Estimation, Optimization, and Parallelism when Data is Sparse or Highly Varying

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Estimation, Optimization, and Parallelism when Data is Sparse or Highly Varying John C. Duchi Michael I. Jordan H. Brendan McMahan November 10, 2013 Abstract We study stochastic optimization problems when the data is sparse, which is in a sense dual to the current understanding of high-dimensional statistical learning and optimization. We highlight both the difficulties—in terms of increased sample complexity that sparse data necessitates—and the potential benefits, in terms of allowing parallelism and asynchrony in the design of algorithms. Concretely, we derive matching upper and lower bounds on the minimax rate for optimization and learning with sparse data, and we exhibit algorithms achieving these rates. We also show how leveraging sparsity leads to (still minimax optimal) parallel and asynchronous algorithms, providing experimental evidence complementing our theoretical results on several medium to large-scale learning tasks. 1 Introduction and problem setting In this paper, we investigate stochastic optimization problems in which the data is sparse. Formally, let {F(·; ξ), ξ ∈ Ξ} be a collection of real-valued convex functions, each of whose domains contains the convex set X ⊂ R d . For a probability distribution P on Ξ, we consider the following optimization problem: minimize x∈X f(x) := E[F(x; ξ)] = Z Ξ F(x; ξ)dP(ξ). (1) By data sparsity, we mean that the sampled data ξ is sparse: samples ξ are assumed to lie in R d , and if we define the support supp(x) of a vector x to the set of indices of its non-zero components, we assume that supp ∇F(x; ξ) ⊂ supp ξ. (2) The sparsity condition (2) means that F(x; ξ) does not “depend” on the values of xj for indices j such that ξj = 0.1 This type of data sparsity is prevalent in statistical optimization problems and machine learning applications, though in spite of its prevalence, study of such problems has been somewhat limited. As a motivating example, consider a text classification problem: data ξ ∈ R d represents words appearing in a document, and we wish to minimize a logistic loss F(x; ξ) = log(1 + exp(hξ, xi)) on the data (we encode the label implicitly with the sign of ξ). Such generalized linear models satisfy the sparsity condition (2), and while instances are of very high dimension, in any given instance, 1Formally, if we define πξ as the coordinate projection that zeros all indices j of its argument where ξj = 0, then F(πξ(x); ξ) = F(x; ξ) for all x, ξ. This is implied by first order conditions for convexity [6, Chapter VI.2] 1very few entries of ξ are non-zero [8]. From a modelling perspective, it thus makes sense to allow a dense predictor x: any non-zero entry of ξ is potentially relevant and important. In a sense, this is dual to the standard approaches to high-dimensional problems; one usually assumes that the data ξ may be dense, but there are only a few relevant features, and thus a parsimonious model x is desirous [2]. So while such sparse data problems are prevalent—natural language processing, information retrieval, and other large data settings all have significant data sparsity—they do not appear to have attracted as much study as their high-dimensional “duals” of dense data and sparse predictors. In this paper, we investigate algorithms and their inherent limitations for solving problem (1) under natural conditions on the data generating distribution. Recent work in the optimization and machine learning communities has shown that data sparsity can be leveraged to develop parallel optimization algorithms [12, 13, 14], but the authors do not study the statistical effects of data sparsity. In recent work, Duchi et al. [4] and McMahan and Streeter [9] develop “adaptive” stochastic gradient algorithms designed to address problems in sparse data regimes (2). These algorithms exhibit excellent practical performance and have theoretical guarantees on their convergence, but it is not clear if they are optimal—in the sense that no algorithm can attain better statistical performance—or whether they can leverage parallel computing as in the papers [12, 14]. In this paper, we take a two-pronged approach. First, we investigate the fundamental limits of optimization and learning algorithms in sparse data regimes. In doing so, we derive lower bounds on the optimization error of any algorithm for problems of the form (1) with sparsity condition (2). These results have two main implications. They show that in some scenarios, learning with sparse data is quite difficult, as essentially each coordinate j ∈ [d] can be relevant and must be optimized for. In spite of this seemingly negative result, we are also able to show that the AdaGrad algorithms of [4, 9] are optimal, and we show examples in which their dependence on the dimension d can be made exponentially better than standard gradient methods. As the second facet of our two-pronged approach, we study how sparsity may be leveraged in parallel computing frameworks to give substantially faster algorithms that still achieve optimal sample complexity in terms of the number of samples ξ used. We develop two new algorithms, asynchronous dual averaging (AsyncDA) and asynchronous AdaGrad (AsyncAdaGrad), which allow asynchronous parallel solution of the problem (1) for general convex f and X . Combining insights of Niu et al.’s Hogwild! [12] with a new analysis, we prove our algorithms can achieve linear speedup in the number of processors while maintaining optimal statistical guarantees. We also give experiments on text-classification and web-advertising tasks to illustrate the benefits of the new algorithms. Notation For a convex function x 7→ f(x), we let ∂f(x) denote its subgradient set at x (if f has two arguments, we say ∂xf(x, y) is the subgradient w.r.t. x). For a positive semi-definite matrix A, we let k·kA be the (semi)norm defined by kvk 2 A := hv, Avi, where h·, ·i is the standard inner product. We let 1 {·} be the indicator function, which is 1 when its argument is true and 0 otherwise. 2 Minimax rates for sparse optimization We begin our study of sparse optimization problems by establishing their fundamental statistical and optimization-theoretic properties. To do this, we derive bounds on the minimax convergence rate of any algorithm for such problems. Formally, let xb denote any estimator for a minimizer of the 2objective (1). We define the optimality gap ǫN for the estimator xb based on N samples ξ 1 , . . . , ξN from the distribution P as ǫN (x, F, b X , P) := f(xb) − inf x∈X f(x) = EP [F(xb; ξ)] − inf x∈X EP [F(x; ξ)] . This quantity is a random variable, since xb is a random variable (it is a function of ξ 1 , . . . , ξN ). To define the minimax error, we thus take expectations of the quantity ǫN , though we require a bit more than simply E[ǫN ]. We let P denote a collection of probability distributions, and we consider a collection of loss functions F specified by a collection F of convex losses F : X × ξ → R. We can then define the minimax error for the family of losses F and distributions P as ǫ ∗ N (X ,P, F) := inf xb sup P ∈P sup F ∈F EP [ǫN (x, F, b X , P)], (3) where the infimum is taken over all possible estimators (optimization schemes) xb. 2.1 Minimax lower bounds Let us now give a more precise characterization of the (natural) set of sparse optimization problems we consider to provide the lower bound. For the next proposition, we let P consist distributions supported on Ξ = {−1, 0, 1} d , and we let pj := P(ξj 6= 0) be the marginal probability of appearance of feature j (j ∈ {1, . . . , d}). For our class of functions, we set F to consist of functions F satisfying the sparsity condition (2) and with the additional constraint that for g ∈ ∂xF(x; ξ), we have that the jth coordinate |gj | ≤ Mj for a constant Mj < ∞. We obtain Proposition 1. Let the conditions of the preceding paragraph hold. Let R be a constant such that X ⊃ [−R, R] d . Then ǫ ∗ N (X ,P, F) ≥ 1 8 R X d j=1 Mj min pj , √pj √ N log 3 . We provide the proof of Proposition 1 in Appendix A.1, providing a few remarks here. We begin by giving a corollary to Proposition 1 that follows when the data ξ obeys a type of power law: let p0 ∈ [0, 1], and assume that P(ξj 6= 0) = p0j −α. We have Corollary 2. Let α ≥ 0. Let the conditions of Proposition 1 hold with Mj ≡ M for all j, and assume the power law condition P(ξj 6= 0) = p0j −α on coordinate appearance probabilities. Then (1) If d > (p0N) 1/α, ǫ ∗ N (X ,P, F) ≥ MR 8 2 2 − α r p0 N (p0N) 2−α 2α − 1 + p0 1 − α d 1−α − (p0N) 1−α α . (2) If d ≤ (p0N) 1/α, ǫ ∗ N (X ,P, F) ≥ MR 8 r p0 N 1 1 − α/2 d 1− α 2 − 1 1 − α/2 . 3For simplicity assume that the features are not too extraordinarly sparse, say, that α ∈ [0, 2], and that number of samples is large enough that d ≤ (p0N) 1/α. Then we find ourselves in regime (2) of Corollary 2, so that the lower bound on optimization error is of order MRr p0 N d 1− α 2 when α < 2, MRr p0 N log d when α → 2, and MRr p0 N when α > 2. (4) These results beg the question of tightness: are they improvable? As we see presently, they are not. 2.2 Algorithms for attaining the minimax rate The lower bounds specified by Proposition 1 and the subsequent specializations are sharp, meaning that they are unimprovable by more than constant factors. To show this, we review a few stochastic gradient algorithms. We first recall stochastic gradient descent, after which we review the dual averaging methods and an extension of both. We begin with stochastic gradient descent (SGD): for this algorithm, we repeatedly sample ξ ∼ P, compute g ∈ ∂xF(x; ξ), then perform the update x ← ΠX (x − ηg), where η is a stepsize parameter and ΠX denotes Euclidean projection onto X . Then standard analyses of stochastic gradient descent (e.g. [10]) show that after N samples ξ, in our setting the SGD estimator xb(N) satisfies E[f(xb(N))] − inf x∈X f(x) ≤ O(1) R2M qPd j=1 pj √ N , (5) where R2 denotes the ℓ2-radius of X . Dual averaging, due to Nesterov [11] and referred to as “follow the regularized leader” in the machine learning literature (see, e.g., the survey article by Hazan [5]) is somewhat more complex. In dual averaging, one again samples g ∈ ∂xF(x; ξ), but instead of updating the parameter vector x one updates a dual vector z by z ← z + g, then computes x ← argmin x∈X hz, xi + 1 η ψ(x) , where ψ(x) is a strongly convex function defined over X (often one takes ψ(x) = 1 2 kxk 2 2 ). The dual averaging algorithm, as we shall see, is somewhat more natural in asynchronous and parallel computing environments, and it enjoys the same type of convergence guarantees (5) as SGD. The AdaGrad algorithm [4, 9] is a slightly more complicated extension of the preceding stochastic gradient methods. It maintains a diagonal matrix S, where upon receiving a new sample ξ, AdaGrad performs the following: it computes g ∈ ∂xF(x; ξ), then updates Sj ← Sj + g 2 j for j ∈ [d]. Depending on whether the dual averaging or stochastic gradient descent (SGD) variant is being used, AdaGrad performs one of two updates. In the dual averaging case, it maintains the dual vector z, which is updated by z ← z + g; in the SGD case, the parameter x is maintained. The updates for the two cases are then x ← argmin x′∈X g, x′ + 1 2η D x ′ − x, S 1 2 (x ′ − x) E 4for stochastic gradient descent and x ← argmin x′∈X z, x′ + 1 2η D x ′ , S 1 2 x ′ E for dual averaging, where η is a stepsize. Then appropriate choice of η shows that after N samples ξ, the averaged parameter xb(N) AdaGrad returns satisfies E[f(xb(N))] − inf x∈X f(x) ≤ O(1)R∞M √ N X d j=1 √pj , (6) where R∞ denotes the ℓ∞-radius of X (e.g. [4, Section 1.3 and Theorem 5], where one takes η ≈ R∞). By inspection, the AdaGrad rate (6) matches the lower bound in Proposition 1 and is thus optimal. It is interesting to note, though, that in the power law setting of Corollary 2 (recall the error order (4)), a calculation shows that the multiplier for the SGD guarantee (5) becomes R∞ √ d max{d (1−α)/2 , 1}, while AdaGrad attains rate at worst R∞ max{d 1−α/2 , log d} (by evaluation of P j √pj ). Thus for α > 1, the AdaGrad rate is no worse, and for α ≥ 2, is more than √ d/ log d better than SGD—an exponential improvement in the dimension. 3 Parallel and asynchronous optimization with sparsity As we note in the introduction, recent works [12, 14] have suggested that sparsity can yield benefits in our ability to parallelize stochastic gradient-type algorithms. Given the optimality of AdaGradtype algorithms, it is natural to focus on their parallelization in the hope that we can leverage their ability to “adapt” to sparsity in the data. To provide the setting for our further algorithms, we first revisit Niu et al.’s Hogwild!. The Hogwild! algorithm of Niu et al. [12] is an asynchronous (parallelized) stochastic gradient algorithm that proceeds as follows. To apply Hogwild!, we must assume the domain X in problem (1) is a product space, meaning that it decomposes as X = X1 × · · · × Xd, where Xj ⊂ R. Fix a stepsize η > 0. Then a pool of processors, each running independently, performs the following updates asynchronously to a centralized vector x: 1. Sample ξ ∼ P 2. Read x and compute g ∈ ∂xF(x; ξ) 3. For each j s.t. gj 6= 0, update xj ← ΠXj (xj − ηgj ) Here ΠXj denotes projection onto the jth coordinate of the domain X . The key of Hogwild! is that in step 2, the parameter x at which g is calculated may be somewhat inconsistent—it may have received partial gradient updates from many processors—though for appropriate problems, this inconsistency is negligible. Indeed, Niu et al. [12] show a linear speedup in optimization time as the number of independent processors grow; they show this empirically in many scenarios, providing a proof under the somewhat restrictive assumption that there is at most one non-zero entry in any gradient g. 53.1 Asynchronous dual averaging One of the weaknesses of Hogwild! is that, as written it appears to only be applicable to problems for which the domain X is a product space, and the known analysis assumes that kgk0 = 1 for all gradients g. In effort to alleviate these difficulties, we now develop and present our asynchronous dual averaging algorithm, AsyncDA. In AsyncDA, instead of asynchronously updating a centralized parameter vector x, we maintain a centralized dual vector z. A pool of processors performs asynchronous additive updates to z, where each processor repeatedly performs the following updates: 1. Read z and compute x := argminx∈X n hz, xi + 1 η ψ(x) o // Implicitly increment “time” counter t and let x(t) = x 2. Sample ξ ∼ P and let g ∈ ∂xF(x; ξ) // Let g(t) = g. 3. For j ∈ [d] such that gj 6= 0, update zj ← zj + gj Because the actual computation of the vector x in asynchronous dual averaging (AsyncDA) is performed locally on each processor in step 1 of the algorithm, the algorithm can be executed with any proximal function ψ and domain X . The only communication point between any of the processors is the addition operation in step 3. As noted by Niu et al. [12], this operation can often be performed atomically on modern processors. In our analysis of AsyncDA, and in our subsequent analysis of the adaptive methods, we require a measurement of time elapsed. With that in mind, we let t denote a time index that exists (roughly) behind-the-scenes. We let x(t) denote the vector x ∈ X computed in the tth step 1 of the AsyncDA algorithm, that is, whichever is the tth x actually computed by any of the processors. We note that this quantity exists and is recoverable from the algorithm, and that it is possible to track the running sum Pt τ=1 x(τ ). Additionally, we require two assumptions encapsulating the conditions underlying our analysis. Assumption A. There is an upper bound m on the delay of any processor. In addition, for each j ∈ [d] there is a constant pj ∈ [0, 1] such that P(ξj 6= 0) ≤ pj . We also require an assumption about the continuity (Lipschitzian) properties of the loss functions being minimized; the assumption amounts to a second moment constraint on the sub-gradients of the instantaneous F along with a rough measure of the sparsity of the gradients. Assumption B. There exist constants M and (Mj ) d j=1 such that the following bounds hold for all x ∈ X : E[k∂xF(x; ξ)k 2 2 ] ≤ M2 and for each j ∈ [d] we have E[|∂xjF(x; ξ)|] ≤ pjMj . With these definitions, we have the following theorem, which captures the convergence behavior of AsyncDA under the assumption that X is a Cartesian product, meaning that X = X1×· · ·×Xd, where Xj ⊂ R, and that ψ(x) = 1 2 kxk 2 2 . Note the algorithm itself can still be efficiently parallelized for more general convex X , even if the theorem does not apply. Theorem 3. Let Assumptions A and B and the conditions in the preceding paragraph hold. Then E X T t=1 F(x(t); ξ t ) − F(x ∗ ; ξ t ) ≤ 1 2η kx ∗ k 2 2 + η 2 TM2 + ηTmX d j=1 p 2 jM2 j . 6We provide the proof of Theorem 3 in Appendix B. As stated, the theorem is somewhat unwieldy, so we provide a corollary and a few remarks to explain and simplify the result. Under a more stringent condition that |∂xjF(x; ξ)| ≤ Mj , Assumption A implies E[k∂xF(x; ξ)k 2 2 ] ≤ Pd j=1 pjM2 j . Thus, without loss of generality for the remainder of this section we take M2 = Pd j=1 pjM2 j , which serves as an upper bound on the Lipschitz continuity constant of the objective function f. We then obtain the following corollary. Corollary 4. Define xb(T) = 1 T PT t=1 x(t), and set η = kx ∗k2 /M √ T. Then E[f(xb(T)) − f(x ∗ )] ≤ M kx ∗k √ 2 T + m kx ∗k2 2M √ T X d j=1 p 2 jM2 j Corollary 4 is almost immediate. To see the result, note that since ξ t is independent of x(t), we have E[F(x(t); ξ t ) | x(t)] = f(x(t)); applying Jensen’s inequality to f(xb(T)) and performing an algebraic manipulation give the corollary. If the data is suitably “sparse,” meaning that pj ≤ 1/m (which may also occur if the data is of relatively high variance in Assumption B) the bound in Corollary 4 simplifies to E[f(xb(T)) − f(x ∗ )] ≤ 3 2 M kx ∗k √ 2 T = 3 2 qPd j=1 pjM2 j kx ∗k2 √ T (7) which is the convergence rate of stochastic gradient descent (and dual averaging) even in nonasynchronous situations (5). In non-sparse cases, setting η ∝ kx ∗k2 / √ mM2T in Theorem 3 recovers the bound E[f(xb(T)) − f(x ∗ )] ≤ O(1)√ m · M kx ∗k √ 2 T . The convergence guarantee (7) shows that after T timesteps, we have error scaling 1/ √ T; however, if we have k processors, then updates can occur roughly k times as quickly, as all updates are asynchronous. Thus in time scaling as n/k, we can evaluate n gradient samples: a linear speedup. 3.2 Asynchronous AdaGrad We now turn to extending AdaGrad to asynchronous settings, developing AsyncAdaGrad (asynchronous AdaGrad). As in the AsyncDA algorithm, AsyncAdaGrad maintains a shared dual vector z among the processors, which is the sum of gradients observed; AsyncAdaGrad also maintains the matrix S, which is the diagonal sum of squares of gradient entries (recall Section 2.2). The matrix S is initialized as diag(δ 2 ), where δj ≥ 0 is an initial value. Each processor asynchronously performs the following iterations: 1. Read S and z and set G = S 1 2 . Compute x := argminx∈X n hz, xi + 1 2η hx, Gxi o // Implicitly increment “time” counter t and let x(t) = x, S(t) = S 2. Sample ξ ∼ P and let g ∈ ∂F(x; ξ) 3. For j ∈ [d] such that gj 6= 0, update Sj ← Sj + g 2 j and zj ← zj + gj 7As in the description of AsyncDA, we note that x(t) is the vector x ∈ X computed in the tth “step” of the algorithm (step 1), and similarly associate ξ t with x(t). To analyze AsyncAdaGrad, we make a somewhat stronger assumption on the sparsity properties of the losses F than Assumption B. Assumption C. There exist constants (Mj ) d j=1 such that for any x ∈ X and ξ ∈ Ξ, we have E[(∂xjF(x; ξ))2 | ξj 6= 0] ≤ M2 j . Indeed, taking M2 = P j pjM2 j shows that Assumption C implies Assumption B with specific constants. We then have the following convergence result, whose proof we provide defer to Appendix C. Theorem 5. In addition to the conditions of Theorem 3, let Assumption C hold. Assume that δ 2 ≥ M2 j m for all j and that X ⊂ [−R∞, R∞] d . Then X T t=1 E F(x(t); ξ t ) − F(x ∗ ; ξ t ) ≤ X d j=1 min 1 η R 2 ∞E " δ 2 + X T t=1 gj (t) 2 1 2 # + ηE "X T t=1 gj (t) 2 1 2 # (1 + pjm), MjR∞pjT . We can also relax the condition on the initial constant diagonal term δ slightly, which gives a qualitatively similar result (see Appendix C.3). Corollary 6. Under the conditions of Theorem 5, assume that δ 2 ≥ M2 j min{m, 6 max{log T, mpj}} for all j. Then X T t=1 E F(x(t); ξ t ) − F(x ∗ ; ξ t ) ≤ X d j=1 min 1 η R 2 ∞E " δ 2 + X T t=1 gj (t) 2 1 2 # + 3 2 ηE X T t=1 gj (t) 2 1 2 (1 + pjm), MjR∞pjT . It is natural to ask in which situations the bound Theorem 5 and Corollary 6 provides is optimal. We note that, as in the case with Theorem 3, we may take an expectation with respect to ξ t and obtain a convergence rate for f(xb(T)) − f(x ∗ ), where xb(T) = 1 T PT t=1 x(t). By Jensen’s inequality, we have for any δ that E " δ 2 + X T t=1 gj (t) 2 1 2 # ≤ δ 2 + X T t=1 E[gj (t) 2 ] 1 2 ≤ q δ 2 + T pjM2 j . For interpretation, let us now make a few assumptions on the probabilities pj . If we assume that pj ≤ c/m for a universal (numerical) constant c, then Theorem 5 guarantees that E[f(xb(T)) − f(x ∗ )] ≤ O(1) 1 η R 2 ∞ + η X d j=1 Mj min (p log(T)/T + pj √ T , pj ) , (8) 8 Auditory Sparse Coding 1 Auditory Sparse Coding Steven R. Ness University of Victoria Thomas Walters Google Inc. Richard F. Lyon Google Inc. CONTENTS 1.1 Summary .................................................................. 3 1.2 Introduction ............................................................... 4 1.2.1 The stabilized auditory image .................................... 6 1.3 Algorithm ................................................................. 7 1.3.1 Pole–Zero Filter Cascade .......................................... 7 1.3.2 Image Stabilization ................................................ 8 1.3.3 Box Cutting ....................................................... 9 1.3.4 Vector Quantization ............................................... 9 1.3.5 Machine Learning ................................................. 10 1.4 Experiments ............................................................... 11 1.4.1 Sound Ranking .................................................... 11 1.4.2 MIREX 2010 ...................................................... 14 1.5 Conclusions ................................................................ 17 1.1 Summary The concept of sparsity has attracted considerable interest in the field of machine learning in the past few years. Sparse feature vectors contain mostly values of zero and one or a few non-zero values. Although these feature vectors can be classified by traditional machine learning algorithms, such as SVM, there are various recently-developed algorithms that explicitly take advantage of the sparse nature of the data, leading to massive speedups in time, as well as improved performance. Some fields that have benefited from the use of sparse algorithms are finance, bioinformatics, text mining [1], and image classification [4]. Because of their speed, these algorithms perform well on very large collections of data [2]; large collections are becoming increasingly 34 Book title goes here relevant given the huge amounts of data collected and warehoused by Internet businesses. In this chapter, we discuss the application of sparse feature vectors in the field of audio analysis, and specifically their use in conjunction with preprocessing systems that model the human auditory system. We present early results that demonstrate the applicability of the combination of auditory-based processing and sparse coding to content-based audio analysis tasks. We present results from two different experiments: a search task in which ranked lists of sound effects are retrieved from text queries, and a music information retrieval (MIR) task dealing with the classification of music into genres. 1.2 Introduction Traditional approaches to audio analysis problems typically employ a shortwindow fast Fourier transform (FFT) as the first stage of the processing pipeline. In such systems a short, perhaps 25ms, segment of audio is taken from the input signal and windowed in some way, then the FFT of that segment is taken. The window is then shifted a little, by perhaps 10ms, and the process is repeated. This technique yields a two-dimensional spectrogram of the original audio, with the frequency axis of the FFT as one dimension, and time (quantized by the step-size of the window) as the other dimension. While the spectrogram is easy to compute, and a standard engineering tool, it bears little resemblance to the early stages of the processing pipeline in the human auditory system. The mammalian cochlea can be viewed as a bank of tuned filters the output of which is a set of band-pass filtered versions of the input signal that are continuous in time. Because of this property, fine-timing information is preserved in the output of cochlea, whereas in the spectrogram described above, there is no fine-timing information available below the 10ms hop-size of the windowing function. This fine-timing information from the cochlea can be made use of in later stages of processing to yield a three-dimensional representation of audio, the stabilized auditory image (SAI)[11], which is a movie-like representation of sound which has a dimension of ‘time-interval’ in addition to the standard dimensions of time and frequency in the spectrogram. The periodicity of the waveform gives rise to a vertical banding structure in this time interval dimension, which provides information about the sound which is complementary to that available in the frequency dimension. A single example frame of a stabilized auditory image is shown in Figure 1.1. While we believe that such a representation should be useful for audio analysis tasks, it does come at a cost. The data rate of the SAI is many times that of the original input audio, and as such some form of dimensionalityAuditory Sparse Coding 5 reduction is required in order to create features at a suitable data rate for use in a recognition system. One approach to this problem is to move from a the dense representation of the SAI to a sparse representation, in which the overall dimensionality of the features is high, but only a limit number of the dimensions are nonzero at any time. In recent years, machine learning algorithms that utilize the properties of sparsity have begun to attract more attention and have been shown to outperform approaches that use dense feature vectors. One such algorithm is the passive-aggressive model for image retrieval (PAMIR), a machine learning algorithm that learns a ranking function from the input data, that is, it takes an input set of documents and orders them based on their relevance to a query. PAMIR was originally developed as a machine vision method and has demonstrated excellent results in this field. There is also growing evidence that in the human nervous system sensory inputs are coded in a sparse manner; that is, only small numbers of neurons are active at a given time [10]. Therefore, when modeling the human auditory system, it may be advantageous to investigate this property of sparseness in relation to the mappings that are being developed. The nervous systems of animals have evolved over millions of years to be highly efficient in terms of energy consumption and computation. Looking into the way sound signals are handled by the auditory system could give us insights into how to make our algorithms more efficient and better model the human auditory system. One advantage of using sparse vectors is that such coding allows very fast computation of similarity, with a trainable similarity measure [4]. The efficiency results from storing, accessing, and doing arithmetic operations on only the non-zero elements of the vectors. In one study that examined the performance of sparse representations in the field of natural language processing, a 20- to 80-fold speedup over LIBSVM was found [7]. They comment that kernel-based methods, like SVM, scale quadratically with the number of training examples and discuss how sparsity can allow algorithms to scale linearly based on the number of training examples. In this chapter, we use the stabilized auditory image (SAI) as the basis of a sparse feature representation which is then tested in a sound ranking task and a music information retrieval task. In the sound raking task, we generate a two-dimensional SAI for each time slice, and then sparse-code those images as input to PAMIR. We use the ability of PAMIR to learn representations of sparse data in order to learn a model which maps text terms to audio features. This PAMIR model can then be used rank a list of unlabeled sound effects according to their relevance to some text query. We present results that show that in certain tasks our methods can outperform highly tuned FFT based approaches. We also use similar sparse-coded SAI features as input to a music genre classification system. This system uses an SVM classifier on the sparse features, and learns text terms associated with music. The system was entered into the annual music information retrieval evaluation exchange evaluation (MIREX 2010).6 Book title goes here Results from the sound-effects ranking task show that sparse auditorymodel-based features outperform standard MFCC features, reaching precision about 73% for the top-ranked sound, compared to about 60% for standard MFCC and 67% for the best MFCC variant. These experiments involved ranking sounds in response to text queries through a scalable online machine learning approach to ranking. 1.2.1 The stabilized auditory image In our system we have taken inspiration from the human auditory system in order to come up with a rich set of audio features that are intended to more closely model the audio features that we use to listen and process music. Such fine timing relations are discarded by traditional spectral techniques. A motivation for using auditory models is that the auditory system is very effective at identifying many sounds. This capability may be partially attributed to acoustic features that are extracted at the early stages of auditory processing. We feel that there is a need to develop a representation of sounds that captures the full range of auditory features that humans use to discriminate and identify different sounds, so that machines have a chance to do so as well. FIGURE 1.1 An example of a single SAI of a sound file of a spoken vowel sound. The vertical axis is frequency with lower frequencies at the bottom of the figure and higher frequencies on the top. The horizontal axis is the autocorrelation lag. From the positions of the vertical features, one can determine the pitch of the sound. This SAI representation generates a 2D image from each section of waveform from an audio file. We then reduce each image in several steps: first cutting the image into overlapping boxes converted to fixed resolution per box; second, finding row and column sums of these boxes and concatenating those into a vector; and finally vector quantizing the resulting medium-Auditory Sparse Coding 7 dimensionality vector, using a separate codebook for each box position. The VQ codeword index is a representation of a 1-of-N sparse code for each box, and the concatenation of all of those sparse vectors, for all the box positions, makes the sparse code for the SAI image. The resulting sparse code is accumulated across the audio file, and this histogram (count of number of occurrences of each codeword) is then used as input to an SVM [5] classifier[3]. This approach is similar to that of the “bag of words” concept, originally from natural language processing, but used heavily in computer vision applications as “bag of visual words”; here we have a “bag of auditory words”, each “word” being an abstract feature corresponding to a VQ codeword. The bag representation is a list of occurrence counts, usually sparse. 1.3 Algorithm In our experiments, we generate a stream of SAIs using a series of modules that process an incoming audio stream through the various stages of the auditory model. The first module filters the audio using the pole–zero filter cascade (PZFC) [9], then subsequent modules find strobe points in this audio, and generate a stream of SAIs at a rate of 50 per second. The SAIs are then cut into boxes and are transformed into a high dimensional dense feature vector [12] which is vector quantized to give a high dimensional sparse feature vector. This sparse vector is then used as input to a machine learning system which performs either ranking or classification. This whole process is shown in diagrammatic form in Figure 1.2 1.3.1 Pole–Zero Filter Cascade We first process the audio with the pole–zero filter cascade (PZFC) [9], a model inspired by the dynamics of the human cochlea. The PZFC is a cascade of a large number of simple filters with an output tap after each stage. The effect of this filter cascade is to transform an incoming audio signal into a set of band-pass filtered versions of the signal. In our case we used a cascade with 95 stages, leading to 95 output channels. Each output channel is halfwave rectified to simulate the output of the inner hair cells along the length of the cochlea. The PZFC also includes an automatic gain control (AGC) system that mimics the effect of the dynamic compression mechanisms seen in the cochlea. A smoothing network, fed from the output of each channel, dynamically modifies the characteristics of the individual filter stages. The AGC can respond to changes in the output on the timescale of milliseconds, leading to very fast-acting compression. One way of viewing this filter cascade is that its outputs are an approximation of the instantaneous neuronal firing rate as a function of cochlear place, modeling both the frequency filtering and8 Book title goes here FIGURE 1.2 A flowchart describing the flow of data in our system. First, either the pole– zero filter cascade (PZFC) or gammatone filterbank filters the input audio signal. Filtered signals then pass through a half-wave rectification module (HCL), and trigger points in the signal are then calculated by the local-max module. The output of this stage is the SAI, the image in which each signal is shifted to align the trigger time to the zero lag point in the image. The SAI is then cut into boxes with the box-cutting module, and the resulting boxes are then turned into a codebook with the vector-quantization module. the automatic gain control characteristics of the human cochlea [8]. The PZFC parameters used for the sound-effects ranking task are described in [9]. We did not do any further tuning of this system to the problems of genre, mood or song classification; this would be a fruitful area of further research. 1.3.2 Image Stabilization The output of the PZFC filterbank is then subjected to a process of strobe finding where large peaks in the PZFC signal are found. The temporal locations of these peaks are then used to initiate a process of temporal integration whereby the stabilized auditory image is generated. These strobe points “stabilize” the signal in a manner analogous to the trigger mechanism in an oscilloscope. When these strobe points are found, a modified form of autocorrelation, known as strobed temporal integration, which is like a sparse version of autocorrelation where only the strobe points are correlated against the sig-Auditory Sparse Coding 9 FIGURE 1.3 The cochlear model, a filter cascade with half-wave rectifiers at the output taps, and an automatic gain control (AGC) filter network that controls the tuning and gain parameters in response to the sound. nal. Strobed temporal integration has the advantage of being considerably less computationally expensive than full autocorrelation. 1.3.3 Box Cutting We then divide each image into a number of overlapping boxes using the same process described in [9]. We start with rectangles of size 16 lags by 32 frequency channels, and cover the SAI with these rectangles, with overlap. Each of these rectangles is added to the set of rectangles to be used for vector quantization. We then successively double the height of the rectangle up to the largest size that fits in an SAI frame, but always reducing the contents of each box back to 16 by 32 values. Each of these doublings is added to the set of rectangles. We then double the width of each rectangle up to the width of the SAI frame and add these rectangles to the SAI frame. The output of this step is a set of 44 overlapping rectangles. The process of box-cutting is shown in Figure 1.4. In order to reduce the dimensionality of these rectangles, we then take their row and column marginals and join them together into a single vector. 1.3.4 Vector Quantization The resulting dense vectors from all the boxes of a frame are then converted to a sparse representation by vector quantization. We first preprocessed a collection of 1000 music files from 10 genres using a PZFC filterbank followed by strobed temporal integration to yield a set of10 Book title goes here FIGURE 1.4 The boxes, or multi-scale regions, used to analyze the stabilized auditory images are generated in a variety of heights, widths, and positions. SAI frames for each file . We then take this set of SAI and apply the boxcutting technique described above. The followed by the calculation of row and column marginals. These vectors are then used to train dictionaries of 200 entries, representing abstract “auditory words”, for each box position, using a k-means algorithm. This process requires the processing of large amounts of data, just to train the VQ codebooks on a training corpus. The resulting dictionaries for all boxes are then used in the MIREX experiment to convert the dense features from the box cutting step on the test corpus songs into a set of sparse features where each box was represented by a vector of 200 elements with only one element being non-zero. The sparse vectors for each box were then concatenated, and these long spare vectors are histogrammed over the entire audio file to produce a sparse feature vector for each song or sound effect. This operation of constructing a sparse bag of auditory words was done for both the training and testing corpora.Auditory Sparse Coding 11 1.3.5 Machine Learning For this system, we used the support vector machine learning system from libSVM which is included in the Marsyas[13] framework. Standard Marsyas SVM parameters were used in order to classify the sparse bag of auditory words representation of each song. It should be noted that SVM is not the ideal algorithm for doing classification on such a sparse representation, and if time permitted, we would have instead used the PAMIR machine learning algorithm as described in [9]. This algorithm has been shown to outperform SVM on ranking tasks, both in terms of execution speed and quality of results. 1.4 Experiments 1.4.1 Sound Ranking We performed an experiment in which we examined a quantitative ranking task over a diverse set of audio files using tags associated with the audio files. For this experiment, we collected a dataset of 8638 sound effects, which came from multiple places. 3855 of the sound files were from commercially available sound effect libraries, of these 1455 were from the BBC sound effects library. The other 4783 audio files were collected from a variety of sources on the internet, including findsounds.com, partnersinrhyme.com, acoustica.com, ilovewaves.com, simplythebest.net, wav-sounds.com, wav-source.com and wavlist.com. We then manually annotated this dataset of sound effects with a small number of tags for each file. Some of the files were already assigned tags and for these, we combined our tags with this previously existing tag information. In addition, we added higher level tags to each file, for example, files with the tags “cat”, “dog” and “monkey” were also given the tags “mammal” and “animal”. We found that the addition of these higher level tags assist retrieval by inducing structure over the label space. All the terms in our database were stemmed, and we used the Porter stemmer for English, which left a total of 3268 unique tags for an average of 3.2 tags per sound file. In order to estimate the performance of the learned ranker, we used a standard three-fold cross-validation experimental setup. In this scheme, two thirds of the data is used for training and one third is used for testing; this process is then repeated for all three splits of the data and results of the three are averaged. We removed any queries that had fewer than 5 documents in either the training set or the test set, and if the corresponding documents had no other tags, these documents were removed as well. To determine the values of the hyperparameters for PAMIR we performed a second level of cross-validation where we iterated over values for the aggressiveness parameter C and the number of training iterations. We found that in12 Book title goes here general system performance was good for moderate values of C and that lower values of C required a longer training time. For the agressiveness parameter, we selected a value of C=0.1, a value which was also found to be optimal in other research [6]. For the number of iterations, we chose 10M, and found that in our experience, the system was not very sensitive to the exact value of these parameters. We evaluated our learned model by looking at the precision within the top k audio files from the test set as ranked by each query. Precision at top k is a commonly used measure in retrieval tasks such as these and measures the fraction of positive results within the top k results from a query. The stabilized auditory image generation process has a number of parameters which can be adjusted including the parameters of the PZFC filter and the size of rectangles that the SAI is cut into for subsequent vector quantization. We created a default set of parameters and then varied these parameters in our experiments. The default SAI box-cutting was performed with 16 lags and 32 channels, which gave a total of 49 rectangles. These rectangles were then reduced to their marginal values which gives a 48 dimension vector, and a codebook of size 256 was used for each box, giving a total of 49 x 256 = 12544 feature dimensions. Starting from these, we then made systematic variations to a number of different parameters and measured their effect on precision of retrieval. For the box-cutting step, we adjusted various parameters including the smallest sized rectangle, and the maximum number of rectangles used for segmentation. We also varied the codebook sizes that we used in the sparse coding step. In order to evaluate our method, we compared it with results obtained using a very common feature extraction method for audio analysis, MFCCs (mel-frequency cepstral coefficients). In order to compare this type of feature extraction with our own, we turned these MFCC coefficients into a sparse code. These MFCC coefficients were calculated with a Hamming window with initial parameters based on a setting optimized for speech. We then changed various parameters of the MFCC algorithm, including the number of cepstral coefficients (13 for speech), the length of each frame (25ms for speech), and the number of codebooks that were used to sparsify the dense MFCC features for each frame. We obtained the best performance with 40 cepstral coefficients, a window size of 40ms and codebooks of size 5000. We investigated the effect of various parameters of the SAI feature extraction process on test-set precision, these results are displayed graphically in Figure 1.5 where the precision of the top ranked sound file is plotted against the number of features used. As one can see from this graph, performance saturates when the number of features approaches 105 which results from the use of 4000 code words per codebook, with a total of 49 codebooks. This particular set of parameters led to a performance of 73%, significantly better than the best MFCC result which achieved a performance of 67%, which represents a smaller error of 18% (from 33 % to 27 % error). It is also notableAuditory Sparse Coding 13 FIGURE 1.5 Ranking at top-1 retrieved result for all the experimental runs described in this section. A few selected experiment names are plotted next to each point, and different experiments are shown by different icons. The convex hull that connects the best-performing experiments is plotted as a solid line. that SAI can achieve better precision-at-top-k consistently for all values of k, albeit with a smaller improvement in relative precision. In table 1.2 results of three queries along with the top five sound files that were returned by the best SAI-based and MFCC-based systems. From this table, one can see that the two systems perform in different ways, this can be expected when one considers the basic audio features that these two systems extract. For example, for the query “gulp”, the SAI system returns “pouring” and “water-dripping”, all three of these share the similarity of involving the movement of water or liquids. When we calculated performance, it was based on textual tags, which are often noisy and incomplete. Due to the nature of human language and perception, people often use different words to describe sounds that are very similar, for example, a Chopin Mazurka could be described with the words “piano”, “soft”, “classical”, “Romantic”, and “mazurka”. To compound this diffi- culty, a song that had a female vocalist singing could be labelled as “woman”, “women”, “female”, “female vocal”, or “vocal”. This type of multi-label problem is common in the field of content based retrieval. It can be alleviated by a number of techniques, including the stemming of words, but due to the varying nature of human language and perception, will continue to remain an issue. In Figure 1.6 the performance of the SAI and MFCC based systems are14 Book title goes here FIGURE 1.6 A comparison of the average precision of the SAI and MFCC based systems. Each point represents a single query, with the horizontal position being the MFCC average precision and the vertical position being the SAI average precision. More of the points appear above the y=x line, which indicates that the SAI based system achieved a higher mean average precision. compared to each other with respect to their average precision. A few select full tag names are placed on this diagram, for the rest, only a plus is shown. This is required because otherwise the text would overlap to such a great degree that it would be impossible to read. In this diagram we plot the average precision of the SAI based system against that of the MFCC based system, with the SAI precision shown along the vertical axis and the MFCC precision shown along the horizontal axis. If the performance of the two systems was identical, all points would lie on the line y=x. Because more points lie above the line than below the line, the performance of the SAI based system is better than that of the MFCC based system.Auditory Sparse Coding 15 top-k SAI MFCC percent error reduction 1 27 33 18 % 2 39 44 12 % 5 60 62 4 % 10 72 74 3 % 20 81 84 4 % TABLE 1.1 A comparison of the best SAI and MFCC configurations. This table shows the percent error at top-k, where error is defined as 1 - precision. Query SAI file (labels) MFCC file (labels) tarzan Tarzan-2 (tarzan, yell) TARZAN (tarzan, yell) tarzan2 (tarzan, yell) 175orgs (steam, whistle) 203 (tarzan) mosquito-2 (mosquito) wolf (mammal, wolves, wolf, ...) evil-witch-laugh (witch, evil, laugh) morse (morse, code) Man-Screams (horror, scream, man) applause 27-Applause-from-audience 26-Applause-from-audience audience 30-Applause-from-audience phase1 (trek, phaser, star) golf50 (golf) fanfare2 (fanfare, trumpet) firecracker 45-Crowd-Applause (crowd, applause) 53-ApplauseLargeAudienceSFX golf50 gulp tite-flamn (hit, drum, roll) GULPS (gulp, drink) water-dripping (water, drip) drink (gulp, drink) Monster-growling (horror, monster, growl) california-myotis-search (blip) Pouring (pour,soda) jaguar-1 (bigcat, jaguar, mammal, ...) TABLE 1.2 A comparison of the best SAI and MFCC configurations. This table shows the percent error at top-k, where error is defined as (1 - precision).16 Book title goes here Algorithm Classification Accuracy SAI/VQ 0.4987 Marsyas MFCC 0.4430 Best 0.6526 Average 0.455 TABLE 1.3 Classical composer train/test classification task Algorithm Classification Accuracy SAI/VQ 0.4861 Marsyas MFCC 0.5750 Best 0.6417 Average 0.49 TABLE 1.4 Music mood train/test classification task 1.4.2 MIREX 2010 All of these algorithms were then ported to the Marsyas music information retrieval framework from AIM-C, and extensive tests were written as described above. These algorithms were submitted to the MIREX 2010 competition as C++ code, which was then run by the organizers on blind data. As of this date, only results for two of the four train/test tasks have been released. One of these is for the task of classifying classical composers and the other is for classifying the mood of a piece of music. There were 40 groups participating in this evaluation, the most ever for MIREX, which gives some indication about how this classification task is increasingly important in the real world. Below I present the results for the best entry, the average of all entries, our entry, and the other entry for the Marsyas system. It is instructive to compare our result to that of the standard Marsyas system because in large part we would like to compare the SAI audio feature to the standard MFCC features, and since both of these systems use the SVM classifier, we partially negate the influence of the machine learning part of the problem. For the classical composer task the results are shown in table 1.3 and for the mood classification task, results are shown in table 1.4 From these results we can see that in the classical composer task we outperformed the traditional Marsyas system which has been tuned over the course of a number of years to perform well. This gives us the indication that the use of these SAI features has promise. However, we underperform the best algorithm, which means that there is work to be done in terms of testing different machine learning algorithms that would be better suited to this type of data. However, in a more detailed analysis of the results, which is shown in 1.7, it is evident that each of the algorithms has a wide range of perfor-Auditory Sparse Coding 17 mance on different classes. This graph shows that the most well predicted in our SAI/VQ classifier overlap significantly with those from the highest scoring classification engines. FIGURE 1.7 Per class results for classical composer In the mood task, we underperform both Marsyas and the leading algorithm. This is interesting and might speak to the fact that we did not tune the parameters of this algorithm for the task of music classification, but instead used the parameters that worked best for the classification of sound effects. Music mood might be a feature that has spectral aspects that evolve over longer time periods than other features. For this reason, it would be important to search for other parameters in the SAI algorithm that would perform well for other tasks in music information retrieval. For these results, due to time constraints, we only used the SVM classifier on the SAI histograms. This has been shown in [9] to be an inferior classifier for this type of sparse, high-dimensional data than the PAMIR algorithm. In the future, we would like to add the PAMIR algorithm to Marsyas and to try these experiments using this new classifier. It was observed that the MIR community is increasingly becoming focused on advanced machine learning techniques, and it is clear that it will be critical to both try different machine learning algorithms on these audio features as well as to perform wider sweeps of parameters for these classifiers. Both of these will be important in increasing the performance of these novel audio features.18 Book title goes here 1.5 Conclusions The use of physiologically-plausible acoustic models combined with a sparsi- fication approach has shown promising results in both the sound effects ranking and MIREX 2010 experiments. These features are novel and hold great promise in the field of MIR for the classification of music as well as other tasks. Some of the results obtained were better than that of a highly tuned MIR system on blind data. In this task we were able to expose the MIR community to these new audio features. These new audio features have been shown to outperform MFCC features in a sound-effects ranking task, and by evaluating these features with machine learning algorithms more suited for these high dimensional, sparse features, we have great hope that we will obtain even better results in future MIREX evaluations.Bibliography [1] Suhrid Balakrishnan and David Madigan. Algorithms for sparse linear classifiers in the massive data setting. J. Mach. Learn. Res., 9:313–337, 2008. [2] L´eon Bottou, Olivier Chapelle, Dennis DeCoste, and Jason Weston. Large-Scale Kernel Machines (Neural Information Processing). The MIT Press, 2007. [3] O. Chappelle, B. Scholkopf, and A. Zien. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006. [4] Gal Chechik, Varun Sharma, Uri Shalit, and Samy Bengio. Large scale online learning of image similarity through ranking. J. Mach. Learn. Res., 11:1109–1135, 2010. [5] Yasser EL-Manzalawy and Vasant Honavar. WLSVM: Integrating LibSVM into Weka Environment, 2005. [6] David Grangier and Samy Bengio. A discriminative kernel-based approach to rank images from text queries. IEEE Trans. Pattern Anal. Mach. Intell., 30(8):1371–1384, 2008. [7] Patrick Haffner. Fast transpose methods for kernel learning on sparse data. In ICML ’06: Proceedings of the 23rd international conference on Machine learning, pages 385–392, New York, NY, USA, 2006. ACM. [8] R. F. Lyon. Automatic gain control in cochlear mechanics. In P Dallos et al., editor, The Mechanics and Biophysics of Hearing, pages 395–420. Springer-Verlag, 1990. [9] Richard F. Lyon, Martin Rehn, Samy Bengio, Thomas C. Walters, and Gal Chechik. Sound retrieval and ranking using auditory sparsecode representations. Neural Computation, 22, 2010. [10] Bruno A. Olshausen and David J. Field. Sparse coding of sensory inputs. Current opinion in neurobiology, 14(4):481–487, 2004. [11] R. D. Patterson. Auditory images: how complex sounds are represented in the auditory system. The Journal of the Acoustical Society of America, 21:183–190, 2000. 1920 Book title goes here [12] Martin Rehn, Richard F. Lyon, Samy Bengio, Thomas C. Walters, and Gal Chechik. Sound ranking using auditory sparse-code representations. ICML 2009 Workshop on Sparse Methods for Music Audio, 2009. [13] G. Tzanetakis. Marsyas-0.2: A case study in implementing music information retrieval systems, chapter 2, pages 31–49. Intelligent Music Information Systems: Tools and Methodologies. Information Science Reference, 2008. Shen, Shepherd, Cui, Liu (eds). Extracting Patterns from Location History Andrew Kirmse Google Inc Mountain View, California akirmse@google.com Tushar Udeshi Google Inc Boulder, Colorado tudeshi@google.com Jim Shuma Google Inc Mountain View, California jshuma@google.com Pablo Bellver Google Inc Mountain View, California pablob@google.com ABSTRACT In this paper, we describe how a user's location history (recorded by tracking the user's mobile device location with his permission) is used to extract the user's location patterns. We describe how we compute the user's commonly visited places (including home and work), and commute patterns. The analysis is displayed on the Google Latitude history dashboard [7] which is only accessible to the user. Categories and Subject Descriptors D.0 [General]: Location based services. General Terms Algorithms. Keywords Location history analysis, commute analysis. 1. INTRODUCTION Location-based services have been gaining in popularity. Most services[4,5] utilize a “check-in” model where a user takes some action on the phone to announce that he has reached a particular place. He can then advertise this to his friends and also to the business owner who might give him some loyalty points. Google Latitude [6] utilizes a more passive model. The mobile device periodically sends his location to a server which shares it with his registered friends. The user also has the option of opting into latitude location history. This allows Google to store the user's location history. This history is analyzed and displayed for the user on a dashboard [7]. A user's location history can be used to provide several useful services. We can cluster the points to determine where he frequents and how much time he spends at each place. We can determine the common routes the user drives on, for instance, his daily commute to work. This analysis can be used to provide useful services to the user. For instance, one can use real-time traffic services to alert the user when there is traffic on the route he is expected to take and suggest an alternate route. We expect many more useful services to arise from location history. It is important to note that a user's location history is stored only if he explicitly opts into this feature. However, once signed in, he can get several useful services without any additional work on his part (like checking in). 2. PREVIOUS WORK Much previous work assumes clean location data sampled at very high frequency. Ashbrook and Starner [2] cluster a user's significant locations from GPS traces by identifying locations where the GPS signal reappears after an absence of 10 minutes or longer. This approach is unable to identify important outdoor places and is also susceptible to spurious GPS signal loss (e.g. in urban canyons or when the recording device is off). In addition they use a Markov model to predict where the user is likely to go next from where he is. Liao, et al [11] attempt to segment a user's day into everyday activities such as “working”, “sleeping” etc. using a hierarchical activity model. Both these papers obtain one GPS reading per second. This is impractical with today's mobile devices due to battery usage. Kang et al [10] use time-based clustering of locations obtained using a “Place Lab Client” to infer the user's important locations. The “Place lab client” infers locations by listening to RF-emissions from known wi-fi access points. This requires less power than GPS. However, their clustering algorithm assumes a continuous trace of one sample per second. Real-world data is not so reliable and often has missing and noisy data as illustrated in Section 3.2. Ananthanarayanan et al [1] describe a technique to infer a user's driving route. They also match users having similar routes to suggest carpool partners. Liao et al [12] use a hierarchical Markov Model to infer a user's transportation patterns including different modes of transportation (e.g. bus, on foot, car etc.). Both these papers use clean regularly-sampled GPS traces as input. 3. LOCATION ANALYSIS 3.1 Input Data For every user, we have a list of timestamped points. Each point has a geolocation (latitude and longitude), an accuracy radius and an input source: 17% of our data points are from GPS and these have an accuracy in the 10 meter range. Points derived from wifi signatures have an accuracy in the 100 meter range and represent 57% of our data. The remaining 26% of our points are derived Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ACM SIGSPATIAL GIS '11, November 1-4, 2011. Chicago, IL, USA Copyright © 2011 ACM ISBN 978-1-4503-1031-4/11/11...$10.00from cell tower triangulation and these have an accuracy in the 1000 meter range. We have a test database of location history of around a thousand users. We used this database to generate the data in this paper. 3.2 Location Filtering The raw geolocations reported from mobile devices may contain errors beyond the measurable uncertainties inherent in the collection method. Hardware or software bugs in the mobile device may induce spurious readings, or variations in signal strength or terrain may cause a phone to connect to a cell tower that is not the one physically closest to the device. Given a stream of input locations, we apply the following filters to account for these errors: 1. Reject any points that fall outside the boundaries of international time zones over land. While this discards some legitimate points over water (presumably collected via GPS), in practice it removes many more false readings. 2. Reject any points with timestamps before the known public launch of the collection software. 3. Identify cases of “jitter”, where the reported location jumps to a distant point and soon returns. As shown in Figure 1, this is surprisingly common. We look for a sequence of consecutive locations {P1, P2, …, Pn} where the following conditions hold: ◦ P1 and Pn are within a small distance threshold D of each other. ◦ P1 and Pn have timestamps within a few hours of each other. ◦ P1 and Pn have high reported accuracy. ◦ P2, …, Pn-1 have low reported accuracy. ◦ P2, …, Pn-1 are farther than D from P1. In such a case, we conclude that the points P2, …, Pn-1 are due to jitter, and discard them. 4. If a pair of consecutive points implies a non-physical velocity, reject the later one. Any points that are filtered are discarded, and are not used in the remaining algorithms described in this paper. 3.3 Computing Frequently Visited Places In this section, we describe the algorithms we use to compute places frequented by a user from his location history. We first filter out the points for which the user is stationary i.e. moving at a very low velocity. These stationary points need to be clustered to extract interesting locations. 3.3.1 Clustering Stationary Points We use two different algorithms for clustering stationary points. 3.3.1.1 Leader-based Clustering For every point, we determine if it belongs to any of the already generated clusters by computing its distance to the cluster leader's location. If this is below a threshold radius, the point is added to the cluster. Psuedocode is in Figure 2. This algorithm is simple and efficient. It runs in O(NC) where N is the number of points and C is the number of clusters. However, the output clusters are dependent on the order of the input points. For example, consider 3 points P1, P2 and P3 which lie on a straight line with a distance of radius between them as shown in Figure 3. If the input points are ordered {P1, P2, P3}, we would get 2 clusters: {P1} and {P2, P3}. But if they are ordered {P2, P1 ,P3} we would get only 1 cluster containing all 3 points. 3.3.1.2 Mean Shift Clustering Mean shift [3] is an iterative procedure that moves every point to the average of the data points in its neighborhood. The iterations are stopped when all points move less than a threshold. This algorithm is guaranteed to converge. We use a weighted average to compute the move position where the weights are inversely proportional to the accuracy. This causes the points to gravitate towards high accuracy points. Once the iterations converge, the moved locations of the points are chosen as cluster centers. All input points within a threshold radius to a cluster center are added to its cluster. We revert back to leader-based clustering if the iterations do not converge or some of the input points remain unclustered. Psuedocode is shown in Figure 4. This algorithm does not suffer from the input-order dependency of leader-based clustering. For the input point set of Figure 3, it will always return a cluster comprising all 3 points. The algorithm generates a smaller number of better located clusters compared to leader-based clustering. For example consider 4 points on the vertices of a square with a diagonal of 2*radius as shown in Figure 5. Leader-based clustering would generate 4 clusters, 1 per Figure 3. Three equidistant points on a line. P2 P3 P 1 radius radius Let points = input points. Let clusters = [] foreach p in points: foreach c in clusters: if distance(c.leader(), p) < radius: Add p to c break else: Create a new cluster c with p as leader clusters.add(c) Figure 2. Leader-based Clustering Algorithm. Figure 1. A set of reported locations exhibiting “jitter”. One of the authors was actually stationary during the time interval represented by these points.point. Mean-shift clustering would return only 1 cluster whose centroid is at the center of the square. The iterative nature of this algorithm makes it expensive. We therefore limit the maximum number of iterations and revert to leader-based clustering if the algorithm does not converge quickly enough. When we ran Mean-shift clustering on our test database, the algorithm converged in 2.4 iterations on an average. 3% of the input points could not be clustered (i.e. we had to revert to leaderbased for them). However, it did not cause a significant reduction in the number of computed clusters (< 1%). We concluded that the marginal improvement in quality did not justify the increased computational cost. 3.3.1.3 Adaptive Radius Clustering The two clustering algorithms described above return clusters of the input points. One possibility would be to deem clusters larger than a threshold as interesting locaitons. However, this is not ideal since the input points have varying accuracy. For instance, if there are three stationary GPS points within close proximity of each other, we have high confidence that the user visited that place as opposed to three stationary cell tower points. We run the clustering algorithms multiple times, increasing the radius as well as the minimum cluster size after every iteration. When a cluster is generated, we check to see if it overlaps an already computed cluster (generated from a smaller radius). If that is the case, we merge it into the larger cluster. Note that adaptive radius clustering can be used in conjunction with any clustering algorithm. From our test database, we found that adaptively increasing the clustering radius from 20 meters to 500 meters and the minimum cluster size from 2 to 4, increased the number of computed visited places by 81% as compared to clustering with a fixed radius of 500 meters and a minimum cluster size of 4. We also surveyed users and found that the majority of the new visited places generated were correct and useful enough to display on the Latitude history dashboard [7]. 3.3.2 Computing Home and Work Locations We use a simple heuristic to determine the user's home and work locations. A user is likely to be at home at night. We filter out the user's points which occur at night and cluster them. The largest cluster is deemed the user's home location. Similarly, work location is derived by clustering points which occur on weekdays in the middle of the day and clustering them. Note that this heuristic will not work for users with non-standard schedules (e.g. work at night or work in multiple locations). Such users have the option of correcting their home and work location on the Latitude history dashboard [7]. These updated locations will be used for other analyses (e.g. commute analysis described in Section 3.4). 3.3.3 Computing Visited Places We do some additional filtering of the input points before clustering for visited places: 1. We remove points which are within a threshold distance of home and work locations. 2. We remove points which are on the user's commute between home and work. These points are determined using the algorithm described in Section 3.4.1. 3. We remove points near airports since these are reported as flights as described in Section 3.5. Without these filters, we get spurious visited places. Even when a user is stationary at home or work, the location reported can jump around, as described in Section 3.2. Without the first filter, we would get multiple visited places near home and work. If a user regularly stops at a long traffic signal on his commute to work, it has a good chance of being clustered to a visited place. This is why we need the second filter. 3.4 Commute Analysis We can deduce a user's driving commute patterns from his location history. The main challenge here is that points are reported infrequently and we have to derive the path the user has taken in between these points. Also, the accuracy of the points can be very low and so one needs to snap the points to the road he is likely to be on. The commutes are analyzed in three steps: (1) Extract sets of commute points from the input. (2) Fit the commute points to a road path. (3) Cluster paths together spatially and (optionally) temporally to generate the most common commutes taken by the user. 3.4.1 Extracting Commute Points Given a source and destination location (e.g. home and work), we extract the points from the user's location history which likely occurred on the user's driving commute from source to destination. We first filter out all points with low accuracy from the input set. We then find pairs of source-destination points. All points between a pair are candidate points for a single commute. The input points are noisy and therefore we do some sanity checks on the commute candidate points: (1) The commute distance should be reasonable. (2) The commute duration should be reasonable. (3) The commute should be at reasonable driving velocity. Figure 5. Four points on a square. Let points = input points Let clusters = [] foreach p in points: Compute Weight(p) Let cluster_centers = points while all cluster_centers move < shift_threshold: foreach p in cluster_centers: Find all points np which are within a threshold distance of p p = ∑ Weight (npi )×npi ∑ Weight( npi ) while not cluster_centers.empty(): Choose p with highest accuracy from cluster_centers Find all points, say rp, in points which are within radius of p Create a new cluster c with rp clusters.add(c) points = points – rp cluster_centers = cluster_centers – Moved(rp) Cluster remaining points with Leader-based clustering. Figure 4. Mean-Shift Clustering Algorithm 2*radius3.4.2 Fitting a Road Path to Commute Points We use the routing engine used to compute driving directions in Google Maps [8] to fit a path to the commute point. For the rest of this paper, we will refer to this routing engine as “Pathfinder”. This is an iterative algorithm. We first query Pathfinder for the route between source and destination. If all the commute points are within the accuracy threshold distance (used in Section 3.4.1), we terminate and return this path. If not, we add the point which is furthest away from the path as a waypoint and query Pathfinder again. If Pathfinder fails, we assume that the waypoint is not valid (for example it might be in water) and drop it. We continue iterating until all commute points are within the accuracy distance threshold. Psuedocode is shown in Figure 6. Two iterations of this algorithm are shown in Figure 7. The small blue markers are the points from the user's history. The large green markers are the Pathfinder waypoints used to generate the path. After the second iteration all the user points are within the accuracy threshold. 3.4.3 Clustering Commutes Given a bag of commute paths and the time intervals the commutes occurred, we cluster them to determine the most frequent commutes. Two commutes are deemed temporally close if they start and end within some threshold of each other on the same day of the week. Two commutes are deemed spatially close if their Hausdorff distance [9] is within a threshold. We use a variant of leader-based clustering (described in Section 3.3.1.1) to generate commute clusters. The largest commute cluster on a particular day of week is the most common route taken by the user. This can be used for traffic alerts as described in Section 4.1. 4. ACKNOWLEDGMENTS Our thanks to Matthieu Devin, Max Braun, Jesse Rosenstock, Will Robinson, Dale Hawkins, Jim Guggemos, and Baris Gultekin for contributing to this project. 5. REFERENCES [1] Ananthanarayanan, G., Haridasan , M., Mohomed , I., Terry, D., Thekkath , C. A. 2009. StarTrack: A Framework for Enabling Track-Based Applications. Proceedings of the 7th international conference on Mobile systems, applications, and services. [2] Ashbrook, D., Starner, T. 2003. Using GPS to Learn Significant Locations and Predict Movement across Multiple Users. Personal and Ubiquitous Computing, Vol. 7, Issue 5. [3] Cheng, Y. C. 1995. Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 8. [4] Facebook places http://www.facebook.com/places. [5] Foursquare http://www.foursquare.com. [6] Google Latitude http://www.google.com/latitude. [7] Google Latitude History Dashboard. http://www.google.com/latitude/history/dashboard [8] Google Maps http://maps.google.com. [9] Huttenlocher D. P., Klanderman, G. A., and Rucklidge W. J. 1993. Comparing Images using the Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 15, No 9. [10] Kang, J.H., Welbourne, W., Stewart, B. and Borriello, G. 2005. Extracting Places from Traces of Locations. SIGMOBILE Mob. Comput. Commun. Rev. 9, 3. [11] Liao, L., Fox, D., and Kautz, H. 2007. Extracting Places and Activities from GPS traces using Hierarchical Conditional Random Fields. International Journal of Robotics Research. [12] Liao, L., Patterson D. J., Fox, D., and Kautz, H. 2007, Learning and Inferring Transportation Routines. Artificial Intelligence. Vol. 171, Issues 5-6. Figure 7. Two iterations of path fitting algorithm. The small markers are the user's points. The large marker are Pathfinder waypoints used to generate the path. Iteration 1 Iteration 2 Let points = input points Let waypoints = [source, destination] Let current_path = Pathfinder route for waypoints while points.size() > 2: Let p be the point in points farthest away from current_path if distance(p, current_path) < threshold: Record current_path as a commute. break Add p to waypoints in the correct position current_path = Pathfinder route for waypoints if Pathfinder fails: Erase p from waypoints Erase p from points Figure 6. Algorithm to fit a path to commute Abstract When we started implementing a refactoring tool for real-world C programs, we recognized that preprocessing and parsing in straightforward and accurate ways would result in unacceptably slow analysis times and an overly-complicated parsing system. Instead, we traded some accuracy so we could parse, analyze, and change large, real programs while still making the refactoring experience feel interactive and fast. Our tradeoffs fell into three categories: using different levels of accuracy in different parts of the analysis, recognizing that collected wisdom about C programs didn't hold for Objective-C programs, and finding ways to exploit delays in typical interaction with the tool. Categories and Subject Descriptors D.2.6 [Software Engineering]: Programming Environments General Terms Design, Language Keywords: refactoring, case study, scalability, Objective-C 1. Introduction Taking software engineering tools from research to development requires addressing the practical details of software development: huge amounts of source code, the nuances of real languages, and multiple build configurations. Making tools useful for real programmers requires either addressing all these sorts of issues, or accepting various trade-offs in order to ship a reasonable software tool. In our case, we wanted to add refactoring to Apple’s Xcode IDE (integrated development environment.) 1 The refactoring feature would manipulate programs written in Objective-C. Objective-C is an object-oriented extension to C, and Apple’s primary development language [1]. In past research [2], I’d found it acceptable to take multiple minutes to perform a transformation on a small Scheme program. The critical requirements for our commercial tool were quite different: • Support the most common and useful transformations. Renaming declarations, replacing a block of code with a call to a new function, and moving declarations up and down a class hierarchy were mandatory features. • Refactor 200,000 line programs. The feature had to work on real, medium-sized applications. The actual amount of code to parse was much larger than the program’s size. Most Mac OS X compilation units pull in headers for common system libraries, requiring at least another 60-120,000 lines of code that would need to be parsed for every compilation unit. Such large sets of headers are not unique to Mac OS X. C programs using large libraries like the Qt user interface library would encounter similar scalability issues. • Interactive behavior. Xcode’s refactoring feature would be part of the source code editor. Users will expect transformations to complete in seconds rather than minutes, and the whole experience would need to feel interactive [3]. Parsing and analyzing programs of this size in straightforward ways would result in an unacceptable user experience. In one of my first experiences with a similar product, renaming a declaration in a 4,200 line C program (with the previously-mentioned 60,000 lines of headers) took two minutes. • Don't force the user to change the program in order to refactor. The competing product previously mentioned could provide much more acceptable performance if the user specified a pre-compiled header—a single header included by all compilation units. However, converting a large existing project to use a pre-compiled header is not a trivial task, and the additional and hidden setup step discourages new users. • Be aware of use of C's preprocessor. The programs being manipulated would make common use of preprocessor macros and conditionally compiled code. If we did not fully address how the preprocessor affected refactoring, we would at least need to be aware of the potential issues. • Reuse existing parsing infrastructure. We realized there wasn’t sufficient time or resources to write a new parser from scratch. Analysis would need to be done by an existing Objective C parser used for indexing global declarations. Refactoring had to work best for our third-party developers—primarily developers writing GUI applications. It should also work well for developers within Apple, but not for those writing low-level operating system or device driver code. Performance and interactivity were key—we wanted to provide an excellent refactoring experience. In order to meet these performance and interactivity goals, we attacked three areas: using different levels of accuracy in different parts of the tool, recognizing differences between our target programmers and typical C programmers, and finding ways to exploit delays in the user’s interaction with the tool. 2. Different Levels of Accuracy In C, each source file is preprocessed and compiled independently as a “compilation unit”. Each can include different headers, or can include the same headers with different inclusion order or Performance Trade-offs Implementing Refactoring Support for Objective-C Robert Bowdidge* rbowdidge@mac.com Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. 3rd Workshop on Refactoring Tools '09, Oct. 26, 2009, Orlando, FL. Copyright © 2009 ACM 978-1-60558-909-1...$10.00 * This work was performed while the author was at Apple, and discusses the initial implementation of refactoring for Xcode 3.0. The author is currently at Google.initial macro settings. As a result, each compilation unit may interpret the same headers different ways, and may parse different declarations in those same headers. For correct parsing, the compiler needs to compile every source file independently, read in header files anew each time, and fully parse all headers. For small programs, this may not matter, but with Mac OS X, each source file includes between 60-120,000 lines of code from header files. Precompiled headers and other optimizations could speed compile times, but not all developers use precompiled headers, nor could we demand that developers use such schemes in order to use refactoring. Naively parsing all source code was not acceptable; we saw parse times of around five seconds to parse a typical set of headers, so five seconds minimum per file per build configuration would be completely unacceptable. We realized two facts about programs that made us question whether we needed compilation-unit-level accuracy. We realized that although programmers have the opportunity for header files to be interpreted differently in each compilation unit, most programmers intend for the headers to be processed the same in all compilation units. (When header files are not processed uniformly, it can cause subtle, nasty bugs that can take days to track down.) We also realized that system header files are not really part of the project, and not targets for refactoring. We needed to correctly parse system header files merely for their information on types and external function declarations. For most refactoring operations, we didn’t care if the my_integer_t type was 4 bytes long or 8; we just needed to know that the name referred to a type. We also knew that correct refactoring transformations shouldn’t change the write-protected system header files. We thus made two assumptions about headers we parsed. First, we decided to parse each header file at most once, and would assume that the files were interpreted the same in each compilation unit. This meant that we could shorten parsing times for at least five seconds per file to five seconds (for all system header files), plus the additional time to only parse the source files and headers in the project. Second, we gathered less position information for system header files. We knew that changes in system header files were both incorrect (because we couldn’t change the existing code in libraries) and uninteresting (because we couldn’t change all other clients of the header file.) We gathered less exact position information for such files, and would flag errors if a transformation would change code in a system header file. We also realized that the user interface needed information about the source code to identify whether refactoring was possible for a given selection, which transformations were possible, and what the default parameters for the transformation would be. Because we wanted the user interface to make these suggestions immediately without waiting for parsing to complete, we used saved information from the Xcode’s declaration index when helping the user propose a refactoring transformation. We did have some issues where indexer information had inaccuracies (when its less accurate parser misparsed certain constructs), but in general we found the information good enough for our first release. 3. The Typical Programmer Dealing with conditional code and multiple build configurations is another major issue for refactoring and source code analysis of C programs. We realized that many of the assumptions about C code did not hold for Objective-C programs, and changed our expectations of what we would implement. C’s preprocessor supports conditional code—code only compiled if certain macros are set. Although some conventions exist for using conditional directives, the criteria triggering a particular block of code usually can be understood only by evaluating the values of the controlling macros at the point the preprocessor would have interpreted the directive. If source code with conditional code was refactored without considering all potential conditions, syntax errors or changed behavior could be introduced. Others have proposed various solutions for handling conditional code. Garrido and Johnson expanded the conditional code to cover entire declarations, and annotated the ASTs to mark the configurations including each declaration [4]. Vittek suggested parsing only the feasible sets of configuration macros, parsing each condition separately, and merging the resulting parse trees [5]. McCloskey and Brewer proposed a new preprocessor amenable to analysis and change, with tools to migrate existing programs to the new preprocessor [6]. We instead chose to parse for a single build configuration—a single set of macros, compiler flags, and include paths. Parsing a single build configuration appeared reasonable because ObjectiveC programs use the preprocessor less than typical C programs, because occurrences of conditional code were unlikely to be refactored, and because remaining uses of conditional code were insensitive to the refactoring changes. Ernst’s survey of preprocessor use found that UNIX utilities varied in their use of preprocessor directives. He found the percentage of preprocessor directives to total non-comment, nonblank (NCNB) lines ranged between 4% and 22% [7]. By contrast, only 3-8% of lines in typical Objective-C programs were preprocessor directives. (Measurements were made on sources for the Osirix medical visualization application, Adium multiprotocol chat client, and Xcode itself.) Within those Objective-C programs, preprocessor directives and conditional code also occurred much more frequently in the code unlikely to be refactored. Many were either in third-party utility code, or in cross-platform C++ code. The utility code was often public-domain source code intended for multiple operating systems. Such code is unlikely to be refactored for fear of complicating merges of newer versions. For applications designed for multiple operating systems, often a core C++ library would be the basis of all versions, and separate user interface code would be written for each operating system. Because our first release would not refactor or parse C++ code, such core code would be irrelevant to refactoring. For the Objective-C portions of the projects, only 2-4% of all lines were preprocessor directives. The preprocessor directives that do appear in Objective-C code are often irrelevant to refactoring. Of Ernst’s eleven categories of conditional code, many are either unlikely to affect the target audience, or are irrelevant to refactoring in general. Include guards are less frequently used in Objective-C because a separate directive (#import) ensures a file is included only once. Conditional directives that always disabled code (“#if (0)”) can be handled in the same way comments are processed. Operating system -specific conditional code is unlikely in Objective-C code because the language is used only on Mac OS X. However, there are three problematic conditional code directives that appear in Objective-C programs: code for debugging, architecture-specific code, and conditional code for enabling and disabling features in the project. Conditional code for debugging is unlikely to be troublesome. The rename transformation will make an incorrect change if a declaration is referenced in conditional code that is not parsed. If the condition is parsed, then the conditional code is not a concern. The most dangerous case occurs when code that needs to be manipulated exists in two conditionally compiled sections of code never parsed at the same time. Luckily, most conditional code controlled by debugging macros only adds code to the debug case, and does not add code to the non-debug case. As long as we parse the program with debug macros set (which should be the default during development), then we should parse all necessary code. Architecture-specific code is more common at Apple because we support two architectures (x86 and PowerPC), both in 32 and 64 bit versions. Most of the architecture-specific conditional code is found in low level system code and device drivers. The external developers we are targeting with refactoring would be working on application software, and would be unlikely to have architecturespecific code. Project-specific features controlled by conditional compilation directives represent a larger risk. Some of these may actually be in use (such as code shared between an iPhone and Mac application), and others may represent dead code. Code may exist on both sides of a condition. For the first release, we only changed code in the current build configuration, and relied on the user to be aware of and avoid changes in project-specific conditional code. 4. Exploiting Interaction Delays A final area for optimization was deciding when parsing and refactoring work would begin during actual use. Even with our previous decisions, parsing speed still wasn’t acceptable. Our rough numbers were that we could parse all the system header files in about 5 seconds, and then could parse an additional ten files a second on a typical machine. Caching the results of the header file parsing was an obvious solution, but we weren’t sure we had the time to implement such caching. A straightforward implementation would start parsing after the user specified the transformation to be performed, and only show results when the transformation was complete. We realized we could speed perceived performance by starting parsing early, and showing partial results before the transformation completed. 4.1 Optimistically Starting Parsing It usually takes a few seconds for a programmer to specify a refactoring transformation. Even for the simple rename, the user needs to indicate that he wants to rename a declaration, then needs to type in the new name. For “extract function”, the additional choices for parameter name and order requires additional time. To improve perceived performance, we began parsing the currently active file and header files as soon as the programmer had selected the “refactor” menu item. For refactoring transformations that only affected a single file, this often meant that as soon as the user specified the parameters for refactoring, the parsing had already been completed, and the transformation would be ready immediately. 4.2 Showing Partial Results When performing transformations changing multiple files, we similarly exploited how programmers would interact with the refactoring tool. We knew that most programmers beginning to use refactoring might want to examine the changes being made to double-check that the transformation was correct. If we assumed that most transformations would be successful (because the programmer was unlikely to try a transformation they thought would break their code), then we could begin showing partial results immediately rather than waiting for the entire transformation to be complete and validated to be safe. Most descriptions of refactoring break each transformation into two parts: the pre-conditions (which indicate the requirements that must be met before a transformation may be performed) and the changes to the source code (which are only performed after the change is believed safe. [8]) Because parse times are liable to be longer than a few seconds, the “check, then perform” approach would not have been interactive. The user would have to wait until all source code was parsed and all refactoring complete before examining any results. Similarly, parse trees for all functions would need to be generated before any refactoring work could begin. If the project being manipulated was particularly large, then the parse trees could consume huge amounts of memory. To make refactoring more palatable on large projects, we designed our transformations to work in several phases so that changes could be presented shown after only some of the code had been parsed and portions of the transformation performed. (See Figure 1). We also could dispose of some parse trees as soon as that code has been analyzed. The seven phases for our transformations are: • check user input: precondition checks that could be done with the initial inputs to the transformation only. • check first file: precondition checks to do after the file containing the selection is parsed. Generally, the analysis performed in this phase only performs initial sanity checks requiring parse trees. For the rename transformation, the phase checks that the declaration can be renamed, if the name is a valid C identifier, and if the declaration is not in a system header file. • perform first file: apply any changes that can be determined after the first file is parsed. Few transformations do work in this phase. • check per-file: precondition checks to do after parsing each compilation unit. • perform per-file: changes to apply after parsing each compilation unit. Most transformations do the bulk of their work in the per-file category. The check and perform parts both look at newly found uses of relevant declarations, and make appropriate changes. Each transformation specifies if the memory for parsed representations of function bodies can be freed before beginning the next file. • check final: precondition checks to do after parsing all files. The after-parsing checks tend to involve existence tests or nonexistence tests—whether any situations exist that indicate the transformation is unsafe such as “did we ever see any declaraparse b.c check per-file perform per-file check per-file perform per-file check per-file perform first file perform per-file check first file parse a.c parse c.c perform final check final Process a.c Process b.c Process c.c Figure 1: Order of processing of interleaved refactoring transformation on three source files a.c, b.c, and c.c. Results of the transformation are incrementally updated after each perform- phase is complete.tions with this name already?” Some of these checks could be done incrementally as each file is parsed. • perform final: changes to apply after parsing all files. The perform final phase is typically used for edits that cannot be constructed until all sources have been parsed. For example, when converting references to a structure’s field to call getter or setter functions, the transformation needs to determine where to place the new accessor functions. The accessors need to be placed in a source file (rather than a header), preferably near existing references to the field or the definition of the structure. Typically, the transformation can place the functions as soon as a likely location is found. If no appropriate location for the new code is found in any source file, the perform final phase chooses an arbitrary location. By breaking up each transformation in this way, the user experience of refactoring becomes more interactive. The refactoring user interface can show the list of files which must be parsed for a transformation. As each file is parsed and changes are identified, the user interface indicates completion and notes the number of changes in that file. Selecting the filename shows a side-by-side view of the source before and after the change. As the transformation progresses, more files and edits are displayed. The user can examine proposed changes as soon as each file is processed. While examining the changes, the user can also choose not to include some changes, or can make additional edits to the changed source code. In this way, the user can both measure progress and can be working productively as the transformation progresses. The interleaved transformation approach has the risk of declaring a transformation unsafe after the user has already examined some changes. This turned out not to be a problem in actual use. Programmers weren’t bothered by the delayed negative answer. We also found very few transformations where we could outright refuse to do a transformation. We might warn the result is incorrect, but we found programmers often wanted the chance to apply those incorrect changes and then fix remaining problems with straight edits. 5. Conclusions Overall, our progress on refactoring matched effort described on similar projects. Our first prototype was completed in three months by one person, and our first release required two years and three people. We found the transformations tended to be easy to write. Most of our parsing effort focused on scalability - getting parsing performance and memory use low, and making sure it worked well inside the IDE. We also found that implementing a polished user interface took the majority of the overall effort, with two of the engineers working full time on refactoring workflow and on making the file comparison view as polished as possible. With the trade-offs described here, we met our performance goals. Our goal at the beginning of the project was to permit refactoring on 200,000 line projects, and be able to rename a frequently-referenced declaration within 30 seconds. On a 2.2 GHz Dual Xeon PowerMac with 1 GB of memory, we renamed declarations in a 270,000 line Objective-C project. We found we could rename a class referenced in 382 places through 123 files in 28 seconds. We could rename a class used in 65 files in 15 seconds. Operations involving only a single file took around 8 seconds; this time was irrespective of the source file because parsing the headers dominated. Most transformations only require parsing a small subset of source files in a project. However, one of the transformations searches all code for iterators that can be converted to use a new language feature. Parsing the entire 270,000 line project for this transformation takes around 90 seconds. This is not acceptable for the interactive transformations, but is adequate for an infrequently run transformation that changes all source files. The refactoring feature as described shipped as part of Xcode 3.0 and Mac OS X 10.5. Building software development tools in industry requires making tradeoffs in both requirements and design. Some are driven by the expected needs of users such as the size of programs to be refactored, or response times expected. Some are driven by scalability issues such as whether to save pre-processed header files in the IDE between refactorings, or whether to re-parse headers from scratch each time. Other tradeoffs occur for business, timing, or staffing reasons, affecting whether a feature might even be implemented, or whether a new parser is written from scratch. As described in this paper, our requirements strongly affected what we could and did implement. The particular tradeoffs we made may not appear to be the "right" or "perfect" decision in all cases, but they are representative of the sorts of decisions that must be made during the process of commercial development. Our three themes of trade-offs—identifying where different levels of accuracy were acceptable, recognizing differences between "our typical user" and "a typical user", and exploiting delays in user interaction to improve responsiveness—suggest ways that other tools can meet their own goals. Acknowledgements Thanks to Michael Van De Vanter and Todd Fernandez for their feedback on a previous version of this paper. Dave Payne originally suggested applying the transformations file-by-file. Andrew Pontious and Yuji Akimoto implemented the refactoring user interface, and kept us focused on an interactive experience. Our approach for incrementally showing refactoring results is also described in U.S. Patent Application 20080052684, “ Stepwise source code refactoring”. References [1] Apple, "Apple Developer Documentation: Objective-C Programming Language," Cupertino, CA 2007. [2] R. W. Bowdidge and W. G. Griswold, "Supporting the Restructuring of Data Abstractions through Manipulation of a Program Visualization," ACM Transactions on Software Engineering and Methodology, vol. 7(2), 1998. [3] D. Bäumer, E. Gamma, and A. Kiezun, "Integrating refactoring support into a Java development tool," in OOPSLA 2001 Companion, 2001. [4] A. Garrido and R. Johnson, "Analyzing Multiple Configurations of a C Program," in 21st IEEE International Conference on Software Maintenance (ICSM), 2005. [5] M. Vittek, "Refactoring Browser with Preprocessor," in 7th European Conference on Software Maintenance and Reengineering, Benevento, Italy, 2003. [6] B. McCloskey and E. Brewer, "ASTEC: a new approach to refactoring C," in 13th ACM SIGSOFT international symposium on Foundations of Software Engineering ESEC/FSE-13, 2005. [7] M. D. Ernst, G. J. Badros, and D. Notkin, "An Empirical Analysis of C Preprocessor Use," IEEE Transactions on Software Engineering, vol. 28, pp. 1146-1170, December 2002. [8] W. F. Opdyke, "Refactoring: A Program Restructuring Aid in Designing Object-Oriented Application Frameworks," University of Illinois, Urbana-Champaign, 1991. Linear-Space Computation of the Edit-Distance between a String and a Finite Automaton Cyril Allauzen1 and Mehryar Mohri2,1 1 Google Research 76 Ninth Avenue, New York, NY 10011, US. 2 Courant Institute of Mathematical Sciences 251 Mercer Street, New York, NY 10012, US. Abstract. The problem of computing the edit-distance between a string and a finite automaton arises in a variety of applications in computational biology, text processing, and speech recognition. This paper presents linear-space algorithms for computing the edit-distance between a string and an arbitrary weighted automaton over the tropical semiring, or an unambiguous weighted automaton over an arbitrary semiring. It also gives an efficient linear-space algorithm for finding an optimal alignment of a string and such a weighted automaton. 1 Introduction The problem of computing the edit-distance between a string and a finite automaton arises in a variety of applications in computational biology, text processing, and speech recognition [8, 10, 18, 21, 14]. This may be to compute the edit-distance between a protein sequence and a family of protein sequences compactly represented by a finite automaton [8, 10, 21], or to compute the error rate of a word lattice output by a speech recognition with respect to a reference transcription [14]. A word lattice is a weighted automaton, thus this further motivates the need for computing the edit-distance between a string and a weighted automaton. In all these cases, an optimal alignment is also typically sought. In computational biology, this may be to infer the function and various properties of the original protein sequence from the one it is best aligned with. In speech recognition, this determines the best transcription hypothesis contained in the lattice. This paper presents linear-space algorithms for computing the edit-distance between a string and an arbitrary weighted automaton over the tropical semiring, or an unambiguous weighted automaton over an arbitrary semiring. It also gives an efficient linear-space algorithm for finding an optimal alignment of a string and such a weighted automaton. Our linear-space algorithms are obtained by using the same generic shortest-distance algorithm but by carefully defining different queue disciplines. More precisely, our meta-queue disciplines are derived in the same way from an underling queue discipline defined over states with the same level.The connection between the edit-distance and the shortest distance in a directed graph was made very early on (see [10, 4–6] for a survey of string algorithms). This paper revisits some of these algorithms and shows that they are all special instances of the same generic shortest-distance algorithm using different queue disciplines. We also show that the linear-space algorithms all correspond to using the same meta-queue discipline using different underlying queues. Our approach thus provides a better understanding of these classical algorithms and makes it possible to easily generalize them, in particular to weighted automata. The first algorithm to compute the edit-distance between a string x and a finite automaton A as well as their alignment was due to Wagner [25] (see also [26]). Its time complexity was in O(|x||A| 2 Q) and its space complexity in O(|A| 2 Q|Σ| + |x||A|Q), where Σ denotes the alphabet and |A|Q the number of states of A. Sankoff and Kruskal [23] pointed out that the time and space complexity O(|x||A|) can be achieved when the automaton A is acyclic. Myers and Miller [17] significantly improved on previous results. They showed that when A is acyclic or when it is a Thompson automaton, that is an automaton obtained from a regular expression using Thompson’s construction [24], the edit-distance between x and A can be computed in O(|x||A|) time and O(|x|+|A|) space. They also showed, using a technique due to Hirschberg [11], that the optimal alignment between x and A can be obtained in O(|x| + |A|) space, and in O(|x||A|) time if A is acyclic, and in O(|x||A| log |x|) time when A is a Thompson automaton. The remainder of the paper is organized as follows. Section 2 introduces the definition of semirings, and weighted automata and transducers. In Section 3, we give a formal definition of the edit-distance between a string and a finite automaton, or a weighted automaton. Section 4 presents our linear-space algorithms, including the proof of their space and time complexity and a discussion of an improvement of the time complexity for automata with some favorable graph structure property. 2 Preliminaries This section gives the standard definition and specifies the notation used for weighted transducers and automata which we use in our computation of the edit-distance. Finite-state transducers are finite automata [20] in which each transition is augmented with an output label in addition to the familiar input label [2, 9]. Output labels are concatenated along a path to form an output sequence and similarly input labels define an input sequence. Weighted transducers are finitestate transducers in which each transition carries some weight in addition to the input and output labels [22, 12]. Similarly, weighted automata are finite automata in which each transition carries some weight in addition to the input label. A path from an initial state to a final state is called an accepting path. A weighted transducer or weighted automaton is said to be unambiguous if it admits no two accepting paths with the same input sequence.The weights are elements of a semiring (K, ⊕, ⊗, 0, 1), that is a ring that may lack negation [12]. Some familiar semirings are the tropical semiring (R+ ∪ {∞}, min, +, ∞, 0) and the probability semiring (R+ ∪ {∞}, +, ×, 0, 1), where R+ denotes the set of non-negative real numbers. In the following, we will only consider weighted automata and transducers over the tropical semiring. However, all the results of section 4.2 hold for an unambiguous weighted automaton A over an arbitrary semiring. The following gives a formal definition of weighted transducers. Definition 1. A weighted finite-state transducer T over the tropical semiring (R+ ∪ {∞}, min, +, ∞, 0) is an 8-tuple T = (Σ, ∆, Q, I, F, E, λ, ρ) where Σ is the finite input alphabet of the transducer, ∆ its finite output alphabet, Q is a finite set of states, I ⊆ Q the set of initial states, F ⊆ Q the set of final states, E ⊆ Q × (Σ ∪ {ǫ}) × (∆ ∪ {ǫ}) × (R+ ∪ {∞}) × Q a finite set of transitions, λ : I → R+ ∪ {∞} the initial weight function, and ρ : F → R+ ∪ {∞} the final weight function mapping F to R+ ∪ {∞}. We define the size of T as |T | = |T |Q + |T |E where |T |Q = |Q| is the number of states and |T |E = |E| the number of transitions of T . The weight of a path π in T is obtained by summing the weights of its constituent transitions and is denoted by w[π]. The weight of a pair of input and output strings (x, y) is obtained by taking the minimum of the weights of the paths labeled with (x, y) from an initial state to a final state. For a path π, we denote by p[π] its origin state and by n[π] its destination state. We also denote by P(I, x, y, F) the set of paths from the initial states I to the final states F labeled with input string x and output string y. The weight T (x, y) associated by T to a pair of strings (x, y) is defined by: T (x, y) = min π∈P (I,x,y,F ) λ(p[π]) + w[π] + ρ[n[π]]. (1) Figure 1(a) shows an example of weighted transducer over the tropical semiring. Weighted automata can be defined as weighted transducers A with identical input and output labels, for any transition. Thus, only pairs of the form (x, x) can have a non-zero weight by A, which is why the weight associated by A to (x, x) is abusively denoted by A(x) and identified with the weight associated by A to x. Similarly, in the graph representation of weighted automata, the output (or input) label is omitted. Figure 1(b) shows an example. 3 Edit-distance We first give the definition of the edit-distance between a string and a finite automaton. Let Σ be a finite alphabet, and let Ω be defined by Ω = (Σ ∪ {ǫ}) × (Σ ∪ {ǫ}) − {(ǫ, ǫ)}. An element of Ω can be seen as a symbol edit operation: (a, ǫ) is a deletion, (ǫ, a) an insertion, and (a, b) with a 6= b a substitution. We will denote by h the natural morphism between Ω∗ and Σ∗ × Σ∗ defined by a:b/.1 0 1 a:b/.2 2/1 a:b/.4 3/.8 b:a/.6 b:a/.3 b:a/.5 a/.1 0 1 a/.2 2/1 a/.4 3/.8 b/.6 b/.3 b/.5 (a) (b) Fig. 1. (a) Example of a weighted transducer T. (b) Example of a weighted automaton A. T(aab, bba) = A(aab) = min(.1 +.2 +.6 +.8, .2 +.4 +.5 +.8). A bold circle indicates an initial state and a double-circle a final state. The final weight ρ[q] of a final state q is indicated after the slash symbol representing q. h((a1, b1)· · ·(an, bn)) = (a1 · · · an, b1 · · · bn). An alignment ω between two strings x and y is an element of Ω∗ such that h(ω) = (x, y). Let c : Ω → R+ be a function associating a non-negative cost to each edit operation. The cost of an alignment ω = ω1 · · · ωn is defined as c(ω) = Pn i=1 c(ωi). Definition 2. The edit-distance d(x, y) of two strings x and y is the minimal cost of a sequence of symbols insertions, deletions or substitutions transforming one string into the other: d(x, y) = min h(ω)=(x,y) c(ω). (2) When c is the function defined by c(a, a) = 0 and c(a, ǫ) = c(ǫ, a) = c(a, b) = 1 for all a, b in Σ such that a 6= b, the edit-distance is also known as the Levenshtein distance. The edit-distance d(x, A) between a string x and a finite automaton A can then be defined as d(x, A) = min y∈L(A) d(x, y), (3) where L(A) denotes the regular language accepted by A. The edit-distance d(x, A) between a string x and a weighted automaton A over the tropical semiring is defined as: d(x, A) = min y∈Σ∗ A(y) + d(x, y) . (4) 4 Algorithms In this section, we present linear-space algorithms both for computing the editdistance d(x, A) between an arbitrary string x and an automaton A, and an optimal alignment between x and A, that is an alignment ω such that c(ω) = d(x, A). We first briefly describe two general algorithms that we will use as subroutines.4.1 General algorithms Composition. The composition of two weighted transducers T1 and T2 over the tropical semiring with matching input and output alphabets Σ, is a weighted transducer denoted by T1 ◦ T2 defined by: (T1 ◦ T2)(x, y) = min z∈Σ∗ T1(x, z) + T2(z, y). (5) T1 ◦ T2 can be computed from T1 and T2 using the composition algorithm for weighted transducers [19, 15]. States in the composition T1 ◦ T2 are identified with pairs of a state of T1 and a state of T2. In the absence of transitions with ǫ inputs or outputs, the transitions of T1 ◦ T2 are obtained as a result of the following matching operation applied to the transitions of T1 and T2: (q1, a, b, w1, q′ 1 ) and (q2, b, c, w2, q′ 2 ) → ((q1, q′ 1 ), a, c, w1 + w2,(q2, q′ 2 )). (6) A state (q1, q2) of T1 ◦T2 is initial (resp. final) iff q1 and q2 are initial (resp. final) and, when it is final, its initial (resp.final) weight is the sum of the initial (resp. final) weights of q1 and q2. In the worst case, all transitions of T1 leaving a state q1 match all those of T2 leaving state q2, thus the space and time complexity of composition is quadratic, that is O(|T1||T2|). Shortest distance. Let A be a weighted automaton over the tropical semiring. The shortest distance from p to q is defined as d[p, q] = min π∈P (p,q) w[π]. (7) It can be computed using the generic single-source shortest-distance algorithm of [13], a generalization of the classical shortest-distance algorithms. This generic shortest-distance algorithm works with an arbitrary queue discipline, that is the order according to which elements are extracted from a queue. We shall make use of this key property in our algorithms. The pseudocode of a simplified version of the generic algorithm for the tropical semiring is given in Figure 2. The complexity of the algorithm depends on the queue discipline selected for S. Its general expression is O(|Q| + C(A) max q∈Q N(q)|E| + (C(I) + C(X))X q∈Q N(q)), (8) where N(q) denotes the number of times state q is extracted from queue S, C(X) the cost of extracting a state from S, C(I) the cost of inserting a state in S, and C(A) the cost of an assignment. With a shortest-first queue discipline implemented using a heap, the algorithm coincides with Dijkstra’s algorithm [7] and its complexity is O((|E| + |Q|) log |Q|). For an acyclic automaton and with the topological order queue discipline, the algorithm coincides with the standard linear-time (O(|Q| + |E|)) shortest-distance algorithm [3].Shortest-Distance(A, s) 1 for each p ∈ Q do 2 d[p] ← ∞ 3 d[s] ← 0 4 S ← {s} 5 while S 6= ∅ do 6 q ← Head(S) 7 Dequeue(S) 8 for each e ∈ E[q] do 9 if (d[s] + w[e] < d[n[e]]) then 10 d[n[e]] ← d[s] + w[e] 11 if (n[e] 6∈ S) then 12 Enqueue(S, n[e]) Fig. 2. Pseudocode of the generic shortest-distance algorithm. 4.2 Edit-distance algorithms The edit cost function c can be naturally represented by a one-state weighted transducer over the tropical semiring Tc = (Σ, Σ, {0}, {0}, {0}, Ec, 1, 1), or T in the absence of ambiguity, with each transition corresponding to an edit operation: Ec = {(0, a, b, c(a, b), 0)|(a, b) ∈ Ω}. Lemma 1. Let A be a weighted automaton over the tropical semiring and let X be the finite automaton representing a string x. Then, the edit-distance between x and A is the shortest-distance from the initial state to a final state in the weighted transducer U = X ◦ T ◦ A. Proof. Each transition e in T corresponds to an edit operation (i[e], o[e]) ∈ Ω, and each path π corresponds to an alignment ω between i[π] and o[π]. The cost of that alignment is, by definition of T , c(ω) = w[π]. Thus, T defines the function: T (u, v) = min ω∈Ω∗ {c(ω): h(ω) = (u, v)} = d(u, v), (9) for any strings u, v in Σ∗ . Since A is an automaton and x is the only string accepted by X, it follows from the definition of composition that U(x, y) = T (x, y) + A(y) = d(x, y) + A(y). The shortest-distance from the initial state to a final state in U is then: min π∈PU (I,F ) w[π] = min y∈Σ∗ min π∈PU (I,x,y,F ) w[π] = min y∈Σ∗ U(x, y) (10) = min y∈Σ∗ d(x, y) + A(y) = d(x, A), (11) that is the edit-distance between x and A. ⊓⊔0 1 a/0 2 b/0 3/0 a/0 0/0 1 a/0 b/0 (a) (b) 0/0 ε:a/1 ε:b/1 a:ε/1 a:a/0 a:b/1 b:ε/1 b:a/1 b:b/0 0,0 0,1 ε:a/1 1,0 a:ε/1 1,1 a:a/0 ε:b/1 a:b/1 a:ε/1 ε:a/1 2,0 b:ε/1 2,1 b:a/1 ε:b/1 b:b/0 b:ε/1 ε:a/1 3,0/0 a:ε/1 3,1 a:a/0 ε:b/1 a:b/1 a:ε/1 ε:a/1 ε:b/1 (c) (d) Fig. 3. (a) Finite automaton X representing the string x = aba. (b) Finite automaton A. (c) Edit transducer T over the alphabet {a, b} where the cost of any insertion, deletion and substitution is 1. (d) Weighted transducer U = X ◦ T ◦ A. Figure 3 shows an example illustrating Lemma 1. Using the lateral strategy of the 3-way composition algorithm of [1] or an ad hoc algorithm exploiting the structure of T , U = X ◦ T ◦ A can be computed in O(|x||A|) time. The shortestdistance algorithm presented in Section 4.1 can then be used to compute the shortest distance from an initial state of U to a final state and thus the edit distance of x and A. Let us point out that different queue disciplines in the computation of that shortest distance lead to different algorithms and complexities. In the next section, we shall give a queue discipline enabling us to achieve a linear-space complexity. 4.3 Edit-distance computation in linear space Using the shortest-distance algorithm described in Section 4.1 leads to an algorithm with space complexity linear in the size of U, i.e. in O(|x||A|). However, taking advantage of the topology of U, it is possible to design a queue discipline that leads to a linear space complexity O(|x| + |A|). We assume that the finite automaton X representing the string x is topologically sorted. A state q in the composition U = X ◦T ◦A can be identified with a triplet (i, 0, j) where i is a state of X, 0 the unique state of T , and j a state of A. Since T has a unique state, we further simplify the notation by identifying each state q with a pair (i, j). For a state q = (i, j) of U, we will refer to i by the level of q. A key property of the levels is that there is a transition in U from q to q ′iff level(q ′ ) = level(q) or level(q ′ ) = level(q) + 1. Indeed, a transition from (i, j) to (i ′ , j′ ) in U corresponds to taking a transition in X (in that case i ′ = i + 1 since X is topologically sorted) or staying at the same state in X and taking an input-ǫ transition in T (in that case i ′ = i). From any queue discipline ≺ on the states of U, we can derive a new queue discipline ≺l over U defined for all q, q′ in U as follows: q ≺l q ′ iff level(q) < level(q ′ ) or level(q) = level(q ′ ) and q ≺ q ′ . (12) Proposition 1. Let ≺ be a queue discipline that requires at most O(|V |) space to maintain a queue over any set of states V . Then, the edit-distance between x and A can be computed in linear space, O(|x| + |A|), using the queue discipline ≺l. Proof. The benefit of the queue discipline ≺l is that when computing the shortest distance to q = (i, j) in U, only the shortest distances to the states in U of level i and i − 1 need to be stored in memory. The shortest distances to the states of level strictly less than i − 1 can be safely discarded. Thus, the space required to store the shortest distances is in O(|A|Q). Similarly, there is no need to store in memory the full transducer U. Instead, we can keep in memory the last two levels active in the shortest-distance algorithm. This is possible because the computation of the outgoing transitions of a state with level i only requires knowledge about the states with level i and i + 1. Therefore, the space used to store the active part of U is in O(|A|E +|A|Q) = O(|A|). Thus, it follows that the space required to compute the edit-distance of x and A is linear, that is in O(|x| + |A|). ⊓⊔ The time complexity of the algorithm depends on the underlying queue discipline ≺. A natural choice is for ≺ is the shortest-first queue discipline, that is the queue discipline used in Dijkstra’s algorithm. This yields the following corollary. Corollary 1. The edit-distance between a string x and an automaton A can be computed in time O(|x||A| log |A|Q) and space O(|x| + |A|) using the queue discipline ≺l. Proof. A shortest-first queue is maintained for each level and contains at most |A|Q states. The cost for the global queue of an insertion, C(I), or an assignment, C(A), is in O(log |A|Q) since it corresponds to inserting in or updating one of the underlying level queues. Since N(q) = 1, the general expression of the complexity (8) leads to an overall time complexity of O(|x||A| log |A|Q) for the shortestdistance algorithm. ⊓⊔ When the automaton A is acyclic, the time complexity can be further improved by using for ≺ the topological order queue discipline. Corollary 2. If the automaton A is acyclic, the edit-distance between x and A can be computed in time O(|x||A|) and space O(|x| + |A|) using the queue discipline ≺l with the topological order queue discipline for ≺.Proof. Computing the topological order for U would require O(|U|) space. Instead, we use the topological order on A, which can be computed in O(|A|), to define the underlying queue discipline. The order inferred by (12) is then a topological order on U. ⊓⊔ Myers and Miller [17] showed that when A is a Thompson automaton, the time complexity can be reduced to O(|x||A|) even when A is not acyclic. This is possible because of the following observation: in a weighted automaton over the tropical semiring, there exists always a shortest path that is simple, that is with no cycle, since cycle weights cannot decrease path weight. In general, it is not clear how to take advantage of this observation. However, a Thompson automaton has additionally the following structural property: a loop-connectedness of one. The loop-connectedness of A is k if in any depth- first search of A, a simple path goes through at most k back edges. [17] showed that this property, combined with the observation made previously, can be used to improve the time complexity of the algorithm. The results of [17] can be generalized as follows. Corollary 3. If the loop-connectedness of A is k, then the edit-distance between x and A can be computed in O(|x||A|k) time and O(|x| + |A|) space. Proof. We first use a depth-first search of A, identify back edges, and mark them as such. We then compute the topological order for A, ignoring these back edges. Our underlying queue discipline ≺ is defined such that a state q = (i, j) is ordered first based on the number of times it has been enqueued and secondly based on the order of j in the topological order ignoring back edges. This underlying queue can be implemented in O(|A|Q) space with constant time costs for the insertion, extraction and updating operations. The order ≺l derived from ≺ is then not topological for a transition e iff e was obtained by matching a back edge in A and level(p[e]) = level(n[e]). When such a transition e is visited, n[e] is reinserted in the queue. When state q is dequeued for the lth time, the value of d[q] is the weight of the shortest path from the initial state to q that goes through at most l −1 back edges. Thus, the inequality N(q) ≤ k + 1 holds for all q and, since the costs for managing the queue, C(I), C(A), and C(X), are constant, the time complexity of the algorithm is in O(|x||A|k). ⊓⊔ 4.4 Optimal alignment computation in linear space The algorithm presented in the previous section can also be used to compute an optimal alignment by storing a back pointer at each state in U. However, this can increase the space complexity up to O(|x||A|Q). The use of back pointers to compute the best alignment can be avoided by using a technique due to Hirschberg [11], also used by [16, 17]. As pointed out in previous sections, an optimal alignment between x and A corresponds to a shortest path in U = X ◦ T ◦ A. We will say that a state q in U is a midpoint of an optimal alignment between x and A if q belongs to a shortest path in U and level(q) = ⌊|x|/2⌋.Lemma 2. Given a pair (x, A), a midpoint of the optimal alignment between x and A can be computed in O(|x|+|A|) space with a time complexity in O(|x||A|) if A is acyclic and in O(|x||A| log |A|Q) otherwise. Proof. Let us consider U = X ◦ T ◦ A. For a state q in U let d[q] denote the shortest distance from the initial state to q, and by d R[q] the shortest distance from q to a final state. For a given state q = (i, j) in U, d[(i, j)] +d R[(i, j)] is the cost of the shortest path going through (i, j). Thus, for any i, the edit-distance between x and A is d(x, A) = minj (d[(i, j)] + d R[(i, j)]). For a fixed i0, we can compute both d[(i0, j)] and d R[(i0, j)] for all j in O(|x||A| log |A|Q) time (or O(|x||A| time if A is acyclic) and in linear space O(|x| + |A|) using the algorithm from the previous section forward and backward and stopping at level i0 in each case. Running the algorithm backward (exchanging initial and final states and permuting the origin and destination of every transition) can be seen as computing the edit-distance between x R and AR, the mirror images of x and A. Let us now set i0 = ⌊|x|/2⌋ and j0 = argminj (d[(i0, j)] + d R[(i0, j)]). It then follows that (i0, j0) is a midpoint of the optimal alignment. Hence, for a pair (x, A), the running-time complexity of determining the midpoint of the alignment is in O(|x||A|) if A is acyclic and O(|x||A| log |A|Q) otherwise. ⊓⊔ The algorithm proceeds recursively by first determining the midpoint of the optimal alignment. At step 0 of the recursion, we first find the midpoint (i0, j0) between x and A. Let x 1 and x 2 be such that x = x 1x 2 and |x 1 | = i0, and let A1 and A2 be the automaton obtained from A by respectively changing the final state to j0 in A1 and the initial state to j0 in A2 . We can now recursively find the alignment between x 1 and A1 and between x 2 and A2 . Theorem 1. An optimal alignment between a string x and an automaton A can be computed in linear space O(|x| + |A|) and in time O(|x||A|) if A is acyclic, O(|x||A| log |x| log |A|Q) otherwise. Proof. We can assume without loss of generality that the length of x is a power of 2. At step k of the recursion, we need to compute the midpoints for 2k string-automaton pairs (x i k , Ai k )1≤i≤2k . Thus, the complexity of step k is in O( P2 k i=1 |x i k ||Ai k | log |Ai k |Q) = O( |x| 2 k P2 k i=1 |Ai k | log |Ai k |Q) since |x i k | = |x|/2 k for all i. When A is acyclic, the log factor can be avoided and the equality P2 k i=1 |Ai k | = O(|A|) holds, thus the time complexity of step k is in O(|x||A|/2 k ). In the general case, each |Ai k | can be in the order of |A|, thus the complexity of step k is in O(|x||A| log |A|Q). 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Communications of the ACM, 11(6):365–375, 1968. 25. R. A. Wagner. Order-n correction for regular languages. Communications of the ACM, 17(5):265–268, May 1974. 26. R. A. Wagner and J. I. Seiferas. Correcting counter-automaton-recognizable languages. SIAM Journal on Computing, 7(3):357–375, August 1978. JMLR: Workshop and Conference Proceedings vol 23 (2012) 44.1–44.3 25th Annual Conference on Learning Theory Open Problem: Better Bounds for Online Logistic Regression H. Brendan McMahan MCMAHAN@GOOGLE.COM Google Inc., Seattle, WA Matthew Streeter MSTREETER@GOOGLE.COM Google Inc., Pittsburgh, PA Editor: Shie Mannor, Nathan Srebro, Robert C. Williamson Abstract Known algorithms applied to online logistic regression on a feasible set of L2 diameter D achieve regret bounds like O(e D log T) in one dimension, but we show a bound of O( √ D + log T) is possible in a binary 1-dimensional problem. Thus, we pose the following question: Is it possible to achieve a regret bound for online logistic regression that is O(poly(D) log(T))? Even if this is not possible in general, it would be interesting to have a bound that reduces to our bound in the one-dimensional case. Keywords: online convex optimization, online learning, regret bounds 1. Introduction and Problem Statement Online logistic regression is an important problem, with applications like click-through-rate prediction for web advertising and estimating the probability that an email message is spam. We formalize the problem as follows: on each round t the adversary selects an example (xt , yt) ∈ R n × {−1, 1}, the algorithm chooses model coefficients wt ∈ R n , and then incurs loss `(wt ; xt , yt) = log(1 + exp(−ytwt · xt)), (1) the negative log-likelihood of the example under a logistic model. For simplicity we assume kxtk2 ≤ 1 so that any gradient kO`(wt)k2 ≤ 1. While conceptually any w ∈ R n could be used as model parameters, for regret bounds we consider competing with a feasible set W = {w | kwk2 ≤ D/2}, the L2 ball of diameter D centered at the origin. Existing algorithms for online convex optimization can immediately be applied. First-order algorithms like online gradient descent (Zinkevich, 2003) achieve bounds like O(D √ T). On a bounded feasible set logistic loss (Eq. (1)) is exp-concave, and so we can use second-order algorithms like Follow-The-Approximate-Leader (FTAL), which has a general bound of O(( 1 α + GD)n log T) (Hazan et al., 2007) when the loss functions are α-exp-concave on the feasible set; we have α = e −D/2 for the logistic loss (see Appendix A), which leads to a bound of O((exp(D) + D)n log T) in the general case, or O(exp(D) log T) in the one-dimensional case. The exponential dependence on the diameter of the feasible set can make this bound worse than the O(D √ T) bounds for practical problems where the post-hoc optimal probability can be close to zero or one. We suggest that better bounds may be possible. In the next section, we show that a simple Follow-The-Regularized-Leader (FTRL) algorithm can achieve a much better result, namely c 2012 H.B. McMahan & M. Streeter.MCMAHAN STREETER O( √ D + log T), for one-dimensional problems where the adversary is further constrained1 to pick xt ∈ {−1, 0, +1}. A single mis-prediction can cost about D/2, and so the additive dependence on the diameter of the feasible set is less than the cost of one mistake. The open question is whether such a bound is achievable for problems of arbitrary finite dimension n. Even the general onedimensional case, where xt ∈ [−1, 1], is not obvious. 2. Analysis in One Dimension We analyze an FTRL algorithm. We can ignore any rounds when xt = 0, and then since only the sign of ytxt matters, we assume xt = 1 and the adversary picks yt ∈ {−1, 1}. The cumulative loss function on P positive examples and N negative examples is c(w; N, P) = P log(1 + exp(−w)) + N log(1 + exp(w)). Let Nt denote the number of negative examples seen through the t’th round, with Pt the corresponding number of positive examples. We play FTRL, with wt+1 = arg min w c(w; Nt + λ, Pt + λ), for a constant λ > 0. This is just FTRL with a regularization function r(w) = c(w; λ, λ). Using the FTRL lemma (e.g., McMahan and Streeter (2010, Lemma 1)), we have Regret ≤ r(w ∗ ) +X T t=1 ft(wt) − ft(wt+1) where ft(w) = `(w; xt , yt). It is easy to verify that r(w) ≤ λ(|w| + 2 log 2). It remains to bound ft(wt) − ft(wt+1). Fix a round t. For compactness, we write N = Nt−1 and P = Pt−1. Suppose that yt = −1, so Nt = N + 1 and Pt = P (the case when yt+1 = +1 is analogous). Since ft is convex, by definition ft(w) ≥ ft(wt) + gt(w − wt) where gt = Oft(wt). Taking w = wt+1 and re-arranging, we have ft(wt) − ft(wt+1) ≤ gt(wt − wt+1) ≤ |gt ||wt − wt+1|. It is easy to verify that |gt | ≤ 1, and also that wt = log P + λ N + λ . Since yt = −1, wt+1 < wt , and so |wt − wt+1| = log P + λ N + λ − log P + λ N + 1 + λ = log(N + 1 + λ) − log(N + λ) = log 1 + 1 N + λ ≤ 1 N + λ . 1. Constraining the adversary in this way is reasonable in many applications. For example, re-scaling each xt so kxtk2 = 1 is a common pre-processing step, and many problems also are naturally featurized by xt,i ∈ {0, 1}, where xt,i = 1 indicates some property i is present on the t’th example. 44.2OPEN PROBLEM: ONLINE LOGISTIC REGRESSION Thus, if we let T − = {t | yt = −1}, we have X t∈T − ft(wt) − ft(wt+1) ≤ X NT N=0 1 N + λ ≤ 1 λ + X NT N=1 1 N ≤ 1 λ + log(NT ) + 1. Applying a similar argument to rounds with positive labels and summing over the rounds with positive and negative labels independently gives Regret ≤ λ(|w ∗ | + 2 log 2) + log(PT ) + log(NT ) + 2 λ + 2. Note log(PT ) + log(NT ) ≤ 2 log T. We wish to compete with w ∗ where |w ∗ | ≤ D/2, so we can choose λ = √ 1 D/2 which gives Regret ≤ O( √ D + log T). References Elad Hazan, Amit Agarwal, and Satyen Kale. Logarithmic regret algorithms for online convex optimization. Mach. Learn., 69, December 2007. H. Brendan McMahan and Matthew Streeter. Adaptive bound optimization for online convex optimization. In COLT, 2010. Martin Zinkevich. Online convex programming and generalized infinitesimal gradient ascent. In ICML, 2003. Appendix A. The Exp-Concavity of the Logistic Loss Theorem 1 The logistic loss function `(wt ; xt , yt) = log(1 + exp(−ytwt · xt)), from Eq. (1), is α-exp-concave with α = exp(−D/2) over set W = {w | kwk2 ≤ D/2} when kxtk2 ≤ 1 and yt ∈ {−1, 1}. Proof Recall that a function ` is α-exp-concave if O 2 exp(−α`(w)) 0. When `(w) = g(w·x) for x ∈ R n , we have O 2 exp(−α`(w)) = O 2f 00(z)xx>, where f(z) = exp(−αg(z)). For the logistic loss, we have g(z) = log(1 + exp(z)) (without loss of generality, we consider a negative example), and so f(z) = (1 + exp(z))−α. Then, f 00(z) = αez (1 + e z ) −α−2 (αez − 1). We need the largest α such that f 00(z) ≤ 0, given a fixed z. We can see by inspection that α = 0 is a zero. Since e z (1 + e z ) −α−2 > 0, from the term (αez − 1) we conclude α = e −z is the largest value of α where f 00(z) ≤ 0. Note that z = wt · xt , and so |z| ≤ D/2 since kxtk2 ≤ 1, and so taking the worst case over wt ∈ W and xt with kxtk2 ≤ 1, we have α = exp(−D/2). 44.3 Online Microsurveys for User Experience Research Abstract This case study presents a critical analysis of microsurveys as a method for conducting user experience research. We focus specifically on Google Consumer Surveys (GCS) and analyze a combination of log data and GCSs run by the authors to investigate how they are used, who the respondents are, and the quality of the data. We find that such microsurveys can be a great way to quickly and cheaply gather large amounts of survey data, but that there are pitfalls that user experience researchers should be aware of when using the method. Author Keywords Microsurveys; user experience research; user research methods ACM Classification Keywords H.5.2. User Interfaces: Theory and methods. Introduction To keep up with fast paced design and development teams, user researchers must develop a toolkit of methods to quickly and efficiently address research questions. One such method is the microsurvey, or a short survey of only one to three questions. There are several commercial microsurveys—including Google Consumer Surveys (GCS), SlimSurveys, and Survata— Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s). CHI 2014, April 26–May 1, 2014, Toronto, Ontario, Canada. ACM 978-1-4503-2474-8/14/04. http://dx.doi.org/10.1145/2559206.2559975 David Huffaker Google Inc. Mountain View, CA 94043 huffaker@google.com Gueorgi Kossinets Google Inc. Mountain View, CA 94043 gkossinets@google.com Kerwell Liao Google Inc. Mountain View, CA 94043 kerwell@google.com Paul McDonald Google Inc. Mountain View, CA 94043 pmcdonald@google.com Aaron Sedley Google Inc. Mountain View, CA 94043 asedley@google.com Victoria Schwanda Sosik Cornell University 301 College Ave. Ithaca, NY 14850 vsosik@cs.cornell.edu Elie Bursztein Google Inc. Mountain View, CA 94043 elieb@google.com Sunny Consolvo Google Inc. Mountain View, CA 94043 sconsolvo@google.com Case Study: Creating Methods CHI 2014, One of a CHInd, Toronto, ON, Canada 889Figure 1. An respondent en asked to answ or share the p social media i the publisher’ Figure 2. Pa interface. To responses by to a multiple to the right. example of how a ncounters GCS. 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Distribution of r urvey questions. r overall findings w closely to the de m rall internet popu ence that GCS is b 4] Usage C 62.04% 21.71% 4.62% 3.81% 1.60% 1.50% 1.30% 0.99% 0.92% 0.82% 0.69% -- mong survey design espondents for the 1 response times in se were that GCS mographic lation,” and biased towards Completion Rate 20.56% 39.37% 27.03% 34.19% 25.30% 34.09% 27.20% 28.49% 29.37% 36.57% 27.79% -- ners and 12 different econds to We ran a s and techno of tablet o phone own (35%, 33 % was lower of demogr and gende networking findings us We also co from Surv Knowledge and techno were simil heavier int being the Overall, w between th of unkno w similar acr representi Responde n We ran a G surveywal are trying options the premium c response w followed b (34%), ma purchasing they then series of GCSs to ology-use questio ownership (PEW = nership (91%, 67 %) or the internet among GCS than raphics, GCS sho w er (see Table 2). 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We found tha = 34%, GCS = 28 % %) and use of ce t for banking (61 % n PEW respondent ws lower rates acr With respect to so ng older American ose to PEW (see T pondents to respo rnational (SSI) an with respect to int Results across the ondents tending t technology adopte e 4). mographic differe es—likely due to th nology usage and ples, with PEW and ow extremes, res ward Surveywalls espondents’ attitud ween them and co ked them which o when trying to acc d that the most po rt microsurvey (47 sponsored by an e-time payment (1 6%), and other (3 open ended text) emographic at the rate %), cell ll phones %, 48%) ts. In terms ross age ocial ns, our Table 3). ondents nd ternet use panels to be the ers, and KN nces he number adoption is d KN pectively. des toward ontent they of five cess opular 7%), advertiser 10%), 3%; which ). Case Study: Creating Methods CHI 2014, One of a CHInd, Toronto, ON, Canada 891Data Quality: Survey Attentiveness As one measure of data quality, we ran a GCS that asked respondents one of several trap questions. For a summary of how respondents performed, see the sidebar to the left. We find that our GCS respondents answered correctly the “Very Often” question less often (73%) than an example of the same trap question being asked on a paper survey (97%) [3]. A trap survey run in Mechanical Turk found only 61% of respondents answering correctly when asked to read an email and answer two questions [2], but this task is arguably harder than the questions we asked. Data Quality: Garbage Open Ended Responses We also analyzed data quality by looking at the rate of garbage responses that we received across 25 GCS questions run for other projects. Examples of these questions include: “which web browser(s) do you use?” and “what does clicking on this image allow you to do?” responses such as “blah”, “who cares”, and “zzzzz” and found that the percentage of garbage responses ranged from 1.8% to 23.4% (Mean = 7.8%). Our analysis revealed that the percentage of “I don’t know” responses tended to correlate with the percentage of garbage responses, suggesting that people were more likely to provide such garbage responses when they were not sure of what the question was asking of them. Conclusion: Best Practices for Microsurveys We find that microsurveys such as Google Consumer Surveys can quickly provide large amounts of data with relatively low setup costs. We also see that the GCS population is fairly representative as compared to other large-scale survey panels. However there are also pitfalls to keep in mind. Our findings from the trap question survey suggests that being concise is important to maximize data quality, which supports GCS’s question length constraints. We also suggest that it is important to appropriately target surveys to a population in order to keep garbage open ended responses to a minimum. If respondents are being asked about something they are unfamiliar with, they are less likely to provide meaningful responses. Finally, multiple answer questions had the lowest completion rate—which is often used as a measure of data quality (e.g. [1])—so we suggest that people think critically about the types of questions they use, and consider using other question types if at all appropriate. With respect to analyzing microsurveys, first it is important to remember that demographics are inferred, and there are many “unknowns”. We also suggest using built-in text clustering tools to categorize open-ended responses, and if desired, following up with multiple choice questions to determine how frequent these categories are. References [1] Dillman, D. A. & Schaefer, D. R. (1998). Development of a standard e-mail methodology: results of an experiment. Public Opinion Quarterly, 62, 3. [2] Downs, J.S., Holbrook, M. B., Sheng, S., & Cranor, L. F. (2010). Are your participants gaming the system? Screening mechanical turk workers. In Proceedings of CHI '10. [3] Hargittai, E. (2005). Survey Measures of WebOriented Digital Literacy. Social Science Computer Review. 23, 3, 371–379. [4] Pew Research (November 2012). A Comparison of Results from Surveys by the Pew Research Center and Google Consumer Surveys. KN GCS SSI For personal purposes, I normally use the Internet (5 = every hour or more, 1 = once per week or less) 3.2 3.5 3.8 Other people often seek my ideas and advice regarding technology (5 = describes me very well, 1 = describes me very poorly) 2.7 3.1 3.2 I am willing to pay more for the latest technology (same as above) 2.3 2.6 3.1 Which of the following best describes when you buy or try out new technology? (5 = Among the first people, 1 = I am usually not interested) 2.5 2.6 3.1 How frequently do you post on social networks? (5 = multiple times a day, 1 = once a month or less) 1.7 2.1 2.4 Trap Questions in GCS  What is the color of a red ball? (90.3% correct)  What is the shape of a red ball? (85.7%)  The purpose of this question is to assess your attentiveness to question wording. For this question please mark the ‘Very Often’ response. (72.5%)  The purpose of this question is to assess your attentiveness to question wording. Ignore the question below, and select “blue” from the answers. What color is a basketball? (57%) Table 4. Technology use and adoption among 3 different survey panels. Case Study: Creating Methods CHI 2014, One of a CHInd, Toronto, ON, Canada 892 Minimizing off-target signals in RNA fluorescent in situ hybridization Aaron Arvey1,2, Anita Hermann3 , Cheryl C. Hsia3 , Eugene Ie2,4, Yoav Freund2 and William McGinnis3,* 1 Computational and Systems Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, 2 Department of Computer Sciences and Engineering, 3 Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093 and 4 Google Inc., Mountain View, CA 94043, USA Received November 4, 2009; Revised December 11, 2009; Accepted January 17, 2010 ABSTRACT Fluorescent in situ hybridization (FISH) techniques are becoming extremely sensitive, to the point where individual RNA or DNA molecules can be detected with small probes. At this level of sensitivity, the elimination of ‘off-target’ hybridization is of crucial importance, but typical probes used for RNA and DNA FISH contain sequences repeated elsewhere in the genome. We find that very short (e.g. 20 nt) perfect repeated sequences within much longer probes (e.g. 350–1500 nt) can produce significant off-target signals. The extent of noise is surprising given the long length of the probes and the short length of non-specific regions. When we removed the small regions of repeated sequence from either short or long probes, we find that the signal-to-noise ratio is increased by orders of magnitude, putting us in a regime where fluorescent signals can be considered to be a quantitative measure of target transcript numbers. As the majority of genes in complex organisms contain repeated k-mers, we provide genome-wide annotations of k-mer-uniqueness at http://cbio.mskcc .org/aarvey/repeatmap. INTRODUCTION The gene expression profiles of individual cells can be drastically different from that of adjacent cells. This is particularly true in developing or heterogeneous tissues such as embryos (1), proliferative adult epithelia (2) and tumors (3). Visualization of RNA expression patterns in fields of cells is often accomplished with fluorescence in situ hybridization (FISH) using antisense probes. Analysis of cellular patterns of gene expression by FISH has provided insight into prognosis (3) and cell fate (4) of tissues. A challenge for the future is to use FISH in tissues to quantify RNA expression levels on a cell-by-cell basis, which requires high resolution, high sensitivity and high signal-to-noise ratios (1,5–8). A major hurdle in making RNA FISH methods quantitative has been increasing sensitivity and specificity to the point where genuine target RNA signals can be distinguished from background. One way to produce probes of high specificity has been to produce chemicallysynthesized oligonucleotides that are directly labeled with fluorophores, and tiled along regions of RNA sequence (6–8). Although directly-labeled oligo probes are elegant, they have not yet been widely applied, in part due to their expense, and in part due to their relatively low signal strength (6–8). One alternative method for single RNA molecule detection employs long haptenylated riboprobes that are enzymatically synthesized from cDNAs (1,5). Such probes are cheaply and easily produced, and when detected with primary and fluorescently-labeled secondary antibodies, they have higher signal intensities and equivalent resolution when compared to probes that are directly labeled with fluorophores (1,5). However, tiling probes have a natural advantage with respect to specificity: if a single probe ‘tile’ hybridizes to an off-target transcript, it is unlikely to generate sufficient signal to pass an intensity threshold that is characteristic of genuine RNA transcripts, which contain multiple tiled binding sites. In contrast, a single haptenylated probe, even if fragmented to sizes in the range of hundreds of nucleotides, may yield strong off-target signals due to the amplification conferred by primary and secondary antibodies. One traditional approach to determine background levels of fluorescence, and thus act as a crude estimate of specificity, has been the use of sense *To whom correspondence should be addressed. Tel: 858 822 0461; Fax: 858 822 3021; Email: wmcginnis@ucsd.edu The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. Published online 17 February 2010 Nucleic Acids Research, 2010, Vol. 38, No. 10 e115 doi:10.1093/nar/gkq042 The Author(s) 2010. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. by on July 11, 2010 http://nar.oxfordjournals.org Downloaded from Collaboration in the Cloud at Google Yunting Sun, Diane Lambert, Makoto Uchida, Nicolas Remy Google Inc. January 8, 2014 Abstract Through a detailed analysis of logs of activity for all Google employees1 , this paper shows how the Google Docs suite (documents, spreadsheets and slides) enables and increases collaboration within Google. In particular, visualization and analysis of the evolution of Google’s collaboration network show that new employees2 , have started collaborating more quickly and with more people as usage of Docs has grown. Over the last two years, the percentage of new employees who collaborate on Docs per month has risen from 70% to 90% and the percentage who collaborate with more than two people has doubled from 35% to 70%. Moreover, the culture of collaboration has become more open, with public sharing within Google overtaking private sharing. 1 Introduction Google Docs is a cloud productivity suite and it is designed to make collaboration easy and natural, regardless of whether users are in the same or different locations, working at the same or different times, or working on desktops or mobile devices. Edits and comments on the document are displayed as they are made, even if many people are simultaneously writing and commenting on or viewing the document. Comments enable real-time discussion and feedback on the document, without changing the document itself. Authors are notified when a new comment is made or replied to, and authors can continue a conversation by replying to the comment, or end the discussion by resolving it, or re-start the discussion by re-opening a closed discussion stream. Because documents are stored in the cloud, users can access any document they own or that has been shared with them anywhere, any time and on any device. The question is whether this enriched model of collaboration matters? There have been a few previous qualitative analyses of the effects of Google Docs on collaboration. For example, the review of Google Docs in [1] suggested that its features should improve collaboration and productivity among college students. A technical report [2] from the University of Southern Queensland, Australia argued that Google Docs can overcome barriers to usability such as difficulty of installation and document version control and help resolve conflicts among co-authors of research papers. There has also been at least one rigorous study of the effect of Google Docs on collaboration. Blau and Caspi [3] ran a small experiment that was designed to compare collaboration on writing documents to merely sharing documents. In their experiment, 118 undergraduate students of the Open University of Israel were randomized to one of five groups in which they shared their written assignments and received feedback from other students to varying degrees, ranging from keeping texts 1Full-time Google employees, excluding interns, part-times, vendors, etc 2Full-time employees who have joined Google for less than 90 days 12 COLLABORATION VISUALIZATION private to allowing in-text suggestions or allowing in-text edits. None of the students had used Google Docs previously. The authors found that only students in the collaboration group perceived the quality of their final document to be higher after receiving feedback, and students in all groups thought that collaboration improves documents. This paper takes a different approach, and looks for the effects of collaboration on a large, diverse organization with thousands of users over a much longer period of time. The first part of the paper describes some of the contexts in which Google Docs is used for collaboration, and the second part analyzes how collaboration has evolved over the last two years. 2 Collaboration Visualization 2.1 The Data This section introduces a way to visualize the events during a collaboration and some simple statistics that summarize how widespread collaboration using Google Docs is at Google. The graphics and metrics are based on the view, edit and comment actions of all full-time employees on tens of thousands of documents created in April 2013. 2.2 A Simple Example To start, a document with three collaborators Adam (A), Bryant (B) and Catherine (C) is shown in Figure 1. The horizontal axis represents time during the collaboration. The vertical axis is broken into three regions representing viewing, editing and commenting. Each contributor is assigned a color. A box with the contributor’s color is drawn in any time interval in which the contributor was active, at a vertical position that indicates what the user was doing in that time interval. This allows us to see when contributors were active and how often they contributed to the document. Stacking the boxes allows us to show when contributors were acting at the same time. Only time intervals in which at least one contributor was active are shown, and gaps in time that are shorter than a threshold are ignored. Gray vertical bars of fixed width are used to represent periods of no activity that are longer than the threshold. In this paper, the threshold is set to be 12 hours in all examples. In Figure 1, an interval represents an hour. Adam and Bryant edited the document together during the hour of 10 AM May 4 and Bryant edited alone in the following hour. The collaboration paused for 8 days and resumed during the hour of 2 pm on May 12. Adam, Bryant and Catherine all viewed the document during that hour. Catherine commented on the document in the next hour. Altogether, the collaboration had two active sessions, with a pause of 8 days between them. Figure 1: This figure shows an example of the collaboration visualization technique. Each colored block except the gray one represents an hour and the gray one represents a period of no activity. The Y axis is the number of users for each action type. This document has three contributors, each assigned a different color. Although we have used color to represent collaborators here, we could instead use color to represent the locations of the collaborators, their organizations, or other variables. Examples with different colorings are given in Sections 2.5 and 2.6. 2 Google Inc.2 COLLABORATION VISUALIZATION 2.3 Collaboration Metrics 2.3 Collaboration Metrics To estimate the percentage of users who concurrently edit a document and the percentage of documents which had concurrent editing, we discretize the timestamps of editing actions into 15 minute intervals and consider editing actions by different contributors in the same 15 minute interval to be concurrent. Two users who edit the same document but always more than 15 minutes apart would not be considered as concurrent, although they would still be considered collaborators. Edge cases in which two collaborators edit the same document within 15 minutes of each other but in two adjacent 15 minute intervals would not be counted as concurrent events. The choice of 15 minutes is arbitrary; however, metrics based on a 15 minute discretization and a 5 minute discretization are little different. The choice of 15 minute intervals makes computation faster. A more accurate approach would be to look for sequences of editing actions by different users with gaps below 15 minutes, but that requires considerably more computing. 2.4 Collaborative Editing Collaborative editing is common at Google. 53% of the documents that were created and shared in April 2013 were edited by more than one employee, and half of those had at least one concurrent editing session in the following six months. Looking at employees instead of documents, 80% of the employees who edited any document contributed content to a document owned by others and 65% participated in at least one 15 minute concurrent editing session in April 2013. Concurrent editing is sticky, in the sense that 76% of the employees who participate in a 15 minute concurrent editing session in April will do so again the following month. There are many use cases for collaborative editing, including weekly reports, design documents, and coding interviews. The following three plots show an example of each of these use cases. Figure 2: Collaboration activity on a design document. The X axis is time in hours and the Y axis is the number of users for each action type. The document was mainly edited by 3 employees, commented on by 18 and viewed by 50+. Google Inc. 32.5 Commenting 2 COLLABORATION VISUALIZATION Figure 2 shows the life of a design document created by engineers. The X axis is time in hours and the Y axis is the number of employees working on the document for each action type. The document was mainly edited by three employees, commented on by 18 employees and viewed by more than 50 employees from three major locations. This document was completed within two weeks and viewed many times in the subsequent month. Design documents are common at Google, and they typically have many contributors. Figure 3 shows the life of a weekly report document. Each bar represents a day and the Y axis is the number of employees who edited and viewed the document in a day. This document has the following submission rules: • Wednesday, AM: Reminder for submissions • Wednesday, PM: All teams submit updates • Thursday, AM: Document is locked The activities on the document exhibit a pronounced weekly pattern that mirrors the submission rules. Weekly reports and meeting notes that are updated regularly are often used by employees to keep everyone up-to-date as projects progress. Figure 3: Collaboration on a weekly report. The X axis is time in days and the Y axis is the number of users for each action type. The activities exhibit a pronounced weekly pattern and reflect the submission rules of the document. Finally, Figure 4 shows the life of a document used in an interview. The X axis represents time in minutes. The document was prepared by a recruiter and then viewed by an engineer. At the beginning of the interview, the engineer edited the document and the candidate then wrote code in the document. The engineer was able to watch the candidate typing. At the end of the interview, the candidate’s access to the document was revoked so no further change could be made, and the document was reviewed by the engineer. Collaborative editing allows the coding interview to take place remotely, and it is an integral part of interviews for software engineers at Google. Figure 4: The activity on a phone interview document. The X axis is time in minutes and the Y axis is the number of users for each action type. The engineer was able to watch the candidate typing on the document during a remote interview. 2.5 Commenting Commenting is common at Google. 30% of the documents created in April 2013 that are shared received comments within six months of creation. 57% of the employees who used Google Docs in April commented at least once in April, and 80% of the users who commented in April commented again in the following month. 4 Google Inc.2 COLLABORATION VISUALIZATION 2.6 Collaboration Across Sites Figure 5: Commenting and editing on a design document. The X axis is time in hours and the Y axis is the number of user actions for each user location. There are four user actions, each assigned a different color. Timestamps are in Pacific time. Figure 5 shows the life of a design document. Here color represents the type of user action (create a comment, reply to a comment, resolve a comment and edit the document), and the Y axis is split into two locations. The document was written by one engineering team and reviewed by another. The review team used commenting to raise many questions, which the engineering team resolved over the next few days. Collaborators were located in London, UK and Mountain View, California, with a nine hour time zone difference, so the two teams were almost ”taking turns” working on the document (timestamps are in Pacific time). There are many similar communication patterns between engineers via commenting to ask questions, have discussions and suggest modifications. 2.6 Collaboration Across Sites Employees use the Docs suite to collaborate with colleagues across the world, as Figure 6 shows. In that figure, employees working from nine locations in eight countries across the globe contributed to a document that was written within a week. The document was either viewed or edited with gaps of less than 12 hours (the threshold for suppressing gaps in the plot) in the first seven days as people worked in their local timezones. After final changes were made to the document, it was reviewed by people in Dublin, Mountain View, and New York. Figure 7 shows one month of global collaborations for full-time employees using Google Docs. The blue dots show the locations of the employees and a line connects two locations if a document is created in one location and viewed in the other. The warmer the color of the line, moving from green to red, the more documents shared between the two locations. Google Inc. 52.6 Collaboration Across Sites 2 COLLABORATION VISUALIZATION Figure 6: Activity on a document. Each user location is assigned a different color. The X axis is time in hours and the Y axis is the number of locations for each action type. Users from nine different locations contributed to the document. Figure 7: Global collaboration on Docs. The blue dots are locations and the dots are connected if there is collaboration on Google Docs between the two locations. 6 Google Inc.3 THE EVOLUTION OF COLLABORATION 2.7 Cross Device Work 2.7 Cross Device Work The advantage of cloud-based software and storage is that a document can be accessed from any device. Figure 8 shows one employee’s visits to a document from multiple devices and locations. When the employee was in Paris, a desktop or laptop was used during working hours and a mobile device during non-working hours. Apparently, the employee traveled to Aix-En-Provence on August 18. On August 18 and the first part of August 19, the employee continued working on the same document from a mobile device while on the move. Figure 8: Visits to a document by one user working on multiple devices and from multiple locations. Not surprisingly, the pattern of working on desktops or laptops during working hours and on mobile devices out of business hours holds generally at Google, as Figure 9 shows. The day of week is shown on the X axis and hour of day in local time on the Y axis. Each pixel is colored according to the average number of employees working in Google Docs in a day of week and time of day slot, with brighter colors representing higher numbers. Pixel values are normalized within each plot separately. Desktop and laptop usage of Google Docs peaks during conventional working hours (9:00 AM to 11:00 AM and 1:00 PM to 5:00 PM), while mobile device usage peaks during conventional commuting and other out-of-office hours (7:00 AM to 9:00 AM and 6:00 PM to 8:00 PM). Figure 9: The average number of active users working in Google Docs in each day of week and time of day slot. The X axis is day of the week and the Y axis is time of the day in local time. Desktop/Laptop usage peaks during working hours while mobile usage peaks at out-of-office working hours. 3 The Evolution of Collaboration 3.1 The Data This section explores changes in the usage of Google Docs over time. Section 2 defined collaborators as users who edited or commented on the same document and used logs of employee editing, viewing and commenting actions to describe collaboration within Google. This section defines collaborators differently using metadata on documents. Metadata is much less rich than the event history logs used in Section 2, but metadata is retained for a much longer period of time. Document metadata includes the document creation time and the last time that the document Google Inc. 73.2 Collaboration for New Employees 3 THE EVOLUTION OF COLLABORATION was accessed, but no other information about its revision history. However, the metadata does include the identification numbers for employees who have subscribed to the document, where a subscriber is anyone who has permission to view, edit or comment on a document and who has viewed the document at least once. Here we use metadata on documents, slides and spreadsheets. We call two employees collaborators (or subscription collaborators to be clear) if one is a subscriber to a document owned by the other and has viewed the document at least once and the document has fewer than 20 subscribers. The owner of the document is said to have shared the document with the subscriber. The number of subscribers is capped at 20 to avoid overcounting collaborators. The more subscribers the document has, the less likely it is that all the subscribers contributed to the document. There is no timestamp for when the employee subscribed to the document in the metadata, so the exact time of the collaboration is not known. Instead, the document creation time, which is known, is taken to be the time of the collaboration. An analysis (not shown here) of the event history data discussed in Section 2 showed that most collaborators join a collaboration soon after a document is created, so taking collaboration time to be document creation time is not unreasonable. To make this assumption even more tenable, we exclude documents for which the time of the last view, comment or edit is more than six months after the document was created. This section uses metadata on documents created between January 1, 2011 and March 31, 2013. We say that two employees had a subscription collaboration in July if they collaborated on a document that was created in July. 3.2 Collaboration for New Employees Here we define the new employees for a given month to be all the employees who joined Google no more than 90 days before the beginning of the month and started using Google Docs in the given month. For example, employees called new in the month of January 2011 must have joined Google no more than 90 days before January 1, 2011 and used Google Docs in January 2011. Each month can include different employees. New employees are said to share a document if they own a document that someone else subscribed to, whether or not the person subscribed to the document is a new employee. Similarly, a new employee is counted as a subscriber, regardless of the tenure of the document creator. Figure 10 shows that collaboration among new employees has increased since 2011. Over the last two years, subscribing has risen from 55% to 85%, sharing has risen from 30% to 50%, and the fraction of users who either share or subscribe has risen from 70% to 90%. In other words, new employees are collaborating earlier in their career, so there is a faster ramp-up and easier access to collective knowledge. Figure 10: This figure shows the percentage of new employees who share, subscribe to others’ documents and either share or subscribe in each one-month period over the last two years. Not only do new employees start collaborating more often (as measured by subscription and sharing), they also collaborate with more people. Figure 11 shows the percentage of new employees with at least a given number of collaborators by month. For example, the percentage of 8 Google Inc.3 THE EVOLUTION OF COLLABORATION 3.3 Collaboration in Sales and Marketing new employees with at least three subscription collaborators was 35% in January 2011 (the bottom red curve) and 70% in March 2013 (the top blue curve), a doubling over two years. It is interesting that the curves hardly cross each other and the curves for the farthest back months lie below those for recent months, suggesting that there has been steady growth in the number of subscription collaborators per new employee over this period. Figure 11: This figure shows the proportion of new employees who have at least a given number of collaborators in each one-month period. Each period is assigned a different color. The cooler the color of the curve, moving from red to blue, the more recent the month. The legend only shows the labels for a subset of curves. The percentage of new employees who have at least three collaborators has doubled from 35% to 70%. To present the data in Figure 11 in another way, Table 1 shows percentiles of the distribution of the number of subscription collaborators per new employee using Google Docs in January 2011 and in January 2013. For example, the lowest 25% of new employees using Google Docs had no such collaborators in January 2011 and two such collaborators in January 2013. 25% 50% 75% 90% 95% January 2011 0 1 4 7 11 January 2013 2 5 10 17 22 Table 1: This table shows the percentile of number of collaborators a new employee have in January 2011 and January 2013. The entire distribution shifts to the right. 3.3 Collaboration in Sales and Marketing Section 3.2 compared new employees who joined Google in different months. This section follows current employees in Sales and Marketing who joined Google before January 1, 2011. That is, the previous section considered changes in new employee behavior over time and this section considers changes in behavior for a fixed set of employees over time. We only analyze subscription collaborations among this fixed set of employees and collaborations with employees not in this set are excluded. Figure 12: This figure shows the percentage of current employees in Sales and Marketing who have at least a given number of collaborators in each onemonth period. Figure 12 shows the percentage of current employees in Sales and Marketing who have at least Google Inc. 93.4 Collaboration Between Organizations 3 THE EVOLUTION OF COLLABORATION a given number of collaborators at several times in the past. There we see that more employees are sharing and subscribing over time because the fraction of the group with at least one subscription collaborator has increased from 80% to 95%. And the fraction of the group with at least three subscription collaborators has increased from 50% to 80%. It shows that many of the employees who used to have no or very few subscription collaborators have migrated to having multiple subscription collaborators. In other words, the distribution of number of subscription collaborators for employees who have been in Sales and Marketing since January 1, 2011 has shifted right over time, which implies that collaboration in that group of employees has increased over time. Finally, the number of documents shared by the employees who have been in Sales and Marketing at Google since January 1, 2011 has nearly doubled over the last two years. Figure 13 shows the number of shared documents normalized by the number of shared documents in January, 2011. Figure 13: This figure shows the number of shared documents created by employees in Sales and Marketing each month normalized by the number of shared documents in January 2011. The number has almost doubled over the last two years. 3.4 Collaboration Between Organizations Collaboration between organizations has increased over time. To show that, we consider hundreds of employees in nine teams within the Sales and Marketing group and the Engineering and Product Management group who joined Google before January 1, 2011, were still active in March 31, 2013 and used Google Docs in that period. Figure 14 represents the Engineering and Product Management employees as red dots and the Sales and Marketing employees as blue dots. The same dots are included in all three plots in Figure 14 because the employees included in this analysis do not change. A line connects two dots if the two employees had at least one subscription collaboration in the month shown. The denser the lines in the graph, the more collaboration, and the more lines connecting red and blue dots, the more collaboration between organizations. Clearly, subscription collaboration has increased both within and across organizations in the past two years. Moreover, the network shows more pronounced communities (groups of connected dots) over time. Although there are nine individual teams, there seems to be only three major communities in the network. Figure 14 indicates that teams can work closely with each other even though they belong to separate departments. We also sampled 187 teams within the Sales and Marketing group and the Engineering and Product Management group. Figure 15 represents teams in Engineering and Product Management as red dots and teams in Sales and Marketing as blue dots. Two dots are connected if the two teams had a least one subscription collaboration between their members in the month. Figure 15 shows that the collaboration between those teams has increased and the interaction between the two organizations has becomed stronger over the past two years. 10 Google Inc.3 THE EVOLUTION OF COLLABORATION 3.4 Collaboration Between Organizations Figure 14: An example of collaboration across organizations. Red dots represent employees in Engineering and Product Management and blue dots represent employees in Sales and Marketing Figure 15: An example of collaboration between teams. Red dots represent teams in Engineering and Product Management and blue dots represent teams in Sales and Marketing Google Inc. 113.5 Cultural Changes in Collaboration 4 CONCLUSIONS 3.5 Cultural Changes in Collaboration Google Docs allows users to specify the access level (visibility) of their documents. The default access level in Google Docs is private, which means that only the user who created the document or the current owner of the document can view it. Employees can change the access level on a document they own and allow more people to access it. For example, the document owner can specify particular employees who are allowed to access the document, or the owner can mark the document as public within Google, in which case any employee can access the document. Clearly, not all documents created in Google can be visible to everyone at Google, but the more documents are widely shared, the more open the environment is to collaboration. Figure 16: This figure shows the percentage of shared documents that are ”public within Google” created in each month. Public sharing is overtaking private sharing at Google. Figure 16 shows the percentage of shared documents in Google created each month between January 1, 2012 and March 31, 2013 that are public within Google. The red line, which is a curve fit to the data to smooth out variability, shows that the percentage has increased about 12% from 48% to 54% in the last year alone. In that sense, the culture of sharing is changing in Google from private sharing to public sharing. 4 Conclusions We have examined how Google employees collaborate with Docs and how that collaboration has evolved using logs of user activity and document metadata. To show the current usage of Docs in Google, we have developed a visualization technique for the revision history of a document and analyzed key features in Docs such as collaborative editing, commenting, access from anywhere and on any device. To show the evolution of collaboration in the cloud, we have analyzed new employees and a fixed group of employees in Sales and Marketing, and computed collaboration network statistics each month. We find that employees are engaged in using the Docs suite, and collaboration has grown rapidly over the last two years. It would also be interesting to conduct a similar analysis for other enterprises and see how long it would take them to reach the benchmark Google has set for collaboration on Docs. Not only has the collaboration on Docs changed at Google, the number of emails, comments on G+, calender meetings between people who work together has also had significant changes over the past few years. How those changes reinforce each other over time would also be an interesting topic to study. Acknowledgements We would like to thank Ariel Kern for her insights about collaboration on Google Docs, Penny Chu and Tony Fagan for their encouragement and support and many thanks to Jim Koehler for his constructive feedback. 12 Google Inc.REFERENCES REFERENCES References [1] Dan R. Herrick (2009). Google this!: using Google apps for collaboration and productivity. Proceeding of the ACM SIGUCCS fall conference (pp. 55-64). [2] Stijn Dekeyser, Richard Watson (2009). Extending Google Docs to Collaborate on Research Papers. Technical Report, The University of Southern Queensland, Australia. [3] Ina Blau, Avner Caspi (2009). What Type of Collaboration Helps? Psychological Ownership, Perceived Learning and Outcome Quality of Collaboration Using Google Docs. Learning in the technological era: Proceedings of the Chais conference on instructional technologies research (pp. 48-55). Google Inc. 13 Page [1] of [40] [March, 2013] WORKING GROUP 4 Network Security Best Practices FINAL Report – BGP Security Best PracticesThe Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [2] of [40] Table of Contents Table of Contents 1 RESULTS IN BRIEF...................................................................................................................................... 3 1.1 CHARTER ........................................................................................................................................................ 3 1.2 EXECUTIVE SUMMARY................................................................................................................................. 3 2 INTRODUCTION........................................................................................................................................... 4 2.1 CSRIC STRUCTURE......................................................................................................................................... 5 2.2 WORKING GROUP [#4] TEAM MEMBERS ..................................................................................................... 7 3 OBJECTIVE, SCOPE, AND METHODOLOGY ................................................................................... 8 3.1 OBJECTIVE ..................................................................................................................................................... 8 3.2 SCOPE .............................................................................................................................................................. 9 3.3 METHODOLOGY ............................................................................................................................................ 9 4 BACKGROUND.............................................................................................................................................. 9 4.1 DEPLOYMENT SCENARIOS .............................................................................................................................. 9 5 ANALYSIS, FINDINGS AND RECOMMENDATIONS .............................................................................10 5.1 BGP SESSION-LEVEL VULNERABILITY ........................................................................................................10 5.1.1 SESSION HIJACKING ...................................................................................................................................................10 5.1.2 DENIAL OF SERVICE (DOS) VULNERABILITY ........................................................................................................12 5.1.3 SOURCE-ADDRESS FILTERING ..................................................................................................................................17 5.2 BGP INJECTION AND PROPAGATION VULNERABILITY...............................................................................20 5.2.1 BGP INJECTION AND PROPAGATION COUNTERMEASURES.................................................................................22 5.2.2 BGP INJECTION AND PROPAGATION RECOMMENDATIONS ................................................................................25 5.3 OTHER ATTACKS AND VULNERABILITIES OF ROUTING INFRASTRUCTURE..............................................26 5.3.1 HACKING AND UNAUTHORIZED 3RD PARTY ACCESS TO ROUTING INFRASTRUCTURE.....................................26 5.3.2 ISP INSIDERS INSERTING FALSE ENTRIES INTO ROUTERS ...................................................................................28 5.3.3 DENIAL-OF-SERVICE ATTACKS AGAINST ISP INFRASTRUCTURE.......................................................................28 5.3.4 ATTACKS AGAINST ADMINISTRATIVE CONTROLS OF ROUTING IDENTIFIERS....................................................30 6 CONCLUSIONS............................................................................................................................................32 7 APPENDIX .....................................................................................................................................................33 7.1 BACKGROUND................................................................................................................................................33 7.1.1 SALIENT FEATURES OF BGP OPERATION..............................................................................................................33 7.1.2 REVIEW OF ROUTER OPERATIONS..........................................................................................................................34 7.2 BGP SECURITY INCIDENTS AND VULNERABILITIES ...................................................................................35 7.3 BGP RISKS MATRIX......................................................................................................................................38 7.4 BGP BCP DOCUMENT REFERENCES............................................................................................................40The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [3] of [40] 1 Results in Brief 1.1 Charter This Working Group was convened to examine and make recommendations to the Council regarding best practices to secure the Domain Name System (DNS) and routing system of the Internet during the period leading up to some significant deployment of protocol extensions such as the Domain Name System Security Extensions (DNSSEC), Secure BGP (Border Gateway Protocol) and the like. The focus of the group is limited to what is possible using currently available and deployed hardware and software. Development and refinement of protocol extensions for both systems is ongoing, as is the deployment of such extensions, and is the subject of other FCC working groups. The scope of Working Group 4 is to focus on currently deployed and available feature-sets and processes and not future or non-widely deployed protocol extensions. 1.2 Executive Summary Routing is what provides reachability between the various end-systems on the Internet be they servers hosting web or email applications; home user machines; VoIP (Voice over Internet Protocol) equipment; mobile devices; connected home monitoring or entertainment systems. Across the length and breadth of the global network it is inter-domain routing that allows for a given network to learn of the destinations available in a distant network. BGP (Border Gateway Protocol) has been used for inter-domain routing for over 20 years and has proven itself a dynamic, robust, and manageable solution to meet these goals. BGP is configured within a network and between networks to exchange information about which IP address ranges are reachable from that network. Among its many features, BGP allows for a flexible and granular expression of policy between a given network and other networks that it exchanges routes with. Implicit in this system is required trust in information learned from distant entities. That trust has been the source of problems from time to time causing reachability and stability problems. These episodes have typically been short-lived but underscored the need for expanding the use of Best Current Practices (BCPs) for improving the security of BGP and the inter-domain routing system. These mechanisms have been described in a variety of sources and this document does not seek to re-create the work done elsewhere but to provide an overview and gloss on the vulnerabilities and methods to address each. Additionally, the applicability of these BCPs can vary somewhat given different deployment scenarios such as the scale of a network’s BGP deployment and the number of inter-domain neighbors. By tailoring advice for these various scenarios, recommendations that may seem confusing or contradictory can be clarified. Further, an appendix includes a table that indexes the risks and countermeasures according to different deployment scenarios. Issues that the working group considered included: • Session hijacking • Denial of service (DoS) vulnerabilities • Source-address filtering • BGP injection and propagation vulnerabilitiesThe Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [4] of [40] • Hacking and unauthorized access to routing infrastructure • Attacks against administrative controls of routing identifiers Working Group 4 recommends that the FCC encourage adoption of numerous best practices for protecting ISPs’ routing infrastructures and addressing risks related to routing that are continuously faced by ISPs.. Inter-domain routing via BGP is a fundamental requirement for ISPs and their customers to connect and interoperate with the Internet. As such, it is a critical service that ISPs must ensure is resilient to operational challenges and protect from abuse by miscreants. SPECIAL NOTE: For brevity, and to address the remit of the CSRIC committee to make recommendations for ISPs, the term ISP is used throughout the paper. However, in most instances the reference or the recommendations are applicable to any BGP service components whether implemented by an ISP or by other organizations that peer to the Internet such as business enterprises, hosting providers, and cloud providers. 2 Introduction CSRIC was established as a federal advisory committee designed to provide recommendations to the Commission regarding Best Practices and actions the Commission may take to ensure optimal operability, security, reliability, and resiliency of communications systems, including telecommunications, media, and public safety communications systems. Due to the large scope of the CSRIC mandate, the committee then divided into a set of Working Groups, each of which was designed to address individual issue areas. In total, some 10 different Working Groups were created, including Working Group 4 on Network Security Best Practices. This Working Group will examine and make recommendations to the Council regarding best practices to secure the Domain Name System (DNS) and routing system of the Internet during the period leading up to a anticipated widespread implementation of protocol updates such as the Domain Name System Security Extensions (DNSSEC) and Secure Border Gateway Protocol (BGPsec) extensions. The Working Group presented its report on DNS Security issues in September 2012. This Final Report – BGP Best Practices documents the efforts undertaken by CSRIC Working Group 4 Network Security Best Practices with respect to securing the inter-domain routing infrastructure that is within the purview of ISPs, enterprises, and other BGP operators. Issues affecting the security of management systems that provide control and designation of routing and IP-space allocation records that BGP is based on were also considered. Routing and BGP related services are necessary and fundamental components of all ISP operations, and there are many established practices and guidelines available for operators to consult. Thus most ISPs have mature BGP/routing management and infrastructures in-place. Still, there remain many issues and exposures that introduce major risk elements to ISPs, since the system itself is largely insecure and unauthenticated, yet provides the fundamental traffic control system of the Internet. This report enumerates the issues the group identified as most critical and/or that may need more attention.Page [5] of [40] 2.1 CSRIC Structure Communications Security, Reliability, and Interoperability Council (CSRIC) III Working Group 5: DNSSEC Implementation Practices for ISPs Working Group 6: Secure BGP Deployment Working Group 4: Network Security Best Practices Working Group 7: Botnet Remediation Working Group 3: E911 Location Accuracy Working Group 8: E911 Best Practices Working Group 2: Next Generation Alerting Working Group 9: Legacy Broadcast Alerting Issues Working Group 1: Next Generation 911 Working Group 10: 911 Prioritization CSRIC Steering Committee Co-Chairs Working Group 1 Co-Chairs Working Group 2 Co-Chairs Working Group 3 Co-Chairs Working Group 4 Chair Working Group 5 Co-Chairs Working Group 6 Chair Working Group 7 Chair Working Group 8 Co-Chairs Working Group 9 Co-Chairs Working Group 10Page [6] of [40]The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [7] of [40] 2.2 Working Group [#4] Team Members Working Group [#4] consists of the members listed below for work on this report. Name Company Rodney Joffe – Co-Chair Neustar, Inc. Rod Rasmussen – Co-Chair Internet Identity Mark Adams ATIS (Works for Cox Communications) Steve Bellovin Columbia University Donna Bethea-Murphy Iridium Rodney Buie TeleCommunication Systems, Inc. Kevin Cox Cassidian Communications, an EADS NA Comp John Crain ICANN Michael Currie Intrado, Inc. Dale Drew Level 3 Communications Chris Garner CenturyLink Joseph Gersch Secure64 Software Corporation Jose A. Gonzalez Sprint Nextel Corporation Kevin Graves TeleCommunication Systems (TCS) Tom Haynes Verizon Chris Joul T-Mobile Mazen Khaddam Cox Kathryn Martin Access Partnership Ron Mathis Intrado, Inc. Danny McPherson Verisign Doug Montgomery NIST Chris Oberg ATIS (Works for Verizon Wireless) Victor Oppleman Packet Forensics Elman Reyes Internet Identity Ron Roman Applied Communication Sciences Heather Schiller Verizon Jason Schiller Google Marvin Simpson Southern Company Services, Inc. Tony Tauber Comcast Paul Vixie Internet Systems Consortium Russ White Verisign Bob Wright AT&T Name Company Rodney Joffe – Co-Chair Neustar, Inc. Rod Rasmussen – Co-Chair Internet Identity Mark Adams ATIS (Works for Cox Communications) Steve Bellovin Columbia University Donna Bethea-Murphy Iridium Rodney Buie TeleCommunication Systems, Inc. Kevin Cox Cassidian Communications, an EADS NA Comp John Crain ICANN Michael Currie Intrado, Inc. Dale Drew Level 3 CommunicationsThe Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [8] of [40] Chris Garner CenturyLink Joseph Gersch Secure64 Software Corporation Jose A. Gonzalez Sprint Nextel Corporation Kevin Graves TeleCommunication Systems (TCS) Tom Haynes Verizon Chris Joul T-Mobile Mazen Khaddam Cox Kathryn Martin Access Partnership Ron Mathis Intrado, Inc. Danny McPherson Verisign Doug Montgomery NIST Chris Oberg ATIS (Works for Verizon Wireless) Victor Oppleman Packet Forensics Elman Reyes Internet Identity Ron Roman Applied Communication Sciences Heather Schiller Verizon Jason Schiller Google Marvin Simpson Southern Company Services, Inc. Tony Tauber Comcast Paul Vixie Internet Systems Consortium Russ White Verisign Bob Wright AT&T Table 1 - List of Working Group Members 3 Objective, Scope, and Methodology 3.1 Objective This Working Group was convened to examine and make recommendations to the Council regarding best practices to secure the Domain Name System (DNS) and routing system of the Internet during the period leading up to what some anticipate might be widespread implementation of protocol updates such as the Domain Name System Security Extensions (DNSSEC) and Secure Border Gateway Protocol (BGPsec) extensions (though the latter outcome is not entirely uncontroversial). DNS is the directory system that associates a domain name with an IP (Internet Protocol) address. In order to achieve this translation, the DNS infrastructure makes hierarchical inquiries to servers that contain this global directory. As DNS inquiries are made, their IP packets rely on routing protocols to reach their correct destination. BGP is the protocol utilized to identify the best available paths for packets to take between points on the Internet at any given moment. This foundational system was built upon a distributed unauthenticated trust model that has been mostly sufficient for over two decades but has some room for improvement. These foundational systems are vulnerable to compromise through operator procedural mistakes as well as through malicious attacks that can suspend a domain name or IP address's availability, or compromise their information and integrity. While there are formal initiatives under way within the IETF (which has been chartered to develop Internet technical standards and protocols) that will improve this situation significantly, global adoption and implementation will take some time. The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [9] of [40] This Working Group will examine vulnerabilities within these areas and recommend best practices to better secure these critical functions of the Internet during the interval of time preceding deployment of more robust, secure protocol extensions. This report covers the BGP portion of these overall group objectives. 3.2 Scope Working Group 4’s charter clearly delineates its scope to focus on two subsets of overall network security, DNS and routing. It further narrows that scope to exclude consideration of the implementation of DNSSEC (tasked to Working Group 5) and secure extensions of BGP (tasked to Working Group 6). While those groups deal with protocol modifications requiring new software and/or hardware deployments; WG4 is geared toward items that either don't require these extensions or are risks which are outside the scope of currently contemplated extensions. For this report regarding BGP, the focus is on using known techniques within the Operator community. Some of these methods and the risks they seek to address are useful even in cases where protocol extensions are used in some future landscape. 3.3 Methodology With the dual nature of the work facing Working Group 4, the group was divided into two subgroups, one focused on issues in DNS security, another in routing security. Starting in December 2011, the entire Working Group met every two weeks via conference call(s) to review research and discuss issues, alternating between sub-groups. The group created a mailing list to correspond and launched a wiki to gather documents and to collectively collaborate on the issues. Additional subject matter experts were occasionally tapped to provide information to the working group via conference calls. The deliverables schedule called for a series of reports starting in June 2012 that would first identify issues for both routing and DNS security, then enumerate potential solutions, and finally present recommendations. The initial deliverables schedule was updated in March in order to concentrate efforts in each particular area for separate reports. This first report on DNS security issues was presented in September 2012, and this, the second report on routing issues, is being published in March 2013. Based on the discussions of the group, a list of BGP risks, potential solutions, and relevant BCP documents was created and refined over the course of the work. Subject matter experts in BGP then drove development of the initial documentation of issues and recommendations. These were then brought together into a full document for review and feedback. Text contributions, as completed, were reviewed, edited and approved by the full membership of Working Group 4. 4 Background 4.1 Deployment Scenarios BGP is deployed in many different kinds of networks of different size and profiles. Many different recommendations exist to improve the security and resilience of the inter-domain routing system. Some of the advice can even appear somewhat contradictory and often the key decision can come down to understanding what is most important or appropriate for a given network considering its size, the number of external connections, number of BGP routers, size The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [10] of [40] and expertise of the staff and so forth. We attempt to tailor the recommendations and highlight which are most significant for a given network operator’s situation. Further background and information on routing operations can be found in the Appendix (Section 7) of this document for readers unfamiliar with this area of practice. 5 Analysis, Findings and Recommendations The primary threats to routing include: • Risks to the routers and exchange of routing information • Routing information that is incorrect or propagates incorrectly • General problems with network operations 5.1 BGP Session-Level Vulnerability When two routers are connected and properly configured, they form a BGP peering session. Routing information is exchanged over this peering session, allowing the two peers to build a local routing table, which is then used to forward actual packets of information. The first BGP4 attack surface is the peering session between two individual routers, along with the routers themselves. Two classes of attacks are included here, session hijacking and denial of service. 5.1.1 Session Hijacking The BGP session between two routers is based on the Transport Control Protocol (TCP), a session protocol also used to transfer web pages, naming information, files, and many other types of data across the Internet. Like all these other connection types, BGP sessions can be hijacked or disrupted, as shown in Figure 1. Figure 1: Session Hijacking In this diagram, the attack host can either take over the existing session between Routers A and B, or build an unauthorized session with Router A. By injecting itself into the peering between Routers A and B, the attacker can inject new routing information, change the routing information being transmitted to Router A, or even act as a “man in the middle,” modifying the routing information being exchanged between these two routers.The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [11] of [40] 5.1.1.1 Session-Level Countermeasures Current solutions to these types of attacks center on secure hash mechanisms, such as HMACMD5 (which has been deprecated) and HMAC-SHA. These mechanisms rely on the peering routers sharing a key (a shared key – essentially a password) that is used to calculate a cryptographic hash across each packet (or message) transmitted between the two routers, and included in the packet itself. The receiving router can use the same key to calculate the hash. If the hash in the packet matches the locally calculated hash, the packet could have only been transmitted by another router that knows the shared key. This type of solution is subject to a number of limitations. First, the key must actually be shared among the routers building peering sessions. In this case, the routers involved in the peering session are in different administrative domains. Coming to some uniform agreement about how keys are generated and communicated (e.g. phone, email, etc.) with the often hundreds of partners and customers of an ISP is an impractical task. There is the possibility that any key sharing mechanism deployed to ease this administrative burden could, itself, come under attack (although such attacks have never been seen in the wild). Lastly, some concerns have been raised that burden of cryptographic calculations could itself become a vector for a Denial-of-Service (DoS) attack by a directed stream of packets with invalid hash components. One way to deny service is to make the processor that is responsible for processing routing updates and maintaining liveness too busy to reliably process these updates in a timely manner. In many routers the processor responsible for calculating the cryptographic hash is also responsible for processing new routing information learned, sending out new routing information, and even transmitting keep-alive messages to keep all existing sessions up. Since calculating the cryptographic hash is computationally expensive, a smaller flood of packets with an invalid hash can consume all the resources of the processor, thus making it easier to cause the processor to be to busy. Other mechanisms, such as the Generalized TTL Security Mechanism (GTSM, described in RFC 5082), focus on reducing the scope of such attacks. This technique relies on a feature of the IP protocol that would prevent an attacker from effectively reaching the BGP process on a router with forged packets from some remote point on the Internet. Since most BGP sessions are built across point-to-point links (on which only two devices can communicate), this approach would prevent most attackers from interfering in the BGP session. Sessions built over a shared LAN, such as is the case in some Internet exchanges, will be protected from those outside the LAN, but will remain vulnerable to all parties that are connected to the LAN. This solution is more complicated to implement when BGP speaking routers are not directly connected. It is possible to count the number of hops between routers and limit the TTL value to only that number of hops. This will provide some protection, limiting the scope of possible attackers to be within that many hops. If this approach is used consider failure scenarios of devices between the pair of BGP speaking routers, what impact those failures will have on the hop count between the routers, and if you want to expand the TTL value to allow the session to remain up for a failure that increases the hop count. The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [12] of [40] 5.1.1.2 Session-Level Current Recommendations For a network with a small (e.g., single-digit) number of eBGP neighbors, it is reasonable to follow the lead of what is specified by the upstream ISPs who may have a blanket policy of how they configure their eBGP sessions. A network with larger numbers of eBGP neighbors may be satisfied that they can manage the number of keys involved either through data-store or rubric. Note that a rubric may not always be feasible as you cannot ensure that your neighbors will always permit you to choose the key. Managing the keys for a large number of routers involved in BGP sessions (a large organization may have hundreds or thousands of such routers) can be an administrative burden. Questions and issues can include: • In what system should the keys be stored and who should have access? • Should keys be unique per usage having only one key for internal usage and another key that is shared for all external BGP sessions? • Should keys be unique per some geographical or geo-political boundary say separate keys per continent or per country or per router? • Should keys be unique to each administrative domain, for example a separate key for each Autonomous System a network peers with? • There is no easy way to roll over keys, as such changing a key is quite painful, as it disrupts the transmission of routing information, and requires simultaneous involvement from parties in both administrative domains. This makes questions of how to deal with the departure of an employee who had access to the keys, or what keys to use when peering in a hostile country more critical. Another consideration is the operational cost of having a key. Some routing domains will depend on their peers to provide the key each time a new session is established, and not bother to make a record of the key. This avoids the problems of how to store the key, and ensure the key remains secure. However if a session needs to be recreated because configuration information is lost either due to accidental deletion of the configuration, or hardware replacement, then the key is no longer known. The session will remain down until the peer can be contacted and the key is re-shared. Often times this communication does not occur, and the peer may simply try to remove the key as a troubleshooting step, and note the session reestablishes. When this happens the peer will often prefer for the session to remain up, leaving the peering session unsecured until the peer can be contacted, and a maintenance window can be scheduled. For unresponsive peers, an unsecured peering session could persist, especially considering that the urgency to address the outage has now passed. Despite these vulnerabilities having been widely known for a decade or more, they have not been implicated in any notable number of incidents. As a result some network operators have not found the cost/benefit trade-offs to warrant the operational cost of deploying such mechanisms while others have. Given these facts, the Working Group recommends that individual network operators continue to make their own determinations in using these countermeasures. 5.1.2 Denial of Service (DoS) Vulnerability Because routers are specialized hosts, they are subject to the same sorts of Denial of Service (DoS) attacks any other host or server is subject to. These attacks fall into three types:The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [13] of [40] 1. Attacks that seek to consume all available interface bandwidth making it difficult for enough legitimate traffic to get through such as UDP floods and reflective attacks 2. Attacks that seek to exhaust resources such as consume all available CPU cycles, memory, or ports so that the system is too busy to respond such as TCP SYN attacks 3. Attacks utilizing specially crafted packets in an attempt to cause the system to crash or operate in an unexpected way such as buffer overflow attacks, or malformed packet attacks that create an exception that is not properly dealt with Bandwidth exhaustion attacks attempt to use so much bandwidth that there is not enough available bandwidth for services to operate properly. This type of an attack can cause routers to fail to receive routing protocol updates or keep-alive messages resulting in out-of-date routing information, routing loops, or interruption of routing altogether, such as happens when a BGP session goes down and the associated routing information is lost. Resource exhaustion attacks target traffic to the router itself, and attempt to make the router exhaust its CPU cycles or memory. In the case of the former, the router’s CPU becomes too busy to properly process routing keepalives and updates causing the adjacencies to go down. In the case of the latter, the attacker sends so much routing protocol information that the router has no available memory to store all of the required routing information. Crafted packets attacks attempt to send a relatively small number of packets that the router does not deal with appropriately. When a router receives this type of packet it may fill up interface buffers and then not forward any traffic on that interface causing routing protocols to crash and restart, reboot, or hang. In some cases the router CPU may restart, reboot, or hang likely causing loss of all topological and routing state. One example was the “protocol 55” attack, where some router vendors simply did not code properly how to deal with this traffic type. Some routers are specialized to forward high rates of traffic. These routers often implement their forwarding capabilities in hardware that is optimized for high throughput, and implement the less demanding routing functions in software. As such bandwidth exhaustion attacks are targeted at the routers interfaces or the backplane between those interfaces, or the hardware responsible for making forwarding decisions. The other types of attack target the software responsible for making the routing decisions. Due to the separation between routing and forwarding, a fourth class of attacks are targeted at exhausting the bandwidth of the internal interconnection between the forwarding components and the routing components. The section below on “Denial-of-Service Attacks on ISP Infrastructure” contains a discussion of disruptive attacks besides those targeting the exchange of BGP routing information. 5.1.2.1 Denial of Service Countermeasures GTSM, described above, can be an effective counter to some forms of DoS attacks against routers, by preventing packets originating outside the direct connection between two BGP peers from being processed by the router under attack. GTSM cannot resolve simple buffer overflow problems, or DoS attacks that exploit weaknesses in packet processing prior to the TTL check, however.The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [14] of [40] Another mechanism currently used to prevent DoS attacks against routers is to simply make the interfaces on which the BGP session is running completely unreachable from outside the local network or the local segment. Using link-local addresses in IPv6, is one technique (with obviously limited applicability). Another approach is applying packet-filters on the relevant address ranges at the network edge. (This process is called infrastructure filtering). Other well-known and widely deployed DoS mitigation techniques can be used to protect routers from attack just as they can be used to protect other hosts. For instance, Control Plane Policing can prevent the routing process on a router from being overwhelmed with high levels of traffic by limiting the amount of traffic accepted by the router directed at the routing processor itself. 5.1.2.2 Denial-of-Service Current Recommendations Since routers are essentially specialized hosts, mechanisms that can be used to protect individual routers and peering sessions from attack are widely studied and well understood. What prevents these techniques from being deployed on a wide scale? Two things: the perception that the problem space is not large enough to focus on, and the administrative burden of actually deploying such defenses. For instance, when GTSM is used with infrastructure filtering, cryptographic measures may appear to be an administrative burden without much increased security. Smaller operators, and end customers, often believe the administrative burden too great to configure and manage any of these techniques. Despite these vulnerabilities having been widely known for a decade or more, they have not been implicated in any notable number of incidents. As a result some network operators have not found the cost/benefit trade-offs to warrant the operational cost of deploying such mechanisms while others have. Given these facts, the Working Group recommends that individual network operators continue to make their own determinations in using these countermeasures. In dealing with vulnerabilities due to “crafted packets”, the vendor should provide notification to customers as the issues are discovered as well as providing fixed software in a timely manner. Customers should make it a point to keep abreast of notifications from their vendors and from various security information clearing-houses. 5.1.2.2.1 Interface Exhaustion Attacks Recommendations include: 1. Understanding the actual forwarding capabilities of your equipment in your desired configuration 2. Examining your queuing configuration 3. Carefully considering which types of traffic share a queue with your routing protocols, and if that traffic can be blocked, rate-limited or forced to another queue 4. Understanding packet filtering capabilities of your equipment, and under what scenarios it is safe to deploy packet filters 5. When it is safe to do so, tactically deploy packet filers upstream from a router that is being attacked The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [15] of [40] The first thing to consider with regard to attacks that attempt to consume all available bandwidth is to determine the actual throughput of the router. It is not safe to assume that a router with two or more 100 Gigabit Ethernet interfaces can receive 100G on one interface and transmit that same 100G out another interface. Some routers can forward at line rate, and some routers cannot. Performance may vary with packet size, for example forwarding traffic in software is more taxing on the CPU as the number of packets increases. The next thing to consider is outbound queuing of routers upstream from the router that is being attacked. Routers typically place routing protocol traffic in a separate Network Control (NC) queue. Determine the characteristics of this queue such as the queue depth and frequency of it being serviced. These values may be tunable. Also consider what types of traffic are placed in this queue, and specifically what traffic an outside attacker can place in this queue. Consider preventing users of the network from being able to place traffic in this queue if they do not need to exchange routing information with your network. For direct customers running eBGP, limit traffic permitted into the NC queue to only traffic required to support their routing protocols. Consider rate-limiting this traffic so no one customer can fill up the queue. Note that rate limits will increase convergence time, so test a customer configuration that is advertising and receiving the largest set of routes, and measure how long it takes to re-learn the routing table after the BGP session is reset with and without the rate limits. If the attack traffic has a particular profile, and all traffic matching that profile can be dropped without impacting legitimate traffic than a packet filter can be deployed upstream from the router that is under attack. Ensure that a deploying a packet filter will not impact the performance of your router by testing packets filters with various types of attacks and packet sizes on your equipment in a lab environment. Ensure that total throughput is not decreased, and that there is not a particular packet per second count that causes the router to crash, or become unresponsive, or stop forwarding traffic reliably, cause routing protocols to time out, etc. If the upstream router belongs to a non-customer network, you will need to work with them to mitigate the attack. Additional bandwidth on the interconnect may allow you to move the bottleneck deeper into your network where you can deal with it. Often the IP destination of these attacks is something downstream from the router. It is possible that some or all of the attack traffic may be destined to the router. In that case some of the mitigation techniques in the next section may also be helpful. 5.1.2.2.2 Resource Exhaustion Attacks Recommendations include: 1. Consider deploying GTSM 2. Consider making router interfaces only reachable by directly connected network 3. Consider only permitting traffic sourced from configured neighbors 4. Consider deploying MD5 5. Deploy maximum prefixes The first set of recommendations is to consider deploying mechanisms that restrict who can send routing protocol traffic to a router. The second set of recommendations restricts how much routing protocol state a neighbor can cause a router to hold.The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [16] of [40] GTSM, described above, can be an effective counter to some forms of DoS attacks against routers, by limiting who can send routing protocol traffic to the router by a configured hop-count radius. GTSM works by preventing packets originating outside the direct connection between two BGP peers from being processed by the router under attack. GTSM cannot resolve simple buffer overflow problems, or DoS attacks that exploit weaknesses in packet processing prior to the TTL check, however. Another mechanism currently used to prevent DoS attacks against routers is to simply make the interfaces on which the BGP session is running completely unreachable from outside the local network or the local segment. Using link-local addresses in IPv6, is one technique (with obviously limited applicability). Another approach is applying packet-filters on the relevant address ranges at the network edge. (This process is called infrastructure filtering). Some routers can dynamically generate packet filters from other portions of the router configuration. This enables one to create an interface packet filter that only allows traffic on the BGP ports from source IP addresses that belong to a configured neighbor. This means attempts to send packets to the BGP port by IP addresses that are not a configured neighbor will be dropped right at the interface. The same session level protections discussed earlier, such as MD5 can also limit who can send routing information to only those routers or hosts that have the appropriate key. As such this is also an effective mechanism to limit who can send routing protocol traffic. While these packets will be processed by the router, and could possibly tax the CPU, they cannot cause the router to create additional routing state such as adding entried to the Routing Information Base (RIB) or Forwarding Information Base (FIB). Lastly each neighbor should be configured to limit the number of prefixes they can send to a reasonable value. A single neighbor accidentally or intentionally de-aggregating all of the address space they are permitted to send could consume a large amount of RIB and FIB memory, especially with the large IPv6 allocations. 5.1.2.2.3 Crafted Packet Attacks Crafted packet attacks typically occur when a router receives some exception traffic that the vendor did not plan for. In some cases it may be possible to mitigate these attacks by filtering the attack traffic if that traffic has a profile that can be matched on and all traffic matching that profile can be discarded. More often then not, this is not the case. In all cases the vendor should provide new code that deals with the exception. Recommendations include keeping current with all SIRT advisories from your vendors. When vulnerability is published move quickly to upgrade vulnerable versions of the code. This may require an upgrade to a newer version of the code or a patch to an existing version. For larger organizations that have extensive and lengthy software certification programs, it is often more reasonable to ask the vendor to provide a patch for the specific version(s) of code that organization is running. If possible the vendor should provide the extent to which the code is modified to quantify how substantial the change is in order to help the provide plan what should be included in the abbreviated software certification tests.The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [17] of [40] For smaller organizations, or organizations that complete little or no software certification, the newer version of code with the fix in place should be deployed. Generally this is deployed cautiously at first to see if issues are raised with a limited field trail, followed by a more widespread deployment. 5.1.2.2.4 Internal Bandwidth Exhaustion Attacks Other well-known and widely deployed DoS mitigation techniques can be used to protect routers from attack just as they can be used to protect other hosts. For instance, Control Plane Policing can prevent the routing process on a router from being overwhelmed with high levels of traffic by limiting the amount of traffic accepted by the router directed at the routing processor itself. One should consider not only the impact on the router CPU, but also the impact on the bandwidth between the forward components and the router CPU. There may be some internal queuing in place on the interconnect between the forwarding components and the router CPU. It may be possible to influence which queue routing protocol traffic is placed in, or with queue traffic generated by customers is placed in, the depths and or servicing of these queues in order to separate and minimize the ability of non-routing traffic to impact routing traffic. If traffic generated by customers (for routing protocols or otherwise) can crowd out a network’s internal routing protocol traffic, then operators may consider separately rate limiting this customer traffic. 5.1.3 Source-address filtering Many Internet security exploits hinge on the ability of an attacker to send packets with spoofed source IP addresses. Masquerading in this way can give the attacker entre to unauthorized access at the device or application level and some BGP vulnerabilities are also in this category. The problem of source-spoofing has long been recognized and countermeasures available for filtering at the interface level. 5.1.3.1 Source-address spoofing example Consider the diagram below which illustrates the legitimate bi-directional traffic flow between two hosts on the left-hand side. An attacker connected to another network can send IP packets with a source address field set to the address of one of the other machines, unless filtering is applied at the point that that attacker’s host or network attaches to AS65002.The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [18] of [40] 5.1.3.2 Source-address spoofing attacks Though most IP transactions are bi-directional, attacks utilizing spoofed source IPs do not require bi-directional communication but instead exploit particular protocol or programmatic semantic weaknesses. Exploits using this technique have covered many areas over the years including these types followed by some examples. • Attacks against services which rely only on source IP of the incoming packet for authorization • rsh, rlogin, NFS, Xwindows, etc. • Attacks where the unreachability of the source can be exploited • TCP SYN floods which exhaust resources on the server • Attacks where the attacker masquerades as the “victim” • Small DNS or SNMP requests resulting in highly asymmetric data flow back toward the victim • Abusive traffic which result in the legitimate user getting blocked from the server or network 5.1.3.3 Source-address filtering challenges The barriers to implementing these countermeasures have ranged from lack of vendor support to lack of solid motivation to implement them. • Lack of proper vendor support: In older implementations of network devices, filtering based on the source address of a packet was performed in software, rather than hardware, and thus had a major impact on the rate of forwarding through the device. Most modern The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [19] of [40] network equipment can perform source filtering in the hardware switching path, eliminating this barrier to deployment. • Lack of scalable deployment and configuration management: In older deployments, filters based on the source of traffic was configured and managed manually, adding a large expense to the entity running the network. This barrier has largely been resolved through remote triggered black hole, unicast Reverse Path Forwarding (uRPF), and loose uRPF options. • Fear of interrupting legitimate traffic, for example in multi-homed situations: Vendors have created flexibility in uRPF filtering to reduce or eliminate this barrier. Future possible additions include “white lists,” which would allow traffic to pass through a uRPF check even though it didn’t meet the rules. • Lack of business motivation: Unilateral application of these features does not benefit or protect a network or its customers directly; rather, it contributes to the overall security of the Internet. Network operators are realizing that objection to incurring this “cost” is being overcome by the realization that if everyone performs this type of filtering, then everyone benefits. 5.1.3.4 Source-address filtering recommendations Filtering should be applied as close as possible to the entry point of traffic. Wherever one host, network, or subnet is attached, a feature such as packet filtering, uRPF, or source-addressvalidation should be used. Ensure adequate support from equipment vendors for subscribermanagement systems (e.g. for Cable and DSL providers) or data-center or campus routers and switches. Stub networks should also filter traffic at their borders to ensure IP ranges assigned to them do not appear in the source field of incoming packets and only those ranges appear in the source field of outgoing packets. Transit networks should likewise use features such as uRPF. Strict mode should be used at a border with a topological stub network and loose mode between transit networks. Transit networks that provide connectivity primarily stub networks, such as consumer ISPs, should consider uRPF strict mode on interfaces facing their customers. If these providers provide a home router to their customers they should consider making uRPF part of the default home router configuration. Transit networks that provide connectivity to a mix of stub networks and multi-homed networks must consider the administrative burden of configuring uRPF strict mode only on stub customers and uRPF loose mode, or no uRPF on customers that are, or become multi-homed. When using uRPF loose mode with the presence of a default route, one must special care to consider configuration options to include or exclude the default route. The value of loose mode uRPF with networks in the default free zone is debatable. It will only prevent traffic with a source address of RFC-1918 space and dark IPs (IP addresses that are not routed on the Internet). Often these dark IP addresses are useful for backscatter techniques and The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [20] of [40] tracing the source(s) of a spoofed DoS attack. It is also important to consider if RFC-1918 addresses are used internal to the transit provider’s network. This practice may become more common if ISPs implement Carrier Grade NAT. It is also worth pointing out that some business customers depend on VPN software that is poorly implemented, and only changes the destination IP address when re-encapsulating a packet. If these customers are using non-routed IP addresses in their internal network then enabling uRPF will break these customers. It is important to measure the impact of forwarding when enabling uRPF. Even when uRPF is implemented in hardware, the router must lookup the destination as well as the source. A double lookup will cause forwarding throughput to be reduced by half. This may have no in the forwarding rate if the throughput of the forwarding hardware is more than twice the rate of all the interfaces it supports. Further, more detailed advice and treatment of this subject can be found in: • IETF BCP38/RFC 2827 Network Ingress Filtering1 • BCP 84/RFC 3704 Ingress Filtering for Multihomed Networks2 5.2 ICANN SAC004 Securing the EdgeBGP Injection and Propagation Vulnerability A second form of attack against the routing information provided by BGP4 is through injection of misleading routing information, as shown in figure 2. Figure 2: A Prefix Hijacking Attack In this network, AS65000 has authority to originate 192.0.2.0/24. Originating a route, in this context, means that computers having addresses within the address space advertised are actually reachable within your network — that a computer with the address 192.0.2.1, for instance, is physically attached to your network. Assume AS65100 would like to attract traffic normally destined to a computer within the 192.0.2.0/24 address range. Why would AS65100 want to do this? There are a number of possible motivations, including: 1 http://tools.ietf.org/html/bcp38 2 http://tools.ietf.org/html/bcp84The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [21] of [40] • A server with an address in this range accepts logins from customers or users, such as a financial web site, or a site that hosts other sensitive information, or information of value • A server with an address in this range processes information crucial to the operation of a business the owner of AS65100 would like to damage in some way, such as a competitor, or a political entity under attack AS65100, the attacker, can easily attract packets normally destined to 192.0.2.0/24 within AS65000 by simply advertising a competing route for 192.0.2.0/24 to AS65002. From within BGP itself, there’s no way for the operators in AS65002 to know which of these two advertisements is correct (or whether both origins are valid – a configuration which does see occasional legitimate use). The impact of the bogus information may be limited to the directly neighboring AS(es) depending on the routing policy of the nearby ASes. The likelihood of the incorrect route being chosen can be improved by two attributes of the route: • A shorter AS Path A shorter AS Path has the semantic value of indicating a topologically “closer” network. In the example above, the normal propagation of the route would show AS65100 as “closer” to AS65001 thus, other factors being equal, more preferred than the legitimate path via AS65000. • A longer prefix Longer prefixes represent more-specific routing information, so a longer prefix is always preferred over a shorter one. For instance, in this case the attacker might advertise 192.0.2.0/25, rather than 192.0.2.0/24, to make the false route to the destination appear more desirable than the real one. • A higher local-preference setting Local-preference is the non-transitive BGP attribute that most network operators use to administratively influence their local routing. Typically, routes learned from a “customer” (i.e., paying) network are preferred over those where the neighboring network has a non-transit relationship or where the operator is paying for transit from the neighboring network. This attribute is more important in the decision algorithm for BGP than AS-path length so routes learned over such a session can draw traffic even without manipulation of the AS-path attribute. This illustration can be used to help describe some related types of risks:The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [22] of [40] Figure 3: BGP Propagation Vulnerabilities Route Leak: In this case, AS65100 is a transit customer of both AS65000 and AS65002. The operator of AS65100 accidentally leaks routing information advertised by AS65000 into its peering session with AS65002. This could possibly draw traffic passing from AS65002 towards a destination reachable through AS65000 through AS65100 when this path was not intended to provide transit between these two networks. Most often such happenings are the result of misconfigurations and can result in overloading the links between AS65100 and the other ASes. As the name suggests, this phenomenon has most often been the result of inadvertent misconfiguration. Occasionally they can result in more malicious outcomes: • Man in the Middle: In this case, all the autonomous systems shown have non-transit relationships. For policy reasons, AS65000 would prefer traffic destined to 192.0.2.0/24 pass through AS65100. To enforce this policy, AS65000 filters the route for 192.0.2.0/24 towards AS65100. In order to redirect traffic through itself (for instance, in order to snoop on the traffic stream), AS65100 generates a route advertisement that makes it appear as if AS65000 has actually advertised 192.0.2.0/24, advertising this route to AS65002, and thus drawing traffic destined to 192.0.2.0/24 through itself. • False Origination: This attack is similar to the man in the middle explained above, however in this case there is no link between AS65000 and AS65100. Any traffic destined to 192.0.2.0/24 into AS65100, is discarded rather than being delivered. Note that all three of these vulnerabilities are variations on a single theme: routing information that should not be propagated based on compliance with some specific policy nonetheless is. 5.2.1 BGP Injection and Propagation Countermeasures 5.2.1.1 Prefix Filtering The key bulwark against entry and propagation of illegitimate routing announcements into the global routing system is prefix-level filtering; typically at the edge between the ISP and their customers. The usual method involves the customer communicating a list of prefixes and downstream ASes which they expect to be reachable through the connection to the ISP. The ISP will then craft a filter applied to the BGP session which explicitly enumerates this list of expected prefixes (a “prefix-list”), perhaps allowing for announcement of some more-specific The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [23] of [40] prefixes within the ranges such as might be needed by the customer to achieve some goals in adjusting the load of the customer’s inbound traffic across their various connections. This configuration forms a “white-list”, in security parlance, of possible downstream destinations but does not validate the overall semantic correctness of the resulting routing table. 5.2.1.1.1 Manual Prefix-Filter Limitations The validity of the information in this list is obviously important and if the customer is either malicious or simply mistaken in the prefixes they communicate, the prefix filter could obviously still leave open a vulnerability to bogus route injection. Thus typically the information communicated by the customer is checked against registration records such as offered in the “whois” information available from Regional Internet Registries (RIRs) and/or others in the address assignment hierarchy. However, there is no information in the RIR information explicitly indicating a mapping between the address-assignment and the origin AS. The reality is that the process of manually checking a filter that is a few thousand lines long, with hundreds of changes a week is tedious and time consuming. Many transit providers do not check at all. Others have a policy to always check, but support staff may grow complacent with updates from certain customers that have long filter lists or have filter lists that change weekly, especially if those customers have never had a questionable prefix update in the past. In these cases they may only spot-check, only check the first few changes and then give up, or grow fatigued and be less diligent the more lines they check. Moreover, the entity name fields in the “whois” information are free-form and often can’t be reliably matched to the entity name used by the ISP’s customer records. Typically it is a fuzzy match between the customer name on record and the company name listed in the “whois” record. A truly malicious actor could order service with a name which is intentionally similar to a company name whose IP addresses they intend to use. Another possibility is that the company name on record is legitimate and an exact match to the company name on the “whois” record, but the customer is a branch office, and the legitimate holder of the IP addresses is the corporate office which has not authorized the branch office to use their space. It is also possible that the customer is the legitimate holder of the address space, but the individual who called in to the provider support team is not authorized to change the routing of the IP block in question. This problem is further complicated when a transit provider’s customer has one or more downstream customers of its own. These relationships are typically hard or impossible to verify. If every transit provider accurately filtered all of the prefixes their customers advertised, and each network that a transit provider peers with could be trusted to also accurately filter all of the prefixes of their customers, then route origination and propagation problems could be virtuailly eliminated. However, managing filters requires thousands of operators examining, devising, and adjusting the filters on millions of devices throughout the Internet. While there are processes and tools within any given network, such highly inconsistent processes, particularly when handling large amounts of data (a tedious process in and of itself), tends to produce an undesirable rate of errors. Each time an individual operator misjudges a particular piece of information, or simply makes a mistake in building a filter, the result is a set of servers (or services) that are unreachable until the mistake is found and corrected. 5.2.1.2 Internet Routing Registry (IRR) The second source of information a provider can use as a basis for filtering received routing The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [24] of [40] information is a voluntary set of databases of routing policy and origination called Internet Routing Registries (IRRs). These IRRs allow providers to register information about their policies towards customers and other providers, and also allow network operators to register which address space they intend to originate. Some providers require their customers to register their address space in an IRR before accepting the customer’s routes, oftentimes the provider will “proxy register” information on the customer’s behalf since most customers are not versed in IRR details. 5.2.1.2.1 IRR Limitations Because IRRs are voluntary, there is some question about the accuracy and timeliness of the information they contain (see Research on Routing Consistency in the Internet Routing Registry by Nagahashi and Esaki for a mostly negative view, and How Complete and Accurate is the Internet Routing Registry by Khan for a more positive view). Anecdotally, RIPE’s IRR is in widespread use today, and some large providers actually build their filtering off this database, so the accuracy level is at least operationally acceptable for some number of operators. Some IRR repositories use an authorization model as well as authentication but none that primarily serve North America perform RPSL authorization using the scheme described in RFC2725 – Routing Policy System Security3 . 5.2.1.3 AS-Path filtering Filters on the AS_PATH contents of incoming BGP announcements can also be part of a defensive strategy to guard against improper propagation of routing information. Some ISPs have used AS-path filters on customer-facing BGP sessions instead of prefix-filters. This approach is generally inadequate to protect against even the most naïve misconfigurations, much less a deliberate manipulation. Often a leak has involved either redistributing BGP routes inadvertently from one of a stub network’s ISPs to the other. Another problem in the past has involved redistributing BGP routes into an internal routing protocol and back to BGP. Where AS-path filters can be useful is to guard against an egregious leak. For instance an ISP would not expect ASNs belonging to known large ISPs to show up in the AS_PATH of updates from an enterprise-type customer network. Applying an AS-path filter to such a BGP session could act as a second line of defense to the specific prefix-list filter. Similarly, if there are networks which the ISP has non-transit relationships with, applying a similar AS-path filter to those sessions (which wouldn’t be candidates for prefix filters) could help guard against a leak resulting in an unintended transit path. 5.2.1.3.1 AS-Path filtering limitations Maintaining such a list of “known” networks which aren’t expected to show up in transit adjacencies can be fairly manual, incomplete and error-prone. Again, applying a filter which validates the neighbor AS is in the path is useless since this state is the norm of what’s expected. 5.2.1.4 Maximum-prefix cut-off threshold Many router feature-sets include the ability to limit the number of prefixes that are accepted from a neighbor via BGP advertisements. When the overall limit is exceeded, the BGP session is torn down on the presumption that this situation is a dangerous error condition. Typically also a threshold can be set at which a warning notification (e.g. log message) to the Operations staff 3 https://tools.ietf.org/html/rfc2725The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [25] of [40] is issued. This way a gradual increase in the number of advertisements will trigger a sensible manual raise in the cut-off threshold without causing an outage. This tool can be used to guard against the most egregious leaks which can, if the numbers are large enough, exhaust the routing table memory on the recipient’s routers and/or otherwise cause widespread network instability. Typical deployments will set the threshold based on the current observed number of advertisements within different bands; for instance, 1-100, 100-1000, 1000-5000, 5000-10000, 10000-50000, 50,000-100,000, 100,000-150,000, 150,000-200,000, 200,000-250,000, 250,000- 300,000. 5.2.1.4.1 Maximum-prefix limitations When the threshold is exceeded, the session is shut down and manual intervention is required to bring it back up. In the case where a network has multiple interconnection points to another network (thus multiple BGP neighbors), all sessions will typically go down at the same time assuming all are announcing the same number of prefixes. In this case, it may be the case that all connectivity between the two networks is lost during this period. Obviously this measure is an attempt to balance two different un-desirable outcomes so must be weighed judiciously. Above 10000 or perhaps 50000 (e.g., a full Internet routing table from a transit provider), applying maximum-prefix thresholds provide limited protection. A small number of neighbors each advertising a unique set of 300,000 routes would fill the memory of the receiving router anyway. However if these neighbors are all advertising a large portion of the Internet routes, with many routes overlapping, then the limit offers some protection. 5.2.1.5 Monitoring Aside from a proactive filtering approach, a network operator can use various vantage points external to their own network (e.g, “route servers” or “looking glasses”) to monitor the prefixes for which they have authority to monitor for competing announcements which may have entered the BGP system. Some tools such as BGPmon have been devised to automate such monitoring. 5.2.1.5.1 Monitoring Limitations Obviously, this approach is reactive rather than proactive and steps would then need to be taken to contact the offending AS and/or intermediate AS(es) to stop the advertisement and/or propagation of the misinformation. Also, the number of such vantage points is limited so a locally impacting bogus route may or may not be detected with this method. 5.2.2 BGP Injection and Propagation Recommendations The most common router software implementations of BGP do not perform filtering of route advertisements, either inbound or outbound, by default. While this situation eases the burden of configuration on network operators (the customers of the router vendors), it has also caused the majority of unintentional inter-domain routing problems to date. Thus it is recommended that network operators of all sizes take extra care in configuration of BGP sessions to keep unintentional routes from being injected and propagated. Stub network operators should configure their outbound sessions to only explicitly allow the The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [26] of [40] prefixes which they expect to be advertising over a particular session. ISPs should explicitly filter their inbound sessions at the boundary with their “customer edge”. The inter-provider connections between large ISPs are impractical locations for filtering given the requirement for significant dynamism in BGP routing and traffic-engineering across the global Internet. However, the cumulative gains accrued when each ISP filters at this “customer edge” are significant enough to lessen the residual risk of not filtering on these “non-customer” BGP sessions. ISPs (and even stub networks) should also consider using AS-path filters and maximum-prefix limits on sessions as a second line of defense to guard against leaks or other pathological conditions. 5.3 Other Attacks and Vulnerabilities of Routing Infrastructure There are many vulnerabilities and attack vectors that can be used to disrupt the routing infrastructure of an ISP outside of the BGP protocol and routing-specific operations. These are just as important to address as issues the working group has identified within the routing space itself. The largest attack surface for routing infrastructure likely lies within the standard operational security paradigm that applies to any critical networked asset. Therefore the working group looked at including BCPs relating to network and operational security as part of addressing these issues, and ISPs should be aware that they are likely to see attacks against their routing infrastructure based on these “traditional” methods of computer and network intrusion. 5.3.1 Hacking and unauthorized 3rd party access to routing infrastructure ISPs and all organizations with an Internet presence face the ever-present risk of hacking and other unauthorized access attempts on their infrastructure from various actors, both on and off network. This was already identified as a key risk for ISPs, and CSRIC 2A – Cyber Security Best Practices was published in March 2011 to provide advice to address these types of attacks and other risks for any ISP infrastructure elements, including routing infrastructure. The current CSRIC III has added a new Working Group 11 that will report out an update to prior CSRIC work in light of recent advancements in cybersecurity practices and a desire of several US government agencies to adopt consensus guidelines to protect government and critical infrastructure computers and networks. A recent SANS publication, Twenty Critical Security Controls for Effective Cyber Defense: Consensus Audit Guidelines (CAG)4 lays out these principals and maps them out versus prior work, including another relevant document, NIST SP-800-53 Recommended Security Controls for Federal Information Systems and Organizations. 5 The SANS publication appears to be a primary driver for Working Group 11’s work. The entire document is available for review, and we have included the 20 topic areas here for reference: 4 http://www.sans.org/critical-security-controls/ 5 http://csrc.nist.gov/publications/PubsSPs.htmlThe Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [27] of [40] Critical Control 1: Inventory of Authorized and Unauthorized Devices Critical Control 2: Inventory of Authorized and Unauthorized Software Critical Control 3: Secure Configurations for Hardware and Software on Laptops, Workstations, and Servers Critical Control 4: Continuous Vulnerability Assessment and Remediation Critical Control 5: Malware Defenses Critical Control 6: Application Software Security Critical Control 7: Wireless Device Control Critical Control 8: Data Recovery Capability Critical Control 9: Security Skills Assessment and Appropriate Training to Fill Gaps Critical Control 10: Secure Configurations for Network Devices such as Firewalls, Routers, and Switches Critical Control 11: Limitation and Control of Network Ports, Protocols, and Services Critical Control 12: Controlled Use of Administrative Privileges Critical Control 13: Boundary Defense Critical Control 14: Maintenance, Monitoring, and Analysis of Audit Logs Critical Control 15: Controlled Access Based on the Need to Know Critical Control 16: Account Monitoring and Control Critical Control 17: Data Loss Prevention Critical Control 18: Incident Response Capability Critical Control 19: Secure Network Engineering Critical Control 20: Penetration Tests and Red Team Exercises Because this work is being analyzed directly by Working Group 11 to address the generic risk to ISPs of various hacking and unauthorized access issues, Working Group 4 will not be commenting in-depth in this area, and refers readers to reports from Working Group 11 for comprehensive, and updated coverage of these risks when they issue their report. We will comment upon current BCPs for ISPs to look to adopt in the interim, and provide further background around risks unique to running BGP servers/routers in this area. An ISP’s routing infrastructure is an important asset to protect, as gaining control of it can lead to a wide variety of harms to ISP customers. Further, an ISP’s staff computers, servers, and networking infrastructure also rely upon their own routers to correctly direct traffic to its intended destinations. The ISP’s own sensitive data and processes could be compromised via hacked routers/servers. Thus routers should be included on the list of network assets that are assigned the highest level of priority for protection under any type of ISP security program. There are many industry standard publications pertaining to overall cybersecurity best practices available for adoption by ISPs or any organization at risk of attack, including prior CSRIC reports. It is incumbent upon ISPs to maintain their overall security posture and be up-to-date on the latest industry BCPs and adopt the practices applicable to their organization. Of particular note is the IETF’s RFC 4778 - Current Operational Security Practices in Internet Service Provider Environments6 which offers a comprehensive survey of ISP security practices. An older IETF publication, but still active BCP, that still applies to ISP environments can be found with BCP 46, aka RFC 3013 Recommended Internet Service Provider Security Services and Procedures7 . NIST also puts out highly applicable advice and BCPs for running 6 http://www.ietf.org/rfc/rfc4778.txt 7 http://www.apps.ietf.org/rfc/rfc3013.txtThe Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [28] of [40] government networks, with the most currently relevant special report, NIST SP-800-53. The ultimate goal of someone attempting unauthorized access to routing infrastructure would be to either deny customer use of those servers or, more likely, insert false entries within the router to misdirect the users of those routers. This is a functional equivalent to route injection and propagation attacks as already described in section 5.2. So the analysis and recommendations presented in section 5.2.1.5 with respect to monitoring for and reacting to route injection and propagation attacks apply in the scenario where an attacker has breached a router to add incorrect entries. 5.3.1.1 Recommendations 1) ISPs should refer to and implement the practices found in CSRIC 2A – Cyber Security Best Practices that apply to securing servers and ensure that routing infrastructure is protected. 2) ISPs should adopt applicable BCPs found in other relevant network security industry approved/adopted publications. Monitor for applicable documents and update. Three documents were identified that currently apply to protecting ISP networks: IETF RFC 4778 and BCP 46 (RFC 3013); NIST special publications series: NIST SP-800-53 3) ISPs should ensure that methods exist within the ISP’s operations to respond to detected or reported successful route injection and propagation attacks, so that such entries can be rapidly remediated. 4) ISPs should consider implementing routing-specific monitoring regimes to assess the integrity of data being reported by the ISP’s routers that meet the particular operational and infrastructure environments of the ISP. 5.3.2 ISP insiders inserting false entries into routers While insider threats can be considered a subset of the more general security threat of unauthorized access and hacking, they deserve special attention in the realm of routing security. ISP insiders have unparalleled access to any systems run by an ISP, and in the case of routers, the ability to modify entries is both trivially easy and potentially difficult to detect. Since routers don’t typically have company-sensitive information, are accessed by thousands of machines continuously, and are not usually hardened or monitored like other critical servers, it is relatively easy for an insider to alter a router’s configuration in a way that adversely affects routing. The analysis and recommendations for this particular threat do not differ significantly from those presented in Section 5.3.1 of this report - Hacking and unauthorized 3rd party access to routing infrastructure. However, it is worth paying special attention to this particular exposure given the liabilities an ISP may be exposed to from such difficult-to-detect activities of its own employees. 5.3.2.1 Recommendations 1) Refer to section 5.3.1.1 for generic hacking threats. 5.3.3 Denial-of-Service Attacks against ISP Infrastructure Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) are some of the oldest and most prolific attacks that ISPs have faced over the years and continue to defend against today. The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [29] of [40] Typically, an external actor who is targeting some Internet presence or infrastructure to make it unusable is behind such attacks. However, DoS/DDoS attacks come in many flavors that can be broadly lumped into two primary categories: logic attacks and resource exhaustion/flooding attacks.8 Logic attacks exploit vulnerabilities to cause a server or service to crash or reduce performance below usable thresholds. Resource exhaustion or flooding attacks cause server or network resources to be consumed to the point where the targeted service no longer responds or service is reduced to the point it is operationally unacceptable. We will examine the latter type of attack in this section of analysis, as resource exhaustion . Logic attacks are largely directed to break services/servers and can be largely addressed with the analysis and recommendations described above with respect to BGP specific issues and also put forward in section 5.3.1 that cover protecting networked assets from various hacking and other attacks. There is a large variety of flooding attacks that an ISP could face in daily operations. These can be targeted at networks or any server, machine, router, or even user of an ISP’s network. From the perspective of routing operations, it is helpful to differentiate between “generic” DoS attacks that could affect any server, and those that exploit some characteristic of BGP that can be utilized to affect routers in particular, which have already been covered. Due to the long history, huge potential impact, and widespread use of various DoS and DDoS attacks, there is an abundance of materials, services, techniques and BCPs available for dealing with these attacks. ISPs will likely have some practices in place for dealing with attacks both originating from their networks and that are being directed at their networks and impacting their services. The IETF’s RFC 4732 Internet Denial-of-Service Considerations9 provides an ISP with a thorough overview of DoS/DDoS attacks and mitigation strategies and provides a solid foundational document. The SANS Institute has published a useful document for ISPs that is another reference document of BCPs against DoS/DDoS attacks entitled A Summary of DoS/DDoS Prevention, Monitoring and Mitigation Techniques in a Service Provider Environment10. As mentioned in section 5.3.1, there are several documents that cover general ISP security concerns, and those typically include prescriptive advice for protecting a network against DoS/DDoS attacks. Such advice can be found in previously cited documents including prior CSRIC reports: CSRIC 2A – Cyber Security Best Practices11, the IETF’s RFC 4778 - Current Operational Security Practices in Internet Service Provider Environments12, BCP 46, RFC 3013 Recommended Internet Service Provider Security Services and Procedures13 and NIST’s special report, NIST SP-800-53. For the most part, an ISP’s routers for interdomain routing must be publicly available in order for the networks they serve to be reachable across the Internet. Thus measures to restrict access 8 http://static.usenix.org/publications/library/proceedings/sec01/moore/moore.pdf 9 http://tools.ietf.org/rfc/rfc4732.txt 10 http://www.sans.org/reading_room/whitepapers/intrusion/summary-dos-ddos-preventionmonitoring-mitigation-techniques-service-provider-enviro_1212 11 http://www.fcc.gov/pshs/docs/csric/WG2A-Cyber-Security-Best-Practices-Final-Report.pdf 12 http://www.ietf.org/rfc/rfc4778.txt 13 http://www.apps.ietf.org/rfc/rfc3013.txtThe Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [30] of [40] that can be implemented for an ISP’s internal infrastructure are unavailable as options for these connecting routers. This leaves an ISP with limited choices for DDoS protection, including the traditional approaches of overprovisioning of equipment and bandwidth and various DoS/DDoS protection services and techniques. 5.3.3.1 Recommendations 1) ISPs should implement BCPs and recommendations for securing an ISP’s infrastructure against DoS/DDoS attacks that are enumerated in the IETF’s RFC 4732 Internet Denial-of-Service Considerations and consider implementing BCPs enumerated in the SANS Institute reference document of BCPs against DoS/DDoS attacks entitled A Summary of DoS/DDoS Prevention, Monitoring and Mitigation Techniques in a Service Provider Environment. 2) ISPs should refer to and implement the BCPs related to DoS/DDoS protection found in CSRIC 2A – Cyber Security Best Practices that apply to protecting servers from DoS/DDoS attacks. 3) ISPs should consider adopting BCPs found in other relevant network security industry approved/adopted publications that pertain to DoS/DDoS issues, and monitor for applicable documents and updates. Four that currently apply to protecting ISP networks from DoS/DDoS threats are IETF RFC 4778 and BCP 46 (RFC 3013); NIST special publications series: NIST SP-800-53; and ISOC Publication Towards Improving DNS Security, Stability, and Resiliency. 4) ISPs should review and apply BCPs for protecting network assets against DoS/DDoS attacks carefully to ensure they are appropriate to protect routing infrastructure. 5.3.4 Attacks against administrative controls of routing identifiers Blocks of IP space and Autonomous Systems Numbers (ASNs) are allocated by various registries around the world. Each of these Regional Internet Registries (RIR’s) is provided IP space and ASN allocation blocks by IANA, to manage under their own rules and practices. Inturn, several of these registries allow for country or other region/use specific registries to suballocate IP space based on their own rules, processes and systems. Each RIR maintains a centralized “whois” database that designates the “owner” of IP spaces or ASN’s within their remit. Access to the databases that control these designations, and thus “rights” to use a particular space or ASN is provided and managed by the RIR’s and sub RIR registries depending upon the region. Processes for authentication and management of these identifier resources are not standardized, and until recently, were relatively unsophisticated and insecure. This presents an administrative attack vector allowing a miscreant to use a variety of account attack methods, from hacking to password guessing to social engineering and more that could allow them to assume control over an ASN or IP space allocation. In other fields, such attacks would be considered “hijacking” or “account take-over” attacks, but the use of the word “hijacking” in the BGP space to include various injection and origin announcements complicates the common taxonomy. Thus for this section, we will refer to account “hijacking” as “account take-over”. The primary concern for most ASN and prefix block owners and the ISPs that service them in such scenarios is the take-over of active space they are using. A miscreant could literally “take The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [31] of [40] over” IP space being routed and used by the victim, much like an origin attack as described in section 5.3. In this case, it would be equivalent to a full take-over, with the majority of the global routing system recognizing the miscreant’s announcement as being the new “legitimate” one, with all the inherent risks previously described. The real owner will have to prove their legitimacy and actual legal ownership/control of the resource that has been taken over. Depending upon the authentication scheme the registry uses, this can prove difficult, especially for legacy space and older ASN registrations. A corollary of this attack scenario, is a miscreant taking over “dormant” IP space or an unused ASN, and thus “squatting” in unused territory14. While not impactful on existing Internet presence, squatting on IP space can lead to many forms of abuse, including the announcement of bogus peering arrangements, if the squatted resource is an ASN. In a take-over scenario, a miscreant typically impersonates or compromises the registrant of the ASN and/or IP space in order to gain access to the management account for that ASN or CIDR block. Until recently, nearly all RIRs and registries used an e-mail authentication scheme to manage registrant change requests. Thus, if the registrant’s e-mail address uses an available domain name, the miscreant can register the domain name, recreate the administration email address, and authenticates himself with the registry. If the domain isn’t available, the criminal could still try to hijack the domain name registration account to gain control of that same domain. If a registry or RIR requires more verification for registrant account management, the criminal use various social engineering tricks against the registry staff to get into the management account. Once a criminal has control of the registration account, they can update the information there to allow them to move to a new peering ISP, create new announcements from their “new” space, or launch any sort of BGP-type attack as listed above. Even more basically, the criminal can simply utilize their new control of the ASN/prefix to have their own abusive infrastructure announced on the Internet for whatever process they would like. This includes direct abuse against the Internet in general (e.g. hosting malware controllers, phishing, on-line scams, etc.), but also the ability to impersonate the original holder of the space they have taken over. Of course they can also intercept traffic originally destined for the legitimate holder of the space as previously described for various route-hijacking scenarios. The end result of an administrative account take-over is likely to be similar to other injection attacks against routing infrastructure as covered in section 5.2. Thus, ISPs will want to consult BCPs covering techniques for monitoring and reacting to those types of attacks. These BCPs cover the general effects of a BGP origin attacks – dealing with service interruptions and the worldwide impacts. ISPs typically do not have direct access or control to RIR or other registry account information that has been compromised in most hijacking attacks. The ISP is dependent upon the affected registry to restore control of the ISP’s management account, or in the case of a serious breach, the registrar/registry’s own services. Once control is re-established, the original, correct information needs to be re-entered and published again. This will usually mean updating 14 For a full description of the taxonomy of hijacking, squatting, and spoofing attacks in routing space, see Internet Address Hijacking, Spoofing and Squatting Attacks - http://securityskeptic.typepad.com/the-security-skeptic/2011/06/internet-address-hijackingspoofing-and-squatting-attacks.htmlThe Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [32] of [40] routing table entries/BGP announcements, and fixing any account information that has been modified. The industry has largely been slow to adopt security measures to protect account access for controlling ASN and CIDR block management that are found in other online services like financial services, e-commerce, or even some ISP management systems. The industry also has many participants with a wide variety of geographical regions, with few standards and requirements for the security of registration systems, and very limited oversight. This means it is often difficult to find support for typical online security tools like multi-factor authentication, multi-channel authentication, and verification of high-value transactions. This has been changing in recent years at the RIR level, with ARIN, RIPE, and several other IP registries at various levels of authority implementing new controls and auditing account information. Despite this, gaps exist, especially with “legacy” data entered many years ago before current management systems and authentication processes were implemented. While there is scant guidance on this topic area for ASN/IP block management, ICANN’s Security and Stability Advisory Committee (SSAC) has released two documents to address these issues in the domain name space which is quite analogous to the provisioning of IP space. These documents provide BCPs for avoiding and mitigating many of these issues. SAC 40 Measures to Protect Domain Registration Services Against Exploitation or Misuse15, addresses issues faced by domain name registrars and offers numerous BCPs and recommendations for securing a registrar against the techniques being used by domain name hijackers. Many of the BCP’s presented there would be applicable to RIR’s and other IP address provisioning authorities, including ISPs managing their own customers. SSAC 44, A Registrant's Guide to Protecting Domain Name Registration Accounts16, provides advice to domain name registrants to put in place to better protect their domains from hijacking. Similar techniques could be used by operators to protect their own IP space allocations. Given the limited choices and practices followed by various IP space allocators, ISPs need to carefully evaluate their security posture and the practices of their RIR’s or other IP space allocators with these BCPs in mind. 5.3.4.1 Recommendations 1) ISPs and their customers should refer to the BCPs and recommendations found in SSAC 44 A Registrant's Guide to Protecting Domain Name Registration Accounts with respect to managing their ASN’s and IP spaces they register and use to provide services. 2) ISPs should review the BCPs and recommendations found in SAC 40 Measures to Protect Domain Registration Services Against Exploitation or Misuse to provide similar protections for IP space they allocate to their own customers. 6 Conclusions Working Group 4 has recommended the adoption of numerous best practices for protecting the inter-domain BGP routing system. As a distributed infrastructure requiring several actors to both enable and protect it, network operators face challenges outside of their direct control in tackling many of the issues identified. The more widely Best Current Practices are utilized, the more robust the whole system will be to both bad actors and simple mistakes. See Appendix 7.3 for a 15 http://www.icann.org/en/groups/ssac/documents/sac-040-en.pdf 16 http://www.icann.org/en/committees/security/sac044.pdfThe Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [33] of [40] tabular display of risks indexed with the appropriate countermeasures as discussed in the body text of the document. 7 Appendix 7.1 Background Note that in order to remain consistent with other CSRIC III reports, considerable portions of “Salient Features of BGP Operation” have been taken verbatim from the Appendix section of the CSRIC III, Working Group 6 Interim Report published March 8, 2012. Other parts have been taken from the NIST (National Institute of Standards and Technologies) report entitled Border Gateway Protocol Security. 7.1.1 Salient Features of BGP Operation This section is intended for non-experts who have a need to understand the origins of BGP security problems. Although unknown to most users, the Border Gateway Protocol (BGP) is critical to keeping the Internet running. BGP is a routing protocol, which means that it is used to update routing information between major systems. BGP is in fact the primary inter-domain routing protocol, and has been in use since the commercialization of the Internet. Because systems connected to the Internet change constantly, the most efficient paths between systems must be updated on a regular basis. Otherwise, communications would quickly slow down or stop. Without BGP, email, Web page transmissions, and other Internet communications would not reach their intended destinations. Securing BGP against attacks by intruders is thus critical to keeping the Internet running smoothly. Many organizations do not need to operate BGP routers because they use Internet service providers (ISP) that take care of these management functions. But larger organizations with large networks have routers that run BGP and other routing protocols. The collection of routers, computers, and other components within a single administrative domain is known as an autonomous system (AS). An ISP typically represents a single AS. In some cases, corporate networks tied to the ISP may also be part of the ISP’s AS, even though some aspects of their administration are not under the control of the ISP. Participating in the global BGP routing infrastructure gives an organization some control over the path traffic traverses to and from its IP addresses (Internet destinations). To participate in the global BGP routing infrastructure, an organization needs: • Assigned IP addresses, grouped into IP network addresses (aka prefixes) for routing. • A unique integer identifier called an Autonomous System Number (ASN). • A BGP router ready to connect to a neighbor BGP router on an Internet Service Provider’s network (or another already connected AS) that is willing to establish a BGP session and exchange routing information and packet traffic with the joining organization. The basic operation of BGP is remarkably simple – each BGP-speaking router can relay The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [34] of [40] messages to its neighbors about routes to network addresses (prefixes) that it already knows, either because it “owns” these prefixes, or it already learned routes to them from another neighbor. As part of traveling from one border router to another, a BGP route announcement incrementally collects information about the ASes that the route “update” traversed in an attribute called AS_PATH. Therefore, every BGP route is constructed hop-by-hop according to local routing policies in each AS. This property of BGP is a source of its flexibility in serving diverse business needs, and also a source of vulnerabilities. The operators of BGP routers can configure routing policy rules that determine which received routes will be rejected, which will be accepted, and which will be propagated further – possibly with modified attributes, and can specify which prefixes will be advertised as allocated to, or reachable through, the router’s AS. In contrast to the simplicity of the basic operation of BGP, a routing policy installed in a BGP router can be very complex. A BGP router can have very extensive capabilities for manipulating and transforming routes to implement the policy, and such capabilities are not standardized, but instead, are largely dictated by AS interconnection and business relationships. A route received from a neighbor can be transformed before a decision is made to accept or reject the route, and can be transformed again before the route is relayed to other neighbors; or, the route may not be disseminated at all. All this works quite well most of the time – largely because of certain historically motivated trust and established communication channels among human operators of the global BGP routing system. This is the trust that a route received from a neighbor accurately describes a path to a prefix legitimately reachable through the neighbor ASes networks, and its attributes have not been tampered with. Notwithstanding the above, the “trust but verify” rule applies: Best Current Practices recommend filtering the routes received from neighbors. While this can be done correctly for well-known direct customers, currently there is no validated repository of the “ground truth” allowing for correct filtering of routes to all networks in the world. Now observe that the BGP protocol itself provides a perfect mechanism for spreading malformed or maliciously constructed routes, unless the BGP players are vigilant in filtering them out from further propagation. However, adequate route filtering may not be in place, and from time to time a malicious or inadvertent router configuration change creates a BGP security incident: malformed or maliciously constructed routing messages will propagate from one AS to another simply by exploiting legitimate route propagation rules, and occasionally can spread to virtually all BGP routers in the world. Because some BGP-speaking routers advertise all local BGP routes to all external BGP peers by default, another example that commonly occurs involves a downstream of two or more upstream ASes advertising routes learned from one upstream ISP to another ISP – both the customer and the ISPs should put controls in place to scope the propagation of all routes to those explicitly allocated to the customer AS, but this is difficult given the lack of “ground truth”. The resulting routing distortions can cause very severe Internet service disruptions, in particular effective disconnection of victim networks or third parties from parts or all of Internet, or forcing traffic through networks that shouldn’t carry it, potentially opening higher-level Internet transactions up to packet snooping or man-in-themiddle attacks. 7.1.2 Review of Router Operations In a small local area network (LAN), data packets are sent across the wire, typically using Ethernet hardware, and all hosts on the network see the transmitted packets. Packets addressed The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [35] of [40] to a host are received and processed, while all others are ignored. Once networks grow beyond a few hosts, though, communication must occur in a more organized manner. Routers perform the task of communicating packets among individual LANs or larger networks of hosts. To make internetworking possible, routers must accomplish these primary functions: • Parsing address information in received packets • Forwarding packets to other parts of the network, sometimes filtering out packets that should not be forwarded • Maintaining tables of address information for routing packets. BGP is used in updating routing tables, which are essential in assuring the correct operation of networks. BGP is a dynamic routing scheme—it updates routing information based on packets that are continually exchanged between BGP routers on the Internet. Routing information received from other BGP routers (often called “BGP speakers”) is accumulated in a routing table. The routing process uses this routing information, plus local policy rules, to determine routes to various network destinations. These routes are then installed in the router’s forwarding table. The forwarding table is actually used in determining how to forward packets, although the term routing table is often used to describe this function (particularly in documentation for home networking routers). 7.2 BGP Security Incidents and Vulnerabilities In this section we classify the observed BGP security incidents, outline the known worst-case scenarios, and attempt to tie the incidents to features of proposed solutions that could prevent them. Many of the larger incidents are believed to have been the result of misconfigurations or mistakes rather than intentional malice or criminal intent. It has long been suspected that more frequent, less visible incidents have been happening with less attention or visibility. BGP security incidents usually originate in just one particular BGP router, or a group of related BGP routers in an AS, by means of changing the router’s configuration leading to announcements of a peculiar route or routes that introduce new paths towards a given destination or trigger bugs or other misbehaviors in neighboring routers in the course of propagation. There are no generally accepted criteria for labeling a routing incident as an “attack”, and – as stressed in the recommendations – lack of broadly accepted routing security metrics that could automatically identify certain routing changes as “routing security violations”. BGP security incidents that were observed to date can be classified as follows: • Route origin hijacking (unauthorized announcements of routes to IP space not assigned to the announcer). Such routing integrity violations may happen under various scenarios: malicious activity, inadvertent misconfigurations (“fat fingers”), or errors in traffic engineering. There are further sub-categories of such suspected security violations:The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [36] of [40] o Hijacking of unused IP space such as repetitive hijacks of routes to prefixes within a large IP blocks assigned to an entity such as US government but normally not routed on the public Internet. Temporarily using these “unused” addresses enables criminal or antisocial activities (spam, network attacks) while complicating efforts to detect and diagnose the perpetrators. o Surgically targeted hijacks of specific routes and de-aggregation attacks on specific IP addresses. They may be hard to identify unless anomaly detection is unambiguous, or the victim is important enough to create a large commotion. Examples: Pakistan Hijacks YouTube17 (advertisement of a more specific is globally accepted, and totally black-holes the traffic to the victim). There may be significantly more such attacks than publicly reported, as they may be difficult to distinguish from legitimate traffic engineering or network re-engineering activities. o Unambiguous massive hijacks of many routes where many distinct legitimate origin ASes are replaced by a new unauthorized origin AS advertising the hijacked routes. Significant recent incidents include a 2010 “China's 18-minute Mystery”18, or a hijacking of a very large portion of the Internet for several hours by TTNet in 200419, or a 2006 ConEd incident20. Without knowing the motivations of the implicated router administrators it is difficult to determine if these and similar incidents were due to malicious intent, or to errors in implementations of routing policy changes. • Manipulation of AS_PATH attribute in transmitted BGP messages executed by malicious, selfish, or erroneous policy configuration. The intention of such attacks is to exploit BGP routers’ route selection algorithms dependent on AS_PATH properties, such as immediate rejection of a route with the router’s own ASN in the AS_PATH (mandated to prevent routing loops), or AS_PATH length. Alternatively, such attacks may target software bugs in distinct BGP implementations (of which quite a few were triggered in recent years with global impact). o For routing incidents triggered by long AS_PATHs see House of Cards21, AfNOG Takes Byte Out of Internet22, Longer is Not Always Better23 for actual examples. o Route leaks - A possibility of “man in the middle” (MITM) AS_PATH attacks detouring traffic via a chosen AS was publicly demonstrated at DEFCON in 17 http://www.renesys.com/blog/2008/02/pakistan-hijacks-youtube-1.shtml 18 http://www.renesys.com/blog/2010/11/chinas-18-minute-mystery.shtml 19 Alin C. Popescu, Brian J. Premore, and Todd Underwood, Anatomy of a Leak: AS9121. NANOG 34, May 16, 2005. 20 http://www.renesys.com/blog/2006/01/coned-steals-the-net.shtml 21 http://www.renesys.com/blog/2010/08/house-of-cards.shtml 22 http://www.renesys.com/blog/2009/05/byte-me.shtml 23 http://www.renesys.com/blog/2009/02/longer-is-not-better.shtmlThe Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [37] of [40] 200824. Two other similar incidents were found in a 7-month period surrounding the DEFCON demo by mining of a BGP update repository conducted in 200925 but were not confirmed as malicious. This can occur either by accident as detailed above, and is sometimes referred to as route “leaks”, or may be intentional. Additionally, such attacks may or may not attempt to obscure the presence of additional ASes in the AS path, should they exist. These are particularly problematic to identify as they require some knowledge of intent by the resource holder and intermediate ASes. o AS_PATH poisoning – sometimes used by operators to prevent their traffic AS from reaching and/or transiting a selected AS, or steer the traffic away from certain paths. It is technically a violation of BGP protocol and could be used harmfully as well. • Exploitations of router packet forwarding bugs, router performance degradation, bugs in BGP update processing o Example of a transient global meltdown caused by a router bug tickled by deaggregation26 and several other cases cited there. There are also BGP vulnerabilities that may have not been exploited in the wild so far, but that theoretically could do a lot of damage. The BGP protocol does not have solid mathematical foundations, and certain bizarre behaviors – such as persistent route oscillations – are quite possible. There have been several RFCs and papers addressing BGP vulnerabilities in the context of protocol standard specification and threat modeling, see the following Request For Comments (RFCs): • RFC 4272 “BGP Security Vulnerabilities Analysis” S. Murphy, Jan 2006. • RFC 4593 “Generic Threats to Routing Protocols”, A. Barbir, S. Murphy and Y. Yang, Oct 2006. • Internet draft draft-foo-sidr-simple-leak-attack-bgpsec-no-help-01 “Route Leak Attacks Against BGPSEC”, D. McPherson and S. Amante, Nov 2011. • Internet draft draft-ietf-sidr-bgpsec-threats-01 “Threat Model for BGP Path Security”, S. Kent and A. Chi, Feb 2012. 24 A. Pilosov and T. Kapela, Stealing the internet, DEFCON 16 August 10, 2008 25C. Hepner and E. Zmijewski, Defending against BGP Man-in-the-Middle attacks, Black Hat DC February 2009 26 J. Cowie, The Curious Incident of 7 November 2011, NANOG 54, February 7, 2012Page [38] of [40] 7.3 BGP Risks Matrix BGP Routing Security Risks Examined by WG 4 Network Operator Role Risks Report Sect. Recommendations Stub network (e.g. Enterprise, Data Center) Session-level threats 5.1.1 • Consider MD5 or GTSM if neighbor recommends it DoS (routers and routing info) 5.1.2 • Control-Plane Policing (rate-limiting) • Keep up-to-date router software Spoofed Source IP Addresses 5.1.3 • Use uRPF (unicast Reverse Path Forwarding) in strict mode or other similar features at access edge of network (e.g. datacenter or campus). • Filter source IP address on packets at network edge to ISPs Incorrect route injection and propagation 5.2.1 • Keep current information in “whois” and IRR (Internet Routing Registry) databases • Outbound prefix filtering • Use monitoring services to check for incorrect routing announcements and/or propagation Other Attacks (e.g., hacking, insider, social engineering) 5.3 • Consider many recommendations about operational security processes Internet Service Provider Network Session-level threats 5.1.1 • Consider a plan to use MD5 or GTSM including flexibility to adjust to different deployment scenario specifics DoS (routers and routing info) 5.1.2 • Control-Plane Policing (rate-limiting)The Communications Security, Reliability and Interoperability Council III Working Group [#4] Draft Report [MARCH, 2013] Page [39] of [40] • Keep up-to-date router software Spoofed Source IP Addresses 5.1.3 • Use uRPF (unicast Reverse Path Forwarding) in strict or loose mode as appropriate (e.g. strict mode at network ingress such as data-center or subscriber edge, loose mode at inter-provider border) Incorrect route injection and propagation 5.2.1 • Keep current information in “whois” and IRR (Internet Routing Registry) databases • Consult current information in “whois” and IRR (Internet Routing Registry) databases when provisioning or updating customer routing • Implement inbound prefix filtering from customers • Consider AS-path filters and maximum-prefix limits as second line of defense • Use monitoring services to check for incorrect routing announcements and/or propagation Other Attacks (e.g., hacking, insider, social engineering) 5.3 • Consider many recommendations about operational security processesPage [40] of [40] 7.4 BGP BCP Document References Network Protection Documents NIST Special Publication 800-54 Border Gateway Protocol (BGP) Security Recommendations WG2A - Cyber Security Best Practices SANS: Twenty Critical Security Controls for Effective Cyber Defense: Consensus Audit Guidelines (CAG) NIST Special Publication 800-53 Recommended Security Controls for Federal Information Systems and Organizations IETF RFC 4778 - Current Operational Security Practices in Internet Service Provider Environments IETF RFC 3013 Recommended Internet Service Provider Security Services and Procedures Source Address verification/filtering IETF BCP38/RFC 2827 Network Ingress Filtering: Defeating Denial of Service Attacks which employ IP Source Address Spoofing BCP 84/RFC 3704 Ingress Filtering for Multihomed Networks BCP 140/RFC 5358 Preventing Use of Recursive Nameservers in Reflector Attacks ICANN SAC004 Securing the Edge DoS/DDoS Considerations IETF RFC 4732 Internet Denial-of-Service Considerations SANS A Summary of DoS/DDoS Prevention, Monitoring and Mitigation Techniques in a Service Provider Environment Scalable Dynamic Nonparametric Bayesian Models of Content and Users ∗ Amr Ahmed1 Eric Xing2 1Research @ Google , 2Carnegie Mellon University amra@google.com, epxing@cs.cmu.edu Abstract Online content have become an important medium to disseminate information and express opinions. With their proliferation, users are faced with the problem of missing the big picture in a sea of irrelevant and/or diverse content. In this paper, we addresses the problem of information organization of online document collections, and provide algorithms that create a structured representation of the otherwise unstructured content. We leverage the expressiveness of latent probabilistic models (e.g., topic models) and non-parametric Bayes techniques (e.g., Dirichlet processes), and give online and distributed inference algorithms that scale to terabyte datasets and adapt the inferred representation with the arrival of new documents. This paper is an extended abstract of the 2012 ACM SIGKDD best doctoral dissertation award of Ahmed [2011]. 1 Introduction Our online infosphere is evolving with an astonishing rate. It is reported that there are 50 million scientific journal articles published thus far [Jinha, 2010], 126 million blogs 1 , an average of one news story published per second, and around 500 million tweets per day. With the proliferation of such content, users are faced with the problem of missing the big picture in a sea of irrelevant and/or diverse content. Thus several unsupervised techniques were proposed to build a structured representation of users and content. Traditionally, clustering is used as a popular unsupervised technique to explore and visualize a document collection. When applied in document modeling, it assumes that each document is generated from a single component (cluster or topic) and that each cluster is a uni-gram distribution over a given vocabulary. This assumption limits the expressive power of the model, and does not allow for modeling documents as a mixture of topics. ∗The dissertation on which this extended abstract is based was the recipient of the 2012 ACM SIGKDD best doctoral dissertation award, [Ahmed, 2011]. 1 http://www.blogpulse.com/ Recently, mixed membership models [Erosheva et al., 2004], also known as admixture models, have been proposed to remedy the aforementioned deficiency of mixture models. Statistically, an object wd is said to be derived from an admixture if it consists of a bag of elements, say {wd1, . . . , wdN }, each sampled independently or coupled in some way, from a mixture model, according to an admixing coefficient vector θ, which represents the (normalized) fraction of contribution from each of the mixture component to the object being modeled. In a typical text modeling setting, each document corresponds to an object, the words thereof correspond to the elements constituting the object, and the document-specific admixing coefficient vector is often known as a topic vector and the model is known as latent Dirichlet allocation (LDA) model due to the choice of a Dirichlet distribution as the prior for the topic vector θ [Blei et al., 2003]. Notwithstanding these developments, existing models can not faithfully model the dynamic nature of online content, represent multiple facets of the same topic and scale to the size of the data on the internet. In this paper, we highlight several techniques to build a structured representation of content and users. First we present a flexible dynamic non-parametric Bayesian process called the Recurrent Chinese Restaurant Process for modeling longitudinal data and then present several applications in modeling scientific publication, social media and tracking of user interests. 2 Recurrent Chinese Restaurant Process Standard clustering techniques assume that the number of clusters is known a priori or can be determined using cross validation. Alternatively, one can consider non-parametric techniques that adapt the number of clusters as new data arrives. The power of non-parametric techniques is not limited to model selection, but they endow the designer with necessary tools to specify priors over sophisticated (possibly in- finite) structures like trees, and provide a principled way of learning these structures form data. A key non-parametric distribution is the Dirichlet process (DP). DP is a distribution over distributions [Ferguson, 1973]. A DP denoted by DP(G0, α) is parameterized by a base measure G0 and a concentration parameter α. We write G ∼ DP(G0, α) for a draw of a distribution G from the Dirichlet process. G itself is a distribution over a given parameter space θ, therefore we can draw parameters θ1:N from G. Integrating out G, the1987 speech Neuroscience NN Classificatio n Methods Control Prob. Models image SOM RL Bayesian Mixtures Generalizat -ion 1990 boosting 1991 Clustering 1995 ICA Kernels 1994 1996 Memory speech Kernels ICA PM Classification Mixtures Control support kernel svm regularization sv vectors feature regression kernel support sv svm machines regression vapnik feature solution Kernels kernel support Svm regression feature machines solution margin pca Kernel svm support regression solution machines matrix feature regularization 1996 1997 1998 1999 -Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing, V.Vapnik, S. E. Golowich and A.Smola - Support Vector Regression Machines H. Drucker, C. Burges, L. Kaufman, A. Smola and V. Vapnik -Improving the Accuracy and Speed of Support Vector Machines, C. Burges and B. Scholkopf - From Regularization Operators to Support Vector Kernels, A. Smola and B. Schoelkopf - Prior Knowledge in Support Vector Kernels, B. Schoelkopf, P. Simard, A. Smola and V.Vapnik - Uniqueness of the SVM Solution, C. Burges and D.. Crisp - An Improved Decomposition Algorithm for Regression Support Vector Machines, P. Laskov ..... Many more Figure 1: Left: the NIPS conference timeline as discovered by the iDTM. Right the evolution of the Topic Kernel Methods. parameters θ follow a Polya urn distribution [Blackwell and MacQueen, 1973], also knows as the Chinese restaurant process (CRP), in which the previously drawn values of θ have strictly positive probability of being redrawn again, thus making the underlying probability measure G discrete with probability one. More formally, θi |θ1:i−1, G0, α ∼ X k mk i − 1 + α δ(φk) + α i − 1 + α G0. (1) where φ1:k denotes the distinct values among the parameters θ, and mk is the number of parameters θ having value φk. By using the DP at the top of a hierarchical model, one obtains the Dirichlet process mixture model, DPM [Antoniak, 1974]. The generative process thus proceeds as follows: G|α, G0 ∼ DP(α, G0), θd|G ∼ G, wd|θd ∼ F(.|θd), (2) where F is a given likelihood function parameterized by θ. Dirichlet process mixture (or CRP) models provide a flexible Bayesian framework, however the full exchangeability assumption they employ makes them an unappealing choice for modeling longitudinal data such as text streams that can arrive or accumulate as epochs, where data points inside the same epoch can be assumed to be fully exchangeable, whereas across the epochs both the structure (i.e., the number of mixture components) and the parametrization of the data distributions can evolve and therefore unexchangeable. In this section, we present the Recurrent Chinese Restaurant Process ( RCRP ) [Ahmed and Xing, 2008] as a framework for modeling these complex longitudinal data, in which the number of mixture components at each time point is unbounded; the components themselves can retain, die out or emerge over time; and the actual parametrization of each component can also evolve over time in a Markovian fashion. In RCRP, documents are assumed to be divided into epochs (e.g., one hour or one day); we assume exchangeability only within each epoch. For a new document at epoch t, a probability mass proportional to α is reserved for generating a new cluster. Each existing cluster may be selected with probability proportional to the sum mkt + m0 kt, where mkt is the number of documents at epoch t that belong to cluster k, and m0 kt is the prior weight for cluster k at time t. If we let ctd denotes the cluster assingment of document d at time t, then: ctd|c1:t−1, ct,1:d−1 ∼ RCRP(α, λ, ∆) (3) to indicate the distribution P(ctd|c1:t−1, ct,1:d−1) ∝ m0 kt + m−td kt existing cluster α new cluster (4) As in the original CRP, the count m−td kt is the number of documents in cluster k at epoch t, not including d. The temporal aspect of the model is introduced via the prior m 0 kt, which is defined as m 0 kt = X ∆ δ=1 e − δ λ mk,t−δ. (5) This prior defines a time-decaying kernel, parametrized by ∆ (width) and λ (decay factor). When ∆ = 0 the RCRP degenerates to a set of independent Chinese Restaurant Processes at each epoch; when ∆ = T and λ = ∞ we obtain a global CRP that ignores time. In between, the values of these two parameters affect the expected life span of a given component, such that the lifespan of each storyline follows a power law distribution [Ahmed and Xing, 2008]. In addition, the distribution φk of each component changes over time in a markovian fashion, i.e.: φkt|φk,t−1 ∼ P(.|φk,t−1). In the following three sections we give various models build on top of RCRP and highlight how inference is performed and scaled to the size of data over the internet. 3 Modeling Scientific Publications With the large number of research publications available online, it is important to develop automated methods that can discover salient topics (research area), when each topicstarted, how each topic developed over time and what are the representative publications in each topic at each year. Mixedmembership models (such as LDA) are static in nature and while several dynamic extensions have been proposed ([Blei and Lafferty, 2006]), non of them can deal with evolving all of the aforementioned aspects. While, the RCRP models can be used for modeling the temporal evolution of research topics, it assumes that each document is generated from a single topic (cluster). To marry these two approaches, we first introduce Hierarchical Dirichlet Processes (HDP [Teh et al., 2006]) and then illustrate our proposed model. Instead of modeling each document wd as a single data point, we could model each document as a DP. In this setting,each word wdn is a data point and thus will be associated with a topic sampled from the random measure Gd, where Gd ∼ DP(α, G0). The random measure Gd thus represents the document-specific mixing vector over a potentially infi- nite number of topics. To share the same set of topics across documents, we tie the document-specific random measures by modeling the base measure G0 itself as a random measure sampled from a DP(γ, H). The discreteness of the base measure G0 ensures topic sharing between all the documents. Now we proceed to introduce our model, iDTM [Ahmed and Xing, 2010b] which allows for infinite number of topics with variable durations. The documents in epoch t are modeled using an epoch specific HDP with high-level base measure denoted as Gt 0 . These epoch-specific base measures {Gt 0} are tied together using the RCRP of Section 2 to evolve the topics’ popularity and distribution over words as time proceeds. To enable the evolution of the topic distribution over words, we model each topic as a logistic normal distribution and evolve its parameters using a Kalman filter. This choice introduces non-conjugacy between the base measure and the likelihood function and we deal with it using a Laplace approximate inference technique proposed in [Ahmed and Xing, 2007]. We applied this model to the collection of papers published in the NIPS conference over 18 years. In Figure 1 we depict the conference timeline and the evolution of the topic ‘Kernel Methods’ alone with popular papers in each year. In addition to modeling temporal evolution of topics, in [Ahmed et al., 2009] we developed a mixed-membership model for retrieving relevant research papers based on multiple modalities: for example figures or key entities in the paper such as genes or protein names (as in biomedical papers). Figures in biomedical papers pose various modeling challenges that we omit here for space limitations. 4 Modeling Social Media News portals and blogs/twitter are the main means to disseminate news stories and express opinions. With the sheer volume of documents and blog entries generated every second, it is hard to stay informed. This section explores methods that create a structured representation of news and opinions. Storylines emerge from events in the real world, such as the Tsunami in Japan, and have certain durations. Each story can be classified under multiple topics such as disaster, rescue and economics. In addition, each storyline focuses on certain Sports games won team final season league held Politics government minister authorities opposition officials leaders group Unrest police attack run man group arrested move India-Pakistan tension nuclear border dialogue diplomatic militant insurgency missile Pakistan India Kashmir New Delhi Islamabad Musharraf Vajpayee UEFA-soccer champions goal leg coach striker midfield penalty Juventus AC Milan Real Madrid Milan Lazio Ronaldo Lyon Tax bills tax billion cut plan budget economy lawmakers Bush Senate US Congress Fleischer White House Republican T O P I C S S T O R Y L I N E S Figure 2: Some example storylines and topics extracted by our system. For each storyline we list the top words in the left column, and the top named entities at the right; the plot at the bottom shows the storyline strength over time. For topics we show the top words. The lines between storylines and topics indicate that at least 10% of terms in a storyline are generated from the linked topic. words and named entities such as the name of the cities or people involved in the event. In [Ahmed et al., 2011b,a] we used RCRP to model storylines. In a nutshell, we emulate the process of generating news articles. A story is characterized by a mixture of topics and the names of the key entities involved in it. Any article discussing this story then draws its words from the topic mixture associated with the story, the associated named entities, and any story-specific words that are not well explained by the topic mixture. The latter modification allows us to improve our estimates for a given story once it becomes popular. In summary, we model news story clustering by applying a topic model to the clusters, while simultaneously allowing for cluster generation using RCRP. Such a model has a number of advantages: estimates in topic models increase with the amount of data available. Modeling a story by its mixture of topics ensures that we have a plausible cluster model right from the start, even after observing only one article for a new story. Third, the RCRP ensures a continuous flow of new stories over time. Finally, a distinct named entity model ensures that we capture the characteristic terms rapidly. In order to infer storyline from text stream, we developed a Sequential Monte Carlo (SMC) algorithm that assigns news articles to storylines in real time. Applying our online model to a collection of news articles extracted from a popular news portal, we discovered the structure shown in Figure 2. This structure enables the user to browse the storylines by topics as well as retrieve relevant storylines based on any combination of the storyline attributes. Note that named entities are extracted by a preprocessing step using standard extractors. Quantitatively, we compared the accuracy of our online clustering with a strong offline algorithm [Vadrevu et al., 2011] with favorable outcome. Second, we address the problem of ideology-bias detection in user generated content such as microblogs. We follow the notion of ideology as defines by Van Dijk [Dijk, 1998]: “a set of general abstract beliefs commonly shared by a grouppalestinian israeli peace year political process state end right government need conflict way security palestinian israeli Peace political occupation process end security conflict way government people time year force negotiation bush US president american sharon administration prime settlement pressure policy washington ariel new middle unit state american george powell minister colin visit internal policy statement express pro previous package work transfer european administration receive arafat state leader roadmap george election month iraq week peace june realistic yasir senior involvement clinton november post mandate terrorism US role Israeli View roadmap phase violence security ceasefire state plan international step implement authority final quartet issue map effort roadmap end settlement implementation obligation stop expansion commitment consolidate fulfill unit illegal present previou assassination meet forward negative calm process force terrorism unit road demand provide confidence element interim discussion want union succee point build positive recognize present timetable Roadmap process israel syria syrian negotiate lebanon deal conference concession asad agreement regional october initiative relationship track negotiation official leadership position withdrawal time victory present second stand circumstance represent sense talk strategy issue participant parti negotiator peace strategic plo hizballah islamic neighbor territorial radical iran relation think obviou countri mandate greater conventional intifada affect jihad time Arab Involvement Palestinian View Israeli Background topic Palestinian Background topic Figure 3: Ideology-detection. Middle topics represent the unbiased portion of each topic, while each side gives the Israeli and Palestinian perspective. At time t At time t+1 At time t+2 At time t+3 User 1 process User 2 process User 3 process Global process m m' n n' Figure 4: A Fully evolving non-parametric process. Top level process evolves the global topics via an RCRP. Each row represents a user process evolving using an RCR process whose topics depends both on the the global topics at each epoch and the previous state of the user at previous epochs. The user process is sparser than the global process as users need not appear in each epoch, moreover users can appear (and leave) at any epoch. of people.” In other words, an ideology is a set of ideas that directs one’s goals, expectations, and actions. For instance, freedom of choice is a general aim that directs the actions of “liberals”, whereas conservation of values is the parallel for “conservatives”. In Ahmed and Xing [2010a] we developed a multi-view mixed-membership model that utilizes a factored representation of topics, where each topic is composed of two parts: an unbiased part (shared across ideologies) and a biased part (different for each ideology). Applying this model on a few ideologically labelled documents as seeds and many unlabeled documents, we were able to identify how each ideology stands with respect to mainstream topics. For instance in Figure 3 we show the result of applying the model to a set of articles written on the middle east conflict by both Israeli and Palestinian writers. Given a new documents, the model can 1) detect its idealogical bias (if any), 2) point where the bias appears (i.e. highlight words and/or biased sentences) and 3) retrieve documents written on the same topic from the opposing ideology. Our model achieves state of the art results in task 1 and 3 while being unique in solving task 2. 0 10 20 30 40 0 0.1 0.2 0.3 0.4 0.5 Propotion Day Baseball Dating Celebrity Health 0 10 20 30 40 0 0.1 0.2 0.3 Propotion Day Baseball Finance Jobs Dating Snooki Tom Cruise Katie Holmes Pinkett Kudrow Hollywood League baseball basketball, doublehead Bergesen Griffey bullpen Greinke skin body fingers cells toes wrinkle layers women men dating singles personals seeking match Dating Baseball Celebrity Health job career business assistant hiring part-time receptionist financial Thomson chart real Stock Trading currency Jobs Finance Figure 5: Dynamic interests of two users. 5 Modeling User Interests Historical user activity is key for building user profiles to predict the user behaviour and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user’s activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages suggests that the user might be shopping for a new vehicle. In Ahmed et al. [2011c] we present a comprehensive statistical framework for user profiling based on the RCRP model which is able to capture such effects in a fully unsupervised fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current. For instance if we represent each user as a bag of the words in their search history, we could use the iDTM model described in Section 3. However, unlink research papers that exist in a given epoch, users exist along multiple epoch (where each epoch here might denote a day). To solve this problem we extend iDTM by modeling each user himself as a RCRP that evolves over time as shown in Figure 4. To deal with the size of data on the internet, we developed a streaming, distributed inference algorithm that distribute users over multiple machines and synchronizing the model parameters using an asynchronous consensus protocol described in more details in [Ahmed et al., 2012; Smola and Narayanamurthy, 2010]. Figure 5 shows qualitatively the output of our model over two users. Quantitatively the discovered interests when used as features in an advertising task results in significant improvement over a strong deployed system. 6 Conclusions Our infosphere is diverse and dynamic. Automated methods that create a structured representation of users and content are key to help users staying informed. We presented a flexible nonparametric Bayesian model called the Recurrent Chinese Restaurant Process and showed how using this formalism (in addition to mixed-membership models) can solve this task. We validated our approach on many domains and showed how to scale the inference to the size of the data on the internet and how to performing inference in online settings.References A. Ahmed and E. P. Xing. On tight approximate inference of the logistic normal topic admixture model. In AISTATS, 2007. A. Ahmed and E. P. Xing. Dynamic non-parametric mixture models and the recurrent chinese restaurant process: with applications to evolutionary clustering. In SDM, pages 219–230. SIAM, 2008. A. Ahmed and E. P. Xing. Staying informed: Supervised and semi-supervised multi-view topical analysis of ideological perspective. In EMNLP, 2010. A. Ahmed and E. P. Xing. Timeline: A dynamic hierarchical dirichlet process model for recovering birth/death and evolution of topics in text stream. In UAI, 2010. A. Ahmed, E. P. Xing, W. W. Cohen, and R. F. Murphy. Structured correspondence topic models for mining captioned figures in biological literature. In KDD, pages 39– 48. ACM, 2009. A. Ahmed, Q. Ho, J. Eisenstein, E. P. Xing, A. J. Smola, , and C. H. Teo. Unified analysis of streaming news. In in WWW 2011, 2011. A. Ahmed, Q. Ho, C. hui, J. Eisenstein, A. Somla, and E. P. Xing. Online inference for the infinte topic-cluster model: Storylines from text stream. In AISTATS, 2011. A. Ahmed, Y. Low, M. Aly, V. Josifovski, and A. J. Smola. Scalable distributed inference of dynamic user interests for behavioral targeting. In KDD, pages 114–122, 2011. A. Ahmed, M. Aly, J. Gonzalez, S. Narayanamurthy, and A.J. Smola. Scalable inference in latent variable models. In Web Science and Data Mining (WSDM), 2012. A. Ahmed. Modeling Users and Content: Structured Probabilistic Representation, and Scalable Online Inference Algorithms. PhD thesis, School of Computer Science, Carnegie Mellon University, 2011. C. E. Antoniak. Mixtures of dirichlet processes with applications to bayesian nonparametric problems. The Annals of Statistics, 2(6):1152–1174, 1974. D. Blackwell and J. MacQueen. Ferguson distributions via polya urn schemes. The Annals of Statistics, 1(2):353–355, 1973. D. M. Blei and J. D. Lafferty. Dynamic topic models. In ICML, volume 148, pages 113–120. ACM, 2006. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, 2003. T. A. Van Dijk. Ideology: A multidisciplinary approach. 1998. E. Erosheva, S. Fienberg, and J. Lafferty. Mixed membership models of scientific publications. PNAS, 101(1), 2004. T. S. Ferguson. A bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2):209–230, 1973. A. E. Jinha. Article 50 million: an estimate of the number of scholarly articles in existence. Learned Publishing, 23(3):258–263, 2010. A. J. Smola and S. Narayanamurthy. An architecture for parallel topic models. In Very Large Databases (VLDB), 2010. Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical dirichlet processes. Journal of the American Statistical Association, 101(576):1566–1581, 2006. S. Vadrevu, C. H. Teo, S. Rajan, K. Punera, B. Dom, A. J. Smola, Y. Chang, and Z. Zheng. Scalable clustering of news search results. In in WSDM 2011, 2011. Towards A Unified Modeling and Verification of Network and System Security Configurations Mohammed Noraden Alsaleh, Ehab Al-Shaer University of North Carolina at Charlotte Charlotte, NC, USA Email: {malsaleh, ealshaer}@uncc.edu Adel El-Atawy Google Inc Mountain View, CA, USA Email: aelatawy@google.com Abstract—Systems and networks access control configuration are usually analyzed independently although they are logically combined to define the end-to-end security property. While systems and applications security policies define access control based on user identity or group, request type and the requested resource, network security policies uses flow information such as host and service addresses for source and destination to define access control. Therefore, both network and systems access control have to be configured consistently in order to enforce endto-end security policies. Many previous research attempt to verify either side separately, but it does not provide a unified approach to automatically validate the logical consistency between both of them. Thus, using existing techniques requires error-prone manual and ad-hoc analysis to validate this link. In this paper, we introduce a cross-layer modeling and verifi- cation system that can analyze the configurations and policies across both application and network components as a single unit. It combines policies from different devices as firewalls, NAT, routers and IPSec gateways as well as basic RBACbased policies of higher service layers. This allows analyzing, for example, firewall polices in the context of application access control and vice versa. Thus, by incorporating policies across the network and over multiple layers, we provide a true endto-end configuration verification tool. Our model represents the system as a state machine where packet header, service request and location determine the state and transitions that conform with the configuration, device operations, and packet values are established. We encode the model as Boolean functions using binary decision diagrams (BDDs). We used an extended version of computational tree logic (CTL) to provide more useful operators and then use it with symbolic model checking to prove or find counter examples to needed properties. The tool is implemented and we gave special consideration to efficiency and scalability. I. INTRODUCTION Users inadvertently trigger a long sequence of operations in many locations and devices by just a simple request. The application requests are encapsulated inside network packets which in turn are routed through the network devices and subjected to different types of routing, access control and transformation policies. Misconfigurations at the different layers in any of the network devices can affect the end to end connection between the hosts them selves and the communicating services running on top of them. Moreover, applications may require to transform requests to another one or more requests with different characteristics. This means that the network layer should guarantee more than one packet flow at the same time in order for the application request to be successful. Although it is already very hard to verify that only the legitimate packets can pass through the network successfully, the consistency between network and application layer access configuration adds another challenge. The different natures of policies from network layer devices to the logic of application access control makes it more complex. In this paper we have extended ConfigChecker [3] to include application layer access control. The ConfigChecker is a model checker for network configuration. It implemented many network devices including: routers, firewalls, IPSec gateways, hosts, and NAT/PAT nodes. ConfigChecker models the transitions based on the packet forwarding at the network layer where the packet header fields along with the location represent the variables for the model checker. We define application layer requests following a loose RBAC model: a 4-tuple of . The request can be created by users or services running on top of hosts in the network. The services in our model can also transform a request into another one or more requests and forward them to a different destination. We have implemented a parallel model checker for application layer configuration. The transitions in the application layer model is determined by the movement of the requests between different services. However, the network and application layer model checkers are not operating separately. Requirements regarding both models can be verified in a single query using our unified query interface. Moreover, inconsistency between network configuration and application layer configuration can be detected. The nature of the problem of verifying network-wide configurations necessitate having a very scalable system in terms of time and space requirements. Larger networks, more complex configurations, and richer variety of devices are all dimensions over which the system should handle gracefully. The application-layer access control depends on different variables than most of network layer policies. We chose to implement a parallel model checker for application access control rather than adding the application layer variables (which correspond to request fields) to the network model checker itself. This can decrease the number of system states and improve the performance. As the case in ConfigChecker, both model checkers are represented as state machines and encoded as boolean function using Binary Decision Diagrams (BDDs). We use an extended version of computational tree logic (CTL) toFig. 1. A simple overview of the framework design and flow provide more useful operators and then use it with symbolic model checking to prove or find counter examples for needed properties regarding both models. The rest of this paper is organized as follows. We first briefly describe our framework components in Section II. We then present the model used for capturing the network and application layer configuration in Section III and Section IV respectively. Section V shows how to query the model for properties, and lists some sample queries. The related work is presented in Section VI. We finally present our conclusion and future remarks in Section VII. II. FRAMEWORK OVERVIEW The framework consists of a few key components: configuration loader, model compiler, and query engine. The duty of each component is described briefly below: • The Configuration Loader parses the main configuration file that points out to the configuration files of network devices. Each file represents a device or entity (e.g., firewall, router, application-layer service, etc). Each con- figuration file consists mainly of two sections: meta-data directives, and policy. The initial directives act as an initialization step to configure the device properties like default gateway, service port, host address, etc. The policy is listed afterwards as a simple list of rules. • The Model Compiler translates the configuration into a Boolean expressions for each policy rule and builds a single expression for each device. These expressions are then combined into a single expression representing the whole network. • The Query Engine is responsible for verifying properties of the system by executing simple scripts based on CTL expressions. Scripts is written using a very limited set of primitives for refining the user output, and for defining the property itself. The model compiler component builds two separate expressions. The first represents the network layer configuration that reflects the packets forwarding and transformation through the network core and end points as described in Section III. The other expression represents the application layer configuration including services and users. This reflects how requests are forwarded and transformed in the service level. Section IV describes this process in more details. Although we can integrate the two expressions and build only one expression that accommodates for both the network and application layer configuration, we chose to split them into two expression. The variables used on each of them are generally independent except for the location variable. Building one expression takes more space because the network configuration will be duplicated for each different combination of application layer variables generating more and more states. This helps our model to scale and avoid state explosion. III. NETWORK MODEL We model the network as a single monolithic finite state machine. The state space is the cross-product of the packet properties by its possible locations in the network. The packet properties include the header information that determines the network response to a specific packet. A. State representation Initially, the only information we need about the packet is the source and destination information contained in the IP header and the current location of the packet in the network. Therefore, we can encode the state of the network with the following characteristic function: σn : IPs × ports × IPd × portd × loc → {true, false} IPs the 32-bit source IP address ports the 16-bit source port number IPd the 32-bit destination IP address portd the 16-bit destination port number loc the 32-bit IP address of the device currently processing the packet The function σn encodes the state of the network by evaluating to true whenever the parameters used as input to the function correspond to a packet that is in the network and false otherwise. If the network contains 5 different packets, then exactly five assignments to the parameters of the function σn will result in true. Note that because we abstract payload information, we cannot distinguish between 2 packets that are at the same device if they also have the same IP header information. Each device in the network can then be modeled by describing how it changes a packet that is currently located at the device. For example, a firewall might remove the packet from the network or it might allow it to move on to the device on the other side of the firewall. A router might change the location of the packet but leave all the header information unchanged. A device performing network address translation might change the location of the packet as well as some of the IP header information. A hub might copy the same packet to multiple new locations. The behavior of each of these devices can be described by a list of rules. Each rule has a condition and an action. The rule condition can be described using a Boolean formula over the bits of the state (the parameters of the characteristic function σn). If the packet at the devicematches (satisfies) a rule condition, then the appropriate action is taken. As described above, the action could involve changing the packet location as well as changing IP header information. In all cases, however, the change can be described by a Boolean formula over the bits of the state. Sometimes the new values are constant (completely determined by the rule itself), and sometimes they may depend on the values of some of the bits in the current state. In either case, a transition relation can be constructed as a relation or characteristic function over two copies of the state bits. An assignment to the bits/variables in the transition relation yields true if the packet described by the first copy of the bits will be transformed into a packet described by the second copy of the bits when it is at the device in question. B. Network devices We integrate the policies of different network devices including firewalls, routers, NAT and IPSec gateways. The details of their policies and how they are encoded into BDDs are discussed thoroughly in [3]. However, we have modified the encoding of hosts to reflect the request transformation performed by the services running on top of each host. The host may be configured to run one or multiple services. Each of which has its own access-control list as will be discussed in Section IV. The service configuration may also specify a set of possible request transformations where the incoming request is transformed into another (sometimes completely new) request. For example, a request to a web server can be translated into an NFS request to load a users home page. The new request will be carried through the network over packets. In our initial model the host receives packet and then forward them into the application layer within the host itself and it cannot forward it to another host. We need to modify this model so that the host will be able to forward packets to the other hosts in order to support the requests transformation performed by the services running on top of it. IV. APPLICATION LAYER MODEL We also model the application layer as a finite state machine. The state space is the cross-product of the application layer request properties by its possible locations in the network. The request properties include the its fields that determine the service response to a specific request. σp : usr × role × obj × act × loc × srv → {true, false} usr the 32-bit user ID role the 32-bit role ID which the user belongs to obj the 32-bit object ID act the 16-bit action ID loc the 32-bit IP address of the device currently processing the request srv the 16-bit service ID In the application layer model the devices in the network are modeled by describing how they change the requests. Only the devices that operates on the application level are considered (i.e., the devices who has a defined users list or services running on top of them). Here, we describe how we define access-control rights for service requests, and how we model these services and integrate them into the application layer state transition diagram. A. Application layer access-control In order to have a homogeneous policy definition across applications, we revert to a simplified RBAC model as a way to specify all application requests and consequently the access-control policy. As in firewall policy, the access-control list of application layer services is defined by specifying an action like (permit or deny) to the requests satisfying certain criteria. This criteria is defined using the request fields < user, role, object, action > or < u, r, o, a > for short. We assume that each host has a list of potential users who can use it to send requests. This list can simply be set to ”any”, to indicate that all defined users can access the host which enables a more powerful model for an adversary against which we want to verify the robustness of the policy. Also, another assumption is that any user can assume any role. This enables a more flexible usage of the model to incorporate more types of services. It is even possible to use one of the request fields in a slightly different meaning. For example, in a web server model; an action can be a POST or GET, the role can be a logged in versus guest visitor and the object and user will have their obvious meanings. On the other hand, for database servers, we might have users, roles, actions, and resources used in their original meaning. When a service receives the a request from other device it first verifies it against its access-control policy. If it satisfies the access-control policy it will be forwarded to the service to be executed or transformed as shown in Fig. 2. If the request does not satisfy the access policy it will be dropped (i.e., there is no valid transition in the finite state machine that goes to the required service). A policy typically is defined as a list of tuples with an assigned action: user role resource action decision ;user black listing 1 * * * deny 2 * * * deny ;admin account 100 1 * * permit ;guests can only read * guest 1-50 3 permit ;a read only resource * * 60 4 permit * * 60 * deny As in firewall policies, we use a first-match methodology. For example, the last two rules allow read access, and then deny every other action. Also, the first few black-listing rules do not conflict with the guest account rule that appears later in the policy. From the common practices in the area of application level and RBAC policies, we believe that it should never be the case that a user or role be specified as a range. The value of user and role IDs are irrelevant, therefore having a range in the specification is hard to have a practical value. Although, this fact does not affect our implementation (i.e., we support single values, ranges, or “any” values in all four fields of the access-control rules).Fig. 2. Service Model. S1, S2, S3, and S4 represent services running on different hosts. The dashed lines represent application requests. Requests are subjected to the access control list of the target service. B. State representation Requests that pass the access-control phase are forwarded to the execution phase of the service. We have simplified the execution phase to one of two options as suggested in Fig. 2. A request can be executed on the service itself, which means that the request life-time ends at this phase, and no further events are triggered. The other possibility is that the service transforms the request into another form by modifying one or more fields (i.e., user, role, object or action) and sends it to another service running on the same or a different host. For example, a request to a web server can be translated into an NFS request to load a user’s home page. Each request transformation is associated with a packet flow in the network level, the host should be able to send the appropriate packet to its gateway based on the service transformation. Only the network devices that support application layer services represented by hosts in our model are included in the application layer model. The requests that leave a host may come from two sources: either a user operating directly on the host or through a service, or a request is transformed from another one. We do not require the destination of each request to be defined in the configuration. We assume that any request instantiated in any host can be directed to any service in the network whose access control list allows the request to pass. To build the transition relation for each host in the network we need the following inputs. • The set U of users who can access the host. Each user is represented by its unique ID in the system. It can also be expressed as a pair. • The set T of possible request transformations that can be performed in the host. The configuration may specify the exact ID and location of the target service, or it can be anonymous. • The set P of access control policies for each service running in the network. These policies are encoded as BDD expressions before building the transition relation of the application layer. Each policy in the set corresponds to a particular service ID (The port number of the service can be used as its ID) running on a particular host. Lets assume that the list UH of pairs represents the users who can access the host H. We need first to encode the possible states that result from having these users on the host H (recall that the state is the product of the request properties in this case by the location in which the request exists). UBDD = _ i∈UH (usr = ui ∧ role = ri ∧ loc = H) (1) where ui and ri are the user and role IDs of the item i in the users list UH. To find the transitions we need to find out which services can be reached starting from the states defined in the expression UBDD (i.e., any service whose access-control policy allows defined requests to pass). Tu = _ i∈indices(P ) (UBDD ∧ P(i) ∧ loc0 = li ∧ srv0 = si) (2) where P(i), li , and si are the policy, location, and the service ID of the target service i respectively. The variables loc0 and srv0 represents the location and service ID variables in the next state of the transition. The expression Tu does not include the transitions that results from request transformation by the service running on the host. The following represents the transition that result from one transformation performed by the service i. usr = ui ∧ role = ri ∧ obj = oi ∧ act = ai loc = H ∧ srv = si usr0 = u 0 i ∧ role0 = r 0 i ∧ obj0 = o 0 i ∧ act0 = a 0 i ∧ P 0 (s 0 i ) loc0 = l 0 i ∧ srv = s 0 i (3) The values < ui , ri , oi , ai > are the properties of the initial request and the values < u0 i , r0 i , o0 i , a0 i > represent the properties of the transformed packet. The values P 0 (s 0 i ), l 0 i and s 0 i are the policy, location and the service ID of the target service to which the request should be transformed. Note that we use P 0 (s 0 i ) instead of P(s 0 i ) to indicate that the transformed request (and not the initial request) should pass the target service access-control policy in order to complete the transition. The disjunction of all the transitions caused by all the possible transformations along with the expression Tu calculated earlier formulate the total transition relation of the host H. V. QUERYING THE MODEL A query in our system takes the form of a Boolean expression that specifies some properties over packet flows, requests and locations, with temporal logic criteria specified using CTL operators. By evaluating the given expression in the context of the built state machine (i.e., states and transitions), we obtain the satisfying assignments to that expression represented in the same symbolic representation as the model itself. The simplest form for the result can be the constant expression “true” (e.g., the property is always satisfied), or “false” (e.g., no one violates the required property), or can be any subset of the space that satisfies the property (e.g., only flows with port 80, or traffic that starts from this location, etc).In the following subsections, we will go over a few examples for reachability and security properties. For each, we show how to construct the query (i.e., the Boolean expression), and what the results look like. Moreover, we discuss how to write the script that extracts the results and data fields in the intended format that makes sense to every specific query. The aim of this presentation is to show the applicability of the system to many types of properties, as well as showing the expressive power of the model. A. Model Checking We have described how to construct a transition relation for each device in the network. Each such transition relation describes a list of outgoing transitions for the device it models. The formulas are constructed with the requirement that the current location be equal to the device being modeled, so these transitions can only be taken when a packet or a request is at the device. To get the transition relation for the entire network, we simply take the disjunction of the formulas for the individual devices. This is applied on both models (The network and the application layer models). The current location of a packet or a request will match the location of one device in the network at most, and so only its transitions will apply. Recall that this global transition relation is a characteristic function for transitions in the model. If we substitute the values for a packet that is in the system into the current state variables of the transition relation, what we are left with is a formula describing what the possible next states of that packet look like. We have all the machinery to perform symbolic model checking. We use BDDs for all the formulas described above and we use standard model checking algorithms to explore the state space and compute states that satisfy various CTL properties. The BuDDy BDD package provides all the required operations (including quantification). For a much more complete description of symbolic model checking, the reader is encouraged to see [7]. The network layer model checker and the application layer model checker are encoded separately. Each of them is working on different variables. However, we may need to verify some requirements using both of them together. Although the application layer requests are transmitted from an application to another, the requests are encapsulated inside network packets. We do not require to have static one-one mapping between each request and the packet flow that should be used to transfer the request. The mapping can be expressed in the query itself by specifying precise network packet characteristics . For this purpose we use the variable loc which is common between the two models and has the same meaning. Fig. 3 shows how the two models are used together to verify the requirements. B. Query structure and features The query in our model checkers retrieves the states that satisfy a given condition. The condition is expressed by Fig. 3. Using two models to run a query. restricting some variables in the model checker to a given value or using CTL operators to express a temporal condition. We also need to specify what information to be retrieved about the states which satisfy the query (i.e., a list of variables to be retrieved). An example query can look like this: Q3 = [loc(10.12.13.14) ∧ EF((¬loc(10.0.0.0/8)) ∧ (¬Q2))] Q3 : extractF ield loc dport Q3 : listBounded 20 loc dport The query Q3 is defined by the given expression (i.e., what flows are in a given location (10.12.13.14) and in the future will be outside of the domain (10.0.0.0/8) and do not satisfy a previously defined query (Q2). The second and third lines are used to format the result of the query. The second line tells the query engine that we are only interested in the variables loc and dport. The third line specifies that we need to display only the first 20 satisfying assignments. If there is no satisfying assignment for the given query nothing will be returned. To handle the queries on both the network layer and application layer models, we introduced the concept of sub-query. Each sub-query is applied on one model. The applicationlayer sub-query should not include variables related to network layer model such as source or destination addresses and port numbers and vice versa. A query can include one or more sub-queries based on the following cases. • It can include only one sub-query. In this case the query is applied only on the appropriate model. The query engine detects the appropriate model based on the variables used. • It can include more than one sub-query of the same type linked by the logical operators such as AND, OR, IMP LIES, etc. In this case all the sub-queries are executed on the appropriate model and the final result is calculated by applying the specified operation. The results of the different sub-queries in this case are identical in terms of the number and type of the variables returned. The linking operation can be directly applied. • It can include multiple sub-queries of different types linked by logical operators. In this case, the results of the different types of sub-queries have different variables and we cannot apply the linking operation directly. Thelocation variable (loc) is common between the two models and it has the same meaning and value for the same device. For different types of sub-queries we apply the logical operations based on the location variable only. For example, if an application-layer sub-query is combined with a network-layer sub-query by the AND operation, we calculate the result of both sub-queries and then calculate the intersection between the location values in both results. Only requests and packet flows whose location falls within the intersection are returned. The result of a query is a list of states that satisfy the query expression. In our model we may have two types of results. The first is a set of network-layer states each represented as a packet flow characteristics and location. The second is a set of application-layer states each represented as a request characteristics and location. The existence of these two types depends on the types of sub-queries included within the query script. C. Example properties Property 1: Conflicting network and application accesscontrol (a): Given a user location and userID, does the current configuration allows the user to access the server machine, while the application layer access-control blocks the connection? The query shown in the Table I specifies the initial properties of flows with certain user information (e.g., a specific source IP ”user addr”, and user identifier in the application layer request) and targeted towards a service residing elsewhere (i.e., server, port0 ). If there is an inconsistency in the configuration the query returns a list of requests that cannot access the specified service and another list of packet flows that can eventually reach the host network layer. We can see that the query combines two different types of sub-queries and restricts the location to a particular source machine. Each sub-query is surrounded by angle brackets ”[ ]”. (b): Does the current configuration blocks the user’s access to the server machine through network layer filtering, while the application’s access-control layer permits such connection? As in the pervious property (a), we try to see if a request that is permitted by the application layer access control will never reach the service. This means that somewhere before reaching the server hosting the service there is a network layer device blocks the traffic, or fails to route it correctly. We use the application layer model to find those requests that can pass from the source to a particular service, and we use the network model to find if the underlaying packet flows who should carry the requests are allowed to flow from the source to the appropriate destination. Property 2: Can a user access a resource under different credentials, if he is prohibited from accessing it under his original identity? In this property, we check if a certain user can masquerade under another identity to access a resource. This forms a back-door to this specific object-action pair. A straight forward example can be a user accessing an NFS server for which he does not have access via a web-server who can retrieve the content in the form of web pages. This can be achieved by an improper request transformation in a service which should not be reachable by the specified user. This is defined formally by evaluating the expression specifying which users cannot access an object, while it can be accessed eventually if the constraint on the user identity is removed. Property 3: What access rights does an object require? (a): What roles can user u use to access object o? It is sometimes essential to know what roles can a user manifest when accessing a specific object, or a group of objects. The query consists of checking the space of requests that can pass through the network and RBAC filtering to reach our object of interest. By restricting the user part of the space, we get the possible roles that can be used. We can also, restrict the action if needed. An addition that can prove practically useful is to add another restriction of origin of request: by filtering the location and source address from which the request originated from other than that of the server. In Table I we show only the condition part of the query neglecting the result format part, we can specify to return only the roles and/or any other fields in the results. (b): Which users can access object o via a role r? This is a similar query to the previous one. This query concerns the different users who can access a given object in a certain capacity. For example, we might want to know who can access a critical file as an administrator. Also, we can add extra restrictions to see who can access this object for writing rather than just reading. Property 4: Is there any conflicts within the application layer access-control? (a): Is there any inconsistencies in allowed actions for a specific object? Such conflicts can arise within the same policy or cross policies. For example, if a user is granted the write access to an object then, most probably, read access should be allowed as well. This query is application dependent, and priority between actions has to be specified explicitly (e.g., ‘delete’ > ‘write’ > ‘read’). We write the query for a service to check if it is possible for some user/role to reach the service via a higher action, but not with a lower one. We represent the general form for such query in Table I, the profiles [high security requirements] and [low security requirements] can be replaced with any combination of constraints on the request fields. For example, to compare rights for reading and writing, the high security profile may be [obj(o)∧act(wr)] and the low security profile may be represented as [obj(o) ∧ act(rd)] for the particular object (o). (b): Are role-role relations consistent? As in the pervious property (a), we might need to verify that the order of role privileges is maintained. In other words, aProperty Query expression form P1 (a): Requests reaching the host but not the service it is running. loc(user addr)∧[src(user addr)∧dest(server)∧dport(port0 )∧ EF(loc(server))]∧[usr(userID)∧ ¬EF(loc(server) ∧ srv(port0 ))] P1 (b): Requests reaching the service but can’t reach the host itself. loc(user addr) ∧ [usr(userID) ∧ EF(loc(server) ∧ srv(port0 ))]∧ [src(user addr) ∧ dest(server) ∧ dport(port0 ) ∧ ¬EF(loc(server))] P2: Backdoor: A user is denied direct access to a service, but can use another service to indirectly access it. loc(user addr) ∧ [usr(userID) ∧ ¬EF(usr(userID) ∧ obj(o) ∧ loc(server) ∧ srv(port0 )) ∧ EF(usr(¬userID) ∧ obj(o) ∧ loc(server) ∧ srv(port0 )] ∧ [EF(loc(server) ∧ dport(port0 ))] P3 (a): What roles and actions can a user use to access a specific object from outside the server domain? ¬loc(server) ∧ [EF(loc(server) ∧ srv(port0 ) ∧ usr(u) ∧ obj(o))]∧ [¬src(server) ∧ EF(loc(server) ∧ dport(port0 ))] P3 (b): What users can access a given object? ¬loc(server) ∧ [¬src(server) ∧ EF(loc(server) ∧ dport(port0 ))]∧ [EF(loc(server) ∧ srv(port0 ) ∧ role(r) ∧ obj(o) ∧ act(a))] P4: Is there any inconsistency between rights of low and high privilege requests? EF(loc(server) ∧ srv(port0 ) ∧ [high security requirements]) ∧¬EF(loc(server) ∧ srv(port0 ) ∧ [low security requirements]) TABLE I EXAMPLES FOR REACHABILITY AND SECURITY PROPERTIES more powerful role should be always capable of performing all actions possible for a weaker role. For example, an administrator should perform at least everything doable by a staff member, and guests should never have more access to other roles. It is defined by checking if the space of possible actionsover-objects that can be performed by role1 but not role2 is empty (given that role1 < role2). In this case the high and low security profiles can be represented as [role(role1)∧obj(o)∧ act(a)] and [role(role2) ∧ obj(o) ∧ act(a)] respectively. VI. RELATED WORK There have been significant research effort in the area of configuration verification and management in the past few years. We can classify the work in this area into two main approaches: top-down and bottom-up. The topdown approaches [20], [5] create clean-slate configurations based on high-level requirements. However, the bottom-up approaches [13], [1], [24] analyze the existing configuration to verify desired properties. We focus our discussion on bottomup approach as it is closer to our work in this paper. There has been considerable work recently in detecting misconfiguration in routing and firewall. Many of these approaches are specific for BGP misconfiguration [9], [17], [11], [4]. [23], [13], [1], [24] focused on conflict analysis of firewalls configuration. A BDD-based modeling and taxonomy of IPSec configuration conflicts was presented in [2], [13]. FIREMAN [24] uses BDD to show conflicts on Linux iptables configurations. In [19] and [21], the authors developed a firewall analysis tool to perform customized queries on a set of filtering rules of a firewall. But no general model of network connections is used in this work. In the field of distributed firewalls, current research mainly focuses on the management of distributed firewall policies. The first generation of global policy management technology is presented in [12], which proposes a global policy definition language along with algorithms for verifying the policy and generating filtering rules. In [6], the authors adopted a better approach by using a modular architecture that separates the security policy and the underlying network topology to allow for flexible modification of the network topology without the need to update the security policy. Similar work has been done in [14] with a procedural policy definition language, and in [16] with an object-oriented policy definition language. In terms of distributed firewall policy enforcement, a novel architecture is proposed in [15] where the authors suggest using a trust management system to enforce a centralized security policy at individual network endpoints based on access rights granted to users or hosts. We found that none of the published work in this area addressed the problem of discovering conflicts in distributed firewall environments. A variety of approaches have been proposed in the area of policy conflict analysis. The most significant attempt for IPSec policy analysis is proposed in [10]. The technique simulates IPSec processing by tracking the protection applied on the traffic in every IPSec device. At any point in the simulation, if packet protection violates the security policy requirements, a policy conflict is reported. Although this approach can discover IPSec policy violations in a certain simulation scenario, there is no guarantee that it discovers every possible violation that may exist. In addition, the proposed technique only discovers IPSec conflicts resulting from incorrect tunnel overlapping, but do not address the other types of conflictsthat we study in this research. Other works attempt to create general models for analyzing network configuration [8], [22]. An approach for formulating and deriving of sufficient conditions of connectivity constraints is presented in [8]. The static analysis approach [22]is one of the most interesting work that is close to ConfigChecker. This work uses graph-based approach to model connectivity of network configuration and use set operations to perform static analysis. The transitive closure, as apposed to a fixed point in our approach, is computed. Thus, it seems that all possible paths are computed explicitly. In addition, considering security devices and properties, providing a rich query interface based on our CTL extension, and utilizing BDDs optimization are major advantages of our work. Anteater [18] is another interesting tool for checking invariants in the data plane. It checks the high-level network invariants represented as instances of boolean satisfiability problems (SAT) against network state using a SAT solver, and reports counterexamples for violations, if exist. Thus, in conclusion, although this body of work has a significant impact on the filed, it is either provide limited analysis due to restriction on specific network or application. Unlike the previous work, our work offers a global configuration verification that is comprehensive, scalable and highly expressive. VII. CONCLUSION We presented an extension to the ConfigChecker tool to incorporate both network and application configurations in a unified system across the entire network. Our extended system models the configuration of various devices in the network layer (hubs, switches, routers, firewalls, IPsec gateways) and access control of application layer services including multiplelevel of request translation. Network and system configuration can be modeled together and used to verify properties using CTL-embedded functions translated into Boolean operations. We show that we can separate variables in two model checkers to reduce the state space and required resources. Yet, both models can be used to run combine queries. Our future work includes enhancements in the model’s performance for even faster execution and lower the construction time. Also, we plan to extending the supported devices, and node types to add more virtual devices and compound devices that can incorporate multi-node functionality as in some modern network-based devices. 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Hiyan Alshawi hiyan@google.com Google Research, Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA, 94043 Daniel Jurafsky jurafsky@stanford.edu Departments of Linguistics and Computer Science, Stanford University, Stanford, CA, 94305 Editors: Jeffrey Heinz, Colin de la Higuera, and Tim Oates Abstract Modern grammar induction systems often employ curriculum learning strategies that begin by training on a subset of all available input that is considered simpler than the full data. Traditionally, filtering has been at granularities of whole input units, e.g., discarding entire sentences with too many words or punctuation marks. We propose instead viewing interpunctuation fragments as atoms, initially, thus making some simple phrases and clauses of complex sentences available to training sooner. Splitting input text at punctuation in this way improved our state-of-the-art grammar induction pipeline. We observe that resulting partial data, i.e., mostly incomplete sentence fragments, can be analyzed using reduced parsing models which, we show, can be easier to bootstrap than more nuanced grammars. Starting with a new, bare dependency-and-boundary model (DBM-0), our grammar inducer attained 61.2% directed dependency accuracy on Section 23 (all sentences) of the Wall Street Journal corpus: more than 2% higher than previous published results for this task. Keywords: Dependency Grammar Induction; Unsupervised Dependency Parsing; Curriculum Learning; Partial EM; Punctuation; Unsupervised Structure Learning. 1. Introduction “Starting small” strategies (Elman, 1993) that gradually increase complexities of training models (Lari and Young, 1990; Brown et al., 1993; Frank, 2000; Gimpel and Smith, 2011) and/or input data (Brent and Siskind, 2001; Bengio et al., 2009; Krueger and Dayan, 2009; Tu and Honavar, 2011) have long been known to aid various aspects of language learning. In dependency grammar induction, pre-training on sentences up to length 15 before moving on to full data can be particularly effective (Spitkovsky et al., 2010a,b, 2011a,b). Focusing on short inputs first yields many benefits: faster training, better chances of guessing larger fractions of correct parse trees, and a preference for more local structures, to name a few. But there are also drawbacks: notably, unwanted biases, since many short sentences are not representative, and data sparsity, since most typical complete sentences can be quite long. We propose starting with short inter-punctuation fragments of sentences, rather than with small whole inputs exclusively. Splitting text on punctuation allows more and simpler word sequences to be incorporated earlier in training, alleviating data sparsity and complex- c 2012 V.I. Spitkovsky, H. Alshawi & D. Jurafsky.ity concerns. Many of the resulting fragments will be phrases and clauses, since punctuation correlates with constituent boundaries (Ponvert et al., 2010, 2011; Spitkovsky et al., 2011a), and may not fully exhibit sentence structure. Nevertheless, we can accommodate these and other unrepresentative short inputs using our dependency-and-boundary models (DBMs), which distinguish complete sentences from incomplete fragments (Spitkovsky et al., 2012). DBMs consist of overlapping grammars that share all information about head-dependent interactions, while modeling sentence root propensities and head word fertilities separately, for different types of input. Consequently, they can glean generalizable insights about local substructures from incomplete fragments without allowing their unrepresentative lengths and root word distributions to corrupt grammars of complete sentences. In addition, chopping up data plays into other strengths of DBMs — which learn from phrase boundaries, such as the first and last words of sentences — by increasing the number of visible edges. Figure 1: Three types of input: (a) fragments lacking sentence-final punctuation are always considered incomplete; (b) sentences with trailing but no internal punctuation are considered complete though unsplittable; and (c) text that can be split on punctuation yields several smaller incomplete fragments, e.g., Bach’s, Air and followed. In modeling stopping decisions, Bach’s is still considered left-complete — and followed right-complete — since the original input sentence was complete. Odds and Ends (a) An incomplete fragment. “It happens.” (b) A complete sentence that cannot be split on punctuation. Bach’s “Air” followed. (c) A complete sentence that can be split into three fragments. 2. Methodology All of our experiments make use of DBMs, which are head-outward (Alshawi, 1996) classbased models, to generate projective dependency parse trees for Penn English Treebank’s Wall Street Journal (WSJ) portion (Marcus et al., 1993). Instead of gold parts-of-speech, we use context-sensitive unsupervised tags,1 obtained by relaxing a hard clustering produced by Clark’s (2003) algorithm using an HMM (Goldberg et al., 2008). As in our original setup without gold tags (Spitkovsky et al., 2011b), training is split into two stages of Viterbi EM (Spitkovsky et al., 2010b): first on shorter inputs (15 or fewer tokens), then on most sentences (up to length 45). Evaluation is against the reference parse trees of Section 23.2 Our baseline system learns DBM-2 in Stage I and DBM-3 (with punctuation-induced constraints) in Stage II, starting from uniform punctuation-crossing attachment probabilities (see Appendix A for details of DBMs). Smoothing and termination of both stages are as in Stage I of the original system. This strong baseline achieves 59.7% directed dependency accuracy — somewhat higher than our previous state-of-the-art result (59.1%, see also Table 1). In all experiments we will only make changes to Stage I’s training, initialized from the same exact trees as in the baselines and affecting Stage II only via its initial trees. 1. http://nlp.stanford.edu/pubs/goldtags-data.tar.bz2:untagger.model 2. Unlabeled dependencies are converted from labeled constituents using deterministic “head-percolation” rules (Collins, 1999) — after discarding punctuation marks, tokens that are not pronounced where they appear (i.e., having gold part-of-speech tags $ and #) and any empty nodes — as is standard practice.Table 1: Directed dependency and exact tree accuracies (DDA / TA) for our baseline, experiments with split data, and previous state-of-the-art on Section 23 of WSJ. Stage I Stage II DDA TA Baseline (§2) DBM-2 constrained DBM-3 59.7 3.4 Experiment #1 (§3) split DBM-2 constrained DBM-3 60.2 3.5 Experiment #2 (§4) split DBM-i constrained DBM-3 60.5 4.9 Experiment #3 (§5) split DBM-0 constrained DBM-3 61.2 5.0 (Spitkovsky et al., 2011b, §5.2) constrained DMV constrained L-DMV 59.1 — Table 2: Feature-sets parametrizing dependency-and-boundary models three, two, i and zero: if comp is false, then so are comproot and both of compdir; otherwise, comproot is true for unsplit inputs, compdir for prefixes (if dir = L) and suffixes (when dir = R). Model PATTACH (root-head) PATTACH (head-dependent) PSTOP (adjacent/not) DBM-3 (Appendix A) (⋄, L, cr, comproot) (ch, dir, cd, cross) (compdir, ce, dir, adj) DBM-2 (§3, Appendix A) (⋄, L, cr, comproot) (ch, dir, cd) (compdir, ce, dir, adj) DBM-i (§4, Appendix B) (⋄, L, cr, comproot) (ch, dir, cd) (compdir, ce, dir) DBM-0 (§5, Appendix B) (⋄, L, cr) iff comproot (ch, dir, cd) (compdir, ce, dir) 3. Experiment #1 (DBM-2): Learning from Fragmented Data In our experience (Spitkovsky et al., 2011a), punctuation can be viewed as implicit partial bracketing constraints (Pereira and Schabes, 1992): assuming that some (head) word from each inter-punctuation fragment derives the entire fragment is a useful approximation in the unsupervised setting. With this restriction, splitting text at punctuation is equivalent to learning partial parse forests — partial because longer fragments are left unparsed, and forests because even the parsed fragments are left unconnected (Moore et al., 1995). We allow grammar inducers to focus on modeling lower-level substructures first,3 before forcing them to learn how these pieces may fit together. Deferring decisions associated with potentially long-distance inter-fragment relations and dependency arcs from longer fragments to a later training stage is thus a variation on the “easy-first” strategy (Goldberg and Elhadad, 2010), which is a fast and powerful heuristic from the supervised dependency parsing setting. We bootstrapped DBM-2 using snippets of text obtained by slicing up all input sentences at punctuation. Splitting data increased the number of training tokens from 163,715 to 709,215 (and effective short training inputs from 15,922 to 34,856). Ordinarily, tree generation would be conditioned on an exogenous sentence-completeness status (comp), using presence of sentence-final punctuation as a binary proxy. We refined this notion, accounting for new kinds of fragments: (i) for the purposes of modeling roots, only unsplit sentences could remain complete; as for stopping decisions, (ii) leftmost fragments (prefixes of complete original sentences) are left-complete; and, analogously, (iii) rightmost fragments (suf- fixes) retain their status vis-`a-vis right stopping decisions (see Figure 1). With this set-up, performance improved from 59.7 to 60.2% (from 3.4 to 3.5% for exact trees — see Table 1). Next, we will show how to make better use of the additional fragmented training data. 3. About which our loose and sprawl punctuation-induced constraints agree (Spitkovsky et al., 2011a, §2.2).4. Experiment #2 (DBM-i): Learning with a Coarse Model In modeling head word fertilities, DBMs distinguish between the adjacent case (adj = T, deciding whether or not to have any children in a given direction, dir ∈ {L, R}) and nonadjacent cases (adj = F, whether to cease spawning additional daughters — see PSTOP in Table 2). This level of detail can be wasteful for short fragments, however, since nonadjacency will be exceedingly rare there: most words will not have many children. Therefore, we can reduce the model by eliding adjacency. On the down side, this leads to some loss of expressive power; but on the up side, pooled information about phrase edges could flow more easily inwards from input boundaries, since it will not be quite so needlessly subcategorized. We implemented DBM-i by conditioning all stopping decisions only on the direction in which a head word is growing, the input’s completeness status in that direction and the identity of the head’s farthest descendant on that side (the head word itself, in the adjacent case — see Table 2 and Appendix B). With this smaller initial model, directed dependency accuracy on the test set improved only slightly, from 60.2 to 60.5%; however, performance at the granularities of whole trees increased dramatically, from 3.5 to 4.9% (see Table 1). 5. Experiment #3 (DBM-0): Learning with an Ablated Model DBM-i maintains separate root distributions for complete and incomplete sentences (see PATTACH for ⋄ in Table 2), which can isolate verb and modal types heading typical sentences from the various noun types deriving captions, headlines, titles and other fragments that tend to be common in news-style data. Heads of inter-punctuation fragments are less homogeneous than actual sentence roots, however. Therefore, we can simplify the learning task by approximating what would be a high-entropy distribution with a uniform multinomial, which is equivalent to updating DBM-i via a “partial” EM variant (Neal and Hinton, 1999). We implemented DBM-0 by modifying DBM-i to hardwire the root probabilities as one over the number of word classes (1/200, in our case), for all incomplete inputs. With this more compact, asymmetric model, directed dependency accuracy improved substantially, from 60.5 to 61.2% (though only slightly for exact trees, from 4.9 to 5.0% — see Table 1). 6. Conclusion We presented an effective divide-and-conquer strategy for bootstrapping grammar inducers. Our procedure is simple and efficient, achieving state-of-the-art results on a standard English dependency grammar induction task by simultaneously scaffolding on both model and data complexity, using a greatly simplified dependency-and-boundary model with interpunctuation fragments of sentences. 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Simple unsupervised grammar induction from raw text with cascaded finite state models. In ACL-HLT, 2011. V. I. Spitkovsky, H. Alshawi, and D. Jurafsky. From Baby Steps to Leapfrog: How “Less is More” in unsupervised dependency parsing. In NAACL-HLT, 2010a. V. I. Spitkovsky, H. Alshawi, D. Jurafsky, and C. D. Manning. Viterbi training improves unsupervised dependency parsing. In CoNLL, 2010b. V. I. Spitkovsky, H. Alshawi, and D. Jurafsky. Punctuation: Making a point in unsupervised dependency parsing. In CoNLL, 2011a. V. I. Spitkovsky, A. X. Chang, H. Alshawi, and D. Jurafsky. Unsupervised dependency parsing without gold part-of-speech tags. In EMNLP, 2011b. V. I. Spitkovsky, H. Alshawi, and D. Jurafsky. Three dependency-and-boundary models for grammar induction. In EMNLP-CoNLL, 2012. K. Tu and V. Honavar. On the utility of curricula in unsupervised learning of probabilistic grammars. In IJCAI, 2011.Appendix A. The Dependency-and-Boundary Models (DBMs 1, 2 and 3) All DBMs begin by choosing a class for the root word (cr). Remainders of parse structures, if any, are produced recursively. Each node spawns off ever more distant left dependents by (i) deciding whether to have more children, conditioned on direction (left), the class of the (leftmost) fringe word in the partial parse (initially, itself), and other parameters (such as adjacency of the would-be child); then (ii) choosing its child’s category, based on direction, the head’s own class, etc. Right dependents are generated analogously, but using separate factors. Unlike traditional head-outward models, DBMs condition their generative process on more observable state: left and right end words of phrases being constructed. Since left and right child sequences are still generated independently, DBM grammars are split-head. DBM-2 maintains two related grammars: one for complete sentences (comp = T), approximated by presence of final punctuation, and another for incomplete fragments. These grammars communicate through shared estimates of word attachment parameters, making it possible to learn from mixtures of input types without polluting root and stopping factors. DBM-3 conditions attachments on additional context, distinguishing arcs that cross punctuation boundaries (cross = T) from lower-level dependencies. We allowed only heads of fragments to attach other fragments as part of (loose) constrained Viterbi EM; in inference, entire fragments could be attached by arbitrary external words (sprawl). All missing families of factors (e.g., those of punctuation-crossing arcs) were initialized as uniform multinomials. Appendix B. Partial Dependency-and-Boundary Models (DBMs i and 0) Since dependency structures are trees, few heads get to spawn multiple dependents on the same side. High fertilities are especially rare in short fragments, inviting economical models whose stopping parameters can be lumped together (because in adjacent cases heads and fringe words coincide: adj = T → h = e, hence ch = ce). Eliminating inessential components, such as the likely-heterogeneous root factors of incomplete inputs, can also yield benefits. Consider the sentence a z . It admits two structures: ya z and xa z . In theory, neither should be preferred. In practice, if the first parse occurs 100p% of the time, a multicomponent model could re-estimate total probability as p n + (1 − p) n , where n may exceed its number of independent components. Only root and adjacent stopping factors are nondeterministic here: PROOT(a ) = PSTOP(z , L) = p and PROOT(z ) = PSTOP(a , R) = 1 − p; attachments are fixed (a can only attach z and vice-versa). Tree probabilities are thus cubes (n = 3): a root and two stopping factors (one for each word, on different sides), P(a z ) = P( ya z ) + P( xa z ) = p z }| { PROOT(a ) PSTOP(a , L) | {z } 1 p z }| { (1 − PSTOP(a , R)) PATTACH(a , R,z ) | {z } 1 p z }| { PSTOP(z , L) PSTOP(z , R) | {z } 1 + 1−p z }| { PROOT(z ) PSTOP(z , R) | {z } 1 1−p z }| { (1 − PSTOP(z , L)) PATTACH(z , L,a ) | {z } 1 1−p z }| { PSTOP(a , R) PSTOP(a , L) | {z } 1 = p 3 + (1 − p) 3 . For p ∈ [0, 1] and n ∈ Z +, p n+(1−p) n ≤ 1, with strict inequality if p /∈ {0, 1} and n > 1. Clearly, as n grows above one, optimizers will more strongly prefer extreme solutions p ∈ {0, 1}, despite lacking evidence in the data. Since the exponent n is related to numbers of input words and independent modeling components, a recipe of short inputs — combined with simpler, partial models — could help alleviate some of this pressure towards arbitrary determinism. Building high-level features using large scale unsupervised learning Quoc%V.%Le% Stanford%University%and%Google% Joint%work%with:%Marc’Aurelio Ranzato,%Rajat Monga,%MaEhieu%Devin,%Kai%Chen,%% Greg%Corrado,%Jeff%Dean,%Andrew%Y.%Ng%pixels% edges% Face%parts% (combinaRon%% of%edges)% Face%detectors% Lee%et%al,%2009.%Sparse%DBNs.% Hierarchy%of%feature%representaRons%Faces% Random%images%from%the%Internet%Quoc%V.%Le% Face%detector% Human%body%detector% Cat%detector% Key%results%Algorithm% Each%RICA%layer%=%1%filtering%layer%+%pooling%layer%+%local%contrast% normalizaRon%layer% See%Le%et%al,%NIPS%11%and%Le%et%al,%CVPR%11%for%applicaRons%on%acRon% recogniRon,%object%recogniRon,%biomedical%imaging% Very%large%model%`>%Cannot%fit%in%a%single%machine% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%`>%Model%parallelism,%Data%parallelism% Pooling Size = 5 Number of maps = 8 Image Size = 200 Number of output channels = 8 Number of input channels = 3 One layer RF size = 18 Input to another layer above (image with 8 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spoQed%eagle%ray,%spoQed%ray,%Aetobatus narinari cownose%ray,%cow\nosed%ray,%Rhinoptera bonasus manta,%manta%ray,%devilfish% AtlanNc%manta,%Manta%birostris devil%ray,%Mobula hypostoma grey%skate,%gray%skate,%Raja%baNs liQle%skate,%Raja%erinacea …% SNngray% MantarayBest%sNmuli% Pooling Size = 5 Number of maps = 8 Image Size = 200 Number of output channels = 8 Number of input channels = 3 One layer RF size = 18 Input to another layer above (image with 8 channels) W H LCN Size = 5 Feature%1% Feature%2% Feature%3% Feature%4% Feature%5% Le,%et%al.,%Building$high>level$features$using$large>scale$unsupervised$learning.%ICML%2012%Pooling Size = 5 Number of maps = 8 Image Size = 200 Number of output channels = 8 Number of input channels = 3 One layer RF size = 18 Input to another layer above (image with 8 channels) W H LCN Size = 5 Feature%7%% Feature%8% Feature%6% Feature%9% Best%sNmuli% Le,%et%al.,%Building$high>level$features$using$large>scale$unsupervised$learning.%ICML%2012%Pooling Size = 5 Number of maps = 8 Image Size = 200 Number of output channels = 8 Number of input channels = 3 One layer RF size = 18 Input to another layer above (image with 8 channels) W H LCN Size = 5 Feature%11%% Feature%10% Feature%12% Feature%13% Best%sNmuli% Le,%et%al.,%Building$high>level$features$using$large>scale$unsupervised$learning.%ICML%2012%0.005%% Random%guess% 9.5%% ?% Feature%learning%% From%raw%pixels% State\of\the\art% (Weston,%Bengio%‘11)% Le,%et%al.,%Building$high>level$features$using$large>scale$unsupervised$learning.%ICML%2012%ImageNet%2009%(10k%categories):%Best%published%result:%17%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%(Sanchez%&%Perronnin%‘11%),%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Our%method:%20%% Using%only%1000%categories,%our%method%>%50%% 0.005%% Random%guess% 9.5%% State\of\the\art% (Weston,%Bengio%‘11)% 15.8%% Feature%learning%% From%raw%pixels% Le,%et%al.,%Building$high>level$features$using$large>scale$unsupervised$learning.%ICML%2012%No%maQer%the%algorithm,%more%features%always%more%successful.% Other%results% \ We%also%have%great%features%for%% \ Speech%recogniNon% \ Word\vector%embedding%for%NLPs%• RICA%learns%invariant%features% • Face%neuron%with%totally%unlabeled%data%% %%%%%%%%with%enough%training%and%data% • State\of\the\art%performances%on%% – AcNon%RecogniNon% – Cancer%image%classificaNon% – ImageNet Conclusions% 80% 82% 84% 86% 88% 90% 92% 94% Cancer%classificaNon% AcNon%recogniNon% AcNon%recogniNon%benchmarks% Feature%visualizaNon% Face%neuron% 0.005%% 9.5%% 15.8%% Random%guess% Best%published%result% Our%method% ImageNet• Q.V.%Le,%M.A.%Ranzato,%R.%Monga,%M.%Devin,%G.%Corrado,%K.%Chen,%J.%Dean,%A.Y.% Ng.%Building)high+level)features)using)large+scale)unsupervised)learning.% ICML,%2012.% • Q.V.%Le,%J.%Ngiam,%Z.%Chen,%D.%Chia,%P.%Koh,%A.Y.%Ng.%Tiled)Convolu8onal)Neural) Networks.%NIPS,%2010.%% • Q.V.%Le,%W.Y.%Zou,%S.Y.%Yeung,%A.Y.%Ng.%Learning)hierarchical)spa8o+temporal) features)for)ac8on)recogni8on)with)independent)subspace)analysis.%CVPR,% 2011.% • Q.V.%Le,%J.%Ngiam,%A.%Coates,%A.%Lahiri,%B.%Prochnow,%A.Y.%Ng.%% On)op8miza8on)methods)for)deep)learning.%ICML,%2011.%% • Q.V.%Le,%A.%Karpenko,%J.%Ngiam,%A.Y.%Ng.%%ICA)with)Reconstruc8on)Cost)for) Efficient)Overcomplete)Feature)Learning.%NIPS,%2011.%% • Q.V.%Le,%J.%Han,%J.%Gray,%P.%Spellman,%A.%Borowsky,%B.%Parvin.%Learning)Invariant) Features)for)Tumor)Signatures.%ISBI,%2012.%% • I.J.%Goodfellow,%Q.V.%Le,%A.M.%Saxe,%H.%Lee,%A.Y.%Ng,%%Measuring)invariances)in) deep)networks.%NIPS,%2009.% References% hQp://ai.stanford.edu/~quocle On the Predictability of Search Trends Yair Shimshoni Niv Efron Yossi Matias Google, Israel Labs Draft date: August 17, 2009 1. Introduction Since Google Trends and Google Insights for Search were launched, they provide a daily insight into what the world is searching for on Google, by showing the relative volume of search traffic in Google for any search query. An understanding of web search trends can be useful for advertisers, marketers, economists, scholars, and anyone else interested in knowing more about their world and what's currently top-of-mind. The trends of some search queries are quite seasonal and have repeated patterns. See, for instance, the search trends for ski in the US and in Australia peak during the winter season; or check out how search trends for basketball correlate with annual league events and how consistent it is year-over-year. When looking at trends of the aggregated volume of search queries related to particular categories, one can also observe regular patterns in at least some of hundreds of categories, like the Food & Drink or Automotive categories. Such trends sequences appear quite predictable, and one would naturally expect the patterns of previous years to repeat looking forward. On the other hand, for many other search queries and categories, the trends are quite irregular and hard to predict. For example, the search trends for Obama, Twitter, Android, or global warming, and trends of aggregate searches in the News & Current Events category. Having predictable trends for a search query or for a group of queries could have interesting ramifications. One could forecast the trends into the future, and use it as a "best guess" for various business decisions such as budget planning, marketing campaigns and resource allocations. One could identify deviation from such forecasting and identify new factors that are influencing the search volume like in the detection of influenza epidemics using search queries [Ginsberg etal. 2009] known as Flu Trends. We were therefore interested in the following questions: • How many search queries have trends that are predictable? • Are some categories more predictable than others? How is the distribution of predictable trends between the various categories? • How predictable are the trends of aggregated search queries for different categories? Which categories are more predictable and which are less so? To learn about the predictability of search trends, and so as to overcome our basic limitation of not knowing what the future will entail, we characterize the predictability of a Trends series based on its historical performance. That is, based on the a posteriori predictability of a sequence determined by the discrepancy of forecast trends applied at some point in the past vs the actual performance.Specifically, we have used a simple forecasting model that learns basic seasonality and general trend. For each trends sequence of interest, we take a point in time, t, which is about a year back, compute a one year forecasting for t based on historical data available at time t, and compare it to the actual trends sequence that occurs since time t. The discrepancy between the forecasting trends and the actual trends characterize the predictability level of a sequence, and when the discrepancy is smaller than a predefined threshold, we denote the trends query as predictable. We investigate time series of search trends provided by Google Insights for Search (I4S), which represent query shares of given search terms (or for aggregations of terms). A query share is the total number of queries for a search term (or an entire search category) in a given geographic region divided by the total number of queries in that region at a given point in time. The query share represents the popularity of a query, or the aggregated search interest that users have in a query, and we will therefore use the term search interest interchangeably with query share. The highlights of our observations can be summarized as follows: • Over half of the most popular Google search queries were found predictable in 12 month ahead forecast, with a mean absolute prediction error of approximately 12% on average. • Nearly half of the most popular queries are not predictable, with respect to the prediction model and evaluation framework that we have used. • Some categories have particularly high fraction of predictable queries; for instance, Health (74%), Food & Drink (67%) and Travel (65%). • Some categories have particularly low fraction of predictable queries; for instance, Entertainment (35%) and Social Networks & Online Communities (27%). • The trends of aggregated queries per categories are much more predictable: 88% of the aggregated category search trends of over 600 categories in Insights for Search are predictable with a mean absolute prediction error of less than 6% on average. • There is a clear association between the existence of seasonality patterns and higher predictability as well as an association between high levels of outliers and lower predictability. Recently the research community has started to use Google search data provided publicly by Google Insights for Search (I4S) as auxiliary indicators for economic forecast. [Choi & Varian 2009] have shown that aggregated search trends of Google Categories can be used as extra indicators and effectively leverage several US econometrics prediction models. [Askitas & Zimmermann 2009] and [Suhoy 2009] have shown similar findings on German and Israeli economic data, respectively. Getting a better insight into the behavior of relevant search trends has therefore high potential applicability for these domains. For queries or aggregated set of queries for which the search trends are predictable, one can use a forecasted trends based on the prediction model as a baseline for identifying deviations in actual trends. Such deviations are of particular interest as they are often indicative of material changes in the domain of the queries. We consider a few examples with observed deviation of actual trends relative to the forecasted trends, including: • Automotive Industry We show that in the recent 12 months there is a positive deviation relative to the forecast baseline (i.e., an increased query share) in the searches of Auto Parts and Vehicle Maintenance while there is a negative deviation (i.e., a decrease in query share) in the searches of Vehicle Shopping and Auto Financing.• US Unemployment In relation with the recent research that showed an improvement in prediction of unemployment rates using Google query shares [Choi and Varian 2009 b], we show that the search interest in the category of Welfare & Unemployment has substantially risen in the last year above the forecast based on the prediction model. We also show that the search interest in the category Jobs has significantly decreased according to the prediction model. • Mexico as Vacation Destination We examine the large decrease in the query share of the category Mexico as a vacation destination, compared to the predictions for the last 12 months. We show that a similar deviation of (actual vs. forecast) query share is not observed for other related categories. • Recession Markers We show several examples that demonstrate possible influences of the recent recession on search behavior, like an observed increase of query share for the category Coupons & Rebate compared to the forecast. We also show a negative deviation between the query share for category Restaurants compared to the forecast, where as the category Cooking and Recipes shows a similar positive deviation. Outline The rest of this paper is organized as follows. In Section 2 we formulate the notion of predictability and describe the method of estimating it along with the evaluation measures, prediction model and the time series data that we use. In Section 3 we describe the experiments we conducted and present their results. In Section 4 we examine the association between the predictability of search interest and the level of seasonality or internal deviation of the underlying search trends. Section 5 will present sensitivity analysis and error diagnostics and in section 6 we discuss the potential use of forecasting as a baseline for identifying deviations from regular search behavior; we demonstrate with some examples that the discrepancies from model predictions can act as signals for recent changes in the query share. 2. Time Series Predictability In this section we define the notion of predictability as we use it in our experiments. Predictability. We characterize the predictability of a time series with respect to a prediction model and a discrepancy measure, as follows. Assume we have: • A time series X={xt-H, ... ,xt+F} with history of size H and a future horizon of size F Denote: X H={xt-H, ... ,xt} and X F={xt+1, ... ,xt+F} • A Prediction model: M, which computes a forecast Y=M(X H ) , where Y={yt+1, ... ,yt+F} • A Discrepancy Measure: D=D(X,Y) • A Threshold: D' Then, we say that X is predictable w.r.t (M,D,D',t,H,F) iff D=D(X,Y) < D' . The size of the discrepancy also characterizes the level of predictability of a series. We will often refer to a trends sequence as predictable (or not-predictable) where the various parameters are implied by the context.Data. The time series that are used in the following experiments are based on Google Insights for Search1 (I4S), which reports the query share for search terms in any time, location and category, as well as capable of reporting the most popular queries within a given time / location / category. A query share is defined as the total number of queries for a search term or a set of terms (e.g., an entire search category) in a given geographic region, divided by the total number of queries in that region, at a given point in time. The I4S categories are organized in a tree-like hierarchical structure, with about 30 root level categories, that are further divided into subcategories in a 3-level taxonomy, to a total of about 600 categories and sub-categories. Each search query is classified by I4S to a single category, nevertheless it will also be counted as a part of the query share to all its 'parent' categories. For each category, I4S calculates an aggregated times series which represents the overall query share of this category (i.e., the combined search interest of all the queries in the category). In order to stay focused on the most influential patterns of the yearly seasonality and overall trend (direction), we are using time series of monthly granularity (i.e., one data point per calendar month) and refer to the entire available period (2004-2009). Obviously search trends with finer granularity (e.g., weekly or daily search data) do capture more patterns of search behavior within the intra-monthly and especially the day-of-week effect, however the fine resolution data is also noisier and thus calls for prediction models with higher complexity and a less homogeneous model space. We leave that for future research. We have extracted time series of the entire available time range (2004-2009)2 that consists of 67 data points, which were partitioned into two parts: 1. The History Period - 55 monthly data points (January 2004 - July 2008) 2. The Forecast Period - 12 monthly data points (August 2008 - July 2009) Throughout the work, we will refer to 3 data sets of time series (with a similar format): 1. Country Data - Includes time series of the query shares for the 10,000 most popular queries in each of these countries: USA, UK, Germany, France and Brazil. 2. Category Data - Includes time series of the query shares for the 1,000 most popular queries in the US, for 10 major I4S categories: Automotive, Entertainment, Finance & Insurance, Food & Drink, Health, Social Networks & Online Communities, Real Estate, Shopping, Telecommunications, and Travel. 3. Aggregated Categories Data - Includes time series of aggregated query shares for about 600 I4S categories, which represent the normalized combined search volume in the US for each respective category. Generic Prediction Model. Our prediction process is based on the STL Procedure [Cleveland et.al. 1990], which is a filtering procedure based on locally weighted least squares for decomposing a given time series X into the Trend, Seasonal and Residual components. STL is basically an EM-like algorithm that calculates the seasonal part assuming knowledge of the trend part (iteratively). To compute the forecast of the future values, we extrapolate the trend sub-series using regression, and use the last seasonal period of the seasonal component. 1. URL: http://www.google.com/insights/search/# 2. The time series data was pulled during July 2009, thus the value for this last month might change.The STL procedure uses 6 configuration parameters, 3 of which are smoothing parameters for the three components, which in general should be chosen per time series. The prediction process in our experiments was using a fixed STL configuration for the forecast of all the time series. Given a sampled archive of search time series, we have used an exhaustive exploration and evaluation process that was searching for the best parameter set from a pre-defined set of optional parameter values. The optimality criterion was minimal mean absolute error and the output was a single parameter set w.r.t. the given sampled archive. By choosing to use a particular (fixed) configuration, rather than adjusting an individual parameters set for each given time series, we are adjusting the configuration to a large set of time series thus simplifying the prediction model and enabling much faster forecast. Prediction Discrepancy function. We define the discrepancy D as a combination of several error metrics between the forecasted trends X F and the actual trends Y, as well as seasonal consistency metrics determined by difference in the auto-correlation between X H and X. Specifically, D is defined as a tuple: D = < MAPE, MaxAPE, NMSSE, MeanAbsACFDiff, MaxAbsACFDiff > based on metrics defined below, we say that D . Thus, we say that a given time series is predictable within the available time frame, w.r.t. the prediction model we use and the above error and consistency metrics, if all the following conditions are fulfilled: 1) The Mean Absolute Prediction Error (MAPE) < 25% 2) The Max Absolute Prediction Error (MaxAPE) < 100% 3) The Normalized Mean Sum of Squared Errors (NMSSE) < 10.0 4) The Mean Absolute Difference of the ACF Coef. Sets (MeanAbsACFDiff) < 0.2 5) The Max Absolute Difference of the ACF Coef. Sets (MaxAbsACFDiff) < 0.4 Predictability Ratio. Given a set A of time series, denote its predictability ratio as the number of predictable time series in A, divided by the total number of time series in A.3. Experiments and Results Comparing the Predictability of Top Queries in Different Countries. We have conducted an experiment to test the predictability of search trends regard to the 10,000 most popular search queries in five countries: Country Predictability Ratio Avg. MAPE (for predictable queries) Avg. MaxAPE (for predictable queries) USA 54.1 11.8 27.1 UK 51.4 12.7 32.1 Germany 56.1 11.8 28.2 France 46.9 12.8 28.8 Brazil 46.3 13.7 30.5 Although the above results show some variability among the different countries, one can see that in general, about half of the time series that correspond to popular queries in Google Web Search are predictable with respect to the given prediction model and discrepancy function / threshold. One can see that among the predictable queries, the mean absolute prediction error (MAPE) is about 12% on average, while the maximum absolute prediction error (MaxAPE) is about 30% on average. The Seasonally of Time Series. Time series in general often include various forms of regularity, like a consistent trend (straight, upward or downward) or seasonal patterns (daily, weekly, monthly, etc). In seasonal time series, the amplitude changes along the time in a regular recurring fashion according to the relevant season. In many practical cases, it is common to use a seasonality adjustment where the seasonal component is subtracted from the time series before the analysis, where there are procedures that decompose time series into their seasonal and trend components [Cleveland and Tiao 1976], [Lytras etal. 2007]. We use such a decomposition to compute a metric that represents the relative portion of seasonality within a time series as follows: Given a time series X = {x1, ... ,xT} and a decomposition of X into a seasonal component S and a Trend (i.e., directional) component Tr then: Seasonality Ratio(X) = ( ∑ |Si| ) / ( ∑ |Tri| ) For each time series we forecast, we compute the respective seasonality ratio. For example, let us examine the time series which represents the search interest for the query Cheesecake (in the US, 2004-2009). The blue curve in the following plot shows the original time series which has a significant seasonal component. The red curve is the seasonality adjusted time series; i.e., the trend component that is left after subtracting the seasonality component. It has an upward trend (with Slope:0.18) plus some variability. The seasonality ratio is 2.64 (which is on the 96 percentile of the 10,000 tested queries) and approximates theratio of the area between the red and blue curves, and the area underneath the red curve. The Deviation of a Time Series. In order to assess the extent to which a time series contains extreme values or outliers with large deviation from the overall pattern, we calculate for each time series the deviation ratio. In general, we compute the sum of the top values in the series divided by the total sum of the series, assuming that a large ratio would indicate the existence of considerable extreme values in the series. We normalize by the relative number of top values under consideration. Given a time series X = {x1, ... ,xT} and an integer w, s.t. 1= Prc(X,w) . We use w=90. Notice that the normalization term, (1-(w/100)) in the denominator, is setting the minimal ratio to be 1. Due to the relatively short time series (of 67 points) and since many cases show seasonal patterns with high and narrow peaks (e.g., like in the plot above), it is possible that these sharp peaks will be considered as outliers, although they are a regular part of the time series' recurring dynamics. To mitigate this, we computed the deviation ratio on the seasonal adjusted time series (i.e., on the Trend component that is left after the seasonal component is subtracted by the decomposition we have described above). The Predictability of Search Categories. In order to assess the predictability of categories, we have extracted the 1000 most popular queries in the US for a selection of 10 root level categories and tested their predictability. In the following table, we present the summary results, where the Predictability column on the left refers to the entire 1,000 queries (per category), and the two error metrics (MAPE and MaxAPE in columns 3 & 4) refer only to the sub-set of Predictable queries within each category. The seasonality and deviation ratios are also referring to the entire category sets. The Predictability per category spans from 74% for the Health category, to 27% for the Social Networks & Online Communities category. In the third column we can see that the Mean Absolute Prediction Error (MAPE), varies from of 9% (in the Health category), to 14.1% (in the Social Networks & Online Communities category). The average MAPE for the 10 categories is 12.35%. Notice that the order of predictability ratio is not equal to the order of the MAPE errorsince the Predictability is based on several other metrics as described in Section 2, however the correlation between them is high (r= -0.85). The variability within the columns of seasonality ratio and deviation ratio represents the differences between the search profiles of the various categories, which correspond to the variability of the categories' predictability ratio. For example notice the relatively high seasonality ratio and low deviation ratio of the Food & Drink category which has 66.7% predictability ratio vs. the opposite situation of the Entertainment category that has 35.4% predictability ratio with a relatively low seasonality ratio and high deviation ratio. Category Name Predictability Ratio MAPE predictable queries MaxAPE predictable queries Seasonality Ratio Deviation Ratio Health 74.00 9.00 20.00 0.73 1.58 Food & Drink 66.70 11.90 26.00 1.20 1.74 Travel 64.70 11.80 27.00 1.09 1.61 Shopping 63.30 12.40 28.00 1.21 1.78 Automotive 57.60 11.20 24.90 0.71 1.84 Finance & Insurance 52.90 13.30 30.60 0.65 2.00 Real Estate 49.50 12.90 29.90 0.72 1.82 Telecommunications 45.60 12.90 29.40 0.32 2.34 Entertainment 35.40 14.00 32.30 0.46 2.49 Social Networks 27.50 14.10 30.10 0.19 2.95 For the above summary results of the 10 categories, the correlation between the Predictability and the Seasonality Ratio is r= 0.80 while the Deviation Ratio has a (negative) correlation of r= -0.94 with the Predictability. In the next section we will further examine the association between these regularity characteristics and the predictability. The Predictability of Aggregated Time Series that represent Categories. We now show the results of an experiment of forecasting aggregated times series that represent the overall query share of categories (i.e., the combined search interest of all the queries in the category). We ran the experiment on the aggregated time series of over 600 I4S categories and computed the average absolute prediction error over a period of 12 months ahead. We found 88% of the aggregated category time series to be predictable. The average MAPE for the entire set of aggregated category time series is 8.15%. (6.7% for Predictable queries only), with STD=4.18%. The Average Maximum Prediction Error (MaxAPE) for the entire set was 19.2% (16.6% for Predictable queries only). In the table below, we show the prediction errors for the aggregated time series for the same 10 root categories we examined above. Notice that the prediction errors are now smaller, which was expected. However, we can also see that the order of the categories is not the same as the respective order in the table of the previous experiment. In general, the aggregated time series should have a higher predictability due to the noise reduction effect of the aggregation. The rightmost column shows the MAPE Reduction Rate, which is the relative improvement of the prediction error (MAPE) of the 1,000 queries per category (in the previousexperiment) and the single MAPE for the aggregated category time series here. All categories (except Social Networks & Online Communities) had their MAPE reduced, starting from 47% improvement for the Finance & Insurance category up to 85% for the Food & Drink Category. Category MAPEMaxAPESeasonality Ratio Deviation Ratio MAPE Reduction Rate Food & Drink 1.76 4.52 0.70 1.18 0.85 Shopping 2.72 6.02 2.77 1.11 0.78 Entertainment 2.74 5.95 0.30 1.16 0.80 Health 2.99 7.69 1.04 1.11 0.67 Automotive 3.27 7.36 1.69 1.12 0.71 Travel 3.94 7.61 1.92 1.12 0.67 Telecommunications 5.2 9.07 0.74 1.20 0.60 Real Estate 5.62 12.8 2.95 1.11 0.56 Finance & Insurance 7.08 17.8 0.61 1.26 0.47 Social Networks 38.6 50.4 0.06 2.46 -1.74 The I4S classification into search categories is based on a hierarchical tree-like taxonomy where each category at the root level of the tree has several sub-categories under it. Thus, a combination of all the categories' prediction error into an overall evaluation of the prediction error, can consist of the average MAPE values of the 27 root level categories. However, a 'regular' (uniform) average which gives the same weight to each category, might be inaccurate. Therefore, we have computed a weighted average of the root categories' MAPE, where the weights are the overall relative search interest of each root category. The MAPE Weighted Average is 4.25%. The following table shows the predication errors for the I4S root categories (sorted by the MAPE): Root Category MAPE MaxAPE Food & Drink 1.76 4.52 Beauty & Personal Care 2.2 7.41 Home & Garden 2.21 4.9 Photo & Video 2.34 8.31 Lifestyles 2.38 5.27 Games 2.59 4.45 Shopping 2.72 6.02 Entertainment 2.74 5.95 Business 2.91 11.5 Health 2.99 7.69 Local 3.24 5.49 Automotive 3.27 7.36 Reference 3.7 8.2 Industries 3.77 7.14 Recreation 3.81 7.58 Computers & Electronics 3.93 7.83Travel 3.94 7.61 Internet 4.87 15 Telecommunications 5.2 9.07 Society 5.57 12.6 Real Estate 5.62 12.8 Sports 5.81 29.3 Arts & Humanities 6.98 11.8 Finance & Insurance 7.08 17.8 Science 10.1 15.5 News & Current Events 16.6 47 Social Networks 38.6 50.4 Average 5.81 12.5 Comparing the Predictability of a Category and its Sub-Categories. It is reasonable to expect that a time series of the aggregated search of a set of queries should in general be more predictable than single queries. The larger the aggregation set is, the smaller would be the variability of the aggregated time series. This has implications on the predictability of categories vs. sub-categories, but also has implications regarding aggregated time series of group of queries such as campaign related queries or brand/topic related queries in general. In order to demonstrate this, we have explored the MAPE and MaxAPE prediction errors of the I4S category Vehicle Brands (in the Automotive category), compared to all its 31 'children' sub-categories. The variability of Prediction Errors (MAPE) within the 31 vehicle brands sub-categories is substantial and varies from 3% to 38%. The average MAPE of the 31 brands is 11.4% (with STD=7.7%) which is quite similar to the average MAPE for the 1,000 most popular queries in the Automotive category (11.2%) as we presented above. As expected, the average MAPE of the 31 sub-categories is larger than the MAPE of the aggregated time series of the Vehicle Brands category which is only 3.39%. We have also calculated the median MAPE (9.3%), as well as the weighted average MAPE (with relative search interest per category as weights) (9.7%). Both the median and the weighted average are lower than the regular average but still much larger than the MAPE for the overall aggregated category of Vehicle Brands.4. Predictability vs. Seasonality and Deviation Ratios Among the 10 categories for which we have analyzed their 1,000 most popular queries, we calculated a correlation of r= 0.80 between the Predictability and the Seasonality Ratio and r= -0.94 between the Predictability and the Deviation Ratio (see table in Section 3). Below, we examine the association between the these two time series' characteristics and the MAPE prediction error in the experiment we conducted on the 10,000 most popular queries in the US. Seasonality and Prediction Errors. Many patterns of search behavior have a strong seasonal component (e.g. holidays shopping, summer vacation, etc.) as implied from the specific market they are in. Occasionally, there is also a directional trend effect (up, down or changing) which might be less visually pronounced due to the confounding seasonal pattern. We have used the Seasonality Ratio (described above) as a representation for the 'level of seasonality' of the queries. Among the 10,000 most popular queries in the US, the Seasonality Ratio varies in the rather large range [0.01,13], from time series with no seasonal component up to extremely seasonal time series. The median Seasonality Ratio is 0.4 and its mean value is 0.8. We could see no significant correlation between the prediction error and the seasonality ratio. In order to visualize this possible association, we have sorted the values of seasonality ratio and created a ('smoothed') arrays of 10 average points3 . Similarly, we have computed a 'smoothed' array of averages for the 10,000 corresponding MAPE prediction errors which were sorted according to the corresponding seasonality ratio. We show here a scatter plot of the 'smoothed' MAPE vs the 'smoothed' seasonality ratio. The plot shows a non-stable 'negative' association between prediction errors and the seasonality. The correlation coefficient between the 'smoothed' arrays is substantial (r=0.55), compared to the insignificant correlation we saw for the entire set. 3. Given a time series {YN}, N=10,000 ; K=10; M=N/K=1,000. We compute an array A={A1, A2,.....,AK} of the averages of K consecutive non-overlapping windows of size M over the time series {YN}, such that Ak= (1/M) ∑Yi, where k=1,..,K and i={1+(k-1)M,..,kM}.For the next plot we have repeated the same process - but for predictable time series only. The result shows a stronger 'negative' association between the MAPE prediction error and the seasonality ratio for Predictable queries. Deviation Ratio and Prediction Errors. The Deviation Ratio, which represents the level of outliers and irregular extreme values in a time series was found to be associated with the Predictability of the search interest time series. For the 10,000 queries we tested, the average deviation ratio was 2.08 (STD=1.9). Only 5% of the Predictable time series had a deviation ratio in the upper quartile and 73% of the predictable time series had a deviation ratio under the median. The correlation coefficient between the deviation ratio and the the MAPE error was r=0.29. The average deviation ratio for the Predictable time series was: 1.50 where as for the non-Predictable queries the average was: 2.77. We have applied the same process as above in order to visually demonstrate the association between MAPE and the deviation ratio. The following plot shows a clear positive association between the (sorted) 'smoothed' array of the deviation Ratio and the corresponding 'smoothed' array of prediction errors (MAPE). The correlation coefficient calculated for the 'smoothed' arrays was r=0.88 (compared to r=0.29 which was computed with the original values). Hence, we can say that the larger the deviation level in the time series, the larger is the prediction error. This can also be seen in the next plot for the the Predictable queries only.5. Sensitivity Analysis and Errors Diagnostics Sensitivity of the Predictability Thresholds. As described earlier, we have chosen a predefined set of thresholds which correspond to the three prediction error metrics (MAPE, MaxAPE, NMSSE) and two consistency metrics. These thresholds are responsible for the trade-off between the Predictability Ratio and the distribution of errors within the Predictable time series. In the following figure we see a sensitivity plot for the Mean Absolute Prediction Error (MAPE), that shows how the Predictability Ratio behaves as a function of the Predictability Threshold. We present a separate analysis for each error measure and not as a conjunction of all the conditions as appears in our Predictability definition. The following plot shows that choosing a Predictability Threshold [MAPE<0.25] 'qualifies' more than 60% of the queries (for a single metric condition). Raising the MAPE threshold by 100% into 0.5, would imply that the Predication Ratio would rise by ~30% (using only the MAPE error metric). Raising the MAPE threshold even more, by 200% into 0.75, would imply that the Predication Ratio would rise by ~50% and will qualify approximately 90% of the queries.The next plots are the sensitivity plots for the MaxAPE and NMSSE error metrics. We can see that both chosen Predictability thresholds (1.0, 10.0) are located much farther into the "Predictable Region" and qualify almost 90% of the queries. Thus, in our experiments we use the MAPE as our primary 'filter' where the MaxAPE and the NMSSE play a secondary role. The following plot displays a similar presentation by showing the number of Predicable time series as a function of the Predictability Threshold (using only the MAPE error measure).Prediction Errors Diagnostics. In this section we show diagnostics plots for the US data (top 10,000 queries). The following figure shows the actual values vs. the predicted values (in log scale), for each of the 12 months in the Forecast Period. The top 12 diagrams refer to the entire set of queries, followed by 12 diagrams for the Predictable queries only. One can clearly see the better prediction performance for the Predictable queries (at the bottom part) as expected. Notice that the performance for the different months deteriorates with time (higher average and STD of the prediction errors) especially towards the later months.In order to learn more on the distribution of the average and maximum prediction errors within the top 10,000 most popular queries in the US, we present the histogram of the MAPE and MaxAPE error measures, with the density estimation superimposed (in red). We can see that both distributions are positively skewed and that the value of the average error is largely affected by the extreme error values. Notice that we have trimmed the data at 0.75 and 3.0 for MAPE and MaxAPE respectively (i.e., 3 x the chosen thresholds), to stay focused on the major part of the distribution.Comparison of the Forecast Performance along the Future Horizon. Since in our experiments we are simultaneously predicting 12 month ahead, it is expected that the forecasts for the later months may have larger prediction errors. We have compared the prediction performance for the 12 consecutive month in the forecast period. The following plot shows the distribution of MAPE prediction errors for each future month. We are showing the average monthly MAPE for the Predictable queries only (among the 10,000 most popular in the US). Notice that the first month is predicted in greater accuracy than the rest, then there is an approximately constant error level for months 2-9, with some increase of the error rate in the last 3 months in the Forecast period. The following plot shows the same type of diagram, but for the Mean Prediction Error (i.e., the 'directional' error measure with the sign). We can learn from this plot that there was a positive bias (upward) in the predictions along all months except the 11'th month. Such systematic tendency of the errors can be explained by a reduction of query share for many queries in the Forecast period (Aug 2008 - July 2009) due to the global economic crisis. Hence the actual search interest values were lower than expected by the prediction model that was based on the previous years. In the following section we present examples of categories (and queries) regarding various markets and brands, for which the actual monthly query shares for the recent 12 months are different than model prediction.6. Search Interest Forecasting as baseline for identifying deviations The aggregated query share of the Google Insights for Search (I4S) categories were used in a recent work of Choi and Varian (2009), that showed how data taken from Google I4S could help to predict economic time series. For example, in the analysis on the US Retail Trade they have used the weekly aggregated time series of categories like: Automotive, Computers & Electronics, Apparel, Sporting Goods, Mass Shopping, Merchants & Department Stores, etc. In a later work [Choi and Varian 2009 b] have applied the same methodology on the U.S. unemployment time series using two sub-categories, Jobs and Welfare & Unemployment. They did not attempt to forecast the Google query share; rather, they have successfully used it as predictors for external economic time series. Other works have shown similar results, regarding the capability of aggregated categories' query share to predict econometrics and unemployment data from Germany [Askitas and Zimmerman 2009] as well as from Israel [Suhoy 2009]. In the following, we will show time series of monthly query share of categories, where the forecast values (in red) were superimposed on the actual values (in blue). The errors made by the prediction model are expressing the deviation between the expected and the actual search behavior, which conveys a valuable information regarding the current state of search interest in the respective categories. Choi and Varian have shown that the users' search interest in several categories as represented by the aggregated query shares indeed have a short term predictive power regarding the actual underlying. The following plots show the aggregated time series of various categories that relate to some major US markets. These category plots, which are ordered by their average MAPE, vary in their Predictability level. From the 10 category plots, we can see that many present a clear seasonal pattern. The first 7 time series showed a relatively low error rate (MAPE<6%), which is in accordance with the substantial regularity of search behavior of the respective categories that was maintained throughout the Forecast period. However, notice that the category of Finance & Insurance which shows a seasonal patterns with some medium irregularities (the seasonality ratio is well above its median), underwent a considerable change in the recent 12 months, highlighting an observed discrepancies between the predicted and the actual monthly search interest. The months of September-October 2008 which were low months in each year during the entire history period are observed as peak month in the Forecast period. This is an example where the prediction model could not anticipate the unexpected exogenous events. The category of Energy and Utility showed the most irregular search behavior (with the lowest Seasonality Ratio and the highest Deviation Ratio among the first 9 categories). In addition to the low regularity of its history, it seems that this category has also underwent a change in the dynamics of search interest, probably since mid year 2008. These contributed to the low prediction results for this category. Another good example for lack of Predictability w.r.t. the prediction model, is the last plot of the Social Networks & Online Communities category that has shown a considerable exponential growth in the forecast period (due to the growing popularity of social networks like Facebook and Twitter), which could not be captured by the prediction model (notice the high deviationratio). We will show below several other examples of the relation between the prediction performance and the external market events.Next, we show several examples where one can use the (posterior) prediction results in order to explore the changing dynamics of users' search behavior and possibly get insights on the relevant markets. Whenever we observe substantial prediction errors, i.e., discrepancies between the actual values vs the predicted values, we can conclude that the regularities in the time series (e.g., seasonality and trend) which were captured by the prediction model, were disturbed in the Forecast period. In cases where the actual values show a regularity that is not in accordance with the history's regularity, one could investigate the reasons for such deviation with relation to known external factors. It is important to emphasize that users' search interest is not necessarily always related to consumer preferences, buying intentions, etc. and can be related sometimes to news or or other associated events. A full discussion on the background and reasons for the following market observations is beyond the scope of this paper. Example: The Automotive Industry. We can see that the forecast for the entire Vehicle brands category for the 12 month period between Aug-08 and Jul-094 shows a relatively low prediction error rate of -2.3% on average. However, as we show below there are some noticeable deviations in different sub-categories. We can see in the next 4 plots that the category Vehicle Shopping shows an average negative deviation of 6% from the prediction model in the last 12 months and that the category Auto 4. The time series data was pulled during July 2009, thus the value for this last month is partial and might be biased.Financing is showing a small negative deviation with average of 2.3% respectively. Notice that both categories of Vehicle Maintenance and Auto Parts are showing a positive average deviation of 4.3% and 5.2% respectively, compared to the predictions.Example: US Unemployment. Choi and Varian (2009 b) have used weekly time series of the I4S aggregated categories Welfare & unemployment and Jobs, to help in short term prediction of "Initial Jobless Claims” reports which are issued by US Department of Labor. In the following plots, we show that the search interest the category Welfare & Unemployment has risen substantially above the forecast by the prediction model. The deviation of Welfare and Unemployment is systematic and relatively quite large. While the average MAPE for the entire set of (aggregated) categories' query shares is 8.1%, with STD 8.2%, the MAPE for Welfare & Unemployment is 31.2% which is 2.8 standard deviations above the overall average MAPE. The actual monthly values for the aggregated query share of the category Jobs are also all higher than forecasted by the model. The time series shows a seasonal pattern with a distinguishable low value in December each year and a relatively constant level in between. At the end of the History period and throughout the Forecast, this regularity is shifted upwards by a confounding volatile factor, which causes large positive prediction errors. The Average Error is almost 9% per month.We present here also the aggregated query share of the category Recruitment & Staffing, for which we can observe a corresponding negative deviation where the model expectations are larger than the actual search interest values. Interestingly, despite a similar seasonal pattern as in the Jobs category, it seems that the change in the users' search behavior in this category has not started until March 2009. Beforehand the predictions were rather accurate and the average monthly deviation is therefore only about (-4.8%). Example: Mexico as Vacation Destination. In this example we show that the search interest for Mexico as a vacation destination has decreased substantially in the recent months. The I4S category Mexico is a sub-category of the Vacation Destinations category (in the Travel root category) which aggregates only the vacation related searches on Mexico. In the next plots we can see that the search interest in the category Mexico is down by almost 15% compared to the predicted. In comparison, we show the respective deviation in the entire category of Vacation Destinations, which is only -1.6% on average in the same forecast period. Notice for a reference that the search interest of another related vacation destination, the Caribbean Islands (with a similar seasonal pattern), also has not shown a deviation of similar magnitude (only -2.5%).We considered the recent outbreak of the Swine Flu pandemic that started to spread in April 2009 as a possible contributor for such a negative deviation of actual-vs-forecast query share for Mexico. We examined the time series of the query share for H1N1 and found it to be highly (anti) correlated (r = -0.93) with the observed deviations for Mexico. As a reference, we show the aggregated query share for the category Infectious Diseases, demonstrating the magnitude of the search interest in this subject (in blue) that was spiking following the Swine Flu outbreak:Example: Recession Markers. The following plots present the aggregated query share for some I4S sub-categories in subjects that might demonstrate the influence of the recent recession on search behavior of consumers, and often appear in articles and blog posts. The change in search interest for the category Coupons & Rebates is visible in the following plot, where one can see an average monthly deviation of 15.9% between the observed query share in the recent 12 month compared to the values predicted by the model. The model has captured the general seasonal pattern, however only accounted for a lower holidays peak and a much more moderate upward trend. Next we see the observed query share of the I4S category Restaurants, that is systematically lower than the model predictions. The time series for the aggregated search interests in this category does not show a seasonal pattern, however there exist an upward trend since 2004, which was apparently broken at September 2008 hence causing negative actual-vs-forecast deviation with a an average of -7.8% per month.Below we can see for reference that the Cooking & Recipes category has a systematic positive deviation of actual-vs-forecast query share. The average monthly deviation of 6.15% represents a higher observed search interest in this category for the entire Forecast period compared to model prediction, with almost a constant deviation since January 2009. Another example is the category Gifts, for which the query share has decreased in the recent 12 months compared to the model predictions, by 11% per month on average. Below we can also see that the category Luxury Goods is showing a negative deviation in the actual-vsforecast query share, of 5.8% per month on average.7. Conclusions We studied the predictability of search trends. We found that over half of the most popular Google search queries are predictable w.r.t. the method we have selected, and that several search categories were considerably more predictable than others; that the aggregated queries of the different categories are more predictable than the individual queries and that almost 90% of I4S categories have predictable query shares. In particular we showed that queries with seasonal time series and lower levels of outliers are more predictable. We considered forecasting as a baseline for identification of deviation of actual-vs-forecast, and considered some concrete examples for situations from the automotive, travel and labor verticals. Further research can include an improved implementation of the prediction model as well as incorporating other forecasting models. We would also like to examine short-term forecasting in finer time granularity. Further analysis on actual-vs-forecast (including confidence estimation) could be conducted in various domains, like market analysis, economy, health, etc. In conjunction with this study, a basic forecasting capability was introduced into Google Insights For Search, which provides forecasting for trends that are identified as predictable. Researchers, marketers, journalists, and others, can use I4S to get a wide picture on search trends which now also includes predictability of single queries and aggregated categories in any area of interest. Acknowledgments We would like to thank Yannai Gonczarowsky for designing and implementing the forecasting capabilities in I4S as well as Nir Andelman, Yuval Netzer and Amit Weinstein for creating the forecasting model library. We thank Hal Varian for his helpful comments. Special thanks to the entire team of Google Insights for Search that made this research possible.References [Askitas and Zimmerman 2009] Nikos Askitas and Kalus F. Zimmerman. Google econometrics and unemployment forecasting. Applied Economics Quarterly, 55:107;120, 2009. URL http://ftp.iza.org/dp4201.pdf [Choi and Varian 2009] Hyunyoung Choi and Hal Varian. Predicting the present with google trends. Technical report, Google, 2009. URL http://google.com/googleblogs/pdfs/google_predicting_the_present.pdf. [Choi and Varian 2009b] Hyunyoung Choi and Hal Varian. Predicting Initial Claims for Unemployment Insurance Using Google Trends. Tech. Report, Google, 2009. URL http://research.google.com/archive/papers/initialclaimsUS.pdf [Cleveland and Tiao 1976] W.P. Cleveland and G.C. Tiao. Decomposition of Seasonal Time Series: A Model for the Census X-11 Program, Journal of the American Statistical Association, Vol. 71, No. 355, 1976 pp. 581-587. [Cleveland etal. 1990] R.B Cleveland, W.S. Cleveland, J.E. McRae and Irma Terpenning. STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Jou. of Official Stat., VOL. 6, No. 1, 1990 pp. 3-73. [Ginsberg etal. 2009] Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski & Larry Brilliant. Detecting influenza epidemics using search engine query data. Nature 457, 1012-1014 (2009). URL http://www.nature.com/nature/journal/v457/n7232/full/nature07634.html [Lytras etal. 2007] Demerta P. Lytras, Roxanne M. Felpausch, and William R. Bell. Determining Seasonality: A Comparison of Diagnostics From X-12-ARIMA (Presented at ICES III, June, 2007). [Suhoy 2009] Tanya Suhoy. Query indices and a 2008 downturn: Israeli data. Tech. Report, Bank of Israel, 2009. URL http://www.bankisrael.gov.il/deptdata/mehkar/papers/dp0906e.pdf Building High-level Features Using Large Scale Unsupervised Learning Quoc V. Le quocle@cs.stanford.edu Marc’Aurelio Ranzato ranzato@google.com Rajat Monga rajatmonga@google.com Matthieu Devin mdevin@google.com Kai Chen kaichen@google.com Greg S. Corrado gcorrado@google.com Jeff Dean jeff@google.com Andrew Y. Ng ang@cs.stanford.edu Abstract We consider the problem of building highlevel, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9- layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting with these learned features, we trained our network to obtain 15.8% accuracy in recognizing 22,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art. Appearing in Proceedings of the 29 th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012. Copyright 2012 by the author(s)/owner(s). 1. Introduction The focus of this work is to build high-level, classspecific feature detectors from unlabeled images. For instance, we would like to understand if it is possible to build a face detector from only unlabeled images. This approach is inspired by the neuroscientific conjecture that there exist highly class-specific neurons in the human brain, generally and informally known as “grandmother neurons.” The extent of class-specificity of neurons in the brain is an area of active investigation, but current experimental evidence suggests the possibility that some neurons in the temporal cortex are highly selective for object categories such as faces or hands (Desimone et al., 1984), and perhaps even specific people (Quiroga et al., 2005). Contemporary computer vision methodology typically emphasizes the role of labeled data to obtain these class-specific feature detectors. For example, to build a face detector, one needs a large collection of images labeled as containing faces, often with a bounding box around the face. The need for large labeled sets poses a significant challenge for problems where labeled data are rare. Although approaches that make use of inexpensive unlabeled data are often preferred, they have not been shown to work well for building high-level features. This work investigates the feasibility of building highlevel features from only unlabeled data. A positive answer to this question will give rise to two significant results. Practically, this provides an inexpensive way to develop features from unlabeled data. But perhaps more importantly, it answers an intriguing question as to whether the specificity of the “grandmother neuron” could possibly be learned from unlabeled data. Informally, this would suggest that it is at least in principle possible that a baby learns to group faces into one classBuilding high-level features using large-scale unsupervised learning because it has seen many of them and not because it is guided by supervision or rewards. Unsupervised feature learning and deep learning have emerged as methodologies in machine learning for building features from unlabeled data. Using unlabeled data in the wild to learn features is the key idea behind the self-taught learning framework (Raina et al., 2007). Successful feature learning algorithms and their applications can be found in recent literature using a variety of approaches such as RBMs (Hinton et al., 2006), autoencoders (Hinton & Salakhutdinov, 2006; Bengio et al., 2007), sparse coding (Lee et al., 2007) and K-means (Coates et al., 2011). So far, most of these algorithms have only succeeded in learning lowlevel features such as “edge” or “blob” detectors. Going beyond such simple features and capturing complex invariances is the topic of this work. Recent studies observe that it is quite time intensive to train deep learning algorithms to yield state of the art results (Ciresan et al., 2010). We conjecture that the long training time is partially responsible for the lack of high-level features reported in the literature. For instance, researchers typically reduce the sizes of datasets and models in order to train networks in a practical amount of time, and these reductions undermine the learning of high-level features. We address this problem by scaling up the core components involved in training deep networks: the dataset, the model, and the computational resources. First, we use a large dataset generated by sampling random frames from random YouTube videos.1 Our input data are 200x200 images, much larger than typical 32x32 images used in deep learning and unsupervised feature learning (Krizhevsky, 2009; Ciresan et al., 2010; Le et al., 2010; Coates et al., 2011). Our model, a deep autoencoder with pooling and local contrast normalization, is scaled to these large images by using a large computer cluster. To support parallelism on this cluster, we use the idea of local receptive fields, e.g., (Raina et al., 2009; Le et al., 2010; 2011b). This idea reduces communication costs between machines and thus allows model parallelism (parameters are distributed across machines). Asynchronous SGD is employed to support data parallelism. The model was trained in a distributed fashion on a cluster with 1,000 machines (16,000 cores) for three days. Experimental results using classification and visualization confirm that it is indeed possible to build highlevel features from unlabeled data. In particular, using a hold-out test set consisting of faces and distractors, we discover a feature that is highly selective for faces. 1This is different from the work of (Lee et al., 2009) who trained their model on images from one class. This result is also validated by visualization via numerical optimization. Control experiments show that the learned detector is not only invariant to translation but also to out-of-plane rotation and scaling. Similar experiments reveal the network also learns the concepts of cat faces and human bodies. The learned representations are also discriminative. Using the learned features, we obtain significant leaps in object recognition with ImageNet. For instance, on ImageNet with 22,000 categories, we achieved 15.8% accuracy, a relative improvement of 70% over the stateof-the-art. Note that, random guess achieves less than 0.005% accuracy for this dataset. 2. Training set construction Our training dataset is constructed by sampling frames from 10 million YouTube videos. To avoid duplicates, each video contributes only one image to the dataset. Each example is a color image with 200x200 pixels. A subset of training images is shown in Appendix A. To check the proportion of faces in the dataset, we run an OpenCV face detector on 60x60 randomly-sampled patches from the dataset (http://opencv.willowgarage.com/wiki/). This experiment shows that patches, being detected as faces by the OpenCV face detector, account for less than 3% of the 100,000 sampled patches 3. Algorithm In this section, we describe the algorithm that we use to learn features from the unlabeled training set. 3.1. Previous work Our work is inspired by recent successful algorithms in unsupervised feature learning and deep learning (Hinton et al., 2006; Bengio et al., 2007; Ranzato et al., 2007; Lee et al., 2007). It is strongly influenced by the work of (Olshausen & Field, 1996) on sparse coding. According to their study, sparse coding can be trained on unlabeled natural images to yield receptive fields akin to V1 simple cells (Hubel & Wiesel, 1959). One shortcoming of early approaches such as sparse coding (Olshausen & Field, 1996) is that their architectures are shallow and typically capture low-level concepts (e.g., edge “Gabor” filters) and simple invariances. Addressing this issue is a focus of recent work in deep learning (Hinton et al., 2006; Bengio et al., 2007; Bengio & LeCun, 2007; Lee et al., 2008; 2009) which build hierarchies of feature representations. In particular, Lee et al (2008) show that stacked sparse RBMs can model certain simple functions of the V2 area ofBuilding high-level features using large-scale unsupervised learning the cortex. They also demonstrate that convolutional DBNs (Lee et al., 2009), trained on aligned images of faces, can learn a face detector. This result is interesting, but unfortunately requires a certain degree of supervision during dataset construction: their training images (i.e., Caltech 101 images) are aligned, homogeneous and belong to one selected category. Figure 1. The architecture and parameters in one layer of our network. The overall network replicates this structure three times. For simplicity, the images are in 1D. 3.2. Architecture Our algorithm is built upon these ideas and can be viewed as a sparse deep autoencoder with three important ingredients: local receptive fields, pooling and local contrast normalization. First, to scale the autoencoder to large images, we use a simple idea known as local receptive fields (LeCun et al., 1998; Raina et al., 2009; Lee et al., 2009; Le et al., 2010). This biologically inspired idea proposes that each feature in the autoencoder can connect only to a small region of the lower layer. Next, to achieve invariance to local deformations, we employ local L2 pooling (Hyv¨arinen et al., 2009; Gregor & LeCun, 2010; Le et al., 2010) and local contrast normalization (Jarrett et al., 2009). L2 pooling, in particular, allows the learning of invariant features (Hyv¨arinen et al., 2009; Le et al., 2010). Our deep autoencoder is constructed by replicating three times the same stage composed of local filtering, local pooling and local contrast normalization. The output of one stage is the input to the next one and the overall model can be interpreted as a nine-layered network (see Figure 1). The first and second sublayers are often known as filtering (or simple) and pooling (or complex) respectively. The third sublayer performs local subtractive and divisive normalization and it is inspired by biological and computational models (Pinto et al., 2008; Lyu & Simoncelli, 2008; Jarrett et al., 2009).2 As mentioned above, central to our approach is the use of local connectivity between neurons. In our experiments, the first sublayer has receptive fields of 18x18 pixels and the second sub-layer pools over 5x5 overlapping neighborhoods of features (i.e., pooling size). The neurons in the first sublayer connect to pixels in all input channels (or maps) whereas the neurons in the second sublayer connect to pixels of only one channel (or map).3 While the first sublayer outputs linear filter responses, the pooling layer outputs the square root of the sum of the squares of its inputs, and therefore, it is known as L2 pooling. Our style of stacking a series of uniform modules, switching between selectivity and tolerance layers, is reminiscent of Neocognition and HMAX (Fukushima & Miyake, 1982; LeCun et al., 1998; Riesenhuber & Poggio, 1999). It has also been argued to be an architecture employed by the brain (DiCarlo et al., 2012). Although we use local receptive fields, they are not convolutional: the parameters are not shared across different locations in the image. This is a stark difference between our approach and previous work (LeCun et al., 1998; Jarrett et al., 2009; Lee et al., 2009). In addition to being more biologically plausible, unshared weights allow the learning of more invariances other than translational invariances (Le et al., 2010). In terms of scale, our network is perhaps one of the largest known networks to date. It has 1 billion trainable parameters, which is more than an order of magnitude larger than other large networks reported in literature, e.g., (Ciresan et al., 2010; Sermanet & LeCun, 2011) with around 10 million parameters. It is worth noting that our network is still tiny compared to the human visual cortex, which is 106 times larger in terms of the number of neurons and synapses (Pakkenberg et al., 2003). 3.3. Learning and Optimization Learning: During learning, the parameters of the second sublayers (H) are fixed to uniform weights, 2The subtractive normalization removes the weighted average of neighboring neurons from the current neuron gi,j,k = hi,j,k − P iuv Guvhi,j+u,i+v The divisive normalization computes yi,j,k = gi,j,k/ max{c,( P iuv Guvg 2 i,j+u,i+v) 0.5 }, where c is set to be a small number, 0.01, to prevent numerical errors. G is a Gaussian weighting window. (Jarrett et al., 2009) 3For more details regarding connectivity patterns and parameter sensitivity, see Appendix B and E.Building high-level features using large-scale unsupervised learning whereas the encoding weights W1 and decoding weights W2 of the first sublayers are adjusted using the following optimization problem minimize W1,W2 Xm i=1 W2WT 1 x (i) − x (i) 2 2 + λ Xk j=1 q ǫ + Hj (WT 1 x(i)) 2 . (1) Here, λ is a tradeoff parameter between sparsity and reconstruction; m, k are the number of examples and pooling units in a layer respectively; Hj is the vector of weights of the j-th pooling unit. In our experiments, we set λ = 0.1. This optimization problem is also known as reconstruction Topographic Independent Component Analysis (Hyv¨arinen et al., 2009; Le et al., 2011a).4 The first term in the objective ensures the representations encode important information about the data, i.e., they can reconstruct input data; whereas the second term encourages pooling features to group similar features together to achieve invariances. Optimization: All parameters in our model were trained jointly with the objective being the sum of the objectives of the three layers. To train the model, we implemented model parallelism by distributing the local weights W1, W2 and H to different machines. A single instance of the model partitions the neurons and weights out across 169 machines (where each machine had 16 CPU cores). A set of machines that collectively make up a single copy of the model is referred to as a “model replica.” We have built a software framework called DistBelief that manages all the necessary communication between the different machines within a model replica, so that users of the framework merely need to write the desired upwards and downwards computation functions for the neurons in the model, and don’t have to deal with the low-level communication of data across machines. We further scaled up the training by implementing asynchronous SGD using multiple replicas of the core model. For the experiments described here, we divided the training into 5 portions and ran a copy of the model on each of these portions. The models communicate updates through a set of centralized “parameter servers,” which keep the current state of all parameters for the model in a set of partitioned servers (we used 256 parameter server partitions for training the model described in this paper). In the simplest 4 In (Bengio et al., 2007; Le et al., 2011a), the encoding weights and the decoding weights are tied: W1 = W2. However, for better parallelism and better features, our implementation does not enforce tied weights. implementation, before processing each mini-batch a model replica asks the centralized parameter servers for an updated copy of its model parameters. It then processes a mini-batch to compute a parameter gradient, and sends the parameter gradients to the appropriate parameter servers, which then apply each gradient to the current value of the model parameter. We can reduce the communication overhead by having each model replica request updated parameters every P steps and by sending updated gradient values to the parameter servers every G steps (where G might not be equal to P). Our DistBelief software framework automatically manages the transfer of parameters and gradients between the model partitions and the parameter servers, freeing implementors of the layer functions from having to deal with these issues. Asynchronous SGD is more robust to failure and slowness than standard (synchronous) SGD. Specifically, for synchronous SGD, if one of the machines is slow, the entire training process is delayed; whereas for asynchronous SGD, if one machine is slow, only one copy of SGD is delayed while the rest of the optimization can still proceed. In our training, at every step of SGD, the gradient is computed on a minibatch of 100 examples. We trained the network on a cluster with 1,000 machines for three days. See Appendix B, C, and D for more details regarding our implementation of the optimization. 4. Experiments on Faces In this section, we describe our analysis of the learned representations in recognizing faces (“the face detector”) and present control experiments to understand invariance properties of the face detector. Results for other concepts are presented in the next section. 4.1. Test set The test set consists of 37,000 images sampled from two datasets: Labeled Faces In the Wild dataset (Huang et al., 2007) and ImageNet dataset (Deng et al., 2009). There are 13,026 faces sampled from non-aligned Labeled Faces in The Wild.5 The rest are distractor objects randomly sampled from ImageNet. These images are resized to fit the visible areas of the top neurons. Some example images are shown in Appendix A. 4.2. Experimental protocols After training, we used this test set to measure the performance of each neuron in classifying faces against distractors. For each neuron, we found its maximum 5http://vis-www.cs.umass.edu/lfw/lfw.tgzBuilding high-level features using large-scale unsupervised learning and minimum activation values, then picked 20 equally spaced thresholds in between. The reported accuracy is the best classification accuracy among 20 thresholds. 4.3. Recognition Surprisingly, the best neuron in the network performs very well in recognizing faces, despite the fact that no supervisory signals were given during training. The best neuron in the network achieves 81.7% accuracy in detecting faces. There are 13,026 faces in the test set, so guessing all negative only achieves 64.8%. The best neuron in a one-layered network only achieves 71% accuracy while best linear filter, selected among 100,000 filters sampled randomly from the training set, only achieves 74%. To understand their contribution, we removed the local contrast normalization sublayers and trained the network again. Results show that the accuracy of best neuron drops to 78.5%. This agrees with previous study showing the importance of local contrast normalization (Jarrett et al., 2009). We visualize histograms of activation values for face images and random images in Figure 2. It can be seen, even with exclusively unlabeled data, the neuron learns to differentiate between faces and random distractors. Specifically, when we give a face as an input image, the neuron tends to output value larger than the threshold, 0. In contrast, if we give a random image as an input image, the neuron tends to output value less than 0. Figure 2. Histograms of faces (red) vs. no faces (blue). The test set is subsampled such that the ratio between faces and no faces is one. 4.4. Visualization In this section, we will present two visualization techniques to verify if the optimal stimulus of the neuron is indeed a face. The first method is visualizing the most responsive stimuli in the test set. Since the test set is large, this method can reliably detect near optimal stimuli of the tested neuron. The second approach is to perform numerical optimization to find the optimal stimulus (Berkes & Wiskott, 2005; Erhan et al., 2009; Le et al., 2010). In particular, we find the normbounded input x which maximizes the output f of the tested neuron, by solving: x ∗ = arg min x f(x; W, H), subject to ||x||2 = 1. Here, f(x; W, H) is the output of the tested neuron given learned parameters W, H and input x. In our experiments, this constraint optimization problem is solved by projected gradient descent with line search. These visualization methods have complementary strengths and weaknesses. For instance, visualizing the most responsive stimuli may suffer from fitting to noise. On the other hand, the numerical optimization approach can be susceptible to local minima. Results, shown in Figure 3, confirm that the tested neuron indeed learns the concept of faces. Figure 3. Top: Top 48 stimuli of the best neuron from the test set. Bottom: The optimal stimulus according to numerical constraint optimization. 4.5. Invariance properties We would like to assess the robustness of the face detector against common object transformations, e.g., translation, scaling and out-of-plane rotation. First, we chose a set of 10 face images and perform distortions to them, e.g., scaling and translating. For outof-plane rotation, we used 10 images of faces rotating in 3D (“out-of-plane”) as the test set. To check the robustness of the neuron, we plot its averaged response over the small test set with respect to changes in scale, 3D rotation (Figure 4), and translation (Figure 5).6 6Scaled, translated faces are generated by standard cubic interpolation. For 3D rotated faces, we used 10 se-Building high-level features using large-scale unsupervised learning Figure 4. Scale (left) and out-of-plane (3D) rotation (right) invariance properties of the best feature. Figure 5. Translational invariance properties of the best feature. x-axis is in pixels The results show that the neuron is robust against complex and difficult-to-hard-wire invariances such as out-of-plane rotation and scaling. Control experiments on dataset without faces: As reported above, the best neuron achieves 81.7% accuracy in classifying faces against random distractors. What if we remove all images that have faces from the training set? We performed the control experiment by running a face detector in OpenCV and removing those training images that contain at least one face. The recognition accuracy of the best neuron dropped to 72.5% which is as low as simple linear filters reported in section 4.3. 5. Cat and human body detectors Having achieved a face-sensitive neuron, we would like to understand if the network is also able to detect other high-level concepts. For instance, cats and body parts are quite common in YouTube. Did the network also learn these concepts? To answer this question and quantify selectivity properties of the network with respect to these concepts, we constructed two datasets, one for classifying human bodies against random backgrounds and one for classifying cat faces against other random distractors. For the ease of interpretation, these datasets have a positive-to-negative ratio identical to the face dataset. The cat face images are collected from the dataset dequences of rotated faces from The Sheffield Face Database – http://www.sheffield.ac.uk/eee/research/iel/research/face. See Appendix F for a sample sequence. Figure 6. Visualization of the cat face neuron (left) and human body neuron (right). scribed in (Zhang et al., 2008). In this dataset, there are 10,000 positive images and 18,409 negative images (so that the positive-to-negative ratio is similar to the case of faces). The negative images are chosen randomly from the ImageNet dataset. Negative and positive examples in our human body dataset are subsampled at random from a benchmark dataset (Keller et al., 2009). In the original dataset, each example is a pair of stereo black-and-white images. But for simplicity, we keep only the left images. In total, like in the case of human faces, we have 13,026 positive and 23,974 negative examples. We then followed the same experimental protocols as before. The results, shown in Figure 6, confirm that the network learns not only the concept of faces but also the concepts of cat faces and human bodies. Our high-level detectors also outperform standard baselines in terms of recognition rates, achieving 74.8% and 76.7% on cat and human body respectively. In comparison, best linear filters (sampled from the training set) only achieve 67.2% and 68.1% respectively. In Table 1, we summarize all previous numerical results comparing the best neurons against other baselines such as linear filters and random guesses. To understand the effects of training, we also measure the performance of best neurons in the same network at random initialization. We also compare our method against several other algorithms such as deep autoencoders (Hinton & Salakhutdinov, 2006; Bengio et al., 2007) and K-means (Coates et al., 2011). Results of these baselines are reported in the bottom of Table 1. 6. Object recognition with ImageNet We applied the feature learning method to the task of recognizing objects in the ImageNet dataset (Deng et al., 2009). We started from a network that already learned features from YouTube and ImageNet images using the techniques described in this paper. We then added one-versus-all logistic classifiers on top of the highest layer of this network. This method of initializing a network by unsupervisedBuilding high-level features using large-scale unsupervised learning Table 1. Summary of numerical comparisons between our algorithm against other baselines. Top: Our algorithm vs. simple baselines. Here, the first three columns are results for methods that do not require training: random guess, random weights (of the network at initialization, without any training) and best linear filters selected from 100,000 examples sampled from the training set. The last three columns are results for methods that have training: the best neuron in the first layer, the best neuron in the highest layer after training, the best neuron in the network when the contrast normalization layers are removed. Bottom: Our algorithm vs. autoencoders and K-means. Concept Random Same architecture Best Best first Best Best neuron without guess with random weights linear filter layer neuron neuron contrast normalization Faces 64.8% 67.0% 74.0% 71.0% 81.7% 78.5% Human bodies 64.8% 66.5% 68.1% 67.2% 76.8% 71.8% Cats 64.8% 66.0% 67.8% 67.1% 74.6% 69.3% Concept Our Deep autoencoders Deep autoencoders K-means on network 3 layers 6 layers 40x40 images Faces 81.7% 72.3% 70.9% 72.5% Human bodies 76.7% 71.2% 69.8% 69.3% Cats 74.8% 67.5% 68.3% 68.5% Table 2. Summary of classification accuracies for our method and other state-of-the-art baselines on ImageNet. Dataset version 2009 (∼9M images, ∼10K categories) 2011 (∼14M images, ∼22K categories) State-of-the-art 16.7% (Sanchez & Perronnin, 2011) 9.3% (Weston et al., 2011) Our method 16.1% (without unsupervised pretraining) 13.6% (without unsupervised pretraining) 19.2% (with unsupervised pretraining) 15.8% (with unsupervised pretraining) learning is also known as “unsupervised pretraining.” During supervised learning with labeled ImageNet images, the parameters of lower layers and the logistic classifiers were both adjusted. This was done by first adjusting the logistic classifiers and then adjusting the entire network (also known as “fine-tuning”). As a control experiment, we also train a network starting with all random weights (i.e., without unsupervised pretraining: all parameters are initialized randomly and only adjusted by ImageNet labeled data). We followed the experimental protocols specified by (Deng et al., 2010; Sanchez & Perronnin, 2011), in which, the datasets are randomly split into two halves for training and validation. We report the performance on the validation set and compare against state-of-theart baselines in Table 2. Note that the splits are not identical to previous work but validation set performances vary slightly across different splits. The results show that our method, starting from scratch (i.e., raw pixels), bests many state-of-the-art hand-engineered features. On ImageNet with 10K categories, our method yielded a 15% relative improvement over previous best published result. On ImageNet with 22K categories, it achieved a 70% relative improvement over the highest other result of which we are aware (including unpublished results known to the authors of (Weston et al., 2011)). Note, random guess achieves less than 0.005% accuracy for this dataset. 7. Conclusion In this work, we simulated high-level class-specific neurons using unlabeled data. We achieved this by combining ideas from recently developed algorithms to learn invariances from unlabeled data. Our implementation scales to a cluster with thousands of machines thanks to model parallelism and asynchronous SGD. Our work shows that it is possible to train neurons to be selective for high-level concepts using entirely unlabeled data. In our experiments, we obtained neurons that function as detectors for faces, human bodies, and cat faces by training on random frames of YouTube videos. These neurons naturally capture complex invariances such as out-of-plane and scale invariances. The learned representations also work well for discriminative tasks. Starting from these representations, we obtain 15.8% accuracy for object recognition on ImageNet with 20,000 categories, a significant leap of 70% relative improvement over the state-of-the-art. Acknowledgements: We thank Samy Bengio, Adam Coates, Tom Dean, Jia Deng, Mark Mao, Peter Norvig, Paul Tucker, Andrew Saxe, and Jon Shlens for helpful discussions and suggestions. References Bengio, Y. and LeCun, Y. Scaling learning algorithms towards AI. In Large-Scale Kernel Machines, 2007. Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. Greedy layerwise training of deep networks. In NIPS, 2007. Berkes, P. and Wiskott, L. Slow feature analysis yields a rich repertoire of complex cell properties. Journal of Vision, 2005. Ciresan, D. C., Meier, U., Gambardella, L. M., andBuilding high-level features using large-scale unsupervised learning Schmidhuber, J. Deep big simple neural nets excel on handwritten digit recognition. CoRR, 2010. Coates, A., Lee, H., and Ng, A. Y. An analysis of singlelayer networks in unsupervised feature learning. In AISTATS 14, 2011. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and FeiFei, L. ImageNet: A Large-Scale Hierarchical Image Database. 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Figure 8. Some example test set images (shown before the whitening step). B. Models Central to our approach in this paper is the use of locally-connected networks. In these networks, neurons only connect to a local region of the layer below. In Figure 9, we show the connectivity patterns of the neural network architecture described in the paper. The actual images in the experiments are 2D, but for simplicity, our images in the visualization are in 1D. Figure 9. Diagram of the network we used with more detailed connectivity patterns. Color arrows mean that weights only connect to only one map. Dark arrows mean that weights connect to all maps. Pooling neurons only connect to one map whereas simple neurons and LCN neurons connect to all maps. C. Model Parallelism We use model parallelism to distribute the storage of parameters and gradient computations to different machines. In Figure 10, we show how the weights are divided and stored in different “partitions,” or more simply, machines (see also (Krizhevsky, 2009)). D. Further multicore parallelism Machines in our cluster have many cores which allow further parallelism. Hence, we split these cores to perform different tasks. In our implementation, the cores are divided into three groups: reading data, sending (or writing) data, and performing arithmetic computations. At every time instance, these groups work in parallel to load data, compute numerical results and send to network or write data to disks. E. Parameter sensitivity The hyper-parameters of the network are chosen to fit computational constraints and optimize the training time of our algorithm. These parameters can be changed at the expense of longer training time or more computational resources. For instance, one could increase the size of the receptive fields at an expense of using more memory, more computation, and more net-Building high-level features using large-scale unsupervised learning Figure 10. Model parallelism with the network architecture in use. Here, it can be seen that the weights are divided according to the locality of the image and stored on different machines. Concretely, the weights that connect to the left side of the image are stored in machine 1 (“partition 1”). The weights that connect to the central part of the image are stored in machine 2 (“partition 2”). The weights that connect to the right side of the image are stored in machine 3 (“partition 3”). work bandwidth per machine; or one could increase the number of maps at an expense of using more machines and memories. These hyper-parameters also could affect the performance of the features. We performed control experiments to understand the effects of the two hyperparameters: the size of the receptive fields and the number of maps. By varying each of these parameters and observing the test set accuracies, we can gain an understanding of how much they affect the performance on the face recognition task. Results, shown in Figure 11, confirm that the results are only slightly sensitive to changes in these control parameters. 12 14 16 18 20 60 65 70 75 80 85 receptive field size test set accuracy 6 7 8 9 10 60 65 70 75 80 85 number of maps test set accuracy Figure 11. Left: effects of receptive field sizes on the test set accuracy. Right: effects of number of maps on the test set accuracy. F. Example out-of-plane rotated face sequence In Figure 12, we show an example sequence of 3D (out-of-plane) rotated faces. Note that the faces are black and white but treated as a color picture in the test. More details are available at the webpage for The Sheffield Face Database dataset – http://www.sheffield.ac.uk/eee/research/ iel/research/face Figure 12. A sequence of 3D (out-of-plane) rotated face of one individual. The dataset consists of 10 sequences. G. Best linear filters In the paper, we performed control experiments to compare our features against “best linear filters.” This baseline works as follows. The first step is to sample 100,000 random patches (or filters) from the training set (each patch has the size of a test set image). Then for each patch, we compute its cosine distances between itself and the test set images. The cosine distances are treated as the feature values. Using these feature values, we then search among 20 thresholds to find the best accuracy of a patch in classifying faces against distractors. Each patch gives one accuracy for our test set. The reported accuracy is the best accuracy among 100,000 patches randomly-selected from the training set. H. Histograms on the entire test set Here, we also show the detailed histograms for the neurons on the entire test sets. The fact that the histograms are distinctive for positive and negative images suggests that the network has learned the concept detectors.Building high-level features using large-scale unsupervised learning Figure 13. Histograms of neuron’s activation values for the best face neuron on the test set. Red: the histogram for face images. Blue: the histogram for random distractors. Figure 14. Histograms for the best human body neuron on the test set. Red: the histogram for human body images. Blue: the histogram for random distractors. I. Most responsive stimuli for cats and human bodies In Figure 16, we show the most responsive stimuli for cat and human body neurons on the test sets. Note that, the top stimuli for the human body neuron are black and white images because the test set images are black and white (Keller et al., 2009). J. Implementation details for autoencoders and K-means In our implementation, deep autoencoders are also locally connected and use sigmoidal activation function. For K-means, we downsample images to 40x40 in order to lower computational costs. We also varied the parameters of autoencoders, K-means and chose them to maximize performances given resource constraints. In our experiments, we used 30,000 centroids for Kmeans. These models also employed parallelism in a similar fashion described in the paper. They also used 1,000 machines for three days. Figure 15. Histograms for the best cat neuron on the test set. Red: the histogram for cat images. Blue: the histogram for random distractors. Figure 16. Top: most responsive stimuli on the test set for the cat neuron. Bottom: Most responsive human body stimuli on the test set for the human body neuron. On-Demand Language Model Interpolation for Mobile Speech Input Brandon Ballinger1, Cyril Allauzen2, Alexander Gruenstein1, Johan Schalkwyk2 1Google, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA 2Google, 76 Ninth Avenue, New York, NY 10011, USA brandonb@google.com, allauzen@google.com, alexgru@google.com, johans@google.com Abstract Google offers several speech features on the Android mobile operating system: search by voice, voice input to any text field, and an API for application developers. As a result, our speech recognition service must support a wide range of usage scenarios and speaking styles: relatively short search queries, addresses, business names, dictated SMS and e-mail messages, and a long tail of spoken input to any of the applications users may install. We present a method of on-demand language model interpolation in which contextual information about each utterance determines interpolation weights among a number of n-gram language models. On-demand interpolation results in an 11.2% relative reduction in WER compared to using a single language model to handle all traffic. Index Terms: language modeling, interpolation, mobile 1. Introduction Entering text on mobile devices is often slow and error-prone in comparison to typing on a full-sized keyboard. Google offers several features on Android aimed at making speech a viable alternative input method: search by voice, voice input into any text field, and a speech API for application developers. To search by voice, users simply tap a microphone icon on the desktop search box, or hold down the physical search button. They can speak any query, and are then shown the Google search results. To use the Voice Input feature, users tap the microphone key on the on-screen keyboard, and then speak to enter text virtually anywhere they would normally type. Users may dictate e-mail and SMS messages, fill in forms on web pages, or enter text into any application. Finally, the Android Speech API is a simple way for developers to integrate speech recognition capabilities into their own applications. While a large portion of usage of the speech recognition service is comprised of spoken queries and dictation of SMS messages, there is a long tail of usage from thousands of other applications. Due to this diversity, choosing an appropriate language model for each utterance (recorded audio) is challenging. Two viable options are to build a single language model to handle all traffic, or to train a language model appropriate to each major use case and then choose the “best” one for each utterance, depending on the context of that utterance. We develop and compare a third option in this paper, in which a development set of utterances from each context is used to optimize interpolation weights among a small number of component language models. Since there may be thousands of such “contexts”, the language models are interpolated ondemand, either during decoding or as a post-processing rescoring phase. On-demand interpolation is performed efficiently via the use of a “compact interpolated” finite state transducer (FST), in which transition weights are dynamically computed. Percent of utterances Voice input 49% Search by Voice 44% Speech API 7% Table 1: Breakdown of speech traffic on Android devices that support Voice Input, Search by Voice, and Speech API. 2. Related Work The technique of creating interpolated language models for different contexts has been used with success in a number of conversational interfaces [1, 2, 3] In this case, the pertinent context is the system’s “dialogue state”, and it’s typical to group transcribed utterances by dialogue state and build one language model per state. Typically, states with little data are merged, and the state-specific language models are interpolated, or otherwise merged. Language models corresponding to multiple states may also be interpolated, to share information across similar states. The technique we develop here differs in two key respects. First, we derive interpolation weights for thousands of recognition contexts, rather than a handful of dialogue states. This makes it impractical to create each interpolated language model offline and swap in the desired one at runtime. Our language models are large, and we only learn the recognition context for a particular utterance when the audio starts to arrive. Second, rather than relying on transcribed utterances from each recognition context to train state-specific language modes, we instead interpolate a small number of language models trained from large corpora. 3. Android Speech Usage Analysis The challenge of supporting a variety of use cases is illustrated by examining the usage of the speech features available on Android. Table 1 breaks down the portion of utterances from the Android platform associated with the three speech features: voice input, search by voice, and the speech API. We note that this distinction isn’t perfect, as some users might, for example, speak a search query into a text box in the browser using the voice input feature. In addition, a large majority of the speech API utterances come from built-in Google applications – Google Maps provides a popular voice-enabled search box, for example. Overall, we observe roughly an even split between searching and dictation. The voice input feature encourages a wide range of usage. Since its launch in January, 2010, users have dictated text into over 8,000 distinct text fields. Table 2 shows the 10 most popular text fields. SMS is extremely popular, with usage levels an order of magnitude greater than any other application. Moreover, among the top 10 fields, 4 of them come from either the built-in SMS application, or one of the many SMS applicaCopyright © 2010 ISCA 26-30 September 2010, Makuhari, Chiba, Japan INTERSPEECH 2010 1812Text Field Usage SMS - Compose 63.1% An SMS app from Market - Compose 4.9% Browser 4.8% Google Talk 4.5% Gmail - Compose 3.3% Android Market - Search 2.4% Email - Compose 1.8% SMS - To 1.3% Maps - Directions Endpoint 1.0% An SMS app from Market - Compose 1.0% Table 2: The 10 most popular voice input text fields and their percent usage. 0 10 20 30 40 50 60 70 80 90 100 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 Number of fields, sorted by usage Cumulative percent of utterances Figure 1: Cumulative usage for the most popular 100 text fields, rank ordered by usage. tions available on the Android Market. Also popular are other dictation-style applications: Gmail, Email, and Google Talk. Android Market and Maps, both of which also appear in the top 10, represent different kinds of utterances – search queries. Finally, the Browser category here actually encompasses a wide range of fields – any text field on any web page. Figure 1 shows the cumulative usage per text field of the 100 most popular text fields, rank ordered by usage. Although the usage is certainly concentrated among a handful of applications, there remains a significant tail. While increasing accuracy for the tail may not have a huge effect on the overall accuracy of the system, it’s important for users to have a seamless experience using voice input: users will have a difficult time discerning that voice input may work better in some text fields than others. 4. Compact Interpolated FST In this setting, we have a relatively small set of language models that is fixed and known in advance. At recognition time, each utterance comes with a custom set of interpolation (or mixture) weights and we need to be able to efficiently compute ondemand the corresponding interpolated model. In a backoff language model, the conditional probability of w ∈ Σ given context h ∈ Σ∗ is recursively defined as P(w | h) = P(w | h) if hw ∈ S αhP(w | h ) otherwise, where P is the adjusted maximum likelihood probability (derived from the training corpus), S is the skeleton of the model, αh is the backoff weight for the context h and h is the longest common suffix of h. The order of the model is maxhw∈S |hw|. Such a language model can naturally be represented by a weighted automaton over the real semiring (R, +, ×, 0, 1) using failure transitions [4]: the set of states is Q = x y φ/.5 xa a/.5 ya a/.4 yb b/.4 yc c/.04 x y φ/.4 xb b/.6 ya a/.6 yb b/.2 yc c/.02 x y φ/(.5,.4) a/(.5,.24) xa xb b/(.2,.6) a/(.4,.6) ya yb b/(.4,.2) c/(.04,.02) yc (a) (b) (c) Figure 2: Outgoing transitions from state x in (a) G1, (b) G2 and (c) I. For λ = (.6, .4)T , PIλ (a | x) = .6 × .5 + .4 × .24. {h ∈ Σ∗ | ∃w ∈ Σ such that hw ∈ S}, for each state h, there is a failure transition from h to h labeled by φ and with weight αh, and for each hw ∈ S, there is a transition from h to the longest suffix of hw that belongs to Q, labeled by w and with weight P(w | h). Given a set G = {G1,...,Gm} of m backoff language models and a vector of mixture weights λ = (λ1,...λm) T , the linear interpolation of G by λ is defined as the language model Iλ assigning the conditional probability: PIλ (w | h) = m i=1 λiPGi (w | h). (1) Using (1) directly to perform on-demand interpolation would be inefficient because for a given pair (w, h) we might need to backoff several times in several of the models and this can become rather expensive when using the automata representation. Instead, we chose to reformulate the interpolated model as a backoff model: PIλ (w | h) = λT phw if hw ∈ S(G), f(λ, αh)PIλ (w | h ) otherwise, where phw = (PG1 (w|h),..., PGm(w|h))T , S(G) = ∪m i=1S(Gi) and αh = (αh(G1),...,αh(Gm))T . There exists a closed-form expression of f(λ, α) that ensure the proper normalization of the model. However, in practice we decided to approximate it by the dot product of λ and αh: f(λ, αh) = λT αh. The benefit of this formulation is that it perfectly fits our requirement. Since the set of models is known in advance we can precompute S(G) and all the relevant vectors (phw and αh) effectively building a generic interpolated model I as a model over Rm. Given a new utterance and a corresponding vector of mixture weights λ, we can obtain the relevant interpolated model Iλ by taking the dot product of each component vector of I with λ. Moreover, this approach also allows for an efficient representation of I as a weighted automaton over the semiring (Rm, +, ◦, 0, 1) (◦ denotes componentwise multiplication), the weight of each transition in the automaton being a vector in Rm. The set of states is Q = {h ∈ Σ∗ | ∃w ∈ Σ such that hw ∈ S(G)}. For each state h, there is a failure transition from h to h labeled by φ and with weight αh, and for each hw ∈ S(G), there is a transition from h to the longest suffix of hw that belongs to Q, labeled by w and with weight phw. Figure 2 illustrates this construction. Given a new utterance and a corresponding vector of mixture weights λ, this automaton can be converted on-demand into a weighted automaton over the real semiring by taking the dot product of λ and the weight vector of each visited transition. 1813 ReFr: An Open-Source Reranker Framework Daniel M. Bikel, Keith B. Hall Google Research, New York, NY {dbikel,kbhall}@google.com Abstract ReFr (http://refr.googlecode.com) is a software architecture for specifying, training and using reranking models, which take the n-best output of some existing system and produce new scores for each of the n hypotheses that potentially induce a different ranking, ideally yielding better results than the original system. The Reranker Framework has some special support for building discriminative language models, but can be applied to any reranking problem. The framework is designed with parallelism and scalability in mind, being able to run on any Hadoop cluster out of the box. While extremely efficient, ReFr is also quite flexible, allowing researchers to explore a wide variety of features and learning methods. ReFr has been used for building state-of-the-art discriminative LM’s for both speech recognition and machine translation systems. Index Terms: language modeling, discriminative language modeling, reranking, structured prediction 1. Introduction Creating effective software tools for research is a tricky business. The classic tension between flexibility and efficiency arises with greater urgency. We want researchers to be able to try out many different ideas easily, but we also want them to be able to have a quick code-test-evaluate cycle. ReFr grew out of the 2011 Johns Hopkins Summer Workshop, from the team using automatically generated confusions to synthesize training data for discriminative language models for speech and machine translation, led by Prof. Brian Roark of OHSU. That approach required tools that would scale up to training data sizes orders of magnitude larger than had previously been used to build discriminative language models, so we not only needed our training and inference to be inherently fast, but we needed to design tools with distributed computing in mind from the outset. This paper describes the tools we have developed to solve not only the immediate research problem of exploring confusions for discriminative language modeling, but also the more general problem of reranking approaches to speech and language processing, including structured prediction. We designed ReFr to have the following properites: • “library quality” code • industrial strength • academic flexibility • easy exploration of different types of features, different update methods (e.g., MIRA-style, direct loss minimization, loss-sensitive) and different learning methods (e.g., perceptron-style, log-linear, kernel methods) • modern, object-oriented design, complete with dynamic factories and dynamic composition for flexibility • parallelizable, especially for distributed-computing environments 2. Data Format for I/O There are two main choices when building discriminative reranking models for speech or machine translation: (a) rescore a lattice or hypergraph or (b) simply use a strict reranking approach applied to n-best lists. For ReFr, early on we decided to use (b) reranking n-best lists. The primary reasons were the flexibility this would allow us in designing features and tools. N-best lists readily allow for sentence-level features in a way that, say, lattices do not. Additionally, it is far easier to de- fine generic schemes of passing around n-best lists than it is for designing schemes to take speech lattices as well as machine translation hypergraphs or other, problem-specific data types. ReFr is meant to be flexible enough to allow for a variety of data sources. In order to avoid the need for overly complex data formats, we have chosen to adopt a formalism which allows one to augment the input format, allowing for flexible feature extraction and data manipulation/analysis. We opted to use a data format which mirrors the data-structures that are used internally for training. The Google protocol buffers[1] provide a programming-language independent specification framework to define data formats. The protocol buffers specification language is used by the protocol buffer tools to generate source-code for serializing and deserializing the data stored in the format. Code is generated to allow for native programming-language encapsulation of the data. For example, in C++ each item of data is stored in an object based on a object oriented data specification (a C++ class) allowing for access to the data.1 3. Core learning framework Consider Algorithm 1, which describes the training procedure for a generic online-learning algorithm. Each training example ei comprises a set of candidate hypotheses, each of which is projected via some function Φ into a feature space, R F . We typically think of Φ as being a suite of feature functions, one per dimension. The model itself is defined as a weight vector in this space, w. Decoding, or inference, is carried out simply by taking the dot product of the model and a test instance. More generally, any kernel function K may be used. The training procedure iterates over the training data T—each iteration is called an epoch—until the NEEDTOKEEPTRAINING() predicate returns false. Often, such a predicate is based on the average loss of the current model on some held-out development data D, which is the purpose of the EVALUATE(D) line in the TRAIN(T) procedure. 1For the 2011 Johns Hopkins Workshop, we were targeting multiple tasks (ASR and MT), and so our toolkit provides a means to convert from two types of text-based n-best formats, one the output of an ASR system, the other the output of an MT system. These conversion tools are not only useful in their own right, but serve as example implementations for any developer converting from their own, proprietary format to the Google Protocol Buffer format used by ReFr. Copyright © 2013 ISCA 25-29 August 2013, Lyon, France INTERSPEECH 2013: Show & Tell Contribution 756Algorithm 1 Training algorithm for online-learning reranking models. Let ei = {c1, . . . , ck} be a training example, where each cj is a candidate hypothesis. Similarly, let di = {c1, . . . , ck} be a held-out development data example, also consisting of k candidate hypotheses. Finally, let K be a kernel function. procedure TRAIN(T = {e1, . . . , en}, D = {d1, . . . , dm}) while NEEDTOKEEPTRAINING() do TRAINONEEPOCH(T) EVALUATE(D) end while end procedure procedure TRAINONEEPOCH(T) foreach training example ei do SCORECANDIDATES(ei) if NEEDTOUPDATE() then UPDATE() end if end for end procedure procedure SCORECANDIDATES(ei) foreach candidate hypothesis cj ∈ ei do cj .score ← K(wt, cj ) end for end procedure Model Candidate Scorer Update Predicate Updater … Figure 1: A pictorial view of how a Model wraps instances of other interfaces that specify the predicates and functions needed to carry out model training. For the basic perceptron, the model starts out at time step 0 as the zero vector; that is, wo = ~0. The update is wt+1 = wt + Rt [Φ (yoracle (ei)) − Φ (ˆy (ei))] , (1) where yoracle is a function that picks out the hypothesis towards which we want to bias our model, yˆ is a function that picks out the candidate hypothesis we want to bias our model against and Rt is a learning rate or step size. Most often, yoracle is defined to pick the hypothesis with the lowest loss relative to some goldstandard truth, and yˆ is defined to pick the candidate hypothesis that scores highest under the current model wt. Most of the variations of this basic learning method involve finding different ways of defining Rt, Φ, yoracle and yˆ, along with the various procedures and predicates shown in Algorithm 1. Therefore, we would like our Reranker Framework to make it easy for the researcher to define these various functions, as well as to specify which ones to use at run-time. ReFr defines a Model interface with virtual methods for all of the functions shown in Algorithm 1. To avoid the exponential blow-up of overriding different combinations of these methods, ReFr also employs dynamic composition. That is, we keep the idea of a Model interface, but additionally have each Model instance wrap a set of predicate/manipulator objects, each of which itself conforms to an interface. Figure 1 shows a pictorial representation of this scheme. As we discussed above, we employ dynamic composition to avoid defining a new subclass of Model every time we wish model file = "my model file"; // model output file model = PerceptronModel( name("my model"), score comparator(DirectLossScoreComparator())); exec feature extractor = ExecutiveFeatureExtractorImpl( feature extractors({NgramFeatureExtractor(n(2)), RankFeatureExtractor()}); training efe = exec feature extractor; dev efe = exec feature extractor; training files = {"training1.gz", "training2.gz"}; devtest files = {"dev1.gz", "dev2.gz"}; Figure 2: An example ReFr configuration file, read by its Interpreter class. to explore a new combination of learning method functions. To do this, ReFr includes a very lightweight and yet powerful interpreter for a language that allows for assignment statements for primitives, vectors of primitives, Factory-constructible objects and vectors of Factory-constructible objects. Figure 2 shows an example ReFr configuration file. The syntax is intentionally very similar to that of C++. This lightweight language provides a flexible mechanism by which to specify how feature extraction, training and inference shall occur. 4. Cluster-based distributed training As Algorithm 1 shows, the basic perceptron algorithm involves “online” updating, and thus it is possible to read in each training example from file each time it is needed, only keeping the model’s parameters persistently in memory. The Reranker Framework allows both the memory-intensive way of training as well as this “streaming mode” version of training, essential for distributed learning. The structured perceptron [2] and it’s variants have proven to be effective in supervised, discriminative language modeling work [3]. We have centered the development of our opensource discriminative learning toolkit around perceptron-style algorithms, which are, by definition, online learning algorithms. Identifying the optimal solution for a distributed online optimization algorithm is still an open research question. We borrow from our previous work on distributed perceptron training in [4, 5] and use the Iterative Parameter Mixtures algorithm for distributed computation. The Reranker Framework makes it easy to switch between single processor and distributed training, which uses the Hadoop implementation of MapReduce [6]. 5. Demo Plan Our demo will consist of a walk-through of all ReFr’s features, followed by a hands-on demonstration of how easy it is to implement a new class of features for the reranker based on the rank of each candidate hypothesis. We will also show how easy it is to integrate that new class of features into training and inference. We will then demonstrate the ease with which one can use the API and the interpreted configuration language to alter the training algorithm. Finally, we will demonstrate the simple way that a user can switch from single processor training to large-scale distributed training. 6. Acknowledgements The authors would like to thank Prof. Brian Roark of Oregon Health and Science University for leading a fantastic team at the 2011 Johns Hopkins Workshop, and we would also like to thank all of our teammates, especially Prof. Izhak Shafran of OHSU and Ph.D. candidate Maider Lehr, who are actively working with and helping us improve ReFr. 7577. References [1] Google, “Protocol buffers,” http://code.google.com/apis/protocolbuffers/. [2] M. Collins, “Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms,” in Proc. EMNLP, 2002, pp. 1–8. [3] B. Roark, M. Sarac¸lar, and M. Collins, “Discriminative n-gram language modeling,” Computer Speech and Language, vol. 21, no. 2, pp. 373 – 392, 2007. [Online]. Available: http: //www.sciencedirect.com/science/article/pii/S0885230806000271 [4] R. McDonald, K. Hall, and G. Mann, “Distributed training strategies for the structured perceptron,” in HLT-NAACL, 2010. [5] K. Hall, S. Gilpin, and G. Mann, “Mapreduce/bigtable for distributed optimization,” in NIPS Workshop on Leaning on Cores, Clusters, and Clouds, 2010. [6] J. Dean and S. Ghemawat, “Mapreduce: simplified data processing on large clusters,” CACM, vol. 51:1, 2008. 758 Accurate and Compact Large Vocabulary Speech Recognition on Mobile Devices Xin Lei1 Andrew Senior2 Alexander Gruenstein1 Jeffrey Sorensen2 1Google Inc., Mountain View, CA USA 2Google Inc., New York, NY USA {xinlei,andrewsenior,alexgru,sorenj}@google.com Abstract In this paper we describe the development of an accurate, smallfootprint, large vocabulary speech recognizer for mobile devices. To achieve the best recognition accuracy, state-of-the-art deep neural networks (DNNs) are adopted as acoustic models. A variety of speedup techniques for DNN score computation are used to enable real-time operation on mobile devices. To reduce the memory and disk usage, on-the-fly language model (LM) rescoring is performed with a compressed n-gram LM. We were able to build an accurate and compact system that runs well below real-time on a Nexus 4 Android phone. Index Terms: Deep neural networks, embedded speech recognition, SIMD, LM compression. 1. Introduction Smartphones and tablets are rapidly overtaking desktop and laptop computers as people’s primary computing device. They are heavily used to access the web, read and write messages, interact on social networks, etc. This popularity comes despite the fact that it is significantly more difficult to input text on these devices, predominantly by using an on-screen keyboard. Automatic speech recognition (ASR) is a natural, and increasingly popular, alternative to typing on mobile sevices. Google offers the ability to search by voice [1] on Android, iOS, and Chrome; Apple’s iOS devices come with Siri, a conversational assistant. On both Android and iOS devices, users can also speak to fill in any text field where they can type (see, e.g., [2]), a capability heavily used to dictate SMS messages and e-mail. A major limitation of these products is that speech recognition is performed on a server. Mobile network connections are often slow or intermittent, and sometimes non-existant. Therefore, in this study, we investigate techniques to build an accurate, small-footprint speech recognition system that can run in real-time on modern mobile devices. Previously, speech recognition on handheld computers and smartphones has been studied in the DARPA sponsored Transtac Program, where speech-to-speech translation systems were developed on the phone [3, 4, 5]. In the Transtac systems, Gaussian mixture models (GMMs) were used to as acoustic models. While the task was a small domain, with limited training data, the memory usage in the resulting systems was moderately high. In this paper, we focus on large vocabulary on-device dictation. We show that deep neural networks (DNNs) can provide large accuracy improvements over GMM acoustic models, with a significantly smaller footprint. We also demonstrate how memory usage can be significantly reduced by performing onthe-fly rescoring with a compressed language model during decoding. The rest of this paper is organized as follows. In Section 2, the embedded GMM acoustic model is described. Section 3 presents the training of embedded DNNs, and the techniques we employed to speed up DNN inference at runtime. Section 4 describes the compressed language models for on-the-fly rescoring. Section 5 shows the experimental results of recognition accuracy and speed on Nexus 4 platform. Finally, Section 6 concludes the paper and discusses future work. 2. GMM Acoustic Model Our embedded GMM acoustic model is trained on 4.2M utterances, or more than 3,000 hours of speech data containing randomly sampled anonymized voice search queries and other dictation requests on mobile devices. The acoustic features are 9 contiguous frames of 13-dimensional PLP features spliced and projected to 40 dimensions by linear discriminant analysis (LDA). Semi-tied covariances [6] are used to further diagonalize the LDA transformed features. Boosted-MMI [7] was used to train the model discriminatively. The GMM acoustic model contains 1.3k clustered acoustic states, with a total of 100k Gaussians. To reduce model size and speed up computation on embedded platforms, the floatingpoint GMM model is converted to a fixed-point representation, similar to that described in [8]. Each dimension of the Gaussian mean vector is quantized into 8 bits, and 16-bit for precision vector. The resulting fixed-point GMM model size is about 1/3 of the floating-point model, and there is no loss of accuracy due to this conversion in our empirical testing. 3. DNNs for Embedded Recognition We have previously described the use of deep neural networks for probability estimation in our cloud-based mobile voice recognition system [9]. We have adopted this system for developing DNN models for embedded recognition, and summarize it here. The model is a standard feed-forward neural network with k hidden layers of nh nodes, each computing a nonlinear function of the weighted sum of the outputs of the previous layer. The input layer is the concatenation of ni consecutive frames of 40-dimensional log filterbank energies calculated on 25ms windows of speech every 10ms. The no softmax outputs estimate the posterior of each acoustic state. We have experimented with conventional logistic nonlinearities and rectified linear units that have recently shown superior performance in our large scale task [10], while also reducing computation. Copyright © 2013 ISCA 25-29 August 2013, Lyon, France INTERSPEECH 2013 662While our server-based model has 50M parameters (k = 4, nh = 2560, ni = 26 and no = 7969), to reduce the memory and computation requirement for the embedded model, we experimented with a variety of sizes and chose k = 6, nh = 512, ni = 16 and no = 2000, or 2.7M parameters. The input window is asymmetric; each additional frame of future context adds 10ms of latency to the system so we limit ourselves to 5 future frames, and choose around 10 frames of past context, trading off accuracy and computation. Our context dependency (CD) phone trees were initially constructed using a GMM training system that gave 14,247 states. By pruning this system using likelihood gain thresholds, we can choose an arbitrary number of CD states. We used an earlier large scale model with the full state inventory that achieved around 14% WER to align the training data, then map the 14k states to the desired smaller inventory. Thus we use a better model to label the training data to an accuracy that cannot be achieved with the embedded scale model. 3.1. Training Training uses conventional backpropagation of gradients from a cross entropy error criterion. We use minibatches of 200 frames with an exponentially decaying learning rate and a momentum of 0.9. We train our neural networks on a dedicated GPU based system. With all of the data available locally on this system, the neural network trainer can choose minibatches and calculate the backpropagation updates. 3.2. Decoding speedup Mobile CPUs are designed primarily for lower power usage and do not have as many or as powerful math units as CPUs used in server or desktop applications. This makes DNN inference, which is mathematically computationally expensive, a particular challenge. We exploit a number of techniques to speed up the DNN score computation on these platforms. As described in [11], we use a fixed-point representation of DNNs. All activations and intermediate layer weights are quantized into 8-bit signed char, and biases are encoded as 32-bit int. The input layer remains floating-point, to better accommodate the larger dynamic ranges of input features. There is no measured accuracy loss resulting from this conversion to fixed-point format. Single Instruction Multiple Data (SIMD) instructions are used to speed up the DNN computation. With our choice of smaller-sized fixed-point integer units, the SIMD acceleration is significantly more efficient, exploiting up to 8 way parallelism in each computation. We use a combination of inline assembly to speed up the most expensive matrix multiplication functions, and compiler intrinsics in the sigmoid and rectified linear calculations. Batched lazy computation [11] is also performed. To exploit the multiple cores present on modern smartphones, we compute the activations up to the last layer in a dedicated thread. The output posteriors of the last layer are computed only when needed by the decoder in a separate thread. Each thread computes results for a batch of frames at a time. The choice of batch size is a tradeoff between computation efficiency and recognition latency. Finally, frame skipping [12] is adopted to further reduce computation. Activations and posteriors are computed only every nb frames and used for nb consecutive frames. In experiments we find that for nb = 2, the accuracy loss is negligible; however for nb ≥ 3, the accuracy degrades quickly. 4. Language Model Compression We create n-gram language models appropriate for embedded recognition by first training a 1M word vocabulary and 18M n-gram Katz-smoothed 4-gram language model using Google’s large-scale LM training infrastructure [13]. The language model is trained using a very large corpus (on the order of 20 billion words) from a variety of sources, including search queries, web documents and transcribed speech logs. To reduce memory usage, we use two language models during decoding. First, a highly-pruned LM is used to build a small CLG transducer [14] that is traversed by the decoder. Second, we use a larger LM to perform on-the-fly lattice rescoring during search, similar to [15]. We have observed that a CLG transducer is generally two to three times larger than a standalone LM, so this rescoring technique significantly reduces the memory footprint. Both language models used in decoding are obtained by shrinking the 1M vocabulary and 18M n-gram LM. We aggressively reduce the vocabulary to the 50K highest unigram terms. We then apply relative entropy pruning [16] as implemented in the OpenGrm toolkit [17]. The resulting finite state model for rescoring LM has 1.4M n-grams, with just 280K states and 1.7M arcs. The LM for first pass decoding contains only unigrams and about 200 bigrams. We further reduce the memory footprint of the rescoring LM by storing it in an extremely memory-efficient manner, discussed below. 4.1. Succinct storage using LOUDS If you consider a backoff language model’s structure, the failure arcs from (n + 1)-gram contexts to n-gram contexts and, ultimately, to the unigram state form a tree. Trees can be stored using 2 bits per node using a level-order unary degree sequence (LOUDS), where we visit the nodes breadth-first writing 1s for the number of (n + 1)-gram contexts and then terminating with a 0 bit. We build a bit sequence similarly for the degree of outbound non-φ arcs. The LOUDS data structure provides first-child, last-child, and parent navigation, so we are able to store a language model without storing any next-state values. As a contiguous, indexfree data object, the language model can be easily memory mapped. The implementation of this model is part of the OpenFst library [18] and covered in detail in [19]. The precise storage requirements, measured in bits, are 4ns + na + (W + L)(ns + na) + W nf + c where ns is the number of states, nf the number of final states, na is the number of arcs, L is the number of bits per wordid, and W is the number of bits per probability value. This is approximately one third the storage required by OpenFst’s vector representation. For the models discussed here, we use 16 bits for both labels and weights. During run time, to support fast navigation in the language model, we build additional indexes of the LOUDS bit sequences to support the operations rankb(i) the number of b valued bits before index i, and its inverse selectb(r). We maintain a two level index that adds an additional 0.251(4ns + na) bits. Here it is important to make use of fast assembly operations such as find first set during decoding, which we do through compiler intrinsics. 6634.2. Symbol table compression The word symbol table for an LM is used to map words to unique identifiers. Symbol tabels are another example of a data structure that can be represented as a tree. In this case we relied upon the implementation contained in the MARISA library [20]. This produces a symbol table that fits in just one third the space of the concatenated strings of the vocabulary, yet provides a bidirectional mapping between integers and vocabulary strings. We are able to store our vocabulary in about 126K bytes, less than 3 bytes per entry in a memory mappable image. The MARISA library assigns the string to integer ids during compression, so we relabel all of the other components in our system to match this assignment. 5. Experimental Results To evaluate accuracy performance, we use a test set of 20,000 anonymized transcribed utterances from users speaking in order to fill in text fields on mobile devices. This biases the test set towards dictation, as opposed to voice search queries, because dictation is more useful than search when no network connection is available. To measure speed performance, we decode a subset of 100 utterances on an Android Nexus 4 (LG) phone. The Nexus 4 is equipped with a 1.5GHz quad-core Qualcomm Snapdragon S4 pro CPU, and 2GB of RAM. It runs the Android 4.2 operating system. To reduce start up loading time, all data files, including the acoustic model, the CLG transducer, the rescoring LM and the symbol tables are memory mapped on the device. We use a background thread to “prefetch” the memory mapped resources when decoding starts, which mitigates the slowdown in decoding for the first several utterances. 5.1. GMM acoustic model The GMM configuration achieves a word error rate (WER) of 20.7% on this task, with an average real-time (RT) factor of 0.63. To achieve this speed, the system uses integer arithmetic for likelihood calculation and decoding. The Mahalanobis distance computation is accelerated using fixed-point SIMD instructions. Gaussian selection is used to reduce the burden of likelihood computation, and further efficiencies come from computing likelihoods for batches of frames. 5.2. Accuracy with DNNs We compare the accuracy of DNNs with different configurations to the baseline GMM acoustic model in Table 1. A DNN with 1.48M parameters already outperforms the GMM in accuracy, with a disk size of only 17% of the GMM’s. By increasing the number of hidden layers from 4 to 6 and number of outputs from 1000 to 2000, we obtain a large improvement of 27.5% relative in WER compared to the GMM baseline. The disk size of this DNN is 26% of the size of the GMMs. For comparison, we also evaluate a server-sized DNN with an order of magnitude of more parameters, and it gives 12.3% WER. Note that all experiments in Table 1 use smaller LMs in decoding. In addition, with an un-pruned server LM, the server DNN achieves 9.9% WER while the server GMM achieves 13.5%. Therefore, compared to a full-size DNN server system, there is a 2.4% absolute loss due to smaller LMs, and 2.8% due to smaller DNN. Compared to the full-size GMM server system, the embedded DNN system is about 10% relatively worse in WER. The impact of frame skipping is evaluated with the DNN 6×512 model. As shown in Table 2, the accuracy performance quickly degrades when nb is larger than 2. Table 2: Accuracy results with frame skipping in a DNN system. nb 1 2 3 4 5 WER (%) 15.1 15.2 15.6 16.0 16.7 5.3. Speed benchmark For speed benchmark, we measure average RT factor as well as 90-percentile RT factor. As shown in Table 3, the baseline GMM system with SIMD optimization gives an average RT factor of 0.63. The fixed-point DNN gives 1.32×RT without SIMD optimization, and 0.75×RT with SIMD. Batched lazy computation improves average RT by 0.06 but degrades the 90- percentile RT performance, probably due to less efficient ondemand computation for difficult utterances. After frame skipping with nb = 2, the speed of DNN system is further improved slightly to 0.66×RT. Finally, the overhead of the compact LOUDS based LM is about 0.13×RT on average. Table 3: Averge real-time (RT) and 90-percentile RT factors of different system settings. Average RT RT(90) GMM 0.63 0.90 DNN (fixed-point) 1.32 1.43 + SIMD 0.75 0.87 + lazy batch 0.69 1.01 + frame skipping 0.66 0.97 + LOUDS 0.79 1.24 5.4. System Footprint Compared to the baseline GMM system, the new system with LM compression and DNN acoustic model achieves a much smaller footprint. The data files sizes are listed in Table 4. Note that conversion of the 34MB floating-point GMM model to a 14MB fixed-point GMM model itself provides a large reduction in size. The use of DNN reduces the size by 10MB, and the LM compression contributed to another 18MB reduction. Our final embedded DNN system size is reduced from 46MB to 17MB, while achieving a big WER reduction from 20.7% to 15.2%. 6. Conclusions In this paper, we have described a fast, accurate and smallfootprint speech recognition system for large vocabulary dictation on the device. DNNs are used as acoustic model, which provides a 27.5% relative WER improvement over the baseline GMM models. The use of DNNs also significantly reduces the memory usage. Various techniques are adopted to speed up the DNN inference at decoding time. In addition, a LOUDS based language model compression reduces the rescoring LM size by more than 60% relative. Overall, the size of the data files of the system is reduced from 46MB to 17MB. 664Table 1: Comparison of GMM and DNNs with different sizes. The input layer is denoted by number of filterbank energies × the context window size (left + current + right). The hidden layers are denoted by number of hidden layers × number of nodes per layer. The number of outputs is the number of HMM states in the model. Model WER (%) Input Layer Hidden Layers # Outputs # Parameters Size GMM 20.7 - - 1314 8.08M 14MB DNN 4×400 22.6 40×(8+1+4) 4×400 512 0.9M 1.5MB DNN 4×480 20.3 40×(10+1+5) 4×480 1000 1.5M 2.4MB DNN 6×512 15.1 40×(10+1+5) 6×512 2000 2.7M 3.7MB Server DNN 12.3 40×(20+1+5) 4×2560 7969 49.3M 50.8MB Table 4: Comparison of data file sizes (in MB) in baseline GMM system and DNN system with and without LOUDS LM compression. AM denotes acoustic model, CLG is the transducer for decoding, LM denotes the rescoring LM, and symbols denote the word symbol table. System AM CLG LM Symbols Total GMM 14 2.7 29 0.55 46 + LOUDS 14 2.7 10.7 0.13 27 DNN 3.7 2.8 29 0.55 36 + LOUDS 3.7 2.8 10.7 0.13 17 Future work includes speeding up rescoring using the LOUDS LM as well as further compression techniques. We also continue to investigate the accuracy performance with different sizes of LM for CLG and rescoring. 7. Acknowledgements The authors would like to thank our former colleague Patrick Nguyen for implementing the portable neural network runtime engine used in this study. Thanks also to Vincent Vanhoucke and Johan Schalkwyk for helpful discussions and support during this work. [9] N. Jaitly, P. Nguyen, A. W. Senior, and V. Vanhoucke, “Application of pretrained deep neural networks to large vocabulary speech recognition,” in Proc. Interspeech, 2012. [10] M. D. Zeiler et al., “On rectified linear units for speech processing,” in Proc. ICASSP, 2013. [11] V. Vanhoucke, A. Senior, and M. Z. Mao, “Improving the speed of neural networks on CPUs,” in Proc. Deep Learning and Unsupervised Feature Learning NIPS Workshop, 2011. [12] V. Vanhoucke, M. Devin, and G. Heigold, “Multiframe deep neural networks for acoustic modeling,” in Proc. ICASSP, 2013. [13] T. Brants, A. C. Popat, P. Xu, F. J. Och, and J. Dean, “Large language models in machine translation,” in EMNLP, 2007, pp. 858–867. [14] M. Mohri, F. Pereira, and M. Riley, “Speech recognition with weighted finite-state transducers,” Handbook of Speech Processing, pp. 559–582, 2008. [15] T. Hori and A. Nakamura, “Generalized fast on-the-fly composition algorithm for WFST-based speech recognition,” in Proc. Interspeech, 2005. [16] A. Stolcke, “Entropy-based pruning of backoff language models,” in DARPA Broadcast News Transcription and Understanding Workshop, 1998, pp. 8–11. [17] B. Roark, R. Sproat, C. Allauzen, M. Riley, J. Sorensen, and T. Tai, “The OpenGrm open-source finite-state grammar software libraries,” in Proceedings of the ACL 2012 System Demonstrations. 2012, ACL ’12, pp. 61–66, Association for Computational Linguistics. 8. References [1] J. Schalkwyk, D. Beeferman, F. Beaufays, B. Byrne, C. Chelba, M. Cohen, M. Kamvar, and B. Strope, “Google search by voice: A case study,” in Advances in Speech Recognition: Mobile Environments, Call Centers and Clinics, pp. 61–90. Springer, 2010. [2] B. Ballinger, C. Allauzen, A. Gruenstein, and J. Schalkwyk, “Ondemand language model interpolation for mobile speech input,” in Proc. Interspeech, 2010. [3] J. Zheng et al., “Implementing SRI’s Pashto speech-to-speech translation system on a smart phone,” in SLT, 2010. [4] J. Xue, X. Cui, G. Daggett, E. Marcheret, and B. Zhou, “Towards high performance LVCSR in speech-to-speech translation system on smart phones,” in Proc. Interspeech, 2012. [5] R. Prasad et al., “BBN Transtalk: Robust multilingual two-way speech-to-speech translation for mobile platforms,” Computer Speech and Language, vol. 27, pp. 475–491, February 2013. [6] M. J. F. Gales, “Semi-tied covariance matrices for hidden Markov models,” IEEE Trans. Speech and Audio Processing, vol. 7, pp. 272–281, 1999. [7] D. Povey, D. Kanevsky, B. Kingsbury, B. Ramabhadran, G. Saon, and K. Visweswariah, “Boosted MMI for model and feature-space discriminative training,” in Proc. ICASSP, 2008. [8] E. Bocchieri, “Fixed-point arithmetic,” Automatic Speech Recognition on Mobile Devices and over Communication Networks, pp. 255–275, 2008. [18] C. Allauzen, M. Riley, J. Schalkwyk, W. Skut, and M. Mohri, “OpenFst: A general and efficient weighted finite-state transducer library,” in Proceedings of the Ninth International Conference on Implementation and Application of Automata, (CIAA 2007). 2007, vol. 4783 of Lecture Notes in Computer Science, pp. 11– 23, Springer, http://www.openfst.org. [19] J. Sorensen and C. Allauzen, “Unary data structures for language models,” in Proc. Interspeech, 2011. [20] S. Yata, “Prefix/Patricia trie dictionary compression by nesting prefix/Patricia tries (japanese),” in Proceedings of 17th Annual Meeting of the Association for Natural Language, Toyohashi, Japan, 2011, NLP2011, https://code.google.com/p/marisa-trie/. 665 Backoff Inspired Features for Maximum Entropy Language Models Fadi Biadsy, Keith Hall, Pedro Moreno and Brian Roark Google, Inc. {biadsy,kbhall,pedro,roark}@google.com Abstract Maximum Entropy (MaxEnt) language models [1, 2] are linear models that are typically regularized via well-known L1 or L2 terms in the likelihood objective, hence avoiding the need for the kinds of backoff or mixture weights used in smoothed ngram language models using Katz backoff [3] and similar techniques. Even though backoff cost is not required to regularize the model, we investigate the use of backoff features in MaxEnt models, as well as some backoff-inspired variants. These features are shown to improve model quality substantially, as shown in perplexity and word-error rate reductions, even in very large scale training scenarios of tens or hundreds of billions of words and hundreds of millions of features. Index Terms: maximum entropy modeling, language modeling, n-gram models, linear models 1. Introduction A central problem in language modeling is how to combine information from various model components, e.g., mixing models trained with differing Markov orders for smoothing or on distinct corpora for adaptation. Smoothing (regularization) for n-gram language models is typically presented as a mechanism whereby higher-order models are combined with lower-order models so as to achieve both the specificity of the higher-order model and the more robust generality of the lower-order model. Most commonly, this combination is effected via an interpolation or backoff mechanism, in which each prefix (history) of an n-gram has a parameter which dictates how much cost is associated with making use of lower-order n-gram estimates, often called the “backoff cost”. This becomes a parameter estimation problem in its own right, either through discounting or mixing parameters; and these are often estimated via extensive parameter tying, heuristics based on count histograms, or both. Log linear models provide an alternative to n-gram backoff or interpolated models for combining evidence from multiple, overlapping sources of evidence, with very different regularization methods. Instead of defining a specific model structure with backoff costs and/or mixing parameters, these models combine features from many sources into a single linear feature vector, and score a word by taking the dot product of the feature vector with a learned parameter vector. Learning can be via locally normalized likelihood objective functions, as in Maximum Entropy (MaxEnt) models [1, 2, 4] or global “whole sentence” objectives [5, 6, 7]. For locally normalized MaxEnt models, which estimate a conditional distribution over a vocabulary given the prefix history (just as the backoff smoothed n-gram models do), the brute-force local normalization over the vocabulary obviates the need for complex backoff schemes to avoid zero probabilities. One can simply toss in n-gram features of all the orders, and learn their relative contribution. Recall, however, that the standard backoff n-gram models do not only contain parameters associated with n-grams; they also contain parameters associated with the backoff weights for each prefix history. For every proper prefix of an n-gram in the model, there will be an associated backoff weight, which penalizes to a greater or lesser extent words that have been previously unseen following that prefix history. For some histories we should have a relatively high expectation of seeing something new, either because the history itself is rare (hence we do not have enough observations yet to be strongly predictive) or it simply predicts relatively open classes of possible words, e.g., “the”, which can precede many possible words, including many that were presumably unobserved following “the” in the training corpus. Other prefixes may be highly predictive so that the expectation of seeing something previously unobserved is relatively low, e.g., “Barack”. Granted, MaxEnt language models (LMs) do not need this information about prefix histories to estimate regularized probabilities. Chen and Rosenfeld [4] survey various smoothing and regularization methods for MaxEnt language models, including reducing the number of features (as L1 regularization does), optimizing to match expected frequencies to discounted counts, or optimizing to modified objectives, such as L2 regularization. In none of these methods are there parameters in the model associated with the sort of “otherwise” semantics of conventional n-gram backoffs. Because such features are not required for smoothing, they are not part of the typical feature set used in log linear language modeling, yet our results demonstrate that they should be. The ultimate usefulness of such features likely depends on the amount of training data available, and we have thus applied highly optimized MaxEnt training to very large data sets. In large scale n-gram modeling, it has been shown that the specific details of the smoothing algorithm is typically less important than the scale. So-called “stupid backoff” [8] is an efficient, scalable estimation method that, despite lack of normalization guarantees, is shown to be extremely effective in very large data set scenarios. While this has been taken to demonstrate that the specifics of smoothing is unimportant as the data gets large, those parameters are still important components of the modeling approach, even if their usefulness is robust to variation in parameter value. We demonstrate that features patterned after backoff weights, and several related generalizations of these features, can in fact make a large difference to a MaxEnt language model, even if the amount of training data is very large. In the next section, we present background for language modeling and cover related work. We then present our MaxEnt training approach, and the new features. Finally, we present experimental results on a range of large scale speech tasks. 2. Background and Related Work Let wi be the word at position i in the string, and let w i−1 i−k = wi−k . . . wi−1 be the prefix history of the string prior to wi, and P a probability estimate assigned to seen n-grams by the specific smoothing method. Then the standard backoff language model formulation is as follows: P(wi | w i−1 i−k ) = ( P(wi | w i−1 i−k ) if c(wi i−k ) > 0 α(w i−1 i−k ) P(wi | w i−1 i−k+1) otherwise This recursive smoothing formulation has two kinds of paramCopyright © 2014 ISCA 14-18 September 2014, Singapore INTERSPEECH 2014 2645eters: n-gram probabilities P(wi | w i−1 i−k ) and backoff weights α(w i−1 i−k ), which are parameters associated with the prefix history w i−1 i−k . MaxEnt models are log linear models score that alternatives by taking the exponential of the dot product between a feature vector and a parameter vector and normalizing. Let Φ(wi−k . . . wi) be a d-dimensional feature vector, θ a ddimensional parameter vector, and V a vocabulary. Then P(wi | w i−1 i−k ) = exp(Φ(wi−k . . . wi) · θ) Z(wi−k . . . wi−1, θ) where Z is a partition function (normalization constant): Z(wi−k, . . . , wi−1, θ) = X v∈V exp(Φ(wi−k, . . . , wi−1v) · θ) Training with a likelihood objective function is a convex optimization problem, with well-studied efficient estimation techniques, such as stochastic gradient descent. Regularization techniques are also well-studied, and include L1 and L2 regularization, or their combination, which are modifications of the likelihood objective to either keep parameter values as close to zero as possible (L2) or reduce the number of features with nonzero parameter weights by pushing many parameters to zero (L1). We employ a distributed approximation to L1, see Section 3.1. The most expensive part of this optimization is the calculation of the partition function, since it requires summing over the entire vocabulary, which can be very large. Efficient methods to enable training with very large corpora and large vocabularies have been investigated over the past decades, from methods to exploit structural overlap between features [9, 10] to methods for decomposing the multi-class language modeling problem into many binary language modeling problems (one versus the rest) and sampling less data to effectively learn the models [11]. For this paper, we employed many optimizations to enable training with very large vocabularies (several hundred thousand words) and very large training sets (>100B words). 3. Methods 3.1. Maximum Entropy training Many features have been used in MaxEnt language models, including standard n-grams and trigger words [1], topic-based features [12] and morphological and sub-word based features [13, 14]. Feature engineering is a major consideration in this sort of modeling, and in Section 3.2 we detail our newly designed feature templates. Before we do so, we present the training methods that allow us to scale up to a very large vocabulary and many training instances. In this work, we wish to scale up MaxEnt language model training to learn from the same amount of data used for standard backoff n-gram language models. We achieve this by exploiting recent work on gradient-based distributed optimization; specifically, distributed stochastic gradient descent (SGD) [15, 16, 17, 18, 19]. We differ slightly from previous work in multiple aspects: (1) we apply a final L1 regularization setp at the end of each reducer using statistics collected from the mappers; (2) We estimate the gradient using a mini-batch of 16 samples where the mini-batch is processed in parallel via multi-threading; (3) We do not perform any binarization or subsampling as in [20]; (4) Unlike [21], we do not peform any clustering of our vocabulary. Algorithm 1 presents our variant of the iterative parameter mixtures (IPM) algorithm based on sampling. This presents a merging of concepts from the original IPM algorithm described in [16] and the distributed sample-based algorithm in [18] as well as the lazy L1 SGD computation from [22]. Algorithm 1 Sample-based Iterative Parameter Mixtures Require: n is the number of samples per worker per epoch Require: Break S into K partitions 1: S ← {D 1 , . . . , Dj , . . . , DK} 2: t ← 0 3: Θt ← 0 4: repeat 5: t ← t + 1 6: {θ 1 1, . . . , θK L } ← IPMMAP(D 1 , . . . , DK, Θt−1, n) 7: Θ 0 t ← IPMREDUCE(θ 1 1, . . . , θj l , . . . , θK L ) 8: Θt ← APPLYL1(Θ0 t) 9: until converged 10: function IPMMAP(D, Θ, n) 11: . IPMMAP processes training data in parallel 12: Θ0 ← Θ 13: for i = 1 . . . n do . n examples from D 14: Sample di from D 15: Θ 0 i ← ApplyLazyL1(ActiveF eatures(di, Θi−1)) 16: Θi ← Θ 0 i − α∇Fdi (Θ0 i) 17: α ← U pdateAlpha(α, i) 18: end for 19: return Θn 20: end function 21: function IPMREDUCE(θ 1 l , . . . , θj l , . . . , θK l ) 22: . IPMREDUCE processes model parameters in parallel 23: θl ← 1 K P j θ j l 24: return θl 25: end function While this is a general paradigm for distributed optimization, we show the MapReduce [23] implementation in Algorithm 1. We begin the process by partitioning the training data S into multiple units D j , processing each of these units with the IPMMAP function on separate processing nodes. On each of these nodes, IPMMAP samples a subset of D j which we call di. This can be a single example or a mini-batch of examples. We perform the Lazy L1 regularization update to the model, compute the gradient of the regularized loss associated with the mini-batch (which can be also be done in parallel), update the local copy of the model parameters Θ, and update the learningrate α. Each node samples n examples from its data partition. Finally, IPMREDUCE collects the local model parameters from each IPMMAP and averages them in parallel. Parallelization here can be done over subsets of the parameter indices (each IPMREDUCE node averages a subset of the parameter space). We refer to each full MapReduce pass as an epoch of training. Starting with the second epoch, the IPMMAP nodes are initialized with the previous epoch’s merged, regularized model. In a general shared distributed framework, which is used at Google, some machines may be slower than others (due to hardware or overload), machines may fail, or jobs may be preempted. When using a large number of machines this is inevitable. To avoid starting the training process over in these cases, and make all others wait for for the lagging machines, we enforce a timeout on our trainers. In other words, all mappers have to finish within a certain amount of time. Therefore, the reducer will merge all models when they either finished processing their samples or timed-out. 3.2. Backoff inspired features MaxEnt language models commonly have n-gram features, which we denote here as a function of the string, the position, 2646and the order as follows: NGram(w1 . . . wn, i, k) = We now introduce some features inspired by the backoff parameters α(w i−1 i−k ) presented in Section 2. We begin with the most directly related features, which we term suffix backoff features. SuffixBackoff(w1 . . . wn, i, k) = These fire if and only if the full n-gram NGram(w1 . . . wn, i, k) is not in the feature dictionary (see section 4.1). This is directly analogous to the backoff weights in standard n-gram models, since it is a parameter associated with the prefix history that fires when the particular n-gram is unobserved. Inspired by the form of this feature, we can introduce other general backoff features. First, rather than just replacing the suffix, we can replace the prefix: PrefixBackoff(w1 . . . wn, i, k) = Next, we can replace multiple words in the feature, to generalize across several such contexts: PrefixBackoffj (w1 . . . wn, i, k) = SuffixBackoffj (w1 . . . wn, i, k) = These features indicate that an n-gram of length k + 1 ending with (PrefixBackoff), or beginning with (SuffixBackoff), the particular j words, in the feature, are not in the feature dictionary. Note that, if j=k−1, then PrefixBackoffj is identical to the earlier defined PrefixBackoff feature, and SuffixBackoffj is identical to SuffixBackoff. For example, suppose that we have the following string S=“we will save the quail eggs” and that the 4-gram “will save the quail” does not exist in our feature dictionary. Then we can fire the following features at word wi=5 = “quail”: SuffixBackoff(S, 5, 3) = < will, save, the, BO > PrefixBackoff(S, 5, 3) = < BO, save, the, quail > SuffixBackoff0(S, 5, 3) = < will, BO3 > SuffixBackoff1(S, 5, 3) = < will, save, BO3 > PrefixBackoff0(S, 5, 3) = < BO3, quail > PrefixBackoff1(S, 5, 3) = < BO3, the, quail > As with n-gram feature templates, we include all such features up to some specified length, e.g., if we have a trigram model, that includes n-grams up to length 3, including unigrams, bigrams and trigrams. Similarly, for our prefix and suffix backoff features, we will have a maximum length and include in our possible feature set all such features of that length or shorter. 4. Experimental results We performed two experiments to evaluate the utility of these new backoff-inspired features in maximum entropy language models trained on very large corpora. First, we examine perplexity improvements when such features are included in the model alongside n-gram features. Next, we look at Word Error Rate (WER) performance when reranking the output of a baseline recognizer, again using different backoff feature templates. In all cases, we fixed the vocabulary and feature budget of the model so that improvements are not simply due to having more parameters in the model. We set the vocabulary of our model to 200 thousand words, by selecting all words from the 2M words in the baseline recognizer vocabulary that had been emitted by the recognizer in the last 6 months of log files. All other words are mapped to “”. We use the same vocabulary in all of our experiments. For our experiments, we focus on the voice search task. Our data sets are assembled and pooled from anonymized supervised and unsupervised spoken queries (such as, search queries, questions, and voice actions) and typed queries to google.com, YouTube, and Google Maps, from desktop and mobile devices. Our overall training set is about 305 billion words (including end of sentence symbols). We divide this set into K subsets. We assign subset D k to trainer k (where, 1 ≤ k ≤ K). Then, we run our distributed training (Algorithm 1) using K machines. Since the amount of training data is very large, trainer k randomly samples data points from its subset D k . Each epoch utilizes a different seed for sampling, which equals to the epoch number. As mentioned above, the trainer may terminate due to completing its subsample or due to a timeout. We fix the timeout threshold for each epoch across all our experiments. In our experiments, the timout is 6 hours. 4.1. Feature Dictionary A feature dictionary maps each feature key (e.g., trigram: “save the quail”) to an index in the paramater vector Θ. As described in Algorithm 2, we build this dictionary by iterating over all strings in our training data and make use of the NGgram function (defined above) to build the ngram feature keys (for every k = 0 . . . 4). Also, for each string, we build the required backoff feature keys (depending on the experiment). Upon collecting all of these keys, we compute the total observed count for each feature key and then retain only the most frequent ones. We assign a different count cutoff for each feature template. We determine these counts based on a classical cross-entropy pruned n-gram model trained on the same data Afterwards, our dictionary maps each key to a unique consecutive index = 0 . . . Dim. In all our experiments, we allocated the same budget of 228 million paramaters. It is important to note that the number of features dedicated for backoff features may significantly vary across backoff-feature types. Note that, while the backoff inspired features detailed in section 3.2 are defined to fire only when the corresponding ngram does not appear in the feature dictionary, they themselves must appear in the feature dictionary in order to fire. If one of these features does not appear frequently enough, it will not appear in the feature dictionary and neither the original n-gram nor the backoff feature will fire. 4.2. Feature Sets In these experiments, all MaxEnt language models include ngrams up to 5-grams. Our backoff inspired features are also Algorithm 2 Dictionary Construction for all w1, w2, . . . , wn ∈ Data do for i ← 1 . . . n do . We use 5-gram features. for k ← 0 . . . 4 do key ← NGram(w1, . . . , wn, i, k) dictk ← dictk ∪ {key} countk[key] ← countk[key] + 1 . Call the backoff functions above. bo key ← SuffixBackoff(w1, . . . , wn, i, k) dictk ← dictk ∪ {bo key} countk[bo key] ← countk[bo key] + 1 end for end for end for . Retain the most frequent features in dictk and map each feature to a unique index, for each k = 0, . . . , 4. 2647Perplexity Figure 1: Perplexity versus number of epochs of training for various feature sets under the same feature budget constraint. Feature sets include: (1) n-gram features (NG); (2) PrefixBackoff (P); (3) SuffixBackoff (S); (4) PrefixBackoff-k (Pk); and (5) SuffixBackoff-k (Sk). Perplexity Figure 1: Perplexity versus number of epochs of training for various feature sets under the same feature budget constraint. Feature sets include: (1) n-gram features (NG); (2) PrefixBackoff (P); (3) SuffixBackoff (S); (4) PrefixBackoff-k (Pk); and (5) SuffixBackoff-k (Sk). Epochs Figure 1: Perplexity versus number of epochs of training for various feature sets under the same feature budget constraint. Feature sets include: (1) n-gram features (NG); (2) PrefixBackoff (P); (3) SuffixBackoff (S); (4) PrefixBackoff-k (Pk); and (5) SuffixBackoff-k (Sk). based on substrings up to length 5, i.e., up to 4 words, either preceded (prefix) or followed (suffix) by the “BO” token in the case of PrefixBackoff and SuffixBackoff features; or “BOj ” up to j = 4 preceding (prefix) or following (suffix) the word. We examine several feature set pools: (1) n-gram features alone (NG); (2) n-gram features plus PrefixBackoff (NG+P) or SuffixBackoff (NG+S); (3) n-gram features plus PrefixBackoffj (NG+Pk) or SuffixBackoffj (NG+Sk); and (4) n-gram features plus PrefixBackoffj and SuffixBackoff (NG+Pk+S) or SuffixBackoffj (NG+Pk+Sk). In each case, feature dictionaries are built, so they may contain more or fewer n-grams as required to include the backoff features in the dictionary. For the current experiments, trials with PrefixBackoffj or SuffixBackoffj only include features with j = 0, i.e., a single word alongside the “BOk” token. Note that the number of such features is relatively constrained compared to the n-gram features and other backoff features – at most k|V | possible features for a vocabulary V . 4.3. Perplexity Perplexity was measured on a held-aside random sample of 5 million words from our pool of data. Figure 1 plots perplexity versus number of epochs (up to 11) for different possible feature sets. Recall that data is randomly sampled from the overall training set, so that this plot also shows behavior as the amount of training data is increased. Table 1 presents perplexities after the epoch 11, along with the number of samples used during the training and number of active features with non-zero parameters. The number of samples varies because some trainers may run faster than others depending on the number and type of features used; since we enforce a timeout, an epoch may vary in the number of samples processed in time. Nonetheless, Figure 1 shows that most models have approached or reached convergence before completing all the 11 epochs. A notable exception is the n-gram only model, which seems to require a few more epochs before reaching convergence – though clearly performance will not reach that of the other trials. This points to another benefit of the backoff features – they also seem to speed convergence for these models. Interestingly, they also seem to considerably reduce the number of active features. The results show a large perplexity improvement due to the use of backoff features, and in particular the generalized Prefix/SuffixBackoff-k features. One potential reason for the improved performance with these generalized backoff features is the relatively small number of them and they fire more often, as discussed in the previous section. Feature Set Description Pplx Samp ActFt NG N-grams only 167.0 137B 197.8M NG+P N-grams + PrefixBackoff 122.6 112B 189.5M NG+S N-grams + SuffixBackoff 109.8 125B 188.9M NG+Pk N-grams + PrefixBackoffk 88.0 100B 170.1M NG+Pk+S N-grams + PrefixBackoffk + SuffixBackoff 85.5 113B 172.6M NG+Sk N-grams + SuffixBackoffk 82.7 126B 160.2M NG+Pk+Sk N-grams + PrefixBackoffk + SuffixBackoffk 80.2 96B 162.4M Table 1: Perplexity (Pplx) after 11 epochs of training, with a fixed feature budget. Also giving number of samples (Samp) used for training each model, in billions; and active features (ActFt), in millions. 4.4. Speech Recognition Rescoring Results We evaluated our models by rescoring n-best outputs from a baseline recognizer. In our experiments, we set n to 500. The acoustic model of the baseline system is a deep-neural networkbased model with 85M parameters, consisting of eight hidden layers with 2560 Rectified Linear hidden units each and softmax outputs for the 14,000 context-dependent state posteriors. The network processes a context window of 26 (20 past and 5 future) frames of speech, each represented with 40 dimensional log mel filterbank energies taken from 25ms windows every 10ms. The system is trained to a Cross-Entropy criterion on a US English data set of 3M anonymized utterances (1,700 hours or about 600 million frames) collected from live voice search dictation trafic. The utterances are hand-transcribed and force-aligned with a previously trained DNN. See [24] for Google’s VoiceSearch system design. The baseline LM is a Katz [3] smoothed 5-gram model pruned to 23M n-grams, trained on the same data using Bayesian interpolation to balance multiple sources. It has vocabulary size of 2M and an OOV rate of 0.57% [25]. The score assigned to each hypothesis by our MaxEnt LM is linearly interpolated with the baseline recognizer’s LM score (with an untuned mixture factor of 0.33). Table 2 presents WER results for multiple anonymized voice-search data sets collected from anonymized and manually transcribed live traffic from mobile devices. These data sets contain regular spoken search queries, questions, and YouTube queries. We achieve modest gains over the baseline system and over rescoring with just ngram features in all of the test sets, achieving, in aggregate, a half a point of improvement over the baseline system. 5. Conclusion In this paper we introduced and explored features for maximum entropy language models inspired by the backoff mechanism of standardly smoothed language models. We found large perplexity improvements over using n-gram features alone, for the same feature budget; and a 0.5% absolute (3.4% relative) WER improvement over the baseline system for our best performing model. Future work will include exploring further variants of our general backoff feature templates and combining with other features beyond n-grams. Table 2: WER results on 7 sub-corpora and overall, for the baseline recognizer (no reranking) versus reranking models trained with different feature sets. Reranking feature set Test Utts / Wds NG+ NG+ NG+ Set (×1000) None NG Pk Sk Pk+Sk 1 22.5 / 98.0 12.7 12.6 12.4 12.4 12.4 2 17.8 / 74.0 12.7 12.5 12.4 12.4 12.3 3 16.2 / 61.1 17.3 17.1 16.7 16.8 16.7 4 18.0 / 64.0 12.8 12.7 12.6 12.6 12.5 5 7.4 / 50.7 16.8 16.6 16.2 16.2 16.2 6 7.3 / 31.9 15.1 15.0 14.8 14.8 14.9 7 19.6 / 69.1 16.5 16.2 15.9 15.9 15.9 all 108.9 / 448.8 14.6 14.4 14.2 14.2 14.1 26486. References [1] R. Lau, R. Rosenfeld, and S. Roukos, “Trigger-based language models: a maximum entropy approach,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1993, pp. 45– 48. [2] R. 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Riley, “Bayesian language model interpolation for mobile speech input.” in INTERSPEECH, 2011, pp. 1429–1432. 2649 Unsupervised Testing Strategies for ASR Brian Strope, Doug Beeferman, Alexander Gruenstein, Xin Lei Google, Inc. bps, dougb, alexgru, xinlei @google.com Abstract This paper describes unsupervised strategies for estimating relative accuracy differences between acoustic models or language models used for automatic speech recognition. To test acoustic models, the approach extends ideas used for unsupervised discriminative training to include a more explicit validation on held out data. To test language models, we use a dual interpretation of the same process, this time allowing us to measure differences by exploiting expected ‘truth gradients’ between strong and weak acoustic models. The paper shows correlations between supervised and unsupervised measures across a range of acoustic model and language model variations. We also use unsupervised tests to assess the non-stationary nature of mobile speech input. Index Terms: speech recognition, unsupervised testing, nonstationary distributions 1. Introduction Current commercial speech recognition systems can use years of unsupervised data to train relatively large, discriminatively optimized, acoustic models (AM). Similarly, web-scale text corpora for estimating language models (LM) are often available online, and unsupervised recognition results themselves can provide an additional source of LM training data. Since there is no human transcription in any of these steps, the remaining use for manual human transcription is for generating test sets, as a final sanity check for validating system parameters and models. In this paper, we augment that strategy with unsupervised evaluations and begin the discussion of whether eventually we might be able to get rid of the need for any explicit human transcription. The motivation for human transcription for testing is obvious. Despite steady advances and relative commercial successes, it is generally accepted that humans are much more accurate transcribers than automatic speech recognition systems [1]. While there are a few notable exceptions where machines were more accurate than humans [2], human transcription accuracy is so much better, we use it unquiestioningly as our best approximate for absolute truth. But there are equally obvious disadvantages to relying on human transcription. While it may feel premature, accepting human performance as absolute truth imposes an upper bound on accuracy. The absolute truth is not absolute, and so we’ll eventually have to figure out how to beat it. In fact with our current processes and tasks, below, we show that human transcribers can be only comparable in accuracy to current ASR systems. Absolute truth is already a problem. In response, we are improving transcription processes, but also considering unsupervised ways to augment traditional testing. Another obvious disadvantage of human transcription is that the tests themselves have to be limited in size and type. Even in a commercially successful research lab, getting extensive tests across every combination of speaker and channel type, recognition context, language, and time period is prohibitive. But a detailed characterization of those types of variations could help prioritize efforts. Similarly when tests are unsupervised, it is easier to update development and evaluation sets to avoid problems related to stale, over-fit tests. This is mostly an empirical paper. The next section describes some of the experiments we ran trying to assess our existing human transcription accuracy. Then we describe the generalizations of unsupervised discriminative training that enable a new evaluation strategy. Next the paper includes evaluations that show correlations between supervised and unsupervised tests, and concludes with unsupervised tests that start to characterize the non-stationary distribution of spoken data coming through Google mobile applications. 2. Problems with human transcriptions Recent efforts have begun to consider human transcription accuracy in the context of increased efficiency. These studies have generally shown that depending on the amount of effort, and the task, individual word error rates can vary from 2-15% [3, 4]. Ef- ficiency pressures on human transcription can lead to transcription noise and bias. 2.1. Early experiments Over the last few years we have seen several simple experiments not work: we have added matched data to our language models and seen error rates get worse; we have added unsupervised acoustic modeling data matched to a new fielded acoustic condition, and seen the error rates on new matched tests go up, but surprisingly, error rates on an old test, with slightly mismatched conditions, go down. For each of these, after tediously examining errors, we found the problem was that we typically “seed” our transcription process with the recognition result from the field. Mostly as a matter of expedience; it is easier for the transcriber to hit return than to type “home depot in palo alto california” yet again, and it can improve reliability since retyping can be error prone. But the power of the suggested transcription is also enough to bias the transcribers into rubber-stamping some of the fielded recognition results. When the transcriber rubber-stamps an error we potentially get penalized twice. The baseline gets credit where it should not, and a new system that corrects that error is falsely penalized for adding an error. The surprising improvement noted on the older, slightly mis-matched test happened because the transcriptions for the older test were seeded with transcriptions from an older system, decorrelating some of the transcription bias with the current baseline. In this case, transcription bias toward the baseline model was a bigger effect than the change in acoustics. Copyright © 2011 ISCA 28-31 August 2011, Florence, Italy INTERSPEECH 2011 16852.2. Multiple attempts To measure the human transcription accuracy more directly we started sending the same data for multiple attempts at human transcription, and we intentionally reduced the quality of our starting seeds to move any bias away from our best systems. For one test we sent 200K Voice Search utterances to be transcribed twice. Ignoring trivial differences like spaces, apostrophes, function words, and others, half of the transcripts agreed, which implies a sentence transcription accuracy of 71%, assuming independence of the attempts. Similarly when we sent the remaining 100K utterances, where transcriptions did not agree, back for two more attempts, we were still left with about 10% of the original set with 4 distinct human transcriptions. Again assuming independence, 10% disagreement in 4 attempts is consistent with 68% accuracy for each attempt. But we believe our system has a sentence accuracy higher than 70%. Looking through the errors many of the problems are related to cultural references, popular names, and businesses that are not obvious to everyone. The cultural and geographic requirements of the voice search task may be unusually difficult. It combines short utterances and wide open semantic contexts to generate surprisingly unfamiliar sounding speech. Finding ways to bring the correct cultural context to the transcriber is another obvious path to pursue. 3. Generalizing unsupervised discriminative training While some published results considered unsupervised maximum likelihood estimation of model parameters [5], many systems use unsupervised discriminative optimization, directly using recognizer output as input [6]. Cynically we might ask what we are learning if we are using the recognition result as truth for discriminatively optimizing its parameters. It is hard to imagine that we can fix the errors it makes, when we use the model to generate truth. But when we look into the details of commonly used discriminative training techniques based on maximum mutual information, we see that the LM used to generate competing hypotheses is not the same LM used to generate truth. To improve the generalization of discriminative training, we use a unigram to describe the space of potential errors [7], but a trigram or higher to give us transcription truth with unsupervised training. One interpretation of unsupervised discriminative training for acoustic models is that we are using the difference between a weak unigram and a relatively stronger trigram to give us a known improvement in relative truth. We do not know that the strong-LM (trigram) result is absolutely correct, we only know that it is better than the result with the weak LM (unigram). When there is a difference, if we can move toward the results of the strong-LM system by changing acoustic model parameters, then we are building a more accurate AM, that also helps with the final system using a stronger LM. With this interpretation, the AM learns from the ‘truth gradient’ between the strong and weak LMs. 3.1. Unsupervised AM testing Extending unsupervised discriminative AM training to unsupervised AM testing involves retesting the criterion used during training in a new test context. More prescriptively, we sample a new set of live data from production logs, and take the recognition result from the fielded system using a strong AM and LM as assumed truth. Then we re-recognize the same data using multiple strong acoustic models and a weak LM. If one of the systems using a weak LM can better approximate the system using a strong LM, then at a minimum, we can say that it is doing a better job of generalizing our training criteria to new data. More directly, we have evidence that one of the strong acoustic models could be more accurate than the rest. For scoring we are assuming truth from the fielded system, not a human transcriber. Therefore, when reporting unsupervised testing results, we count traditional word error rates, but because there is no human transcription, we report it as a word difference rate (WDR), to highlight that, for example, in the case of unsupervised AM tests, it is the word differences between the systems with the strong and weak LM. 3.2. Unsupervised LM testing To use the same strategy for LM testing we reverse the roles of the AM and the LM. For better generalization of discriminative AM testing, we used a weak LM to generate more competing alternates. That establishes a truth gradient that generally changes around 1/3 of the words. The dual for LM testing is to use a weak AM instead. To get a truth gradient of a similar magnitude with our systems, we backed off to a context-dependent acoustic model that uses around 1/10th the number of parameters of our strong models, and only uses maximum likelihood training. Then as above, we test with multiple strong LMs and assume that the LM that can move the results of the system using the weak AM closest to the results of the production system (with the strong AM), is the most accurate LM. With unsupervised LM testing we again report WDR and not WER, where the magnitude of the difference is now from the difference between the strong AM and the weak AM. 3.3. Relative measures In this paper we are ignoring the harder problem of measuring absolute accuracy. Instead we focus on relative differences between different acoustic or language models. Others have predicted absolute error measures using statistics from the training set as represented in the final acoustic models [8], without looking at testing data. But here we are interested in estimating relative performance across production data that was unseen during training. Our goal is to assess whether new models or new approaches are helping on new data, and whether the data might be changing from the distributions used during training. 4. Correlating supervised and unsupervised measures First we show that the performance on unsupervised offline tests for the AM and for the LM correlate with more traditional supervised tests. Our production data started with primarily Voice Search queries intended for google.com, but over time has included increasing amounts of general Voice Input traffic which includes a large fraction of short person-to-person messages. To start the analyses, we consider these data streams separately. For Voice Search, our traditional supervised test is built from the 200K utterance set that we sent for multiple transcriptions. For this test we exclude the 10% of the utterances where we got 4 distinct human transcriptions and sample a test set randomly from the remaining 90%. Similarly for the supervised 1686Voice Input test, we sent utterances twice and selected from the utterances with at least 80% agreement between human transcriptions. On the utterances where not all the words agreed, we randomly chose one of the human transcriptions as truth. This led to a test that excluded about 28% of the utterances. Both of these supervised tests are biased in that they only include the utterances that we could reliably transcribe. The Voice Search test has 27K utterances and 87K words. The Voice Input test has 49K utterances and 320K words. For the first unsupervised tests here, we sampled production logs for a single day of traffic. We found the median recognizer confidence for each task and then randomly selected a few hundred thousand utterances that were above median confidence for each task. For all unsupervised experiments we used the recognition results from the field as truth. Our recognition configuration for both systems is fairly standard and described in the literature. Specifically we use a PLP front-end [11] together with LDA and STC [12], and optimize our acoustic models using BMMI [13] on mostly unsupervised data mixed from both tasks. Our language models are n-grams, with Katz interpolation and entropy pruning, and the fielded Voice Input system also includes dynamic interpolation [14]. The Voice Search system used trigrams and the Voice Input system included 4-grams. 4.1. AM experiments The AM experiments use a weak LM (in this case a unigram) for each task estimated from the few hundred thousand high confidence utterances sampled for that day’s test. All the utterances in the test were also used to train the LM, so there is no OOV. This step is consistent with the matched unigram we train for discriminative acoustic model training. For Voice Search, the resulting unigram had 17K words, and for Voice Input there were 18K unique words. The acoustic models we tested here were trained using 11M (mostly unsupervised) utterances from a mix of both tasks. The parameter we vary for these experiments is the size of the acoustic models. We use the same decision tree and context state definitions for all models, but we vary the number of Gaussians assigned to each state. Each model is trained with the same number of iterations through all the data. The final model sizes range from 100K to 1M Gaussians. Decoder parameters are set in production mode, which generally means we lose around 0.5% absolute from the best possible accuracy to have faster than real-time search. # Gauss Sup VS Unsup VS Sup VI Unsup VI 100K 16.0 36.0 14.5 24.8 200K 15.3 34.4 13.6 22.8 340K 14.6 33.9 13.4 22.7 500K 14.3 33.3 13.2 22.3 1M 13.9 33.0 12.9 21.8 Table 1: WER in % on supervised (Sup) and WDR in % on unsupervised (Unsup) AM tests for Voice Search (VS) and Voice Input (VI). 4.2. LM experiments For the LM experiments we vary the number of n-grams used for the Voice Input task from around 2M to 30M by varying our final entropy pruning threshold. Unlike the production system used to generate truth for the unsupervised tests, for these tests the LM is a static n-gram. We show results with two different weak acoustic models (A/B). Condition A is a context-dependent model estimated using maximum likelihood criteria with 2 Gaussians per state for a total of 16K Gaussians. Condition B uses a similar model with a variable number of Gaussians across model states, and a total of 40K Gaussians. On supervised tests, these weak acoustic models have around two to three times the error rates of final strong production models. n-grams Sup PPL Sup WER Unsup A/B WDR 1.9M 109 15.2 38.1/25.9 3.8M 98 14.4 36.8/24.5 7.6M 92 14.1 36.0/23.8 15M 87 13.9 35.5/23.2 30M 85 13.7 35.1/22.8 Table 2: Comparing supervised (Sup) and unsupervised (Unsup) LM tests for Voice Input. WER/WDR are in %, PPL is perplexity. Unsup A and B are for different sized AMs. The relative improvement in both AM and LM experiments is consistently around 10% for a 10x increase in model size. Correlations between supervised and unsupervised tests range between 0.98 and 0.99. 5. Additional experiments Varying model size is a controlled way to generate accuracy differences. Here we include additional unsupervised measurements that show expected differences in the context of other AM and LM modeling efforts. 5.1. CMLLR To evaluate an implementation of constrained maximum likelihood linear regression [9] for adaptation, we started by testing with read speech corpora from several data collections [10] used to initialize acoustic models in a new context. With a large and regular amounts of acoustic data per speaker, we see the typical improvements of 6-10% relative, over a matched discriminative baseline. To estimate the accuracy impact of CMLLR on the production system, (where the actual distributions of amount of data per user is not imposed by the strict specifications of a data collection) we used unsupervised testing. Here we sampled all personalized users over a 30 day period, and measured the change in WDR with a weak LM and either the production AM or the production AM with CMLLR. Further we break the differences in WDR down by the amount of data available for each speaker. # Utts No Adapt Adapt 1-20 25.7 25.4 20-50 26.6 25.6 50-100 25.8 24.6 100-200 23.5 22.5 Table 3: WDR in % on adaptation tests. Input is binned by the number of utterances for a given user. From the table, it is clear that we are seeing a similar relative difference as we saw with more traditional read speech tests, and we are further able to characterize the expected satu- 1687ration of the relatively small number of parameters in CMLLR after around 20 voice input utterances. 5.2. LM update At one point we updated our language model to include a rescoring pass more explicitly matched to recent Voice Search queries. By testing this update with recent unsupervised tests we are able to show the expected win on new voice search type utterances. # Model Config Sup VS Unsup VS Original 14.6 30.0 Updated 14.6 28.6 Table 4: WER in % on supervised (Sup) and WDR in % unsupervised (Unsup) LM tests for Voice Search. One interpretation of these results is that we are updating the LM to better represent the recent query data which itself is better matched to the recent unsupervised test. It also suggests that the distribution of our data might be moving. 5.3. Estimating non-stationary distributions Finally we ran two sweeps of AM tests to estimate how stationary the acoustics for our system have been over the last 14 months. The first system is trained using the Voice Search supervised data available at the beginning of the 14 months, and the second uses only unsupervised data sampled from the last 3 months. Therefore, one model represents our initial estimate of the distribution, and the other approximates a most recent distribution. Both systems use around 350K gaussians. To evaluate the AM performance, we use a weak LM estimated from a year’s worth of production data. Figure 1: Change in WDR over time with two different AMs. Both lines show that the distribution of the data has shifted away from the original supervised data, and toward the recent unsupervised data. Additional unsupervised tests will illuminate the causes of this change in more detail. We currently suspect an increase in the fraction of voice input recognition, but it is already obvious that the distribution of the acoustics for this data is changing. The plot also suggests that with a single AM the change of WDR across conditions may also be informative. Note that since we are generalizing from the same criteria we used for AM training, and we are getting rid of some of the necessity of human transcription, we are concerned about converging away from reality. The ground is a little firmer for the LM side, since our current LM processes are in fact not yet learning from AM truth gradients the same way our unsupervised AM training learns from LM truth gradients. From the AM side, our current unsupervised tests are simply checking whether the training optimizations extend to unseen data. Pragmatically, because it is unsupervised we also have the opportunity to test that generalization with a range of weak LMs and with a range of input data, and thereby to increase our con- fidence in the generalization. Moreover, reducing the accuracy improvement provided by a strong LM seems like a safe requirement to impose on AM training. But from an experiment perspective, we have to remember what gradient we are exploiting and not cheat. In other words, augmenting the AM with features directly related to the strong LM would not lead to improvements. We also monitor coarse signals related to application use (counts of user actions in response to recognition results) to give us additional complimentary evidence of successful generalization. 6. Conclusions This paper extends unsupervised discriminative training to an unsupervised testing strategy suitable for evaluating AM and LM changes. We show strong correlations with traditional testing strategies when we change AM or LM model size. We also show expected gains on unsupervised measures with other types of AM and LM changes, and use the unsupervised measures to begin to characterize the stationarity of the input data to Google mobile. Together with unsupervised training, unsupervised testing enables development paths that no longer impose human performance as the upper bound for accuracy. 7. References [1] R. Lippmann, “Speech recognition by machines and humans,” Speech Communication, July 1997. [2] T. Kristjansson, J. Hershey, P. Olsen, S. Rennie, R. Gopinath, “Super-Human Multi-Talker Speech Recognition: The IBM 2006 Speech Separation Challenge System,” Proc. ICSLP, 2006. [3] S. Novotney, C. Callison-Burch, “Cheap, Fast and Good Enough: Automatic Speech Recognition with Non-Expert Transcription,” Proc. NAACL, 2010. [4] A. Gruenstein, I. McGraw, A. Sutherland, “A Self-Transcribing Speech Corpus: Collecting Continuous Speech with an Online Educational Game,” Proc. SLaTE, 2009. [5] J. Ma, R. Schwartz, “Unsupervised versus supervised training of acoustic models,” Proc. ICSLP, 2008. [6] L. Wang, M. Gales, P. Woodland, “Unsupervised Training for Mandarin Broadcast News Conversation Transcription,” Proc ICASSP, 2007. [7] P.C. Woodland, D. Povey, “Large scale discriminative training of hidden Markov models for speech recognition,” Comp. Speech & Lang., Jan. 2002. [8] Y. Deng, M. Mahajan, A. Acero, “Estimating Speech Recognition Error Rate without Acoustic Test Data,” Proc. Eurospeech, 2003. [9] M. J. F. Gales, “Maximum likelihood linear transformations for HMM-based speech recognition,” Comp. Speech & Lang., Vol 12.2 1998. [10] T. Hughes, K. Nakajima, L. Ha, A. Vasu, P. Moreno, M. LeBeau, “Building transcribed speech corpora quickly and cheaply for many languages,” Proc ICSLP, 2010. [11] H. Hermansky, “Perceptual linear predictive (PLP) analysis of speech,” JASA, v87.4, 1990. [12] M. Gales, “Semi-Tied Covariance Matrices for Hidden Markov Models,” Proc. IEEE Trans. SAP, May 2000. [13] D. Povey, D. Kanevsky, B. Kingsbury, B. Ramabhadran, G. Saon, K. Visweswariah, “Boosted MMI for model and feature-space discriminative training,” Proc. ICASSP, 2008. [14] B. Ballinger, C. Allauzen, A. Gruenstein, J. Schalkwyk, “OnDemand Language Model Interpolation for Mobile Speech Input,” Proc. ICSLP, 2010. 1688 Parallel Algorithms for Unsupervised Tagging Sujith Ravi Google Mountain View, CA 94043 sravi@google.com Sergei Vassilivitskii Google Mountain View, CA 94043 sergeiv@google.com Vibhor Rastogi∗ Twitter San Francisco, CA vibhor.rastogi@gmail.com Abstract We propose a new method for unsupervised tagging that finds minimal models which are then further improved by Expectation Maximization training. In contrast to previous approaches that rely on manually specified and multi-step heuristics for model minimization, our approach is a simple greedy approximation algorithm DMLC (DISTRIBUTEDMINIMUM-LABEL-COVER) that solves this objective in a single step. We extend the method and show how to ef- ficiently parallelize the algorithm on modern parallel computing platforms while preserving approximation guarantees. The new method easily scales to large data and grammar sizes, overcoming the memory bottleneck in previous approaches. We demonstrate the power of the new algorithm by evaluating on various sequence labeling tasks: Part-of-Speech tagging for multiple languages (including lowresource languages), with complete and incomplete dictionaries, and supertagging, a complex sequence labeling task, where the grammar size alone can grow to millions of entries. Our results show that for all of these settings, our method achieves state-of-the-art scalable performance that yields high quality tagging outputs. 1 Introduction Supervised sequence labeling with large labeled training datasets is considered a solved problem. For ∗The research described herein was conducted while the author was working at Google. instance, state of the art systems obtain tagging accuracies over 97% for part-of-speech (POS) tagging on the English Penn Treebank. However, learning accurate taggers without labeled data remains a challenge. The accuracies quickly drop when faced with data from a different domain, language, or when there is very little labeled information available for training (Banko and Moore, 2004). Recently, there has been an increasing amount of research tackling this problem using unsupervised methods. A popular approach is to learn from POS-tag dictionaries (Merialdo, 1994), where we are given a raw word sequence and a dictionary of legal tags for each word type. Learning from POStag dictionaries is still challenging. Complete wordtag dictionaries may not always be available for use and in every setting. When they are available, the dictionaries are often noisy, resulting in high tagging ambiguity. Furthermore, when applying taggers in new domains or different datasets, we may encounter new words that are missing from the dictionary. There have been some efforts to learn POS taggers from incomplete dictionaries by extending the dictionary to include these words using some heuristics (Toutanova and Johnson, 2008) or using other methods such as type-supervision (Garrette and Baldridge, 2012). In this work, we tackle the problem of unsupervised sequence labeling using tag dictionaries. The first reported work on this problem was on POS tagging from Merialdo (1994). The approach involved training a standard Hidden Markov Model (HMM) using the Expectation Maximization (EM) algorithm (Dempster et al., 1977), though EM does notperform well on this task (Johnson, 2007). More recent methods have yielded better performance than EM (see (Ravi and Knight, 2009) for an overview). One interesting line of research introduced by Ravi and Knight (2009) explores the idea of performing model minimization followed by EM training to learn taggers. Their idea is closely related to the classic Minimum Description Length principle for model selection (Barron et al., 1998). They (1) formulate an objective function to find the smallest model that explains the text (model minimization step), and then, (2) fit the minimized model to the data (EM step). For POS tagging, this method (Ravi and Knight, 2009) yields the best performance to date; 91.6% tagging accuracy on a standard test dataset from the English Penn Treebank. The original work from (Ravi and Knight, 2009) uses an integer linear programming (ILP) formulation to find minimal models, an approach which does not scale to large datasets. Ravi et al. (2010b) introduced a two-step greedy approximation to the original objective function (called the MIN-GREEDY algorithm) that runs much faster while maintaining the high tagging performance. Garrette and Baldridge (2012) showed how to use several heuristics to further improve this algorithm (for instance, better choice of tag bigrams when breaking ties) and stack other techniques on top, such as careful initialization of HMM emission models which results in further performance gains. Their method also works under incomplete dictionary scenarios and can be applied to certain low-resource scenarios (Garrette and Baldridge, 2013) by combining model minimization with supervised training. In this work, we propose a new scalable algorithm for performing model minimization for this task. By making an assumption on the structure of the solution, we prove that a variant of the greedy set cover algorithm always finds an approximately optimal label set. This is in contrast to previous methods that employ heuristic approaches with no guarantee on the quality of the solution. In addition, we do not have to rely on ad hoc tie-breaking procedures or careful initializations for unknown words. Finally, not only is the proposed method approximately optimal, it is also easy to distribute, allowing it to easily scale to very large datasets. We show empirically that our method, combined with an EM training step outperforms existing state of the art systems. 1.1 Our Contributions • We present a new method, DISTRIBUTED MINIMUM LABEL COVER, DMLC, for model minimization that uses a fast, greedy algorithm with formal approximation guarantees to the quality of the solution. • We show how to efficiently parallelize the algorithm while preserving approximation guarantees. In contrast, existing minimization approaches cannot match the new distributed algorithm when scaling from thousands to millions or even billions of tokens. • We show that our method easily scales to both large data and grammar sizes, and does not require the corpus or label set to fit into memory. This allows us to tackle complex tagging tasks, where the tagset consists of several thousand labels, which results in more than one million entires in the grammar. • We demonstrate the power of the new method by evaluating under several different scenarios—POS tagging for multiple languages (including low-resource languages), with complete and incomplete dictionaries, as well as a complex sequence labeling task of supertagging. Our results show that for all these settings, our method achieves state-of-the-art performance yielding high quality taggings. 2 Related Work Recently, there has been an increasing amount of research tackling this problem from multiple directions. Some efforts have focused on inducing POS tag clusters without any tags (Christodoulopoulos et al., 2010; Reichart et al., 2010; Moon et al., 2010), but evaluating such systems proves dif- ficult since it is not straightforward to map the cluster labels onto gold standard tags. A more popular approach is to learn from POS-tag dictionaries (Merialdo, 1994; Ravi and Knight, 2009), incomplete dictionaries (Hasan and Ng, 2009; Garrette and Baldridge, 2012) and human-constructed dictionaries (Goldberg et al., 2008).Another direction that has been explored in the past includes bootstrapping taggers for a new language based on information acquired from other languages (Das and Petrov, 2011) or limited annotation resources (Garrette and Baldridge, 2013). Additional work focused on building supervised taggers for noisy domains such as Twitter (Gimpel et al., 2011). While most of the relevant work in this area centers on POS tagging, there has been some work done for building taggers for more complex sequence labeling tasks such as supertagging (Ravi et al., 2010a). Other related work include alternative methods for learning sparse models via priors in Bayesian inference (Goldwater and Griffiths, 2007) and posterior regularization (Ganchev et al., 2010). But these methods only encourage sparsity and do not explicitly seek to minimize the model size, which is the objective function used in this work. Moreover, taggers learned using model minimization have been shown to produce state-of-the-art results for the problems discussed here. 3 Model Following Ravi and Knight (2009), we formulate the problem as that of label selection on the sentence graph. Formally, we are given a set of sequences, S = {S1, S2, . . . , Sn} where each Si is a sequence of words, Si = wi1, wi2, . . . , wi,|Si| . With each word wij we associate a set of possible tags Tij . We will denote by m the total number of (possibly duplicate) words (tokens) in the corpus. Additionally, we define two special words w0 and w∞ with special tags start and end, and consider the modified sequences S 0 i = w0, Si , w∞. To simplify notation, we will refer to w∞ = w|Si|+1. The sequence label problem asks us to select a valid tag tij ∈ Tij for each word wij in the input to minimize a specific objective function. We will refer to a tag pair (ti,j−1, tij ) as a label. Our aim is to minimize the number of distinct labels used to cover the full input. Formally, given a sequence S 0 i and a tag tij for each word wij in S 0 i , let the induced set of labels for sequence S 0 i be Li = |S 0 i [ | j=1 {(ti,j−1, tij )}. The total number of distinct labels used over all sequences is then φ = ∪i Li | = [ i |S [i|+1 j=1 {(ti,j−1, tij )}|. Note that the order of the tokens in the label makes a difference as {(NN, VP)} and {(VP, NN)} are two distinct labels. Now we can define the problem formally, following (Ravi and Knight, 2009). Problem 1 (Minimum Label Cover). Given a set S of sequences of words, where each word wij has a set of valid tags Tij , the problem is to find a valid tag assignment tij ∈ Tij for each word that minimizes the number of distinct labels or tag pairs over all sequences, φ = S i S|Si|+1 j=1 {(ti,j−1, tij )}| . The problem is closely related to the classical Set Cover problem and is also NP-complete. To reduce Set Cover to the label selection problem, map each element i of the Set Cover instance to a single word sentence Si = wi1, and let the valid tags Ti1 contain the names of the sets that contain element i. Consider a solution to the label selection problem; every sentence Si is covered by two labels (w0, ki) and (ki , w∞), for some ki ∈ Ti1, which corresponds to an element i being covered by set ki in the Set Cover instance. Thus any valid solution to the label selection problem leads to a feasible solution to the Set Cover problem ({k1, k2, . . .}) of exactly half the size. Finally, we will use {{. . .}} notation to denote a multiset of elements, i.e. a set where an element may appear multiple times. 4 Algorithm In this Section, we describe the DISTRIBUTEDMINIMUM-LABEL-COVER, DMLC, algorithm for approximately solving the minimum label cover problem. We describe the algorithm in a centralized setting, and defer the distributed implementation to Section 5. Before describing the algorithm, we briefly explain the relationship of the minimum label cover problem to set cover. 4.1 Modification of Set Cover As we pointed out earlier, the minimum label cover problem is at least as hard as the Set Cover prob-1: Input: A set of sequences S with each words wij having possible tags Tij . 2: Output: A tag assignment tij ∈ Tij for each word wij approximately minimizing labels. 3: Let M be the multi set of all possible labels generated by choosing each possible tag t ∈ Tij . M = [ i   |S [i|+1 j=1 [ t 0∈Ti,j−1 t∈Tij {{(t 0 , t)}}   (1) 4: Let L = ∅ be the set of selected labels. 5: repeat 6: Select the most frequent label not yet selected: (t 0 , t) = arg max(s 0 ,s)∈L/ |M ∩ (s 0 , s)|. 7: For each bigram (wi,j−1, wij ) where t 0 ∈ Ti,j−1 and t ∈ Tij tentatively assign t 0 to wi,j−1 and t to wij . Add (t 0 , t) to L. 8: If a word gets two assignments, select one at random with equal probability. 9: If a bigram (wij , wi,j+1) is consistent with assignments in (t, t0 ), fix the tentative assignments, and set Ti,j−1 = {t 0} and Tij = t. Recompute M, the multiset of possible labels, with the updated Ti,j−1 and Tij . 10: until there are no unassigned words Algorithm 1: MLC Algorithm 1: Input: A set of sequences S with each words wij having possible tags Tij . 2: Output: A tag assignment tij ∈ Tij for each word wij approximately minimizing labels. 3: (Graph Creation) Initialize each vertex vij with the set of possible tags Tij and its neighbors vi,j+1 and vi,j−1. 4: repeat 5: (Message Passing) Each vertex vij sends its possibly tags Tij to its forward neighbor vij+1. 6: (Counter Update) Each vertex receives the the tags Ti,j−1 and adds all possible labels {(s, s0 )|s ∈ Ti,j−1, s0 ∈ Tij} to a global counter (M). 7: (MaxLabel Selection) Each vertex queries the global counter M to find the maximum label (t, t0 ). 8: (Tentative Assignment) Each vertex vij selects a tag tentatively as follows: If one of the tags t, t0 is in the feasible set Tij , it tentatively selects the tag. 9: (Random Assignment) If both are feasible it selects one at random. The vertex communicates its assignment to its neighbors. 10: (Confirmed Assignment) Each vertex receives the tentative assignment from its neighbors. If together with its neighbors it can match the selected label, the assignment is finalized. If the assigned tag is T, then the vertex vij sets the valid tag set Tij to {t}. 11: until no unassigned vertices exist. Algorithm 2: DMLC Implementation lem. An additional challenge comes from the fact that labels are tags for a pair of words, and hence are related. For example, if we label a word pair (wi,j−1, wij ) as (NN, VP), then the label for the next word pair (wij , wi,j+1) has to be of the form (VP, *), i.e., it has to start with VP. Previous work (Ravi et al., 2010a; Ravi et al., 2010b) recognized this challenge and employed two phase heuristic approaches. Eschewing heuristics, we will show that with one natural assumption, even with this extra set of constraints, the standard greedy algorithm for this problem results in a solution with a provable approximation ratio of O(log m). In practice, however, the algorithm performs far better than the worst case ratio, and similar to the work of (Gomes et al., 2006), we find that the greedy approach selects a cover approximately 11% worse than the optimum solution. 4.2 MLC Algorithm We present in Algorithm 1 our MINIMUM LABEL COVER algorithm to approximately solve the minimum label cover problem. The algorithm is simple, efficient, and easy to distribute. The algorithm chooses labels one at a time, selecting a label that covers as many words as possible inevery iteration. For this, it generates and maintains a multi-set of all possible labels M (Step 3). The multi-set contains an occurrence of each valid label, for example, if wi,j−1 has two possible valid tags NN and VP, and wij has one possible valid tag VP, then M will contain two labels, namely (NN, VP) and (VP, VP). Since M is a multi-set it will contain duplicates, e.g. the label (NN, VP) will appear for each adjacent pair of words that have NN and VP as valid tags, respectively. In each iteration, the algorithm picks a label with the most number of occurrences in M and adds it to the set of chosen labels (Step 6). Intuitively, this is a greedy step to select a label that covers the most number of word pairs. Once the algorithm picks a label (t 0 , t), it tries to assign as many words to tags t or t 0 as possible (Step 7). A word can be assigned t 0 if t 0 is a valid tag for it, and t a valid tag for the next word in sequence. Similarly, a word can be assigned t, if t is a valid tag for it, and t 0 a valid tag for the previous word. Some words can get both assignments, in which case we choose one tentatively at random (Step 8). If a word’s tentative random tag, say t, is consistent with the choices of its adjacent words (say t 0 from the previous word), then the tentative choice is fixed as a permanent one. Whenever a tag is selected, the set of valid tags Tij for the word is reduced to a singleton {t}. Once the set of valid tags Tij changes, the multi-set M of all possible labels also changes, as seen from Eq 1. The multi-set is then recomputed (Step 9) and the iterations repeated until all of words have been tagged. We can show that under a natural assumption this simple algorithm is approximately optimal. Assumption 1 (c-feasibility). Let c ≥ 1 be any number, and k be the size of the optimal solution to the original problem. In each iteration, the MLC algorithm fixes the tags for some words. We say that the algorithm is c-feasible, if after each iteration there exists some solution to the remaining problem, consistent with the chosen tags, with size at most ck . The assumption encodes the fact that a single bad greedy choice is not going to destroy the overall structure of the solution, and a nearly optimal solution remains. We note that this assumption of cfeasibility is not only sufficient, as we will formally show, but is also necessary. Indeed, without any assumptions, once the algorithm fixes the tag for some words, an optimal label may no longer be consistent with the chosen tags, and it is not hard to find contrived examples where the size of the optimal solution doubles after each iteration of MLC. Since the underlying problem is NP-complete, it is computationally hard to give direct evidence verifying the assumption on natural language inputs. However, on small examples we are able to show that the greedy algorithm is within a small constant factor of the optimum, specifically it is within 11% of the optimum model size for the POS tagging problem using the standard 24k dataset (Ravi and Knight, 2009). Combined with the fact that the final method outperforms state of the art approaches, this leads us to conclude that the structural assumption is well justified. Lemma 1. Under the assumption of c-feasibility, the MLC algorithm achieves a O(c log m) approximation to the minimum label cover problem, where m = P i |Si | is the total number of tokens. Proof. To prove the Lemma we will define an objective function φ¯, counting the number of unlabeled word pairs, as a function of possible labels, and show that φ¯ decreases by a factor of (1−O(1/ck)) at every iteration. To define φ¯, we first define φ, the number of labeled word pairs. Consider a particular set of labels, L = {L1, L2, . . . , Lk} where each label is a pair (ti , tj ). Call {tij} a valid assignment of tokens if for each wij , we have tij ∈ Tij . Then the score of L under an assignment t, which we denote by φt , is the number of bigram labels that appear in L. Formally, φt(L) = | ∪i,j {{(ti,j−1, tij ) ∩ L}}|. Finally, we define φ(L) to be the best such assignment, φ(L) = maxt φt(L), and φ¯(L) = m − φ(L) the number of uncovered labels. Consider the label selected by the algorithm in every step. By the c-feasibility assumption, there exists some solution having ck labels. Thus, some label from that solution covers at least a 1/ck fraction of the remaining words. The selected label (t, t0 ) maximizes the intersection with the remaining feasible labels. The conflict resolution step ensures that in expectation the realized benefit is at least a half of the maximum, thereby reducing φ¯ by at least a(1 − 1/2ck) fraction. Therefore, after O(kc log m) operations all of the labels are covered. 4.3 Fitting the Model Using EM Once the greedy algorithm terminates and returns a minimized grammar of tag bigrams, we follow the approach of Ravi and Knight (2009) and fit the minimized model to the data using the alternating EM strategy. In this step, we run an alternating optimization procedure iteratively in phases. In each phase, we initialize (and prune away) parameters within the two HMM components (transition or emission model) using the output from the previous phase. We initialize this procedure by restricting the transition parameters to only those tag bigrams selected in the model minimization step. We train in conjunction with the original emission model using EM algorithm which prunes away some of the emission parameters. In the next phase, we alternate the initialization by choosing the pruned emission model along with the original transition model (with full set of tag bigrams) and retrain using EM. The alternating EM iterations are terminated when the change in the size of the observed grammar (i.e., the number of unique bigrams in the tagging output) is ≤ 5%. 1 We refer to our entire approach using greedy minimization followed by EM training as DMLC + EM. 5 Distributed Implementation The DMLC algorithm is directly suited towards parallelization across many machines. We turn to Pregel (Malewicz et al., 2010), and its open source version Giraph (Apa, 2013). In these systems the computation proceeds in rounds. In every round, every machine does some local processing and then sends arbitrary messages to other machines. Semantically, we think of the communication graph as fixed, and in each round each vertex performs some local computation and then sends messages to its neighbors. This mode of parallel programming directs the programmers to “Think like a vertex.” The specific systems like Pregel and Giraph build infrastructure that ensures that the overall system 1 For more details on the alternating EM strategy and how initialization with minimized models improve EM performance in alternating iterations, refer to (Ravi and Knight, 2009). is fault tolerant, efficient, and fast. In addition, they provide implementation of commonly used distributed data structures, such as, for example global counters. The programmer’s job is simply to specify the code that each vertex will run at every round. We implemented the DMLC algorithm in Pregel. The implementation is straightforward and given in Algorithm 2. The multi-set M of Algorithm 1 is represented as a global counter in Algorithm 2. The message passing (Step 3) and counter update (Step 4) steps update this global counter and hence perform the role of Step 3 of Algorithm 1. Step 5 selects the label with largest count, which is equivalent to the greedy label picking step 6 of Algorithm 1. Finally steps 6, 7, and 8 update the tag assignment of each vertex performing the roles of steps 7, 8, and 9, respectively, of Algorithm 1. 5.1 Speeding up the Algorithm The implementation described above directly copies the sequential algorithm. Here we describe additional steps we took to further improve the parallel running times. Singleton Sets: As the parallel algorithm proceeds, the set of feasible sets associated with a node slowly decreases. At some point there is only one tag that a node can take on, however this tag is rare, and so it takes a while for it to be selected using the greedy strategy. Nevertheless, if a node and one of its neighbors have only a single tag left, then it is safe to assign the unique label 2 . Modifying the Graph: As is often the case, the bottleneck in parallel computations is the communication. To reduce the amount of communication we reduce the graph on the fly, removing nodes and edges once they no longer play a role in the computation. This simple modification decreases the communication time in later rounds as the total size of the problem shrinks. 6 Experiments and Results In this Section, we describe the experimental setup for various tasks, settings and compare empirical performance of our method against several existing 2We must judiciously initialize the global counter to take care of this assignment, but this is easily accomplished.baselines. The performance results for all systems (on all tasks) are measured in terms of tagging accuracy, i.e. % of tokens from the test corpus that were labeled correctly by the system. 6.1 Part-of-Speech Tagging Task 6.1.1 Tagging Using a Complete Dictionary Data: We use a standard test set (consisting of 24,115 word tokens from the Penn Treebank) for the POS tagging task. The tagset consists of 45 distinct tag labels and the dictionary contains 57,388 word/tag pairs derived from the entire Penn Treebank. Per-token ambiguity for the test data is about 1.5 tags/token. In addition to the standard 24k dataset, we also train and test on larger data sets— 973k tokens from the Penn Treebank, 3M tokens from PTB+Europarl (Koehn, 2005) data. Methods: We evaluate and compare performance for POS tagging using four different methods that employ the model minimization idea combined with EM training: • EM: Training a bigram HMM model using EM algorithm (Merialdo, 1994). • ILP + EM: Minimizing grammar size using integer linear programming, followed by EM training (Ravi and Knight, 2009). • MIN-GREEDY + EM: Minimizing grammar size using the two-step greedy method (Ravi et al., 2010b). • DMLC + EM: This work. Results: Table 1 shows the results for POS tagging on English Penn Treebank data. On the smaller test datasets, all of the model minimization strategies (methods 2, 3, 4) tend to perform equally well, yielding state-of-the-art results and large improvement over standard EM. When training (and testing) on larger corpora sizes, DMLC yields the best reported performance on this task to date. A major advantage of the new method is that it can easily scale to large corpora sizes and the distributed nature of the algorithm still permits fast, efficient optimization of the global objective function. So, unlike the earlier methods (such as MIN-GREEDY) it is fast enough to run on several millions of tokens to yield additional performance gains (shown in last column). Speedups: We also observe a significant speedup when using the parallelized version of the DMLC algorithm. Performing model minimization on the 24k tokens dataset takes 55 seconds on a single machine, whereas parallelization permits model minimization to be feasible even on large datasets. Fig 1 shows the running time for DMLC when run on a cluster of 100 machines. We vary the input data size from 1M word tokens to about 8M word tokens, while holding the resources constant. Both the algorithm and its distributed implementation in DMLC are linear time operations as evident by the plot. In fact, for comparison, we also plot a straight line passing through the first two runtimes. The straight line essentially plots runtimes corresponding to a linear speedup. DMLC clearly achieves better runtimes showing even better than linear speedup. The reason for this is that distributed version has a constant overhead for initialization, independent of the data size. While the running time for rest of the implementation is linear in data size. Thus, as the data size becomes larger, the constant overhead becomes less significant, and the distributed implementation appears to complete slightly faster as data size increases. Figure 1: Runtime vs. data size (measured in # of word tokens) on 100 machines. For comparison, we also plot a straight line passing through the first two runtimes. The straight line essentially plots runtimes corresponding to a linear speedup. DMLC clearly achieves better runtimes showing a better than linear speedup. 6.1.2 Tagging Using Incomplete Dictionaries We also evaluate our approach for POS tagging under other resource-constrained scenarios. Obtain-Method Tagging accuracy (%) te=24k te=973k tr=24k tr=973k tr=3.7M 1. EM 81.7 82.3 2. ILP + EM (Ravi and Knight, 2009) 91.6 3. MIN-GREEDY + EM (Ravi et al., 2010b) 91.6 87.1 4. DMLC + EM (this work) 91.4 87.5 87.8 Table 1: Results for unsupervised part-of-speech tagging on English Penn Treebank dataset. Tagging accuracies for different methods are shown on multiple datasets. te shows the size (number of tokens) in the test data, tr represents the size of the raw text used to perform model minimization. ing a complete dictionary is often difficult, especially for new domains. To verify the utility of our method when the input dictionary is incomplete, we evaluate against standard datasets used in previous work (Garrette and Baldridge, 2012) and compare against the previous best reported performance for the same task. In all the experiments (described here and in subsequent sections), we use the following terminology—raw data refers to unlabeled text used by different methods (for model minimization or other unsupervised training procedures such as EM), dictionary consists of word/tag entries that are legal, and test refers to data over which tagging evaluation is performed. English Data: For English POS tagging with incomplete dictionary, we evaluate on the Penn Treebank (Marcus et al., 1993) data. Following (Garrette and Baldridge, 2012), we extracted a word-tag dictionary from sections 00-15 (751,059 tokens) consisting of 39,087 word types, 45,331 word/tag entries, a per-type ambiguity of 1.16 yielding a pertoken ambiguity of 2.21 on the raw corpus (treating unknown words as having all 45 possible tags). As in their setup, we then use the first 47,996 tokens of section 16 as raw data and perform final evaluation on the sections 22-24. We use the raw corpus along with the unlabeled test data to perform model minimization and EM training. Unknown words are allowed to have all possible tags in both these procedures. Italian Data: The minimization strategy presented here is a general-purpose method that does not require any specific tuning and works for other languages as well. To demonstrate this, we also perform evaluation on a different language (Italian) using the TUT corpus (Bosco et al., 2000). Following (Garrette and Baldridge, 2012), we use the same data splits as their setting. We take the first half of each of the five sections to build the word-tag dictionary, the next quarter as raw data and the last quarter as test data. The dictionary was constructed from 41,000 tokens comprised of 7,814 word types, 8,370 word/tag pairs, per-type ambiguity of 1.07 and a per-token ambiguity of 1.41 on the raw data. The raw data consisted of 18,574 tokens and the test contained 18,763 tokens. We use the unlabeled corpus from the raw and test data to perform model minimization followed by unsupervised EM training. Other Languages: In order to test the effectiveness of our method in other non-English settings, we also report the performance of our method on several other Indo-European languages using treebank data from CoNLL-X and CoNLL-2007 shared tasks on dependency parsing (Buchholz and Marsi, 2006; Nivre et al., 2007). The corpus statistics for the five languages (Danish, Greek, Italian, Portuguese and Spanish) are listed below. For each language, we construct a dictionary from the raw training data. The unlabeled corpus from the raw training and test data is used to perform model minimization followed by unsupervised EM training. As before, unknown words are allowed to have all possible tags. We report the final tagging performance on the test data and compare it to baseline EM. Garrette and Baldridge (2012) treat unknown words (words that appear in the raw text but are missing from the dictionary) in a special manner and use several heuristics to perform better initialization for such words (for example, the probability that an unknown word is associated with a particular tag isconditioned on the openness of the tag). They also use an auto-supervision technique to smooth counts learnt from EM onto new words encountered during testing. In contrast, we do not apply any such technique for unknown words and allow them to be mapped uniformly to all possible tags in the dictionary. For this particular set of experiments, the only difference from the Garrette and Baldridge (2012) setup is that we include unlabeled text from the test data (but without any dictionary tag labels or special heuristics) to our existing word tokens from raw text for performing model minimization. This is a standard practice used in unsupervised training scenarios (for example, Bayesian inference methods) and in general for scalable techniques where the goal is to perform inference on the same data for which one wishes to produce some structured prediction. Language Train Dict Test (tokens) (entries) (tokens) DANISH 94386 18797 5852 GREEK 65419 12894 4804 ITALIAN 71199 14934 5096 PORTUGUESE 206678 30053 5867 SPANISH 89334 17176 5694 Results: Table 2 (column 2) compares previously reported results against our approach for English. We observe that our method obtains a huge improvement over standard EM and gets comparable results to the previous best reported scores for the same task from (Garrette and Baldridge, 2012). It is encouraging to note that the new system achieves this performance without using any of the carefully-chosen heuristics employed by the previous method. However, we do note that some of these techniques can be easily combined with our method to produce further improvements. Table 2 (column 3) also shows results on Italian POS tagging. We observe that our method achieves significant improvements in tagging accuracy over all the baseline systems including the previous best system (+2.9%). This demonstrates that the method generalizes well to other languages and produces consistent tagging improvements over existing methods for the same task. Results for POS tagging on CoNLL data in five different languages are displayed in Figure 2. Note that the proportion of raw data in test versus train 50 60 70 80 90 DANISH GREEK ITALIAN PORTUGUESE SPANISH 79.4 66.3 84.6 80.1 83.1 77.8 65.6 82 78.5 81.3 EM DMLC+EM Figure 2: Part-of-Speech tagging accuracy for different languages on CoNLL data using incomplete dictionaries. (from the standard CoNLL shared tasks) is much smaller compared to the earlier experimental settings. In general, we observe that adding more raw data for EM training improves the tagging quality (same trend observed earlier in Table 1: column 2 versus column 3). Despite this, DMLC + EM still achieves significant improvements over the baseline EM system on multiple languages (as shown in Figure 2). An additional advantage of the new method is that it can easily scale to larger corpora and it produces a much more compact grammar that can be efficiently incorporated for EM training. 6.1.3 Tagging for Low-Resource Languages Learning part-of-speech taggers for severely lowresource languages (e.g., Malagasy) is very challenging. In addition to scarce (token-supervised) labeled resources, the tag dictionaries available for training taggers are tiny compared to other languages such as English. Garrette and Baldridge (2013) combine various supervised and semi-supervised learning algorithms into a common POS tagger training pipeline to addresses some of these challenges. They also report tagging accuracy improvements on low-resource languages when using the combined system over any single algorithm. Their system has four main parts, in order: (1) Tag dictionary expansion using label propagation algorithm, (2) Weighted model minimization, (3) Expectation maximization (EM) training of HMMs using auto-supervision, (4) MaxEnt Markov Model (MEMM) training. The entire procedure results in a trained tagger model that can then be applied to tag any raw data.3 Step 2 in this procedure involves 3 For more details, refer (Garrette and Baldridge, 2013). "We consider a model of repeated online auctions in which an ad with an uncertain click-through rate faces a random distribution of competing bids in each auction and there is discounting of payoffs. We formulate the optimal solution to this explore/exploit problem as a dynamic programming problem and show that efficiency is maximized by making a bid for each advertiser equal to the advertiser's expected value for the advertising opportunity plus a term proportional to the variance in this value divided by the number of impressions the advertiser has received thus far. We then use this result to illustrate that the value of incorporating active exploration into a machine learning system in an auction environment is exceedingly small." Accepted for publication in the Annals of Applied Statistics (in press), 09/2014 INFERRING CAUSAL IMPACT USING BAYESIAN STRUCTURAL TIME-SERIES MODELS By Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott Google, Inc. E-mail: kbrodersen@google.com Abstract An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response in a synthetic control that would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, statespace models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including local trends, seasonality, and the time-varying influence of contemporaneous covariates. Using a Markov chain Monte Carlo algorithm for posterior inference, we illustrate the statistical properties of our approach on simulated data. We then demonstrate its practical utility by estimating the causal effect of an online advertising campaign on search-related site visits. We discuss the strengths and limitations of state-space models in enabling causal attribution in those settings where a randomised experiment is unavailable. The CausalImpact R package provides an implementation of our approach. 1. Introduction. This article proposes an approach to inferring the causal impact of a market intervention, such as a new product launch or the onset of an advertising campaign. Our method generalizes the widely used ‘difference-in-differences’ approach to the time-series setting by explicitly modelling the counterfactual of a time series observed both before and after the intervention. It improves on existing methods in two respects: it provides a fully Bayesian time-series estimate for the effect; and it uses model averaging to construct the most appropriate synthetic control for modelling the counterfactual. The CausalImpact R package provides an implementation of our approach (http://google.github.io/CausalImpact/). Inferring the impact of market interventions is an important and timely Keywords and phrases: causal inference, counterfactual, synthetic control, observational, difference in differences, econometrics, advertising, market research 12 K.H. BRODERSEN ET AL. problem. Partly because of recent interest in ‘big data,’ many firms have begun to understand that a competitive advantage can be had by systematically using impact measures to inform strategic decision making. An example is the use of ‘A/B experiments’ to identify the most effective market treatments for the purpose of allocating resources (Danaher and Rust, 1996; Seggie, Cavusgil and Phelan, 2007; Leeflang et al., 2009; Stewart, 2009). Here, we focus on measuring the impact of a discrete marketing event, such as the release of a new product, the introduction of a new feature, or the beginning or end of an advertising campaign, with the aim of measuring the event’s impact on a response metric of interest (e.g., sales). The causal impact of a treatment is the difference between the observed value of the response and the (unobserved) value that would have been obtained under the alternative treatment, i.e., the effect of treatment on the treated (Rubin, 1974; Hitchcock, 2004; Morgan and Winship, 2007; Rubin, 2007; Cox and Wermuth, 2001; Heckman and Vytlacil, 2007; Antonakis et al., 2010; Kleinberg and Hripcsak, 2011; Hoover, 2012; Claveau, 2012). In the present setting the response variable is a time series, so the causal effect of interest is the difference between the observed series and the series that would have been observed had the intervention not taken place. A powerful approach to constructing the counterfactual is based on the idea of combining a set of candidate predictor variables into a single ‘synthetic control’ (Abadie and Gardeazabal, 2003; Abadie, Diamond and Hainmueller, 2010). Broadly speaking, there are three sources of information available for constructing an adequate synthetic control. The first is the time-series behaviour of the response itself, prior to the intervention. The second is the behaviour of other time series that were predictive of the target series prior to the intervention. Such control series can be based, for example, on the same product in a different region that did not receive the intervention, or on a metric that reflects activity in the industry as a whole. In practice, there are often many such series available, and the challenge is to pick the relevant subset to use as contemporaneous controls. This selection is done on the pre-treatment portion of potential controls; but their value for predicting the counterfactual lies in their post-treatment behaviour. As long as the control series received no intervention themselves, it is often reasonable to assume the relationship between the treatment and the control series that existed prior to the intervention to continue afterwards. Thus, a plausible estimate of the counterfactual time series can be computed up to the point in time where the relationship between treatment and controls can no longer be assumed to be stationary, e.g., because one of the controls received treatment itself. In a Bayesian framework, a third source ofBAYESIAN CAUSAL IMPACT ANALYSIS 3 information for inferring the counterfactual is the available prior knowledge about the model parameters, as elicited, for example, by previous studies. We combine the three preceding sources of information using a statespace time-series model, where one component of state is a linear regression on the contemporaneous predictors. The framework of our model allows us to choose from among a large set of potential controls by placing a spikeand-slab prior on the set of regression coefficients, and by allowing the model to average over the set of controls (George and McCulloch, 1997). We then compute the posterior distribution of the counterfactual time series given the value of the target series in the pre-intervention period, along with the values of the controls in the post-intervention period. Subtracting the predicted from the observed response during the post-intervention period gives a semiparametric Bayesian posterior distribution for the causal effect (Figure 1). Related work. As with other domains, causal inference in marketing requires subtlety. Marketing data are often observational and rarely follow the ideal of a randomised design. They typically exhibit a low signal-to-noise ratio. They are subject to multiple seasonal variations, and they are often confounded by the effects of unobserved variables and their interactions (for recent examples, see Seggie, Cavusgil and Phelan, 2007; Stewart, 2009; Leeflang et al., 2009; Takada and Bass, 1998; Chan et al., 2010; Lewis and Reiley, 2011; Lewis, Rao and Reiley, 2011; Vaver and Koehler, 2011, 2012). Rigorous causal inferences can be obtained through randomised experiments, which are often implemented in the form of geo experiments (Vaver and Koehler, 2011, 2012). Many market interventions, however, fail to satisfy the requirements of such approaches. For instance, advertising campaigns are frequently launched across multiple channels, online and offline, which precludes measurement of individual exposure. Campaigns are often targeted at an entire country, and one country only, which prohibits the use of geographic controls within that country. Likewise, a campaign might be launched in several countries but at different points in time. Thus, while a large control group may be available, the treatment group often consists of no more than one region, or a few regions with considerable heterogeneity among them. A standard approach to causal inference in such settings is based on a linear model of the observed outcomes in the treatment and control group before and after the intervention. One can then estimate the difference between (i) the pre-post difference in the treatment group and (ii) the pre-post difference in the control group. The assumption underlying such differencein-differences (DD) designs is that the level of the control group provides4 K.H. BRODERSEN ET AL. 20 60 100 140 (a) Y Model fit Prediction X1 X2 -40 0 20 60 (b) Point-wise impact Ground truth -2000 0 2000 6000 (c) 2013-01 2013-02 2013-03 2013-04 2013-05 2013-06 2013-07 2013-08 2013-09 2013-10 2013-11 2013-12 2014-01 2014-02 2014-03 2014-04 2014-05 2014-06 Cumulative impact Ground truth Figure 1. Inferring causal impact through counterfactual predictions. (a) Simulated trajectory of a treated market (Y ) with an intervention beginning in January 2014. Two other markets (X1, X2) were not subject to the intervention and allow us to construct a synthetic control (cf. Abadie and Gardeazabal, 2003; Abadie, Diamond and Hainmueller, 2010). Inverting the state-space model described in the main text yields a prediction of what would have happened in Y had the intervention not taken place (posterior predictive expectation of the counterfactual with pointwise 95% posterior probability intervals). (b) The difference between observed data and counterfactual predictions is the inferred causal impact of the intervention. Here, predictions accurately reflect the true (Gamma-shaped) impact. A key characteristic of the inferred impact series is the progressive widening of the posterior intervals (shaded area). This effect emerges naturally from the model structure and agrees with the intuition that predictions should become increasingly uncertain as we look further and further into the (retrospective) future. (c) Another way of visualizing posterior inferences is by means of a cumulative impact plot. It shows, for each day, the summed effect up to that day. Here, the 95% credible interval of the cumulative impact crosses the zeroline about five months after the intervention, at which point we would no longer declare a significant overall effect.BAYESIAN CAUSAL IMPACT ANALYSIS 5 an adequate proxy for the level that would have been observed in the treatment group in the absence of treatment (see Lester, 1946; Campbell, Stanley and Gage, 1963; Ashenfelter and Card, 1985; Card and Krueger, 1993; Angrist and Krueger, 1999; Athey and Imbens, 2002; Abadie, 2005; Meyer, 1995; Shadish, Cook and Campbell, 2002; Donald and Lang, 2007; Angrist and Pischke, 2008; Robinson, McNulty and Krasno, 2009; Antonakis et al., 2010). DD designs have been limited in three ways. First, DD is traditionally based on a static regression model that assumes i.i.d. data despite the fact that the design has a temporal component. When fit to serially correlated data, static models yield overoptimistic inferences with too narrow uncertainty intervals (see also Solon, 1984; Hansen, 2007a,b; Bertrand, Duflo and Mullainathan, 2002). Second, most DD analyses only consider two time points: before and after the intervention. In practice, the manner in which an effect evolves over time, especially its onset and decay structure, is often a key question. Third, when DD analyses are based on time series, previous studies have imposed restrictions on the way in which a synthetic control is constructed from a set of predictor variables, which is something we wish to avoid. For example, one strategy (Abadie and Gardeazabal, 2003; Abadie, Diamond and Hainmueller, 2010) has been to choose a convex combination (w1, . . . , wJ ), wj ≥ 0, P wj = 1 of J predictor time series in such a way that a vector of pre-treatment variables (not time series) X1 characterising the treated unit before the intervention is matched most closely by the combination of pre-treatment variables X0 of the control units w.r.t. a vector of importance weights (v1, . . . , vJ ). These weights are themselves determined in such a way that the combination of pre-treatment outcome time series of the control units most closely matches the pre-treatment outcome time series of the treated unit. Such a scheme relies on the availability of interpretable characteristics (e.g., growth predictors), and it precludes non-convex combinations of controls when constructing the weight vector W. We prefer to select a combination of control series without reference to external characteristics and purely in terms of how well they explain the pre-treatment outcome time series of the treated unit (while automatically balancing goodness of fit and model complexity through the use of regularizing priors). Another idea (Belloni et al., 2013) has been to use classical variable-selection methods (such as the Lasso) to find a sparse set of predictors. This approach, however, ignores posterior uncertainty about both which predictors to use and their coefficients. The limitations of DD schemes can be addressed by using state-space6 K.H. BRODERSEN ET AL. models, coupled with highly flexible regression components, to explain the temporal evolution of an observed outcome. State-space models distinguish between a state equation that describes the transition of a set of latent variables from one time point to the next and an observation equation that specifies how a given system state translates into measurements. This distinction makes them extremely flexible and powerful (see Leeflang et al., 2009, for a discussion in the context of marketing research). The approach described in this paper inherits three main characteristics from the state-space paradigm. First, it allows us to flexibly accommodate different kinds of assumptions about the latent state and emission processes underlying the observed data, including local trends and seasonality. Second, we use a fully Bayesian approach to inferring the temporal evolution of counterfactual activity and incremental impact. One advantage of this is the flexibility with which posterior inferences can be summarised. Third, we use a regression component that precludes a rigid commitment to a particular set of controls by integrating out our posterior uncertainty about the influence of each predictor as well as our uncertainty about which predictors to include in the first place, which avoids overfitting. The remainder of this paper is organised as follows. Section 2 describes the proposed model, its design variations, the choice of diffuse empirical priors on hyperparameters, and a stochastic algorithm for posterior inference based on Markov chain Monte Carlo (MCMC). Section 3 demonstrates important features of the model using simulated data, followed by an application in Section 4 to an advertising campaign run by one of Google’s advertisers. Section 5 puts our approach into context and discusses its scope of application. 2. Bayesian structural time-series models. Structural time-series models are state-space models for time-series data. They can be defined in terms of a pair of equations yt = Z T (2.1) t αt + t αt+1 = Ttαt + Rtηt (2.2) , where t ∼ N (0, σ2 t ) and ηt ∼ N (0, Qt) are independent of all other unknowns. Equation (2.1) is the observation equation; it links the observed data yt to a latent d-dimensional state vector αt . Equation (2.2) is the state equation; it governs the evolution of the the state vector αt through time. In the present paper, yt is a scalar observation, Zt is a d-dimensional output vector, Tt is a d × d transition matrix, Rt is a d × q control matrix, t is a scalar observation error with noise variance σt , and ηt is a q-dimensionalBAYESIAN CAUSAL IMPACT ANALYSIS 7 system error with a q × q state-diffusion matrix Qt , where q ≤ d. Writing the error structure of equation (2.2) as Rtηt allows us to incorporate state components of less than full rank; a model for seasonality will be the most important example. Structural time-series models are useful in practice because they are flexible and modular. They are flexible in the sense that a very large class of models, including all ARIMA models, can be written in the state-space form given by (2.1) and (2.2). They are modular in the sense that the latent state as well as the associated model matrices Zt , Tt , Rt , and Qt can be assembled from a library of component sub-models to capture important features of the data. There are several widely used state-component models for capturing the trend, seasonality, or effects of holidays. A common approach is to assume the errors of different state-component models to be independent (i.e., Qt is block-diagonal). The vector αt can then be formed by concatenating the individual state components, while Tt and Rt become block-diagonal matrices. The most important state component for the applications considered in this paper is a regression component that allows us to obtain counterfactual predictions by constructing a synthetic control based on a combination of markets that were not treated. Observed responses from such markets are important because they allow us to explain variance components in the treated market that are not readily captured by more generic seasonal submodels. This approach assumes that covariates are unaffected by the effects of treatment. For example, an advertising campaign run in the United States might spill over to Canada or the United Kingdom. When assuming the absence of spill-over effects, the use of such indirectly affected markets as controls would lead to pessimistic inferences; that is, the effect of the campaign would be underestimated (cf. Meyer, 1995). 2.1. Components of state. Local linear trend. The first component of our model is a local linear trend, defined by the pair of equations µt+1 = µt + δt + ηµ,t δt+1 = δt + ηδ,t (2.3) where ηµ,t ∼ N (0, σ2 µ ) and ηδ,t ∼ N (0, σ2 δ ). The µt component is the value of the trend at time t. The δt component is the expected increase in µ between times t and t + 1, so it can be thought of as the slope at time t.8 K.H. BRODERSEN ET AL. The local linear trend model is a popular choice for modelling trends because it quickly adapts to local variation, which is desirable when making short-term predictions. This degree of flexibility may not be desired when making longer-term predictions, as such predictions often come with implausibly wide uncertainty intervals. There is a generalization of the local linear trend model where the slope exhibits stationarity instead of obeying a random walk. This model can be written as µt+1 = µt + δt + ηµ,t δt+1 = D + ρ(δt − D) + ηδ,t, (2.4) where the two components of η are independent. In this model, the slope of the time trend exhibits AR(1) variation around a long-term slope of D. The parameter |ρ| < 1 represents the learning rate at which the local trend is updated. Thus, the model balances short-term information with information from the distant past. Seasonality. There are several commonly used state-component models to capture seasonality. The most frequently used model in the time domain is (2.5) γt+1 = − S X−2 s=0 γt−s + ηγ,t, where S represents the number of seasons, and γt denotes their joint contribution to the observed response yt . The state in this model consists of the S − 1 most recent seasonal effects, but the error term is a scalar, so the evolution equation for this state model is less than full rank. The mean of γt+1 is such that the total seasonal effect is zero when summed over S seasons. For example, if we set S = 4 to capture four seasons per year, the mean of the winter coefficient will be −1 × (spring + summer + autumn). The part of the transition matrix Tt representing the seasonal model is an S−1 × S−1 matrix with −1’s along the top row, 1’s along the subdiagonal, and 0’s elsewhere. The preceding seasonal model can be generalized to allow for multiple seasonal components with different periods. When modelling daily data, for example, we might wish to allow for an S = 7 day-of-week effect, as well as an S = 52 weekly annual cycle. The latter can be handled by setting Tt = IS−1, with zero variance on the error term, when t is not the start of a new week, and setting Tt to the usual seasonal transition matrix, with nonzero error variance, when t is the start of a new week.BAYESIAN CAUSAL IMPACT ANALYSIS 9 Contemporaneous covariates with static coefficients. Control time series that received no treatment are critical to our method for obtaining accurate counterfactual predictions since they account for variance components that are shared by the series, including in particular the effects of other unobserved causes otherwise unaccounted for by the model. A natural way of including control series in the model is through a linear regression. Its coefficients can be static or time-varying. A static regression can be written in state-space form by setting Zt = β Txt and αt = 1. One advantage of working in a fully Bayesian treatment is that we do not need to commit to a fixed set of covariates. The spike-andslab prior described in Section 2.2 allows us to integrate out our posterior uncertainty about which covariates to include and how strongly they should influence our predictions, which avoids overfitting. All covariates are assumed to be contemporaneous; the present model does not infer on a potential lag between treated and untreated time series. A known lag, however, can be easily incorporated by shifting the corresponding regressor in time. Contemporaneous covariates with dynamic coefficients. An alternative to the above is a regression component with dynamic regression coefficients to account for time-varying relationships (e.g., Banerjee, Kauffman and Wang, 2007; West and Harrison, 1997). Given covariates j = 1 . . . J, this introduces the dynamic regression component x T t βt = X J j=1 xj,tβj,t βj,t+1 = βj,t + ηβ,j,t, (2.6) where ηβ,j,t ∼ N (0, σ2 βj ). Here, βj,t is the coefficient for the j th control series and σβj is the standard deviation of its associated random walk. We can write the dynamic regression component in state-space form by setting Zt = xt and αt = βt and by setting the corresponding part of the transition matrix to Tt = IJ×J , with Qt = diag(σ 2 βj ). Assembling the state-space model. Structural time-series models allow us to examine the time series at hand and flexibly choose appropriate components for trend, seasonality, and either static or dynamic regression for the controls. The presence or absence of seasonality, for example, will usually be obvious by inspection. A more subtle question is whether to choose static or dynamic regression coefficients. When the relationship between controls and treated unit has been stable in the past, static coefficients are an attractive option. This is because10 K.H. BRODERSEN ET AL. a spike-and-slab prior can be implemented efficiently within a forward- filtering, backward-sampling framework. This makes it possible to quickly identify a sparse set of covariates even from tens or hundreds of potential variables (Scott and Varian, 2013). Local variability in the treated time series is captured by the dynamic local level or dynamic linear trend component. Covariate stability is typically high when the available covariates are close in nature to the treated metric. The empirical analyses presented in this paper, for example, will be based on a static regression component (Section 4). This choice provides a reasonable compromise between capturing local behaviour and accounting for regression effects. An alternative would be to use dynamic regression coefficients, as we do, for instance, in our analyses of simulated data (Section 3). Dynamic coefficients are useful when the linear relationship between treated metrics and controls is believed to change over time. There are a number of ways of reducing the computational burden of dealing with a potentially large number of dynamic coefficients. One option is to resort to dynamic latent factors, where one uses xt = But +νt with dim(ut) J and uses ut instead of xt as part of Zt in (2.1), coupled with an AR-type model for ut itself. Another option is latent thresholding regression, where one uses a dynamic version of the spike-and-slab prior as in Nakajima and West (2013). The state-component models are assembled independently, with each component providing an additive contribution to yt . Figure 2 illustrates this process assuming a local linear trend paired with a static regression component. 2.2. Prior distributions and prior elicitation. Let θ generically denote the set of all model parameters and let α = (α1, . . . , αm) denote the full state sequence. We adopt a Bayesian approach to inference by specifying a prior distribution p(θ) on the model parameters as well as a distribution p(α0|θ) on the initial state values. We may then sample from p(α, θ|y) using MCMC. Most of the models in Section 2.1 depend solely on a small set of variance parameters that govern the diffusion of the individual state components. A typical prior distribution for such a variance is (2.7) 1 σ 2 ∼ G ν 2 , s 2 , where G (a, b) is the Gamma distribution with expectation a/b. The prior parameters can be interpreted as a prior sum of squares s, so that s/ν is a prior estimate of σ 2 , and ν is the weight, in units of prior sample size, assigned to the prior estimate.BAYESIAN CAUSAL IMPACT ANALYSIS 11 𝜇1 𝑦1 𝒩 𝑦𝑡 𝜇𝑡 + 𝑥𝑡 T𝛽𝜚, 𝜎𝑦 2 𝜇𝑛 … 𝑦𝑛 𝜇𝑛+1 𝑦 𝑛+1 𝜇𝑚 𝑦 𝑚 𝑥1 𝑥𝑛 𝑥𝑛+1 𝑥𝑚 𝒩 𝜇𝑡 𝜇𝑡−1 + 𝛿𝑡−1, 𝜎𝜇 2 𝒩 𝛿𝑡 𝛿𝑡−1, 𝜎𝛿 2 … … … … … 𝜎𝜇 𝜎𝛿 𝜎𝑦 𝜇0 local trend local level observed (𝑦) and counterfactual (𝑦 ) activity controls control selection pre-intervention period post-intervention period 𝛿1 𝛿𝑛+1 𝛿𝑚 𝜚 diffusion parameters 𝒩 𝛽𝜚 𝑏𝜚, 𝜎𝜖 2 Σ𝜚 −1 −1 𝛿0 𝛿𝑛 𝜎𝜖 𝛽𝜚 regression coefficients observation noise Figure 2. Graphical model for the static-regression variant of the proposed state-space model. Observed market activity y1:n = (y1, . . . , yn) is modelled as the result of a latent state plus Gaussian observation noise with error standard deviation σy. The state αt includes a local level µt, a local linear trend δt, and a set of contemporaneous covariates xt, scaled by regression coefficients β%. State components are assumed to evolve according to independent Gaussian random walks with fixed standard deviations σµ and σδ (conditionaldependence arrows shown for the first time point only). The model includes empirical priors on these parameters and the initial states. In an alternative formulation, the regression coefficients β are themselves subject to random-walk diffusion (see main text). Of principal interest is the posterior predictive density over the unobserved counterfactual responses y˜n+1, . . . , y˜m. Subtracting these from the actual observed data yn+1, . . . , ym yields a probability density over the temporal evolution of causal impact.12 K.H. BRODERSEN ET AL. We often have a weak default prior belief that the incremental errors in the state process are small, which we can formalize by choosing small values of ν (e.g., 1) and small values of s/ν. The notion of ‘small’ means different things in different models; for the seasonal and local linear trend models our default priors are 1/σ2 ∼ G(10−2 , 10−2 s 2 y ), where s 2 y = P t (yt − y¯) 2/(n − 1) is the sample variance of the target series. Scaling by the sample variance is a minor violation of the Bayesian paradigm, but it is an effective means of choosing a reasonable scale for the prior. It is similar to the popular technique of scaling the data prior to analysis, but we prefer to do the scaling in the prior so we can model the data on its original scale. When faced with many potential controls, we prefer letting the model choose an appropriate set. This can be achieved by placing a spike-andslab prior over coefficients (George and McCulloch, 1993, 1997; Polson and Scott, 2011; Scott and Varian, 2013). A spike-and-slab prior combines point mass at zero (the ‘spike’), for an unknown subset of zero coefficients, with a weakly informative distribution on the complementary set of non-zero coefficients (the ‘slab’). Contrary to what its name might suggest, the ‘slab’ is usually not completely flat, but rather a Gaussian with a large variance. Let % = (%1, . . . , %J ), where %j = 1 if βj 6= 0 and %j = 0 otherwise. Let β% denote the non-zero elements of the vector β and let Σ−1 % denote the rows and columns of Σ−1 corresponding to non-zero entries in %. We can then factorize the spike-and-slab prior as (2.8) p(%, β, 1/σ2 ) = p(%) p(σ 2 |%) p(β%|%, σ2 ). The spike portion of (2.8) can be an arbitrary distribution over {0, 1} J in principle; the most common choice in practice is a product of independent Bernoulli distributions, (2.9) p(%) = Y J j=1 π %j j (1 − πj ) 1−%j , where πj is the prior probability of regressor j being included in the model. Values for πj can be elicited by asking about the expected model size M, and then setting all πj = M/J. An alternative is to use a more specific set of values πj . In particular, one might choose to set certain πj to either 1 or 0 to force the corresponding variables into or out of the model. Generally, framing the prior in terms of expected model size has the advantage that the model can adapt to growing numbers of predictor variables without having to switch to a hierarchical prior (Scott and Berger, 2010).BAYESIAN CAUSAL IMPACT ANALYSIS 13 For the ‘slab’ portion of the prior we use a conjugate normal-inverse Gamma distribution, β%|σ 2 ∼ N b%, σ2 (Σ−1 % ) −1 (2.10) 1 σ 2 ∼ G ν 2 , s 2 (2.11) . The vector b in equation (2.10) encodes our prior expectation about the value of each element of β. In practice, we usually set b = 0. The prior parameters in equation (2.11) can be elicited by asking about the expected R2 ∈ [0, 1] as well as the number of observations worth of weight ν the prior estimate should be given. Then s = ν(1 − R2 )s 2 y . The final prior parameter in (2.10) is Σ−1 which, up to a scaling factor, is the prior precision over β in the full model, with all variables included. The total information in the covariates is XTX, and so 1 nXTX is the average information in a single observation. Zellner’s g-prior (Zellner, 1986; Chipman et al., 2001; Liang et al., 2008) sets Σ−1 = g nXTX, so that g can be interpreted as g observations worth of information. Zellner’s prior becomes improper when XTX is not positive definite; we therefore ensure propriety by averaging XTX with its diagonal, (2.12) Σ−1 = g n n wXTX + (1 − w) diag XTX o with default values of g = 1 and w = 1/2. Overall, this prior specification provides a broadly useful default while providing considerable flexibility in those cases where more specific prior information is available. 2.3. Inference. Posterior inference in our model can be broken down into three pieces. First, we simulate draws of the model parameters θ and the state vector α given the observed data y1:n in the training period. Second, we use the posterior simulations to simulate from the posterior predictive distribution p(y˜n+1:m|y1:n) over the counterfactual time series y˜n+1:m given the observed pre-intervention activity y1:n. Third, we use the posterior predictive samples to compute the posterior distribution of the pointwise impact yt−y˜t for each t = 1, . . . , m. We use the same samples to obtain the posterior distribution of cumulative impact. Posterior simulation. We use a Gibbs sampler to simulate a sequence (θ, α) (1) ,(θ, α) (2) , . . . from a Markov chain whose stationary distribution is p(θ, α|y1:n). The sampler alternates between: a data-augmentation step that simulates from p(α|y1:n, θ); and a parameter-simulation step that simulates from p(θ|y1:n, α).14 K.H. BRODERSEN ET AL. The data-augmentation step uses the posterior simulation algorithm from Durbin and Koopman (2002), providing an improvement over the earlier forward-filtering, backward-sampling algorithms by Carter and Kohn (1994), Fr¨uhwirth-Schnatter (1994), and de Jong and Shephard (1995). In brief, because p(y1:n, α|θ) is jointly multivariate normal, the variance of p(α|y1:n, θ) does not depend on y1:n. We can therefore simulate (y ∗ 1:n , α∗ ) ∼ p(y1:n, α|θ) and subtract E(α ∗ |y ∗ 1:n , θ) to obtain zero-mean noise with the correct variance. Adding E(α|y1:n, θ) restores the correct mean, which completes the draw. The required expectations can be computed using the Kalman filter and a fast mean smoother described in detail by Durbin and Koopman (2002). The result is a direct simulation from p(α|y1:n, θ) in an algorithm that is linear in the total (pre- and post-intervention) number of time points (m), and quadratic in the dimension of the state space (d). Given the draw of the state, the parameter draw is straightforward for all state components other than the static regression coefficients β. All state components that exclusively depend on variance parameters can translate their draws back to error terms ηt , accumulate sums of squares of η, and because of conjugacy with equation (2.7) the posterior distribution will remain Gamma distributed. The draw of the static regression coefficients β proceeds as follows. For each t = 1, . . . , n in the pre-intervention period, let ˙yt denote yt with the contributions from the other state components subtracted away, and let y˙ 1:n = ( ˙y1, . . . , y˙n). The challenge is to simulate from p(%, β, σ2 |y˙ 1:n), which we can factor into p(%|y1:n)p(1/σ2 |%, y˙ 1:n)p(β|%, σ, y˙ 1:n). Because of conjugacy, we can integrate out β and 1/σ2 and be left with (2.13) %|y˙ 1:n ∼ C(y˙ 1:n) |Σ −1 % | 1 2 |V −1 % | 1 2 p(%) S N 2 −1 % , where C(y˙ 1:n) is an unknown normalizing constant. The sufficient statistics in equation (2.13) are V −1 % = XTX % + Σ−1 % β˜ % = (V −1 % ) −1 (XT % y˙ 1:n + Σ−1 % b%) N = ν + n S% = s + y˙ T 1:ny˙ 1:n + b T % Σ −1 % b% − β˜T % V −1 % β˜ %. To sample from (2.13), we use a Gibbs sampler that draws each %j given all other %−j . Each full-conditional is easy to evaluate because %j can only assume two possible values. It should be noted that the dimension of all matrices in (2.13) is P j %j , which is small if the model is truly sparse. There are many matrices to manipulate, but because each is small the overallBAYESIAN CAUSAL IMPACT ANALYSIS 15 algorithm is fast. Once the draw of % is complete, we sample directly from p(β, 1/σ2 |%, y˙ 1:n) using standard conjugate formulae. For an alternative that may be even more computationally efficient, see Ghosh and Clyde (2011). Posterior predictive simulation. While the posterior over model parameters and states p(θ, α|y1:n) can be of interest in its own right, causal impact analyses are primarily concerned with the posterior incremental effect, (2.14) p (y˜n+1:m | y1:n, x1:m). As shown by its indices, the density in equation (2.14) is defined precisely for that portion of the time series which is unobserved: the counterfactual market response ˜yn+1, . . . , y˜m that would have been observed in the treated market, after the intervention, in the absence of treatment. It is also worth emphasizing that the density is conditional on the observed data (as well as the priors) and only on these, i.e., on activity in the treatment market before the beginning of the intervention as well as activity in all control markets both before and during the intervention. The density is not conditioned on parameter estimates or the inclusion or exclusion of covariates with static regression coefficients, all of which have been integrated out. Thus, through Bayesian model averaging, we neither commit to any particular set of covariates, which helps avoid an arbitrary selection; nor to point estimates of their coefficients, which prevents overfitting. The posterior predictive density in (2.14) is defined as a coherent (joint) distribution over all counterfactual data points, rather than as a collection of pointwise univariate distributions. This ensures that we correctly propagate the serial structure determined on pre-intervention data to the trajectory of counterfactuals. This is crucial, in particular, when forming summary statistics, such as the cumulative effect of the intervention on the treatment market. Posterior inference was implemented in C++ with an R interface. Given a typically-sized dataset with m = 500 time points, J = 10 covariates, and 10,000 iterations (see Section 4 for an example), this implementation takes less than 30 seconds to complete on a standard computer, enabling nearinteractive analyses. 2.4. Evaluating impact. Samples from the posterior predictive distribution over counterfactual activity can be readily used to obtain samples from the posterior causal effect, i.e., the quantity we are typically interested in. For each draw τ and for each time point t = n + 1, . . . , m, we set φ (τ) t := yt − y˜ (τ) t (2.15) ,16 K.H. BRODERSEN ET AL. yielding samples from the approximate posterior predictive density of the effect attributed to the intervention. In addition to its pointwise impact, we often wish to understand the cumulative effect of an intervention over time. One of the main advantages of a sampling approach to posterior inference is the flexibility and ease with which such derived inferences can be obtained. Reusing the impact samples obtained in (2.15), we compute for each draw τ X t t 0=n+1 φ (τ) t (2.16) 0 ∀t = n + 1, . . . , m. The preceding cumulative sum of causal increments is a useful quantity when y represents a flow quantity, measured over an interval of time (e.g., a day), such as the number of searches, sign-ups, sales, additional installs, or new users. It becomes uninterpretable when y represents a stock quantity, usefully defined only for a point in time, such as the total number of clients, users, or subscribers. In this case we might instead choose, for each τ , to draw a sample of the posterior running average effect following the intervention, 1 t − n X t t 0=n+1 φ (τ) t (2.17) 0 ∀t = n + 1, . . . , m. Unlike the cumulative effect in (2.16), the running average is always interpretable, regardless of whether it refers to a flow or a stock. However, it is more context-dependent on the length of the post-intervention period under consideration. In particular, under the assumption of a true impact that grows quickly at first and then declines to zero, the cumulative impact approaches its true total value (in expectation) as we increase the counterfactual forecasting period, whereas the average impact will eventually approach zero (while, in contrast, the probability intervals diverge in both cases, leading to more and more uncertain inferences as the forecasting period increases). 3. Application to simulated data. To study the characteristics of our approach, we analysed simulated (i.e., computer-generated) data across a series of independent simulations. Generated time series started on 1 January 2013 and ended on 30 June 2014, with a perturbation beginning on 1 January 2014. The data were simulated using a dynamic regression component with two covariates whose coefficients evolved according to independent random walks, βt ∼ N (βt−1, 0.012 ), initialized at β0 = 1. The covariates themselves were simple sinusoids with wavelengths of 90 days and 360 days, respectively.BAYESIAN CAUSAL IMPACT ANALYSIS 17 12 14 16 18 20 22 24 (a) 2013-01 2013-02 2013-03 2013-04 2013-05 2013-06 2013-07 2013-08 2013-09 2013-10 2013-11 2013-12 2014-01 2014-02 2014-03 2014-04 2014-05 2014-06 observed predicted true (b) effect size (%) proportion of intervals excluding zero 0 0.1 1 10 25 50 0.0 0.2 0.4 0.6 0.8 1.0 50 100 150 0.0 0.2 0.4 0.6 0.8 1.0 (c) campaign duration (days) proportion of intervals containing truth Figure 3. Adequacy of posterior uncertainty. (a) Example of one of the 256 datasets created to assess estimation accuracy. Simulated observations (black) are based on two contemporaneous covariates, scaled by time-varying coefficients plus a time-varying local level (not shown). During the campaign period, where the data are lifted by an effect size of 10%, the plot shows the posterior expectation of counterfactual activity (blue), along with its pointwise central 95% credible intervals (blue shaded area), and, for comparison, the true counterfactual (green). (b) Power curve. Following repeated application of the model to simulated data, the plot shows the empirical frequency of concluding that a causal effect was present, as a function of true effect size, given a post-intervention period of 6 months. The curve represents sensitivity in those parts of the graph where the true effect size is positive, and 1 − specificity where the true effect size is zero. Error bars represent 95% credible intervals for the true sensitivity, using a uniform Beta(1, 1) prior. (c) Interval coverage. Using an effect size of 10%, the plot shows the proportion of simulations in which the pointwise central 95% credible interval contained the true impact, as a function of campaign duration. Intervals should contain ground truth in 95% of simulations, however much uncertainty its predictions may be associated with. Error bars represent 95% credible intervals. The latent state underlying the observed data was generated using a local level that evolved according to a random walk, µt ∼ N (µt−1, 0.1 2 ), initialized at µ0 = 0. Independent observation noise was sampled using t ∼ N (0, 0.1 2 ). In summary, observations yt were generated using yt = βt,1zt,1 + βt,2zt,2 + µt + t . To simulate the effect of advertising, the post-intervention portion of the preceding series was multiplied by 1 +e, where e (not to be confused with ) represented the true effect size specifying the (uniform) relative lift during the campaign period. An example is shown in Figure 3a. Sensitivity and specificity. To study the properties of our model, we began by considering under what circumstances we successfully detected a causal18 K.H. BRODERSEN ET AL. effect, i.e., the statistical power or sensitivity of our approach. A related property is the probability of not detecting an absent impact, i.e., specificity. We repeatedly generated data, as described above, under different true effect sizes. We then computed the posterior predictive distribution over the counterfactuals, and recorded whether or not we would have concluded a causal effect. For each of the effect sizes 0%, 0.1%, 1%, 10%, and 100%, a total of 2 8 = 256 simulations were run. This number was chosen simply on the grounds that it provided reasonably tight intervals around the reported summary statistics without requiring excessive amounts of computation. In each simulation, we concluded that a causal effect was present if and only if the central 95% posterior probability interval of the cumulative effect excluded zero. The model used throughout this section comprised two structural blocks. The first one was a local level component. We placed an inverse-Gamma prior on its diffusion variance with a prior estimate of s/ν = 0.1σy and a prior sample size ν = 32. The second structural block was a dynamic regression component. We placed a Gamma prior with prior expectation 0.1σy on the diffusion variance of both regression coefficients. By construction, the outcome variable did not exhibit any local trends or seasonality other than the variation conveyed through the covariates. This obviated the need to include an explicit local linear trend or seasonality component in the model. In a first analysis, we considered the empirical proportion of simulations in which a causal effect had been detected. When taking into account only those simulations where the true effect size was greater than zero, these empirical proportions provide estimates of the sensitivity of the model w.r.t. the process by which the data were generated. Conversely, those simulations where the campaign had had no effect yield an estimate of the model’s specificity. In this way, we obtained the power curve shown in Figure 3b. The curve shows that, in data such as these, a market perturbation leading to a lift no larger than 1% is missed in about 90% of cases. By contrast, a perturbation that lifts market activity by 25% is correctly detected as such in most cases. In a second analysis, we assessed the coverage properties of the posterior probability intervals obtained through our model. It is desirable to use a diffuse prior on the local level component such that central 95% intervals contain ground truth in about 95% of the simulations. This coverage frequency should hold regardless of the length of the campaign period. In other words, a longer campaign should lead to posterior intervals that are appropriately widened to retain the same coverage probability as the nar-BAYESIAN CAUSAL IMPACT ANALYSIS 19 rower intervals obtained for shorter campaigns. This was approximately the case throughout the simulated campaign (Figure 3c). Estimation accuracy. To study the accuracy of the point estimates supported by our approach, we repeated the preceding simulations with a fixed effect size of 10% while varying the length of the campaign. When given a quadratic loss function, the loss-minimizing point estimate is the posterior expectation of the predictive density over counterfactuals. Thus, for each generated dataset i, we computed the expected causal effect for each time point, φˆ i,t := hφt | y1, . . . , ym, x1, . . . , xmi ∀t = n + 1, . . . , m; i = 1, . . . , 256. (3.1) To quantify the discrepancy between estimated and true impact, we calculated the absolute percentage estimation error, ai,t := φˆ i,t − φt φt (3.2) . This yielded an empirical distribution of absolute percentage estimation errors (Figure 4a; blue), showing that impact estimates become less and less accurate as the forecasting period increases. This is because, under the local linear trend model in (2.3), the true counterfactual activity becomes more and more likely to deviate from its expected trajectory. It is worth emphasizing that all preceding results are based on the assumption that the model structure remains intact throughout the modelling period. In other words, even though the model is built around the idea of multiple (non-stationary) components (i.e., a time-varying local trend and, potentially, time-varying regression coefficients), this structure itself remains unchanged. If the model structure does change, estimation accuracy may suffer. We studied the impact of a changing model structure in a second simulation in which we repeated the procedure above in such a way that 90 days after the beginning of the campaign the standard deviation of the random walk governing the evolution of the regression coefficient was tripled (now 0.03 instead of 0.01). As a result, the observed data began to diverge much more quickly than before. Accordingly, estimations became considerably less reliable (Figure 4a, red). An example of the underlying data is shown in Figure 4b. The preceding simulations highlight the importance of a model that is sufficiently flexible to account for phenomena typically encountered in sea-20 K.H. BRODERSEN ET AL. 0 50 100 150 200 250 300 (a) absolute % error 2014-01 2014-02 2014-03 2014-04 2014-05 2014-06 15 20 25 (b) 2014-01 2014-02 2014-03 2014-04 2014-05 2014-06 Figure 4. Estimation accuracy. (a) Time series of absolute percentage discrepancy between inferred effect and true effect. The plot shows the rate (mean ± 2 s.e.m.) at which predictions become less accurate as the length of the counterfactual forecasting period increases (blue). The well-behaved decrease in estimation accuracy breaks down when the data are subject to a sudden structural change (red), as simulated for 1 April 2014. (b) Illustration of a structural break. The plot shows one example of the time series underlying the red curve in (a). On 1 April 2014, the standard deviation of the generating random walk of the local level was tripled, causing the rapid decline in estimation accuracy seen in the red curve in (a).BAYESIAN CAUSAL IMPACT ANALYSIS 21 sonal empirical data. This rules out entirely static models in particular (such as multiple linear regression). 4. Application to empirical data. To illustrate the practical utility of our approach, we analysed an advertising campaign run by one of Google’s advertisers in the United States. In particular, we inferred the campaign’s causal effect on the number of times a user was directed to the advertiser’s website from the Google search results page. We provide a brief overview of the underlying data below (see Vaver and Koehler, 2011, for additional details). The campaign analysed here was based on product-related ads to be displayed alongside Google’s search results for specific keywords. Ads went live for a period of 6 consecutive weeks and were geo-targeted to a randomised set of 95 out of 190 designated market areas (DMAs). The most salient observable characteristic of DMAs is offline sales. To produce balance in this characteristic, DMAs were first rank-ordered by sales volume. Pairs of regions were then randomly assigned to treatment/control. DMAs provide units that can be easily supplied with distinct offerings, although this finegrained split was not a requirement for the model. In fact, we carried out the analysis as if only one treatment region had been available (formed by summing all treated DMAs). This allowed us to evaluate whether our approach would yield the same results as more conventional treatment-control comparisons would have done. The outcome variable analysed here was search-related visits to the advertiser’s website, consisting of organic clicks (i.e., clicks on a search result) and paid clicks (i.e., clicks on an ad next to the search results, for which the advertiser was charged). Since paid clicks were zero before the campaign, one might wonder why we could not simply count the number of paid clicks after the campaign had started. The reason is that paid clicks tend to cannibalize some organic clicks. Since we were interested in the net effect, we worked with the total number of clicks. The first building block of the model used for the analyses in this section was a local level component. For the inverse-Gamma prior on its diffusion variance we used a prior estimate of s/ν = 0.1σy and a prior sample size ν = 32. The second structural block was a static regression component. We used a spike-and-slab prior with an expected model size of M = 3, an expected explained variance of R2 = 0.8 and 50 prior df. We deliberately kept the model as simple as this. Since the covariates came from a randomised experiment, we expected them to already account for any additional local linear trends and seasonal variation in the response variable. If one suspects22 K.H. BRODERSEN ET AL. that a more complex model might be more appropriate, one could optimize model design through Bayesian model selection. Here, we focus instead on comparing different sets of covariates, which is critical in counterfactual analyses regardless of the particular model structure used. Model estimation was carried out using 10 000 MCMC samples. Analysis 1: Effect on the treated, using a randomised control. We began by applying the above model to infer the causal effect of the campaign on the time series of clicks in the treated regions. Given that a set of unaffected regions was available in this analysis, the best possible set of controls was given by the untreated DMAs themselves (see below for a comparison with a purely observational alternative). As shown in Figure 5a, the model provided an excellent fit on the precampaign trajectory of clicks (including a spike in ‘week −2’ and a dip at the end of ‘week −1’). Following the onset of the campaign, observations quickly began to diverge from counterfactual predictions: the actual number of clicks was consistently higher than what would have been expected in the absence of the campaign. The curves did not reconvene until one week after the end of the campaign. Subtracting observed from predicted data, as we did in Figure 5b, resulted in a posterior estimate of the incremental lift caused by the campaign. It peaked after about three weeks into the campaign, and faded away after about one week after the end of the campaign. Thus, as shown in Figure 5c, the campaign led to a sustained cumulative increase in total clicks (as opposed to a mere shift of future clicks into the present, or a pure cannibalization of organic clicks by paid clicks). Specifically, the overall effect amounted to 88 400 additional clicks in the targeted regions (posterior expectation; rounded to three significant digits), i.e., an increase of 22%, with a central 95% credible interval of [13%, 30%]. To validate this estimate, we returned to the original experimental data, on which a conventional treatment-control comparison had been carried out using a two-stage linear model (Vaver and Koehler, 2011). This analysis had led to an estimated lift of 84 700 clicks, with a 95% confidence interval for the relative expected lift of [19%, 22%]. Thus, with a deviation of less than 5%, the counterfactual approach had led to almost precisely the same estimate as the randomised evaluation, except for its wider intervals. The latter is expected, given that our intervals represent prediction intervals, not confi- dence intervals. Moreover, in addition to an interval for the sum over all time points, our approach yields a full time series of pointwise intervals, which allows analysts to examine the characteristics of the temporal evolution of attributable impact. The posterior predictive intervals in Figure 5b widen more slowly than inBAYESIAN CAUSAL IMPACT ANALYSIS 23 4000 8000 12000 (a) US clicks Model fit Prediction pre-intervention intervention post-intervention -2000 0 2000 (b) Point-wise impact 0e+00 1e+05 (c) Cumulative impact week -4 week -3 week -2 week -1 week 0 week 1 week 2 week 3 week 4 week 5 week 6 week 7 Figure 5. Causal effect of online advertising on clicks in treated regions. (a) Time series of search-related visits to the advertiser’s website (including both organic and paid clicks). (b) Pointwise (daily) incremental impact of the campaign on clicks. Shaded vertical bars indicate weekends. (c) Cumulative impact of the campaign on clicks.24 K.H. BRODERSEN ET AL. the illustrative example in Figure 1. This is because the large number of controls available in this data set offers a much higher pre-campaign predictive strength than in the simulated data in Figure 1. This is not unexpected, given that controls came from a randomised experiment, and we will see that this also holds for a subsequent analysis (see below) that is based on yet another data source for predictors. A consequence of this is that there is little variation left to be captured by the random-walk component of the model. A reassuring finding is that the estimated counterfactual time series in Figure 5a eventually almost exactly rejoins the observed series, only a few days after the end of the intervention. Analysis 2: Effect on the treated, using observational controls. An important characteristic of counterfactual-forecasting approaches is that they do not require a setting in which a set of controls, selected at random, was exempt from the campaign. We therefore repeated the preceding analysis in the following way: we discarded the data from all control regions and, instead, used searches for keywords related to the advertiser’s industry, grouped into a handful of verticals, as covariates. In the absence of a dedicated set of control regions, such industry-related time series can be very powerful controls as they capture not only seasonal variations but also market-specific trends and events (though not necessarily advertiser-specific trends). A major strength of the controls chosen here is that time series on web searches are publicly available through Google Trends (http://www.google.com/trends/). This makes the approach applicable to virtually any kind of intervention. At the same time, the industry as a whole is unlikely to be moved by a single actor’s activities. This precludes a positive bias in estimating the effect of the campaign that would arise if a covariate was negatively affected by the campaign. As shown in Figure 6, we found a cumulative lift of 85 900 clicks (posterior expectation), or 21%, with a [12%, 30%] interval. In other words, the analysis replicated almost perfectly the original analysis that had access to a randomised set of controls. One feature in the response variable which this second analysis failed to account for was a spike in clicks in the second week before the campaign onset; this spike appeared both in treated and untreated regions and appears to be specific to this advertiser. In addition, the series of point-wise impact (Figure 6b) is slightly more volatile than in the original analysis (Figure 5). On the other hand, the overall point estimate of 85 900, in this case, was even closer to the randomised-design baseline (84 700; deviation ca. 1%) than in our first analysis (88 400; deviation ca. 4%). In summary, the counterfactual approach effectively obviated the need for the original randomised experiment. Using purely observationalBAYESIAN CAUSAL IMPACT ANALYSIS 25 4000 8000 12000 (a) US clicks Model fit Prediction pre-intervention intervention post-intervention -2000 0 2000 (b) Point-wise impact 0e+00 1e+05 (c) Cumulative impact week -4 week -3 week -2 week -1 week 0 week 1 week 2 week 3 week 4 week 5 week 6 week 7 Figure 6. Causal efect of online advertising on clicks, using only searches for keywords related to the advertiser’s industry as controls, discarding the original control regions as would be the case in studies where a randomised experiment was not carried out. (a) Time series of clicks on to the advertiser’s website. (b) Pointwise (daily) incremental impact of the campaign on clicks. (c) Cumulative impact of the campaign on clicks. The plots show that this analysis, which was based on observational covariates only, provided almost exactly the same inferences as the first analysis (Figure 5) that had been based on a randomised design.26 K.H. BRODERSEN ET AL. variables led to the same substantive conclusions. Analysis 3: Absence of an effect on the controls. To go one step further still, we analysed clicks in those regions that had been exempt from the advertising campaign. If the effect of the campaign was truly specific to treated regions, there should be no effect in the controls. To test this, we inferred the causal effect of the campaign on unaffected regions, which should not lead to a significant finding. In analogy with our second analysis, we discarded clicks in the treated regions and used searches for keywords related to the advertiser’s industry as controls. As summarized in Figure 7, no significant effect was found in unaffected regions, as expected. Specifically, we obtained an overall non-significant lift of 2% in clicks with a central 95% credible interval of [−6%, 10%]. In summary, the empirical data considered in this section showed: (i) a clear effect of advertising on treated regions when using randomised control regions to form the regression component, replicating previous treatmentcontrol comparisons (Figure 5); (ii) notably, an equivalent finding when discarding control regions and instead using observational searches for keywords related to the advertiser’s industry as covariates (Figure 6); (iii) reassuringly, the absence of an effect of advertising on regions that were not targeted (Figure 7). 5. Discussion. The increasing interest in evaluating the incremental impact of market interventions has been reflected by a growing literature on applied causal inference. With the present paper we are hoping to contribute to this literature by proposing a Bayesian state-space model for obtaining a counterfactual prediction of market activity. We discuss the main features of this model below. In contrast to most previous schemes, the approach described here is fully Bayesian, with regularizing or empirical priors for all hyperparameters. Posterior inference gives rise to complete-data (smoothing) predictions that are only conditioned on past data in the treatment market and both past and present data in the control markets. Thus, our model embraces a dynamic evolution of states and, optionally, coefficients (departing from classical linear regression models with a fixed number of static regressors) and enables us to flexibly summarize posterior inferences. Because closed-form posteriors for our model do not exist, we suggest a stochastic approximation to inference using MCMC. One convenient consequence of this is that we can reuse the samples from the posterior to obtain credible intervals for all summary statistics of interest. Such statistics include, for example, the average absolute and relative effect caused by theBAYESIAN CAUSAL IMPACT ANALYSIS 27 4000 8000 12000 (a) US clicks Model fit Prediction pre-intervention intervention post-intervention -2000 0 2000 (b) Point-wise impact 0e+00 1e+05 (c) Cumulative impact week -4 week -3 week -2 week -1 week 0 week 1 week 2 week 3 week 4 week 5 week 6 week 7 Figure 7. Causal effect of online advertising on clicks in non-treated regions, which should not show an effect. Searches for keywords related to the advertiser’s industry are used as controls. Plots show inferences in analogy with Figure 5. (a) Time series of clicks to the advertiser’s website. (b) Pointwise (daily) incremental impact of the campaign on clicks. (c) Cumulative impact of the campaign on clicks.28 K.H. BRODERSEN ET AL. intervention as well as its cumulative effect. Posterior inference was implemented in C++ and R and, for all empirical datasets presented in Section 4, took less than 30 seconds on a standard Linux machine. If the computational burden of sampling-based inference ever became prohibitive, one option would be to replace it by a variational Bayesian approximation (see Mathys et al., 2011; Brodersen et al., 2013, for examples). Another way of using the proposed model is for power analyses. In particular, given past time series of market activity, we can define a point in the past to represent a hypothetical intervention and apply the model in the usual fashion. As a result, we obtain a measure of uncertainty about the response in the treated market after the beginning of the hypothetical intervention. This provides an estimate of what incremental effect would have been required to be outside of the 95% central interval of what would have happened in the absence of treatment. The model presented here subsumes several simpler models which, in consequence, lack important characteristics, but which may serve as alternatives should the full model appear too complex for the data at hand. One example is classical multiple linear regression. In principle, classical regression models go beyond difference-in-differences schemes in that they account for the full counterfactual trajectory. However, they are not suited for predicting stochastic processes beyond a few steps. This is because ordinary leastsquares estimators disregard serial autocorrelation; the static model structure does not allow for temporal variation in the coefficients; and predictions ignore our posterior uncertainty about the parameters. Put differently: classical multiple linear regression is a special case of the state-space model described here in which (i) the Gaussian random walk of the local level has zero variance; (ii) there is no local linear trend; (iii) regression coefficients are static rather than time-varying; (iv) ordinary least squares estimators are used which disregard posterior uncertainty about the parameters and may easily overfit the data. Another special case of the counterfactual approach discussed in this paper is given by synthetic control estimators that are restricted to the class of convex combinations of predictor variables and do not include time-series effects such as trends and seasonality (Abadie, Diamond and Hainmueller, 2010; Abadie, 2005). Relaxing this restriction means we can utilize predictors regardless of their scale, even if they are negatively correlated with the outcome series of the treated unit. Other special cases include autoregressive (AR) and moving-average (MA) models. These models define autocorrelation among observations rather thanBAYESIAN CAUSAL IMPACT ANALYSIS 29 latent states, thus precluding the ability to distinguish between state noise and observation noise (Ataman, Mela and Van Heerde, 2008; Leeflang et al., 2009). In the scenarios we consider, advertising is a planned perturbation of the market. This generally makes it easier to obtain plausible causal inferences than in genuinely observational studies in which the experimenter had no control about treatment (see discussions in Berndt, 1991; Brady, 2002; Hitchcock, 2004; Robinson, McNulty and Krasno, 2009; Winship and Morgan, 1999; Camillo and d’Attoma, 2010; Antonakis et al., 2010; Lewis and Reiley, 2011; Lewis, Rao and Reiley, 2011; Kleinberg and Hripcsak, 2011; Vaver and Koehler, 2011). The principal problem in observational studies is endogeneity: the possibility that the observed outcome might not be the result of the treatment but of other omitted, endogenous variables. In principle, propensity scores can be used to correct for the selection bias that arises when the treatment effect is correlated with the likelihood of being treated (Rubin and Waterman, 2006; Chan et al., 2010). However, the propensityscore approach requires that exposure can be measured at the individual level, and it, too, does not guarantee valid inferences, for example in the presence of a specific type of selection bias recently termed ‘activity bias’ (Lewis, Rao and Reiley, 2011). Counterfactual modelling approaches avoid these issues when it can be assumed that the treatment market was chosen at random. Overall, we expect inferences on the causal impact of designed market interventions to play an increasingly prominent role in providing quantitative accounts of return on investment (Danaher and Rust, 1996; Seggie, Cavusgil and Phelan, 2007; Leeflang et al., 2009; Stewart, 2009). This is because marketing resources, specifically, can only be allocated to whichever campaign elements jointly provide the greatest return on ad spend (ROAS) if we understand the causal effects of spend on sales, product adoption, or user engagement. At the same time, our approach could be used for many other applications involving causal inference. Examples include problems found in economics, epidemiology, biology, or the political and social sciences. With the release of the CausalImpact R package we hope to provide a simple framework serving all of these areas. 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Google, Inc. 1600 Amphitheatre Parkway Mountain View CA 94043, U.S.A. Estimating reach curves from one data point Georg M. Goerg Google Inc. Last update: November 21, 2014 Abstract Reach curves arise in advertising and media analysis as they relate the number of content impressions to the number of people who have seen it. This is especially important for measuring the effectiveness of an ad on TV or websites (Nielsen, 2009; PricewaterhouseCoopers, 2010). For a mathematical and datadriven analysis, it would be very useful to know the entire reach curve; advertisers, however, often only know its last data point, i.e., the total number of impressions and the total reach. In this work I present a new method to estimate the entire curve using only this last data point. Furthermore, analytic derivations reveal a surprisingly simple, yet insightful relationship between marginal cost per reach, average cost per impression, and frequency. Thus, advertisers can estimate the cost of an additional reach point by just knowing their total number of impressions, reach, and cost. A comparison of the proposed one-data point method to two competing regression models on TV reach curve data, shows that the proposed methodology performs only slightly poorer than regression fits to a collection of several points along the curve. 1 Introduction Let k+ reach, rk, be the percentage of the population that is exposed to a campaign at least k times. As usual, we measure impressions in gross rating points (GRPs), which is calculated as number of impressions divided by total (target) population multiplied by 100 (measured in percent). Equipped with a functional form of the reach curve, a variety of quantities of interest can be computed, e.g., marginal cost per reach or maximum possible reach. Advertisers, however, often only have two points of the reach curve rk(g): rk(0) = 0 and rk(G) = R ∈ [0, 100], (1) where G ≥ 0 is the total GRPs and R is total reach. With this information alone one is tempted to use a linear approximation r (1) k (g) = R G g. However, reach curves are not linear and in particular, the marginal reach per GRP would equal average reach per GRP (= 1/frequency); thus (1) alone is not helpful to get a better estimate of marginal GRP (and thus cost) per reach at g = G. While the behavior of rk(g) around g = G is in general unknown, the tangent at g = 0 can be approximated quite well: starting with no exposure, adding an infinitesimally small unit of GRPs (say ) one reaches · ι % of the population, where ι = ι(k) is the reciprocal of the expected number of impressions needed for the first person to see k impressions. One can lower bound ι by 1/k. For k = 1, the bound is tight, ι = 1; getting an exact expression of ι for k > 1 is ongoing research.1 That is, for small g the reach curve can be approximated with a line through (0, 0) with slope ι: rk(g) ≈ g · ι for small g. (2) Thus, approximately, lim G→0 ∂ ∂g rk(g = G) = ι. (3) Combining (1) with (3) allows us to estimate a two-parameter model. Section 2 reviews parametric models for reach curves. Section 3 derives the parameter estimates based on 1 In practice we found that ι = (k + log2 k)−1 gives good fits for several k ≥ 1. 12.2 Conditional Logit 2 REACH CURVE MODELS the total GRP and reach. Simulations and comparisons to full least squares estimates are presented in Section 4. Finally, Section 5 summarizes the main findings and discusses future work. Details on the TV reach curve data and analytical derivations can be found in the Appendix. 2 Reach curve models Let X ≥ 0 be the number of content impressions, e.g., TV shows, websites, or commercials. For a probabilistic view of reach curves, it is useful to decompose k+ reach as P (X ≥ k,reachable) = P (X ≥ k | reachable) · P (reachable) (4) ⇔ rk = pk · ρ, (5) where ρ is the maximum possible reach, and pk is the probability of being reached k times, given that an individual is indeed reachable. This distinction allows us to model ρ and pk with separate probabilistic models. Since reach is usually denoted in percent, we also use percent for maximum possible reach ρ ∈ [0, 100], while we use proportions for pk ∈ [0, 1]. For further analytical derivations it is necessary to parametrize pk(g). Below we review two functional forms which are parsimonious (2 + 1 parameters), have excellent empirical fits, and lend themselves for simple analytical derivations. 2.1 Gamma-Mixture Jin et al. (2012) propose a Poisson distribution for the impressions g, with an exponential prior distribution with rate β on the Poisson rate λ. This yields a model of the form pk(g) = 1 − β g + β . (6) The exponential prior can be generalized to a Γ(α, β) distribution, which yields rk(g) = ρ 1 − β β + g α . (7) By construction, (6) is nested in (7), which can be tested using a hypothesis test for H0 : α = 1. 2.1.1 Marginal reach The derivative of (7) with respect to g equals2 ∂ ∂g pk(g) = α β β g + β α+1 , (8) with limg→0 ∂ ∂g rk(g) = ρα β . (9) Eq. (9) has three degrees of freedom; since only two data points are available, one parameters has to be fixed. Given the nested structure of the exponential model, it is natural to set α ≡ 1. 2.2 Conditional Logit As an alternative we propose a logistic regression logit(pk(g)) = β0 + β1 · log g, (10) where logit(p) = log p 1−p , and β0 and β1 are intercept and slope.3 Using the logit inverse expit(x) = e x 1+e x = 1 1+e−x , Eq. (10) can be rewritten as pk = expit(β0 + β1 log g) = e β0+β1 log g 1 + e β0+β1 log g (11) = 1 − 1 1 + e β0 · g β1 (12) = 1 − e −β0 e−β0 + g β1 (13) which shows similarity to (7). In fact, identifying β ≡ e −β0 , both models coincide if α = 1 and β1 = 1, respectively. Again, this can be tested using a two-sided hypothesis test for H0 : β1 = 1. The Logit conditional model can also be interpreted as the baseline Gamma mixture model with α ≡ 1, but with transformed GRPs, ˜g = g β1 , in (7). Here β1 can be interpreted as a parameter that measures the efficiency of GRPs: for β1 > 1 GRPs are more efficient than baseline; for β1 = 1 GRPs are spent according to the baseline model; and for β1 < 1 are not spent as efficiently as expected. For an empirical estimates see Section 4. 2See Section B.1 for details. 3We deliberately do not use α and β to parametrize intercept and slope, as it is prone to confusion with the (reversed) roles of α and β in (8). Google Inc. 23.1 ρ 1. (15) Thus for the logit model one has to assume β1 = 1 to use the linear approximation of R(g) at g = 0 for 1+ reach.4 3 Methodology Equipped with the two parameter model r(g; ρ, β) = ρ 1 − β β + g = ρ g β + g ∈ [0, ρ], (16) we can use the tangent approximation in (3) and total GRP and reach to estimate ρ and β. Note that β ≥ 0 is a saturation parameter and controls how efficient GRPs are: for small β reach grows quickly with GRPs, for large β it grows slowly. Its derivative equals r 0 (g; ρ, β) = ρ β (β + g) 2 , (17) which at g = 0 evaluates to r 0 (0) = ρ β . This gives a system of two equations (maximum GRP and reach & marginal reach at 0) with two unknowns, ρ ∈ [0, 100] and β > 0: ρ β = ι ⇔ ρ = β · ι, (18) ρ G β + G = R ⇔ ρ = R(G + β) G . (19) First note that for 1+ reach, ρ ≡ β since ι(k = 1) = 1. Moreover, ρ in (19) satisfies ρ ≥ 0 for all β, but it satisfies ρ ≤ 100 only for β ≤ G · 100−R R . 4For k > 1, the Logit model with β1 > 1 might become useful as the marginal k+ reach for the very first impression is 0. However, one then has to estimate three parameters again, which is not possible without any further assumptions or more than one data point. Solving for β and plugging in to ρ = ρ(β) gives ρb = min G · R G − R/ι, 100 , (20) and βb = (ρb ι = G·R/ι G−R/ι , if ρ < 100, G · 100−R R , if ρ = 100. (21) Condition ρb ≤ 100 is equivalent to G ≤ 100 ι R 100−R ; thus GRPs must be less or equal to a constant times the odds ratio of reach. Plugging them back into (16) yields expressions for reach solely as a function of R and G (details see Appendix B). According to (21) we consider the two scenarios separately. 3.1 ρ 1 − F θ pz > 1 − F. The first inequality will be close to an equality when pz and hence θ is small. For our applications 1 − F θ/pz is a reasonable approximation to the variance ratio. The second inequality reflects the fact that pooling the data cannot possibly be better than what we would get with an SSP of size n + N. From var( f ˆθI)/var(ˆθS) ≈ 1 − F θ/pz we see that using the BRP is effectively like multiplying the SSP sample size n by 1/(1−F θ/pz). Our greatest precision gains come when a high fraction of online reaches are incremental, that is, when θ/pz is largest. In our application this proportion ranges from 20% to 50% when aggregated to the campaign level. See Table 2.1 in Section 2. 3.2 Gain from the CIA alone Here we evaluate the variance reduction that would follow from the CIA. In that case, we could take advantage of the Z–Y independence, and estimate θ by ˆθC = Z¯ S(1 − Y¯ S). It is shown in the Appendix that the delta method variance of ˆθC satisfies var( f ˆθC) var(ˆθS) = 1 − py(1 − pz) 1 − θ > 1 − py, (2) when the CIA holds. This can represent a dramatic improvement, when the online reach pz and incremental reach θ are both small while the TV reach py is large. If the CIA holds, our application data suggest the variance reduction can be from 50% to 80%. The reverse setting with tiny TV reach and large online reach would not be favorable to ˆθC, but our data are not of that type. 3.3 Gain from the CIA and IDA Finally, suppose that both the CIA and IDA hold. If we apply both assumptions, we can get the estimator ˆθI,C = (fZ¯ S + FZ¯B)(1 − Y¯ S). We already gain a lot4 Example campaigns 7 from the CIA, so it is interesting to see how much more the IDA adds when the CIA holds. We show in the Appendix that under both assumptions, var( f ˆθI,C) var( f ˆθC) = f(1 − py)(1 − pz) + pypz (1 − py)(1 − pz) + pypz . If both reaches are high then we gain little, but if both reaches are small then we reduce the variance by almost a factor of f, when adding the IDA to the CIA. In our case we expect that the television reach is large but the online reach is small, fitting neither of these extremes. Consider a campaign with f = 1/3, py = 2/3 and pz = 99/100, similar to the soap campaigns. For such a campaign, var( f ˆθI,C) var( f ˆθC) = (1/9) × .99 + (2/3) × .01 (1/3) × .99 + (2/3) × .01 .= .34, so the combined assumptions then allow a nearly three-fold variance reduction compared to CIA alone. 4 Example campaigns Our data enrichment scheme is described in Section 5. Here we illustrate the results from that scheme on six marketing campaigns and discuss the differences among different algorithms. In addition to data enrichment, we also show results from tree structured models. Those split the data into groups and recursively split the groups. More about tree fitting is in Section 5. One model fits a tree to the SSP data alone and another one works with the pooled SSP and BRP data. For all three of those methods we have aggregated the predictions over the age variable, which takes six levels. In addition, we show the empirical results for age, which amount to recording the percentage of incremental reaches, that is, data with Z(1 − Y ) = 1, at each unique level of age in the SSP. There is no corresponding empirical prediction fully disaggregated by age, gender, income and education, because of the great many empty cells that would cause. We found the age related patterns of incremental reach particularly interesting. Figure 4.1 shows estimated incremental reach for all three models and the empirical counts, on all six campaigns, averaged over age groups. The beer campaign is particularly telling. The empirical data show a decreasing trend of incremental reach with age. The tree fit to SSP-only data yields a fit that is constant in age. The tree model had to explore splitting the data on all four variables without a prior focus on age. There were only 23 incremental reach events for beer in the SSP data set. With such a small number of events and four predictors, there is considerable possibility of overfitting. Cross-validation lead to a model that grouped the entire SSP into one set, that is, the tree had no splits. Both pooling and data enrichment were able to borrow strength from the BRP as well as take advantage of approximate independence of television and web exposure. They then recover the trend with age.5 Data enrichment for incremental reach 8 Age level Incremental reach (%) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1 2 3 4 5 6 ● ● ● ● ● ● Beer 2 4 6 8 10 ● ● ● ● ● ● Chrome 0.0 0.5 1.0 1.5 1 2 3 4 5 6 ● ● ● ● ● ● Salt 0.0 0.5 1.0 1.5 2.0 ● ● ● ● ● ● Soap 1 1 2 3 4 5 6 0.5 1.0 1.5 2.0 ● ● ● ● ● ● Soap 2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 ● ● ● ● ● ● Soap 3 ● Emp SSP Pool DEIR Fig. 4.1: Estimated incremental reach by age, for six campaigns and three models: SSP, Pooling and DEIR as described in the text. Empirical counts are marked by Emp. The Salt campaign had a similarly small number of incremental reaches and once again the SSP only tree was constant. Fitting a tree to the SSP data always gave a flatter fit versus age than did DEIR which in turn was flatter than what we would get simply pooling the data. Section 6 gives simulations in which DEIR has greater accuracy than using pooling or SSP only. 5 Data enrichment for incremental reach For a given sample we would like to combine incremental reach estimates ˆθS, ˆθI , ˆθC and ˆθI,C whose assumptions are: none, IDA, CIA and IDA+CIA, respectively. The latter three add some value if their corresponding assumptions are nearly true, but our information about how well those assumptions hold comes from the same data we are using to form the estimates. The circumstances are similar to those in data enriched linear regression (Chen5 Data enrichment for incremental reach 9 et al., 2013). In that problem there is a regression model Yi = XT i β + εi which holds in the SSP and a biased regression model Yi = XT i (β + γ) + εi holds in the BRP. The estimates are found by minimizing S(λ) = X i∈S (Yi − XT i β) 2 + X i∈B (Yi − XT i (β + γ))2 + λ X i∈S (XT i γ) 2 , (3) over β and γ for a nonnegative penalty factor λ. The εi are independent with mean 0 and variance σ 2 S in the SSP and σ 2 B in the BRP. Taking λ = 0 amounts to fitting regressions separately in the two samples yielding an estimate βˆ that does not use the BRP at all. The limit λ → ∞ corresponds to pooling the two data sets, which would be optimal if there were no bias, i.e., if γ = 0. The specific penalty in (3) discourages the estimated γ from making large changes to the SSP; it is one of several penalties considered in that paper. Varying λ from 0 to ∞ gives a family of estimators that weight the SSP to varying degrees. The optimal λ is unknown. An oracle that knew γ and the error variance in the two data sets would be able to compute the optimal λ under a mean squared error loss. Chen et al. (2013) get a formula for the oracle’s λ and then plug estimates of γ and the variances into that formula. They show, under conditions, that the resulting plugin estimate gives better estimates of β than using the SSP only would. The conditions are that the Y values are normally distributed, and that the model have at least 5 regression parameters and 10 error degrees of freedom. The normality assumption allows a technical lemma due to Stein (1981) to be used and we believe that gains from using the BRP do not require normality. In principle we might multiply the sum of squared errors in the BRP by τ = σ 2 S /σ2 B if that ratio is known. If σ 2 BRP > σ2 SSP then we should put less weight on the BRP sample relative to the SSP sample. However the same effect is gained by increasing λ. Since the algorithm searches for optimal λ over a wide range it is less important to precisely specify τ . Chen et al. (2013) took τ = 1, simply summing all squared errors, and we will generalize that approach. For the present setting we must modify the method. First our responses are binary, not Gaussian. Second we have four estimators to combine, not two. Third, those estimators are dependent, being fit to overlapping data sets. 5.1 Modification for binary response To address the binary response there are two reasonable choices. One is to employ logistic regression. The other is to use tree-structured regression and then pool the estimators at the leaves of the tree. Regarding prediction accuracy, there is no unique best algorithm. There will be data sets for which simple logistic regression outperforms tree based classifiers and vice versa. For this paper we have adopted trees. Tree structured models have two practical advantages. First, the resulting cells that they select correspond to empirically determined market segments, which are then interpretable. Sec-5 Data enrichment for incremental reach 10 Data set Source Imputed V Assumptions D0 SSP ZS(1 − YS) none D1 BRP ZB(1 − YbSSP(XB, ZB)) IDA D2 SSP ZbSSP(XS)(1 − YbSSP(XS)) CIA D3 SSP ZbSSP+BRP(XS)(1 − YbSSP(XS)) CIA & IDA Tab. 5.1: Four incremental reach data sets and their imputed incremental reaches. The hats denote model-imputed values. For example YbSSP(XB, ZB) is a predictive model for Y based on values X and Z fit using data from SSP and evaluated at X = XB and Z = XB (from BRP). ond, within any of those cells, the model is intercept-only. Then both logistic regression and least squares reduce to a simple average. Each leaf of the regression tree defines a subset of the data that we call a cell. There are cells 1, . . . , C. The SSP has nc observations in cell c and the BRP has Nc observations there. For each cell and each set of assumptions we use a linear regression model relating an incremental reach quantity like Vei to an intercept. When there are no assumptions then Vei is the observed incremental reach for i ∈ S. Otherwise we may take advantage of the assumptions to impute values Vei using more of the data. The incremental reach values for each set of assumptions are given in Table 5.1. The predictive models shown there are all fit using rpart. For k = 0, 1, 2, 3 let Vek be vector of imputed responses under any of the assumptions from Table 5.1 and Xek their corresponding predictors. The regression framework minimizes kVe0 − Xe0βk 2 + X 3 k=1 kVek − Xek(β + γk)k 2 + X 3 k=1 λkkXe0γkk 2 . (4) over β and γk for penalties λk. In our setting each Xek is a column vector of ones of length mk. For cell c, m1k = Nc and m0k = m2k = m3k = nc. 5.2 Search for λk It is very convenient to search for suitable weights in the simplex ∆(K) = {(ω0, ω1, . . . , ωK) | ωk > 0, X K k=0 ωk = 1} because it is a bounded set, unlike the set [0, ∞] K of usable vectors λ = (λ1, . . . , λK). Chen et al. (2013) remark that it is more reasonable to use a common set of λk over all cells, stemming from unequal sample sizes. The search we use combines the advantages of both approaches.5 Data enrichment for incremental reach 11 Our search strategy for the simplex is to choose a grid of weight vectors ωg = (ωg0, ωg1, . . . , ωgK) ∈ ∆(K) , g = 1, . . . , G. For each vector ωg we find a vector λg = (λ1, . . . , λK) such that X C c=1 pcωk,c = ωgk, k = 0, 1, . . . , K, where pc is the proportion of our target population in cell c. That is, the population average weight of ωk,c matches ωgk. These weights give us the vector λg = (λg1, . . . , λgK). Using λg in the penalty criterion (4) specifies the weights we use within each cell. Our algorithm chooses the tree and the vector ω jointly using cross-validation. It is computationally expensive to make high dimensional searches. With K factors there is a K − 1 dimensional space of weights to search. Adding in the tree size gives a K’th dimension. As a result, combining all of our estimators requires us to search a 4 dimensional grid of values. We have chosen to set one of the ωk to 0 to reduce the search space from 4 dimensions to 3. We always retained the unbiased estimate ˆθS along with two others. In some computations reported in section A.4 of the Appendix we find only small differences among setting ω1 = 0, or ω2 = 0 or ω3 = 0. The best outcome was setting ω1 = 0. That has the effect of removing the estimate based on IDA only. As we saw in section 3, the IDA-only model had the least potential to improve our estimate. As a bonus, all three of the retained submodels have the same sample sizes and then common λ over cells coincides with common ω over cells. In the special case with ω1 = 0 we find after some calculus that the minimizer of (4) has βˆ c = V¯ 0c + P k∈{2,3} λk 1+λk V¯ kc 1 + P k∈{2,3} λk 1+λk ≡ X k∈{0,2,3} ωkc(λ)Vkc (5) where V¯ kc is the simple average of Vek over i ∈ S for cell c. Our default grid takes all values of ω whose coefficients are integer multiples of 10%. Populations D0, D2 and D3 all have the sample size n and of these only D0 is surely unbiased. An observation in D0 is worth at least as much as an observation in D2 or D3 and so we require ω0 > max{ω2, ω3}. Figure 5.1 shows this region and the set of 24 weight combinations that we use. 5.3 Search for tree size Here we give a brief review of regression trees in order to define our algorithm. For a full description see the monograph by Breiman et al. (1985). The version we use is the function rpart (Therneau and Atkinson, 1997) in the R programming language (R Core Team, 2012).5 Data enrichment for incremental reach 12 Weight region ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Weight points Fig. 5.1: The left panel shows the simplex of weights applied to data sets D0, D2 and D3 with the unbiased data set D0 in the lower left. The shaded region has the valid weights. The right panel shows that region with points for the 24 weights we use in our algorithm. Regression trees are built from splits of the set of subjects. A split uses one of the features in X and creates two subsets based on the values of that feature. For example it might split males from females or it might split those with the two smallest education levels from the others. Such a split defines two subpopulations of our target population and it equally defines two subsamples of our sample. A regression tree is a recursively defined set of splits. After the subjects are split into two groups based on one variable, each of those two groups may then be split again, using the same or different variables. Recursive splitting of splits yields a tree structure with subsets of subjects in the leaf nodes. Given a tree, we predict for subjects by a rule based on the leaf to which they belong. That rule uses the average within the subject’s leaf node. The tree is found by a greedy search that minimizes a measure of prediction error. In our case, the measure R(T), is the sum of squared prediction errors. By construction any tree with more splits than T has lower error and this brings a risk of overfitting. To counter overfitting, rpart adds a penalty proportional to the number |T| of leaves in tree T. The penalized criterion is R(T) + α|T| where the parameter α > 0 is chosen by M-fold cross-validation. This reduces the potentially complicated problem of choosing a tree to the simpler problem of selecting a scalar penalty parameter α. The rpart function has one option that we have changed from the default. That parameter is cp, the complexity parameter. The default is 10−2 . The cp parameter stops tree growing early if a proposed split improves R(T) by less6 Numerical investigation 13 than a factor of cp. We set cp = 10−4 . Our choice creates somewhat larger trees to get more choices to use in cross-validation. 5.4 The algorithm Here is a summary of the entire algorithm. First we make the following preprocessing steps. 1) Fit a large tree T by rpart relating observed incremental reaches Vi to predictor variables Xi in the SSP data. This tree returns a nested sequence of subtrees T0 ⊂ T1 ⊂ · · · ⊂ TL ⊂ T . Each T` corresponds to a critical value α` of the penalty. Choosing α` from this list selects the tree T`. The value L is data-dependent, and chosen by rpart. 2) Specify a grid of values ωg for g = 1, . . . , G. Here ωg = (ωg0, ωg1, . . . , ωgK) with ωgk > 0 and PG k=0 ωgk = 1. 3) Randomly partition SSP data (Xi , Yi , Zi) into M folds Sm for m = 1, . . . , M each of roughly equal size n/M. For fold m the SSP will contain ∪Sm0 for all m0 6= m. We call this S−m. The BRP for fold m is the entire BRP. We also considered using a bootstrap sample for the fold m BRP, but that was more expensive and less accurate in our numerical investigation as described in section A.4 of the Appendix. After this precomputation, our algorithm proceeds to the cross-validation shown in Figure 5.2 to make a joint selection of the tree penalty parameter α` and the simplex grid point ωg. Let the chosen values be α∗ and ω∗. We select the tree T∗ from step 1 above, corresponding to penalty parameter α∗. We treat each leaf node of T∗ as a cell c. We translate ω∗ into the corresponding λc in every cell c of tree T∗. Then we minimize (4) using this λc and the resulting βˆ c is our estimate Vbc of incremental reach in cell c. After choosing the tuning parameters ωg and α` by cross-validation, we use these parameters on the whole data set to make our final prediction. 6 Numerical investigation In order to measure the effect of data enriched estimates on incremental reach, we conducted a simulation where we knew the ground truth. Our goal is to predict for ensembles, not for individuals, so we constructed two large populations in which ground truth was known to us, simulated our process of subsampling them, and scored predictions against the ground truth incremental reach probabilities. To make our large samples realistic, we built them from our real data. We created S- and B-populations by replicating our SSP (respectively BRP) records 100 times each. Then in each simulation, we form an SSP by drawing 6000 observations at random from the S-population, and a BRP by drawing 13,000 observations at random from the B-population.6 Numerical investigation 14 for ` = 1, . . . , L do // initialize error sum of squares for g = 1, . . . , G do SSE`,g ← 0 for m = 1, . . . , M do // folds construct Table 5.1 for fold m, using S−m and B fit tree Tm for fold m by rpart prune tree Tm to T1,m, . . . , TL,m, tree T`,m uses α` for ` = 1, . . . , L do // tree sizes define cells S−m,c and Bc, c = 1, . . . , C from leaves of T`,m for g = 1, . . . , G do // simplex weights convert ωg into λg for c = 1, . . . , C do // cells compute Vek for k = 0, 2, 3 in cell c get Vbc = βˆ c from the weighted average (5) Vc ← 1 |Sm,c| X i∈Sm,c Vi // held out incr. reach pc ← fraction of true S population in cell c SSE`,g ←SSE`,g + pc(Vbc − Vc) 2 Fig. 5.2: Data enrichment for incremental reach (deir) algorithm. After precomputation described on page 13 we run this cross-validation algorithm to choose the complexity parameter α` and the weights ωg, as the joint minimizers ` ∗ and g ∗ of SSE`,g. The values pc come from a census or from the SSP if the census does not have the variables we need. We use M = 10. For each campaign, we apply deir with this sample data to estimate the incremental reach Vˆ (x). We used 10–fold cross-validation. The mean square estimation error (MSE) is P x p(x)(Vˆ (x) − V (x))2 . This sum is taken over all x values in the SSP. The simulation above was repeated 1000 times. The root mean square error was divided by the true incremental reach to get a relative RMSE. We consider two comparison methods. The first is to use the SSP only. That method computes ˆθS within the leaves of a tree. The tree is found by rpart. The second comparison is a tree fit by rpart to the pooled SSP and BRP data and using both CIA and IDA. We do not compare to the empirical fractions because many of them are from empty cells. Figure 6.1 compares the relative errors in the SSP only method to data6 Numerical investigation 15 DEIR RMSE (%) SSP RMSE (%) 1 2 3 1 2 3 ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● Chrome 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Salt 0 1 2 3 4 5 6 0 1 2 3 4 ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Soap 1 0.5 1.0 1.5 2.0 2.5 3.0 0.5 1.0 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Soap 2 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Soap 3 Fig. 6.1: Performance comparison, SSP only versus data enrichment, predictive relative mean square errors. There is one panel for each of 6 campaigns with one point for each of 1000 replicates. The reference line is the forty-five degree line. enrichment. Data enrichment is consistently better over all 6 campaigns we simulated in the great majority of replications. It is clear that the populations are similar enough that using the larger data set improves estimation of incremental reach. Under the IDA we can pool the SSP and BRP together using rpart on the combined data to estimate Pr(Z = 1 | X). Under the CIA we can multiply this estimate by Pr(Y = 0 | X) fit by rpart to the SSP, see Table 5.1 under the assumption CIA & IDA. This method, as an implementation of statistical matching, uses two separate applications of rpart each with their own built in cross-validation. Figure 6.2 compares the relative errors of statistical matching to data enrichment. Data enrichment is consistently better over all 6 campaigns we simulated in the great majority of replications. We also investigate for each estimator, how much of the predictive error is contributed by bias. It is well known that predictive mean square error can be decomposed as the sum of variance and squared bias. These quantities are typically unknown in practice, but can be evaluated in simulation studies.6 Numerical investigation 16 DEIR RMSE (%) Pool RMSE (%) 0.6 0.8 1.0 1.2 1 2 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Beer 5 6 7 8 9 10 3 4 5 6 7 8 9 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Chrome 0.6 0.8 1.0 1.2 1.4 0.5 1.0 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Salt 0.5 1.0 1.5 0 1 2 3 4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Soap 1 0.8 1.0 1.2 1.4 1.6 1.8 0.5 1.0 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Soap 2 0.4 0.6 0.8 1.0 1.2 0.0 0.5 1.0 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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There is one panel for each of 6 campaigns with one point for each of 1000 replicates. The reference line is the forty-five degree line. Table 6.1 reports the fractions of squared bias in predictive mean square errors for each method in all six studies. We see there that the error for statistical matching (data pooling) is dominated by bias while the error for SSP only is dominated by variance. These results are not surprising because the SSP only method has no sampling bias (only algorithmic bias) while the pooled data set has maximal sampling bias. The proportion of bias for DEIR is in between these extremes. Here we have less population bias than a typical data fusion situation because the TV and online-only panels were recruited in the same way. The bottom of Table 6.1 shows that DEIR is able to trade off bias and variance more effectively than SSP only or data pooling, because DEIR attains the smallest predictive mean squared error. Conclusions Predictions of incremental reach can be improved by making use of additional data. That improvement comes only if certain strong assumptions are true or at6 Numerical investigation 17 bias2 /mse Beer Chrome Salt Soap 1 Soap 2 Soap 3 SSP 0.35 0.42 0.26 0.12 0.28 0.12 Pool 0.88 0.82 0.88 0.88 0.88 0.93 DEIR 0.49 0.59 0.47 0.33 0.47 0.39 mse Beer Chrome Salt Soap 1 Soap 2 Soap 3 SSP 1.02 7.76 0.89 0.84 1.26 0.66 Pool 0.82 7.39 0.80 0.86 1.12 0.78 DEIR 0.61 5.42 0.48 0.52 0.68 0.42 Tab. 6.1: The upper rows show the fraction bias2 /mse of the mean squared prediction error due to bias for 3 methods to estimate incremental reach in 6 campaigns. The lower rows show the total mse, that is bias2 + var. least approximately true. Our only guide to the accuracy of those assumptions may come from the data themselves. Our data enriched incremental reach estimate uses a shrinkage strategy to pool estimates using different assumptions. Cross-validating the level of pooling gave us an algorithm that worked better than either ignoring the additional data or treating it the same as the unbiased data. Acknowledgment This project was not part of Art Owen’s Stanford responsibilities. His participation was done as a consultant at Google. The authors would like to thank Penny Chu, Tony Fagan, Yijia Feng, Jerome Friedman, Yuxue Jin, Daniel Meyer, Jeffrey Oldham and Hal Varian for support and constructive comments. References Breiman, L., Friedman, J. H. Olshen, R. A., and Stone, C. J. (1985). Classifi- cation and Regression Trees. Chapman & Hall/CRC, Baton Rouge, FL. Chen, A., Owen, A. B., and Shi, M. (2013). Data enriched linear regression. Technical report, Google. http://arxiv.org/abs/1304.1837. Collins, J. and Doe, P. (2009). Developing an integrated television, print and consumer behavior database from national media and purchasing currency data sources. In Worldwide Readership Symposium, Valencia. Doe, P. and Kudon, D. (2010). Data integration in practice: connecting currency and proprietary data to understand media use. ARF Audience Measurement 5.0.A Appendix 18 D’Orazio, M., Di Zio, M., and Scanu, M. (2006). Statistical Matching: Theory and Practice. Wiley, Chichester, UK. Gilula, Z., McCulloch, R. E., and Rossi, P. E. (2006). A direct approach to data fusion. Journal of Marketing Research, XLIII:73–83. Jin, Y., Shobowale, S., Koehler, J., and Case, H. (2012). The incremental reach and cost efficiency of online video ads over TV ads. Technical report, Google. Lehmann, E. L. and Romano, J. P. (2005). Testing statistical hypotheses. Springer, New York, Third edition. Little, R. J. A. and Rubin, D. B. (2009). Statistical Analysis with Missing Data. John Wiley & Sons Inc., Hoboken, NJ, 2nd edition. R Core Team (2012). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. R¨assler, S. (2004). Data fusion: identification problems, validity, and multiple imputation. Austrian Journal of Statistics, 33(1&2):153–171. Singh, A. C., Mantel, H., Kinack, M., and Rowe, G. (1993). Statistical matching: Use of auxiliary information as an alternative to the conditional independence assumption. Survey Methodology, 19:59–79. Stein, C. M. (1956). Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. In Proceedings of the Third Berkeley symposium on mathematical statistics and probability, volume 1, pages 197–206. Stein, C. M. (1981). Estimation of the mean of a multivariate normal distribution. The Annals of Statistics, 9(6):1135–1151. The Nielsen Company (2011). The cross-platform report. Quarter 2, U.S. Therneau, T. M. and Atkinson, E. J. (1997). An introduction to recursive partitioning using the RPART routines. Technical Report 61, Mayo Clinic. A Appendix A.1 Variance reduction by IDA Recall that f = n/(n + N) and F = N/(n + N) are sample size proportions of the two data sets. Under the IDA we may estimate incremental reach by ˆθI = (fZ¯ S + FZ¯B) V¯ S Z¯ S = V¯ I f + F Z¯B Z¯ S .A Appendix 19 By the delta method (Lehmann and Romano, 2005), var(ˆθI) is approximately var( f ˆθI) = var(V¯ S) ∂ ˆθI ∂V¯ S 2 + var(Z¯B) ∂ ˆθI ∂Z¯B 2 + var(Z¯ S) ∂ ˆθI ∂Z¯ S 2 + 2cov(V¯ S,Z¯ S) ∂ ˆθI ∂V¯ S ∂ ˆθI ∂Z¯ S , with partial derivatives evaluated with expectations E(V¯ S), E(Z¯ S), and E(Z¯B) replacing the corresponding random quantities. The other two covariances are zero because the S and B samples are independent. From the binomial distribution we have var(V¯ S) = θ(1 − θ)/n, var(Z¯B) = pz(1 − pz)/N and var(Z¯ S) = pz(1 − pz)/n. Also cov(V¯ S,Z¯ S) = 1 n E(ViZi) − E(Vi)E(Zi) = θ(1 − pz)/n. After some calculus, var( f ˆθI) = θ(1 − θ) n + pz(1 − pz) N θ 2F 2 p 2 z + pz(1 − pz) n θ 2F 2 p 2 z − 2 θ(1 − pz) n θF pz = var(ˆθS) + θ 2F(1 − pz) pz F N + F n − 2 n = var(ˆθS) − θ 2F(1 − pz) pz 1 n = var(ˆθS) 1 − F 1 − pz pz θ 1 − θ . A.2 Variance reduction by CIA Applying the delta method to ˆθC = Z¯ S(1 − Y¯ S), we find that var( f ˆθC) = var(Z¯ S) ∂ ˆθC ∂Z¯ S 2 + var(Y¯ S) ∂ ˆθC ∂Y¯ S 2 + 2cov(Y¯ S,Z¯ S) ∂ ˆθC ∂Y¯ S ∂ ˆθC ∂Z¯ S = var(Z¯ S)(1 − py) 2 + var(Y¯ S)p 2 z + 2cov(Y¯ S,Z¯ S)(1 − py)pz. Here var(Z¯ S) = pz(1 − pz)/n, var(Z¯ S) = pz(1 − pz)/n, and under conditional independence cov(Y¯ S,Z¯ S) = 0. Thus var( f ˆθC) = 1 n pz(1 − pz)(1 − py) 2 + py(1 − py)p 2 z = 1 n pz(1 − pz)(1 − py) 2 + py(1 − py)p 2 z = pz(1 − py) n (1 − pz)(1 − py) + pypz . When the CIA holds, θ = pz(1 − py). Note that var(ˆθS) = θ(1 − θ)/n. After some algebraic simplification we find that var( f ˆθC) var(ˆθS) = 1 − py(1 − pz) 1 − θ .A Appendix 20 A.3 Variance reduction by CIA and IDA When both assumptions hold we can estimate θ by ˆθI,C = (fZ¯ S + FZ¯B)(1 − Y¯ S). Under these assumptions, Z¯ S, Z¯B and Y¯ S are all independent, and var( f ˆθI,C) equals var(Z¯ S) ∂ ˆθI,C ∂Z¯ S 2 + var(Z¯B) ∂ ˆθI,C ∂Z¯B 2 + var(Y¯ S) ∂ ˆθI,C ∂Y¯ S 2 = pz(1 − pz) n f 2 (1 − py) 2 + pz(1 − pz) N F 2 (1 − py) 2 + py(1 − py) n p 2 z = pz(1 − py) n f(1 − py)(1 − pz) + pypz after some simplification. As a result var( f ˆθI,C) var( f ˆθC) = f(1 − py)(1 − pz) + pypz (1 − py)(1 − pz) + pypz . A.4 Alternative algorithms We faced some design choices in our algorithm. First, we had to decide which estimators to include in our algorithm. We always include the unbiased choice ˆθS as well as two others. Second, we had to decide whether to use the entire BRP or to bootstrap sample it. We ran all six choices on simulations of all six data sets where we knew the correct answer. Table A.1 shows the mean squared errors for the six possible estimators on each of the six data sets. In every case we divided the mean squared error by that for the estimator combining ˆθS, ˆθC , and ˆθI,C without the bootstrap. We only see small differences, but the evidence favors choosing λI = 0 as well as not bootstrapping. Our default method is consistently the best in this table, although only by a small amount. We saw that data enrichment is consistently better than either pooling the data or ignoring the large sample, and by much larger amounts than we see in Table A.1. As a result, any of the data enrichment methods in this table would make a big improvement over either pooling the samples or ignoring the BRP.A Appendix 21 Estimators ˆθS, ˆθI , ˆθC ˆθS, ˆθI , ˆθI,C ˆθS, ˆθC , ˆθI,C BRP All Boot All Boot All Boot Beer 1.02 1.02 1.00 1.01 1 1.01 Chrome 1.04 1.04 1.01 1.01 1 1.00 Salt 1.04 1.04 1.01 1.01 1 1.01 Soap 1 1.04 1.05 1.01 1.02 1 1.00 Soap 2 1.05 1.05 1.01 1.03 1 1.01 Soap 3 1.02 1.02 1.01 1.00 1 1.00 Tab. A.1: Relative performance of our estimators on six problems. The relative errors are mean squared prediction errors normalized to the case that uses ˆθS, ˆθC , ˆθI,C without bootstrapping. The relative error for that case is 1 by definition. Coupled and k-Sided Placements: Generalizing Generalized Assignment Madhukar Korupolu1 , Adam Meyerson1 , Rajmohan Rajaraman2 , and Brian Tagiku1 1 Google, 1600 Amphitheater Parkway, Mountain View, CA. Email: {mkar,awmeyerson,btagiku}@google.com 2 Northeastern University, Boston, MA 02115. Email: rraj@ccs.neu.edu Abstract. In modern data centers and cloud computing systems, jobs often require resources distributed across nodes providing a wide variety of services. Motivated by this, we study the Coupled Placement problem, in which we place jobs into computation and storage nodes with capacity constraints, so as to optimize some costs or profits associated with the placement. The coupled placement problem is a natural generalization of the widely-studied generalized assignment problem (GAP), which concerns the placement of jobs into single nodes providing one kind of service. We also study a further generalization, the k-Sided Placement problem, in which we place jobs into k-tuples of nodes, each node in a tuple offering one of k services. For both the coupled and k-sided placement problems, we consider minimization and maximization versions. In the minimization versions (MinCP and MinkSP), the goal is to achieve minimum placement cost, while incurring a minimum blowup in the capacity of the individual nodes. Our first main result is an algorithm for MinkSP that achieves optimal cost while increasing capacities by at most a factor of k + 1, also yielding the first constant-factor approximation for MinCP. In the maximization versions (MaxCP and MaxkSP), the goal is to maximize the total weight of the jobs that are placed under hard capacity constraints. MaxkSP can be expressed as a k-column sparse integer program, and can be approximated to within a factor of O(k) factor using randomized rounding of a linear program relaxation. We consider alternative combinatorial algorithms that are much more efficient in practice. Our second main result is a local search based approximation algorithm that yields a 15- approximation and O(k 3 )-approximation for MaxCP and MaxkSP respectively. Finally, we consider an online version of MaxkSP and present algorithms that achieve logarithmic competitive ratio under certain necessary technical assumptions. 1 Introduction The data center has become one of the most important assets of a modern business. Whether it is a private data center for exclusive use or a shared public cloud data center, the size and scale of the data center continues to rise. As a companygrows, so too must its data center to accommodate growing computational, storage and networking demand. However, the new components purchased for this expansion need not be the same as the components already in place. Over time, the data center becomes quite heterogeneous [1]. This complicates the problem of placing jobs within the data center so as to maximize performance. Jobs often require resources of more than one type: for example, compute and storage. Modern data centers typically separate computation from storage and interconnect the two using a network of switches. As such, when placing a job within a data center, we must decide which computation node and which storage node will serve the job. If we pick nodes that are far apart, then communication latency may become too prohibitive. On the other hand, nodes are capacitated, so picking nodes close together may not always be possible. Most prior work in data center resource management is focussed on placing one type of resource at a time: e.g., placing storage requirements assuming job compute location is fixed [2, 3] or placing compute requirements assuming job storage location is fixed [4, 5]. One sided placement methods cannot suitably take advantage of the proximities and heterogeneities that exist in modern data centers. For example, a database analytics application requiring high throughput between its compute and storage elements can benefit by being placed on a storage node that has a nearby available compute node. In this paper, we study Coupled Placement (CP), which is the problem of placing jobs into computation and storage nodes with capacity constraints, so as to optimize costs or profits associated with the placement. Coupled placement was first addressed in [6] in a setting where we are required to place all jobs and we wish to minimize the communication latency over all jobs. They show that this problem, which we call MinCP, is NP-hard and investigate the performance of heuristic solutions. Another natural formulation is where the goal is to maximize the total number of jobs or revenue generated by the placement, subject to capacity constraints. We refer to this problem as MaxCP. We also study a generalization of Coupled Placement, the k-Sided Placement Problem (kSP), which considers k ≥ 2 kinds of resources. 1.1 Problem definition In the coupled placement problem, we are given a bipartite graph G = (U, V, E) where U is a set of compute nodes and V is a set of storage nodes. We have capacity functions C : U → R and S : V → R for the compute and storage nodes, respectively. We are also given a set T of jobs, each of which needs to be allocated to one compute node and one storage node. Each job may prefer some compute-storage node pairs more than others, and may also consume different resources at different nodes. To capture these heterogeneities, we have for each job j a function fj : E → R, a processing requirement pj : E → R and a storage requirement sj : E → R. We note that without loss of generality, we can assume that the capacities are unit, since we can scale the processing and storage requirements of individual nodes accordingly.We consider two versions of the coupled placement problems. For the maximization version MaxCP, we view fj as a payment function. Our goal is to select a subset A ⊆ T of jobs and an assignment σ : A → E such that all capacities are observed and our total profit P j∈A fj (σ(j)) is maximized. For the minimization version MinCP, we view fj as a cost function. Our goal is to find an assignment σ : T → E such that all capacities are observed and our total cost P j∈A fj (σ(j)) is minimized. A generalization of the coupled placement problem is k-sided placement (kSP), in which we have k different sets of nodes, S1, . . . , Sk, each set of nodes providing a distinct service. For each i, we have a capacity function Ci : Si → R that gives the capacity of a node in Si to provide the ith service. We are given a set T of jobs, each of which needs each kind of service; the exact resource needs may depend on the particular k-tuple of nodes from Q i Si to which it is assigned. That is, for each job j, we have a demand function dj : Q i Si → Rk . We also have another function fj : Q i Si → R. As for coupled placement, we can assume that the capacities are unit, since we can scale the demands of individual nodes accordingly. Similar to coupled placement, we consider two versions of kSP, MinkSP and MaxkSP. 1.2 Our Results All of the variants of CP and kSP are NP-hard, so our focus is on approximation algorithms. Our first set of results consist of the first non-trivial approximation algorithms for MinCP and MinkSP. Under hard capacity constraints, it is easy to see that it is NP-hard to achieve any bounded approximation ratio to cost minimization. So we consider approximation algorithms that incur a blowup in capacity. We say that an algorithm is α-approximate for the minimization version if its cost is at most that of an optimal solution, while incurring a blowup factor of at most α in the capacity of any node. – We present a (k + 1)-approximation algorithm for MinkSP using iterative rounding, yielding a 3-approximation for MinCP. We next consider the maximization version. MaxkSP can be expressed as a k-column sparse integer packing program (k-CSP). From this, it is immediate that MaxkSP can be approximated to within an O(k) approximation factor by applying randomized rounding to a linear programming relaxation [7]. An Ω(k/ log k)-inapproximability result for k-set packing due to [16] implies the same hardness result for MaxkSP. Our second main result is a simpler approximation algorithm for MaxCP and MaxkSP based on local search. – We present a local search based 15-approximation algorithm for MaxCP. We extend it to MaxkSP and obtain an O(k 3 )-approximation. The local search result applies directly to a version where we can assign tasks fractionally but only to a single pair of machines (this is like assigning a task with lower priority and may have additional applications). We then describe asimple rounding scheme to obtain an integral version. The rounding technique involves establishing a one-to-one correspondence between fractional assignments and machines. This is much like the cycle-removing rounding for GAP; there is a crucial difference, however, since coupled and k-sided placements assign jobs to tuples of machines. Finally, we study the online version of MaxCP, in which tasks arrive online and must be irrevocably assigned or rejected immediately upon arrival. – We extend the techniques of [8] to the case where the capacity requirement for a job is arbitrarily machine-dependent. This enables us to achieve competitive ratio logarithmic in the ratio of best to worst value-per-capacity density, under necessary technical assumptions about the maximum job size. 1.3 Related Work The coupled and k-sided placement problems are natural generalizations of the Generalized Assignment Problem (GAP), which can be viewed as a 1-sided placement problem. In GAP, which was first introduced by Shmoys and Tardos [9], the goal is assign items of various sizes to bins of various capacities. A subset of items is feasible for a bin if their total size is no more than the bin’s capacity. If we are required to assign all items and minimize our cost (MinGAP), Shmoys and Tardos [9] give an algorithm for computing an assignment that achieves optimal cost while doubling the capacities of each bin. A previous result by Lenstra et al. [10] for scheduling on unrelated machines show it is NP-hard to achieve optimal cost without incurring a capacity blowup of at least 3/2. On the other hand, if we wish to maximize our profit and are allowed to leave items unassigned (MaxGAP), Chekuri and Khanna [11] observe that the (1, 2)-approximation for MinGAP implies a 2-approximation for MaxGAP. This can be improved to a ( e e−1 )-approximation using LP-based techniques [12]. It is known that MaxGAP is APX-hard [11], though no specific constant of hardness is shown. On the experimental side, most prior work in data center resource management focusses on placing one type of resource at a time: for example, placing storage requirements assuming job compute location is fixed (file allocation problem [2], [13, 14, 3]) or placing compute requirements assuming job storage location is fixed [4, 5]. These in a sense are variants of GAP. The only prior work on Coupled Placement is [6], where they show that MinCP is NP-hard and experimentally evaluate heuristics: in particular, a fast approach based on stable marriage and knapsacks is shown to do well in practice, close to the LP optimal. The MaxkSP problem is related to the recently studied hypermatching assignment problem (HAP) [15], and special cases, including k-set packing, and a uniform version of the problem. A (k + 1 + ε)-approximation is given for HAP in [15], where other variants of HAP are also studied. While the MaxkSP problem can be viewed as a variant of HAP, there are critical differences. For instance, in MaxkSP, each task is assigned at most one tuple, while in the hypermatching problem each client (or task) is assigned a subset of the hyperedges. Hence, the MaxkSP and HAP problems are not directly comparable. The k-set packing canbe captured as a special case of MaxkSP, and hence the Ω(k/ log k)-hardness due to [16] applies to MaxkSP as well. 2 The minimization version Next, we consider the minimization version of the Coupled Placement problem, MinCP. We write the following integer linear program for MinCP, where xtuv is the indicator variable for the assignment of t to pair (u, v), u ∈ U, v ∈ V . Minimize: X t,u,v xtuvft(u, v) Subject to: X u,v xtuv ≥ 1, ∀t ∈ T, X t,v pt(u, v)xtuv ≤ cu, ∀u ∈ U, X t,u st(u, v)xtuv ≤ dv, ∀v ∈ V, xtuv ∈ {0, 1}, ∀t ∈ T, u ∈ U, v ∈ V. We refer the first set of constraints as satisfaction constraints, the second and third set as capacity constraints (processing and storage). We consider the linear relaxation of this program which replaces the integrality constraints above with 0 ≤ xtuv ≤ 1, ∀t ∈ T, u ∈ U, v ∈ V . 2.1 A 3-approximation algorithm for MinCP We now present algorithm IterRound, based on iterative rounding [21], which achieves a 3-approximation for MinCP. We start with a basic algorithm that achieves a 5-approximation by identifying tight constraints with a small number of variables. Each iteration of this algorithm repeats the following round until all variables have been rounded. 1 Extreme point: Compute an extreme point solution x to the current LP. 2 Eliminate variable or constraint: Execute one of these two steps. By Lemma 3, one of these steps can always be executed if the LP is nonempty. a Remove from the LP all variables xtuv that take the value 0 or 1 in x. If xtuv is 1, then assign job t to the pair (u, v), remove the job t and its associated variables from the LP, and reduce cu by pt(u, v) and dv by st(u, v). b Remove from the LP any tight capacity constraint with at most 4 variables. Fix an iteration of the algorithm, and an extreme point x. Let nt, nc, and ns denote the number of tight task satisfaction constraints, computation constraints, and storage constraints, respectively, in x. Note that every task satisfaction constraint can be assumed to be tight, without loss of generality. Let N denote the number of variables in the LP. Since x is an extreme point, if all variables in x take values in (0, 1), then we have N = nt + nc + ns.Lemma 1. If all variables in x take values in (0, 1), then nt ≤ N/2. Proof. Since a variable only occurs once over all satisfaction constraints, if nt > N/2, there exists a satisfaction constraint that has exactly one variable. But then, this variable needs to take value 1, a contradiction. Lemma 2. If nt ≤ N/2, then there exists a tight capacity constraint that has at most 4 variables. Proof. If nt ≤ N/2, then ns + nc = N − nt ≥ N/2. Since each variable occurs in at most one computation constraint and at most one storage constraint, the total number of variable occurrences over all tight storage and computation constraints is at most 2N, which is at most 4(ns +nc). This implies that at least one of these tight capacity constraints has at most 4 variables. Using Lemmas 1 and 2, we can argue that the above algorithm yields a 5- approximation. Step 2a does not cause any increase in cost or capacity. Step 2b removes a constraint, hence cannot increase cost; since the removed constraint has at most 4 variables, the total demand allocated on the relevant node is at most the demand of four tasks plus the capacity already used in earlier iterations. Since each task demand is at most the capacity of the node, we obtain a 5- approximation with respect to capacity. Studying the proof of Lemma 2 more closely, one can separate the case nt < N/2 from the nt = N/2; in the former case, one can, in fact, show that there exists a tight capacity constraint with at most 3 variables. Together with a careful consideration of the nt = N/2 case, one can improve the approximation factor to 4. We now present an alternative selection of tight capacity constraint that leads to a 3-approximation. One interesting aspect of this step is that the constraint being selected may not have a small number of variables. We replace step 2b by the following. 2b Remove from the LP any tight capacity constraint in which the number of variables is at most two more than the sum of the values of the variables. Lemma 3. If all variables in x take values in (0, 1), then there exists a tight capacity constraint in which the number of variables is at most two more than the sum of the values of the variables. Proof. Since each variable occurs in at most two tight capacity constraints, the total number of occurrences of all variables across the tight capacity constraints is 2N − s for some nonnegative integer s. Since each satisfaction constraint is tight, each variable appears in 2 capacity constraints, and each variable takes on value less than 1, the sum of all the variables over the tight capacity constraints is at least 2nt − s. Therefore, the sum, over all tight capacity constraints, of the difference between the number of variables and their sum is at most 2(N − nt). Since there are N − nt tight capacity constraints, for at least one of these constraints, the difference between the number of variables and their sum is at most 2.Lemma 4. Let u be a node with a tight capacity constraint, in which the number of variables is at most 2 more than the sum of the variables. Then, the sum of the capacity requirements of the tasks partially assigned to u is a most the current available capacity of u plus twice the capacity of u. Proof. Let ` be the number of variables in the constraint for u, and let the associated tasks be numbered 1 through `. Let the demand of task j for the capacity of node u be dj . Then, the capacity constraint for u is P j djxj = bc(u), where bc(u) is the available capacity of u in the current LP. We know that ` − P i xi ≤ 2. Since di ≤ C(u), the capacity of u: X j dj = bc(u) +X ` j=1 (1 − xj )dj ≤ bc(u) + (` − Xm j=` xj )C(u) ≤ bc(u) + 2C(u). Theorem 1. IterRound is a polynomial-time 3-approximation algorithm for MinCP. Proof. By Lemma 3, each iteration of the algorithm removes either a variable or a constraint from the LP. Hence the algorithm is polynomial time. The elimination of a variable that takes value 0 or 1 does not change the cost. The elimination of a constraint can only decrease cost, so the final solution has cost no more than the value achieved by the original LP. Finally, when a capacity constraint is eliminated, by Lemma 4, we incur a blowup of at most 3 in capacity. 2.2 A (k + 1)-approximation algorithm for MinkSP It is straightforward to generalize the the algorithm of the preceding section to obtain a k + 1-approximation to MinkSP. We first set up the integer LP for MinkSP. For a given element e ∈ Q i Si , we use ei to denote the ith coordinate of e. Let xte be the indicator variable that t is assigned to e ∈ Q i Si . Minimize: X t,e xteft(e) Subject to: X e xte ≥ 1, ∀t ∈ T, X t,e:ei=u (dt(e))ixte ≤ Ci(u), ∀1 ≤ i ≤ k, u ∈ U, xte ∈ {0, 1}, ∀t ∈ T, e ∈ E The algorithm, which we call IterRound(k), is identical to IterRound of Section 2.1 except that step 2b is replaced by the following. 2b Remove from the LP any tight capacity constraint in which the number of variables is at most k more than the sum of the values of the variables. The claims and proofs are almost identical to the k = 2 case and are moved to Appendix A. A natural question to ask is whether a linear approximation factor of MinkSP is unavoidable for polynomial time algorithms. Unfortunately, we donot have any non-trivial results in this direction. We have been able to show that the MinkSP linear program has an integrality that grows as Ω(log k/ log log k) (see Appendix A). 3 The maximization problems We present approximation algorithms for the maximization versions of coupled placement and k-sided placement problems. We first observe, in Section 3.1, that these problems reduce to column sparse integer packing. We next present, in Section 3.2, an alternative combinatorial approach based on local search. 3.1 An LP-based approximation algorithm One can write a positive integer linear program for MaxCP. Let xtuv denote the indicator variable for the the assignment of job t to the pair (u, v), u ∈ U, v ∈ V . The goal is then to Maximize: X t,u,v xtuvft(u, v) Subject to: X u,v xtuv ≤ 1, ∀t ∈ T, X t,v pt(u, v)xtuv ≤ cu, ∀u ∈ U, X t,u st(u, v)xtuv ≤ dv, ∀v ∈ V, xtuv ∈ {0, 1}, ∀t ∈ T, u ∈ U, v ∈ V. Note that we can deal with capacities on u, v by scaling the pt(u, v) and st(u, v) values appropriately. The above LP can be easily extended to MaxkSP (see Appendix B). These linear programs are 3- and k-column sparse packing programs, respectively, and can be approximated to within a factor of 15.74 and ek + o(k), respectively using a clever randomized rounding approach. We next give a combinatorial approach based on local search which is likely to be much more efficient in practice. 3.2 Approximation algorithms based on local search Before giving the details, we start with a few helpful definitions. For any u ∈ U, Fu = Σt,vxtuvft(u, v). Similarly, for any v ∈ V , Fv = Σt,uxtuvft(u, v). We set µ = 1 n maxt,u,v ft(u, v). It follows that the optimum solution is at least nµ and at most n 2µ. The local search algorithm will maintain the following two invariants: (1) For each t, there is at most one pair (u, v) for which xtuv > 0; (2) All the linear program inequalities hold. It’s easy to set an initial state where the invariant holds (all xtuv = 0). The local search algorithm proceeds in the following steps: While ∃t, u, v : ft(u, v) > Fu pt(u,v) cu + Fv st(u,v) dv + Σu0 ,v0xtu0v 0ft(u 0 , v0 ) + µ:1. Set xtuv = 1 and set xtu0v 0 = 0 for all (u 0 , v0 ) 6= (u, v). 2. While Σt,vpt(u, v)xtuv > cu, reduce xtuv for the job with minimum cuft(u, v)/pt(u, v) such that xtuv > 0. 3. While Σu,vst(u, v)xtuv > dv, reduce xtuv for the job with minimum dvft(u, v)/st(u, v) such that xtuv > 0 Theorem 2. The local search algorithm maintains the two stated invariants. Proof. The first invariant is straightforward, because the only time we increase an xtuv value we simultaneously set all other values for the same t to zero. The only time the linear program inequalities can be violated is immediately after setting xtuv = 1. However, the two steps immediately after this operation will reduce the values of other jobs so as to satisfy the inequalities (and this is done without increasing any xtuv so no new constraint can be violated). Theorem 3. The local search algorithm produces a 3+ approximate fractional solution satisfying the invariants. Proof. When the algorithm terminates, we have for all t, u, v: ft(u, v) ≤ Fu pt(u,v) cu + Fv st(u,v) dv + Σu0 ,v0xtu0v 0ft(u 0 , v0 )µ. We sum this over t, u, v representing the optimum integer assignments: OP T ≤ ΣuFu + ΣvFv + Σt,u,vxtuvft(u, v) + OP T. Each summation simplifies to the algorithm’s objective value, giving the result. Theorem 4. The local search algorithm runs in polynomial time. Proof. Setting xtuv = 1 and setting all other xtu0v 0 = 0 adds ft(u, v)−Σu0v 0xtu0v 0ft(u 0 , v0 ) to the algorithm’s objective. The next two steps of the algorithm (making sure the LP inequalities hold) reduce the objective by at most Fu pt(u,v) cu + Fv st(u,v) dv . It follows that each iteration of the main loop increases the solution value by at least µ. By definition of µ, this can happen at most n 2/ times. Each selection of (t, u, v) can be done in polynomial time (at worst, by simply trying all tuples). Rounding Phase: When the local search algorithm terminates, we have a fractional solution with the additional guarantee from the first invariant. Note that we can extend this to the k-sided version if we increase the approximation factor to k+1+. Below, we give two different rounding schemes. The first works for general values of k and loses an O(k 2 ) factor, for an overall approximation factor of O(k 3 ). The second is specific to the k = 2 case and obtains a better approximation. 1. We randomly make each assignment with probability p times the fractional value (so pxtuv for Coupled Placement), for some p to be defined later. 2. For each assigned job t, if the other jobs t 0 6= t assigned to any one of its assigned machines violate the corresponding linear program constraint, we immediately drop job t. For Coupled Placement this means if P t 06=t,v pt 0 (u, v)xt 0uv > 1 for any t, u we set xtuv = 0.3. Note that we may still violate linear program constraints, but for any particular machine the constraint would be satisfied if we dropped any one of its assigned jobs. We divide the assigned jobs into k + 1 groups. These groups should guarantee that for any machine with at least two assigned jobs, not all its jobs are members of the same group. We then select the group with largest total objective value as our final solution. Theorem 5. For the k-sided version, the rounding scheme runs in poly-time and achieves an O(k 2 )-approximation over the fractional approximation factor (so an overall factor of O(k 3 ) using local search) for appropriate choice of p. Proof. The first two steps finish with a solution of value at least p(1 − p) k times the optimum in expectation. This is because for any job t, the probability of placing this job in step one is exactly p times its fractional value. Consider any machine m where the job is assigned; the expected total size of the other jobs t 0 6= t assigned to this machine is at most pcm and thus the probability that these other jobs exceed cm is at most p. The probability that none of the k machines where t is assigned exceed capacity from other jobs will be at most (1 − p) k . We may still violate constraints. Dividing into k + 1 groups and picking the best gives a result which is at least 1 k+1 p(1−p) k times optimum without violating constraints. Selecting p = 1 k gives the desired approximation factor. It remains to show that the division into groups can be performed in polytime. We start with all machines unmarked. For each group, we select a maximal set of jobs no two of which are assigned the same unmarked machine. We then mark all machines to which one of our current group of jobs is assigned. Note that immediately before we select group i, each remaining job is assigned to at most k−i+1 unmarked machines. For i = 1 this is obvious. Inductively, suppose that job j is assigned to more than k −i unmarked machines immediately before selecting group i + 1. Before selecting group i, job j was assigned to at most k −i+ 1 unmarked machines, and since we never “unmark” a machine it follows that job j was assigned to exactly k − i + 1 unmarked machines both before and after the selection of group i. But then none of the jobs selected in group i are assigned to any of the unmarked machines assigned to job j (else they would have become marked after selection of group i). So we can augment group i with job j without violating the constraint that no two jobs of group i are on the same unmarked machine. This contradicts the maximality of group i. We thus conclude that immediately before we select group k + 1, each remaining job is assigned only to marked machines. Thus group k + 1 selects all remaining jobs (maximality) and the jobs are divided into k+1 groups. Consider any machine m with at least two assigned jobs. Let group i be the first group to contain a job from m. Thus prior to selection of group i, we had not selected any job which was assigned to m and m was unmarked. So group i cannot include more than one job from machine m without violating the condition that no two jobs share an unmarked machine. It follows that there are at least two distinct groups which contain jobs from machine m (group i and also some later group).For MaxCP, we can improve the approximation factor. We refer the reader to Appendix B for details. Theorem 6. For MaxCP, there exists a polynomial-time algorithm based on local search that achieves a 15 + approximation for MaxCP. 4 Online MaxCP and MaxkSP We now study the online version of MaxCP, in which jobs arrive in an online fashion. When a job arrives we must irrevocably assign it or reject it. Our goal is to maximize our total value at the end of the instance. We apply the techniques of [8] to obtain a logarithmic competitive online algorithm under certain assumptions. We first note that online MaxCP differs from the model considered in [8] in that a job’s computation/storage requirements need not be the same. As demonstrated in [8] certain assumptions have to be made to achieve competitive ratios of any interest. We extend these assumptions for the MaxCP model as follows: Assumption 1 There exists F such that for all t, u, v either ft(u, v) = 0 or 1 ≤ ft(u, v) ≤ F min( pt(u,v) cu , st(u,v) dv ). Assumption 2 For = min( 1 2 , 1 ln 2F +1 ), for all t, u, v: pt(u, v) ≤ cu and st(u, v) ≤ dv. It is not hard to show that they (or some similar flavor of these assumptions) are in fact necessary to obtain any interesting competitive ratios (proof in Appendix C). Theorem 7. No deterministic online algorithm can be competitive over classes of instances where either one of the following is true: (i) job size is allowed to be arbitrarily large relative to capacities, or (ii) job values and resource requirements are completely uncorrelated. A small modification to the algorithm of [8] gives an O(log F)-competitive algorithm. Moreover, the lower bound of Ω(log F) shown in [8] applies to online MaxCP as well. (See Appendix D for proof.) Theorem 8. There exists a deterministic O(log F)-competitive algorithm for online MaxCP under Assumptions 1 and 2. For MaxkSP, this can be extended to a O(log kF)-competitive algorithm. Moreover, any online deterministic algorithm for online MaxCP has competitive ratio Ω(log F), and for online MaxkSP has competitive ratio Ω(log kF). Theorem 9. There exist a randomized O(log F)-competitive algorithm (in expectation) for online MaxCP under assumption 1 even if we weaken assumption 2 to require only that = 1 2 . No deterministic online algorithm for the problem can accomplish such a result.Acknowledgments We would like to thank Aravind Srinivasan for helpful discussions, and for pointing us to the Ω(k/ log k)-hardness result for k-set packing, in particular. We thank anonymous referees for helpful comments on an earlier version of the paper, and are especially grateful to a referee who generously offered the key insights leading to improved results for MinCP and MinkSP. References 1. Patterson, D.A.: Technical perspective: the data center is the computer. Communications of the ACM 51 (January 2008) 105–105 2. Dowdy, L.W., Foster, D.V.: Comparative models of the file assignment problem. ACM Surveys 14 (1982) 3. Anderson, E., Kallahalla, M., Spence, S., Swaminathan, R., Wang, Q.: Quickly finding near-optimal storage designs. ACM Transactions on Computer Systems 23 (2005) 337–374 4. 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In: Proceedings of the Symposium on Theory of Computing. (1995) 268–276 21. Lau, L., Ravi, R., Singh, M.: Iterative Methods in Combinatorial Optimization. Cambridge Texts in Applied Mathematics. Cambridge University Press (2011) A Proofs for MinkSP Fix an iteration of the algorithm, and an extreme point x. Let nt denote the number of tight satisfaction constraints, and ni denote the number of tight capacity constraints on the ith side. Since x is an extreme point, if all variables in x take values in (0, 1), then we have N = nt + P i ni . Lemma 5. If all variables in x take values in (0, 1), then there exists a tight capacity constraint in which the number of variables is at most k more than the sum of the variables. Proof. Since each variable occurs in at most k tight capacity constraints, the total number of occurrences of all variables across the tight capacity constraints is kN − s for some nonnegative integer s. Since each satisfaction constraint is tight, each variable appears in k capacity constraints, and each variable takes on value at most 1, the sum of all the variables over the tight capacity constraints is at least knt − s. Therefore, the sum, over all tight capacity constraints, of the difference between the number of variables and their sum is at most k(N − nt). Since the number of tight capacity constraints is N −nt, for at least one of these constraints, the difference between the number of variables and their sum is at most k. Lemma 6. Let u be a side-i node with a tight capacity constraint, in which the number of variables is at most k more than the sum of the variables. Then, the sum of the capacity requirements of the tasks partially assigned to u is at most the available capacity of u plus kCi(u). Proof. Let ` be the number of variables in the constraint for u, and let the associated tasks be numbered 1 through `. Let the demand of task j for the capacity of node u be dj . Then, the capacity constraint for u is P j djxj = bc(u).We know that m − P i xi ≤ k. We also have di ≤ Ci(u). Letting bc(u) denote the current capacity of u, we now derive X i di = bc(u) +Xm j=1 (1 − xi)di ≤ bc(u) + (m − Xm j=1 xi)Ci(u) ≤ bc(u) + kCi(u). Theorem 10. IterRound(k) is a polynomial-time k + 1-approximation algorithm for MinkSP. Proof. By Lemma 5, each iteration of the algorithm removes either a variable or a constraint from the LP. Hence the algorithm is polynomial time. The elimination of a variable that takes value 0 or 1 neither changes cost nor incurs capacity blowup. The elimination of a constraint can only decrease cost, so the final solution has cost no more than the value achieved by the original LP. Finally, by Lemma 6, we incur a blowup of at most 1 + k in capacity. We now show that the MinkSP LP has an integrality gap of Ω(log k/ log log k). We recursively construct an integrality gap instance with ` t sides, for parameters ` and t, with two nodes per side one with infinite capacity and the other with unit capacity, such that any integral solution has at least t tasks on the unit-capacity node on some side, while there is a fractional solution with load of at most t/` on the unit-capacity node of each side. Setting t = ` and k = ` ` , we obtain an instance in which the capacity used by the fractional solution is 1, while any integral solution has load ` = Θ(log k/ log log k). Each task can be placed on one tuple from a subset of tuples; for a given tuple, the demand of the task on each side of the tuple is one. We start with the construction for t = 1. We introduce a task that has ` choices, the ith choice consisting of the unit-capacity node from side i and infinite capacity nodes on all other sides. Clearly, any integral solution uses up unit capacity of one unitcapacity node, while there is a fractional solution (1/` for each choice) that uses only 1/` fraction of each unit capacity node. Given a construction for ` t sides, we show how to extend to ` t+1 sides. We take ` identical copies of the instance with ` t sides and combine the tuples for each task in such a way that for any i, any integral placement places exactly the same task on side i of each copy. Now we add task t + 1 which can be placed in one of ` tuples: unit capacity node on all sides of copy i and infinite capacity node on all other sides, for each i. Clearly, any integral solution will have to add one more task to a unit-capacity node of a side that already has load t, yielding a load t + 1, while a fractional solution can assign load of at most 1/` to the unit-capacity nodes of each side.B Proofs for MaxkSP and MaxCP We first present the linear program for MaxkSP (recall the definition in Section 1.1). Let xte denote the indicator variable for the assignment of job t to the k-tuple e. Maximize: X t,e xteft(e) Subject to: X e xte ≤ 1, ∀t ∈ T, X t,e (dt(e))ixte ≤ Ci(ei), ∀i ∈ {1, . . . , k}, xte ∈ {0, 1}, ∀t ∈ T, e ∈ Q i Si . We now present the improved approximation algorithm for MaxCP. The idea is to obtain a one-to-one correspondance between fractional assignments and machines. Essentially we view the machines as nodes of a graph where the edges are the fractional assignments (this is similar to the rounding for generalized assignment). If we have a cycle, the idea is to shift the fractions around the cycle (i.e. increase one xtuv then decrease some xt 0vw and increase some xt 00wx and so forth). Applying this directly on a single cycle may violate some constraints; while we try to increase and decrease the fractions in such a way that constraints hold, since each job has different “size” on its two endpoints we may wind up violating the constraint P t,v xtuvpt(u, v) at a single node u. This prevents us from doing a simple cycle elimination as in generalized assignment. However, if we have two adjoining (or connected) cycles the process can be made to work. The remaining case is a single cycle, where we can assign each edge to one of its endpoints. Generalized assignment rounding would now proceed to integrally assign each job to its corresponding machine; we cannot do this because each job requires two machines, and each machine thus has multiple fractional assignments (all but one of which “correspond” to some other machine). Lemma 7. Given any fractional solution which satisfies the local search invariants, we can produce an alternative fractional solution (also satisfying the local search invariants and with equal or greater value). This new fractional solution labels each job t with 0 < xtuv < 1, with either u or v, guaranteeing that each u is labeled with at most one job. Proof. Consider a graph where the nodes are machines, and we have an edge (u, v) for any fractional assignment 0 < xtuv < 1. If any node has degree zero or one, we remove that node and its assigned edge (if any), labeling the removed edge with the node that removed it. We continue this process until all remaining nodes have degree at least two. If there is a node of degree three, then there must exist two (distinct but not necessarily edge-disjoint) cycles with a path between them (possibly a path of length zero); since the graph is bipartite all cycles are even in length. We can alternately increase and decrease the fractional assignments of edges along a cycle such that the total load P t,v pt(u, v)xtuv changesonly on a single node u where the path between cycles intersects this cycle. We can do the same along the other cycle. We can then do the same thing along the path, and equalize the changes (multiplicatively) such that there is no overall change in load, but at least one edge has its fractional value changing. If this process decreases the value, we can reverse it to increase the value. This allows us to modify the fractional solution in a way that increases the number of integral assignments without decreasing the value. After applying this repeatedly (and repeating the node/edge removal process above where necessary), we are left with a graph that consists only of node-disjoint cycles. Each of the remaining edges will be labeled with one of its two endpoints (one to each). The overall effect is that we have a one-to-one labeling correspondance between fractional assignments and machines (each fractional edge to one of its two assigned machines). Note however that since each job is assigned to two machines and labeled with only one of the two, this does not imply that each machine has only one fractional assignment. Once this is done, we consider three possible solutions. One consists of all the integral assignments. The second considers only those assignments which are fractional and labeled with nodes u. For each node v, we select a subset of its fractional assignments to make integrally, so as to maximize the value without violating capacity of v. We cannot violate capacity of u because we select at most one job for each such machine. The result has at least 1 2 the value of assignments labeled with nodes u. For the third solution, we do the same but with the roles of u, v reversed. We select the best of these three solutions; our choice obtains at least 1 5 of the overall value. Proof of Theorem 6: The algorithm sketch contains most of the proof. We need to establish that we can get at least 1 2 the fractional value on a single machine integrally. This can be done by selecting jobs in decreasing order of density (ft(u, v)/pt(u, v)) until we overflow the capacity. Including the job that overflows capacity, this must be better than the fractional solution. Thus we can select either everything but the job that overflows capacity, or that job by itself. We also need to establish the 1 5 value claim. If we were to select the integral assignments with probability 1 5 and each of the other two solutions with probability 2 5 , we would get an expected 1 5 of the fractional solution. Deterministially selecting the best of the three solutions can only be better than this. ut C Proof of Theorem 7 We first show that if resource requirements are large compared to capacities, payment functions ft are exactly equal to the total amount of resources and each job requires the same amount over all resources/dimensions (but different jobs can require different amounts), then no deterministic online algorithm can be competitive. Consider a graph G with a single compute node and a single data storage node. Each node has one-dimensional compute/storage capacity of L. A jobarrives requesting 1 unit of computing and storage and will pay 2. Clearly, any competitive deterministic algorithm must accept this job, in case this is the only job. However, a second job arrives requesting L units of computing and storage and will pay 2L. In this case, the algorithm is L-competitive, and L can be arbitrarily large. Next, we show that if resource requirements are small relative to capacities, payment functions ft are arbitrary and resource requirements are identical, then no deterministic online algorithm can be competitive. This instance satisfies Assumption 2 but not Assumption 1. Consider again a graph G with a single compute node and single data storage node each with one-dimensional, unit capacities. We will use up to k + 1 jobs, each requiring 1/k units of computing and storage. The i-th job, 0 ≤ i ≤ k, will pay Mi for some large value M. Now, consider any deterministic algorithm. If it fails to accept any job j < k, then if job j is the last job, it will be Ω(M)- competitive. If the algorithm accepts jobs 0 up through k − 1 then it will not be able to accept job k and will be Ω(M)-competitive. In all cases it has competitive ratio at least Ω(M) and M and k can be arbitrarily large. Similarly, if resource requirements are small relative to capacities, payment functions ft are exactly equal to the total amount of resources requested and resource requirements are arbitrary, then no deterministic online algorithm can be competitive. Consider once more a graph G with a single compute node and single data store node with one-dimensional compute/storage capacities. However, this time the compute capacity will be 1 and the storage capacity will be some very large L. We will use up to k+1 jobs, each requiring 1/k units of computing. The i-th job, 0 ≤ i ≤ k, will require the appropriate amount of storage so that its value is Mi for very large M. Assuming L = O(kMk ), all these storage requirements are at most 1/k of L. Note that storage can accommodate all jobs, but computing can accommodate at most k jobs. Any deterministic algorithm will have competitive ratio Ω(M) and k, M and L can be suitably large. Thus, it follows that some flavor of Assumptions 1 and 2 are necessary to achieve any interesting competitive result. D Proof of Theorem 8 We adapt the framework of [8] to solve the online MaxCP problem. This framework uses an exponential cost function to place a price on remaining capacity of a node. If the value obtained from a task can cover the cost of the capacity it consumes, we admit the task. In the algorithm below, e is the base of the natural logarithm. We first show that our algorithm will not exceed capacities. Essentially, this occurs because the cost will always be sufficiently high. Lemma 8. Capacity constraints are not violated at any time during this algorithm.Algorithm 1 Online algorithm for MaxCP. 1: λu(1) ← 0, λv(1) ← 0 for all u ∈ U, v ∈ V 2: for each new task j do 3: costu(j) ← 1 2 (e λu(j) ln(2F +1) 1− − 1) 4: costv(j) ← 1 2 (e λv(j) ln(2F +1) 1− − 1) 5: For all uv let Ztuv = pj (u,v) cu costu(j) + sj (u,v) dv costv(j) 6: Let uv maximize fj (u, v) subject to Zjuv < fj (u, v) 7: if such uv exist with fj (u, v) > 0 then 8: Assign j to uv 9: λu(j + 1) ← λu(j) + pj (u,v) cu 10: λv(j + 1) ← λv(j) + sj (u,v) dv 11: For all other u 0 6= u let λu0 (j + 1) ← λu0 (j) 12: For all other v 0 6= v let λv0 (j + 1) ← λv0 (j) 13: else 14: Reject task j 15: For all u let λu(j + 1) ← λu(j) 16: For all v let λv(j + 1) ← λv(j) 17: end if 18: end for Proof. Note that λu(n + 1) will be 1 cu Σt,vpt(u, v)xtuv, since any time we assign a job j to uv we immediately increase λu(j + 1) by the appopriate amount. Thus if we can prove λu(n + 1) ≤ 1 we will not violate capacity of u. Initially we had λu(1) = 0 < 1, so suppose that the first time we exceed capacity is after the placement of job j. Thus we have λu(j) ≤ 1 < λu(j + 1). By applying assumption 2 we have λu(j) > 1 − . From this it follows that costu(j) > 1 2 (e ln(2F +1) − 1) = F, and since these costs are always non-negative we must have had Zjuv > pj (u,v) cu F ≥ fj (u, v) by applying assumption 1. But then we must have rejected job j and would have λu(j + 1) = λu(j) Identical reasoning applies to v ∈ V . Next, we bound the algorithms revenue from below using the sum of the node costs. Lemma 9. Let A(j) be the total objective value Σt,u,vxtuvft(u, v) obtained by P the algorithm immediately before job j arrives. Then (3e ln(2F + 1))A(j) ≥ u∈U costu(j) + P v∈V costv(j). Proof. The proof will be by induction on j; the base case where j = 1 is immediate since no jobs have yet arrived or been scheduled and costu(1) = costv(1) = 0 for all u and v. Consider what happens when job j arrives. If this job is rejected, neither side of the inequality changes and the induction holds. Otherwise, suppose job j is assigned to uv. We have: A(j + 1) = A(j) + fj (u, v)We can bound the new value of the righthand side by observing that since costu has derivative increasing in the value of λu, the new value will be at most the new derivative times the increase in λu. It follows that: costu(j + 1) ≤ costu(j) + (λu(j + 1) − λu(j))1 2 ( ln(2F + 1) 1 − )(e λu(j+1) ln(2F +1) 1− ) costu(j + 1) ≤ costu(j) + pj (u, v) cu ( ln(2F + 1) 1 − )(1 2 e λu(j) ln(2F +1) 1− )(e ln(2F +1) 1− ) costu(j + 1) ≤ costu(j) + pj (u, v) cu ln(2F + 1) 1 − (costu(j) + 1 2 )(e ln(2F +1)) Applying assumption 2 gives: costu(j + 1) ≤ costu(j) + (2e ln(2F + 1))(pj (u, v) cu costu(j) + 1 4 ) Identical reasoning can be applied to costv, allowing us to show that the increase in the righthand side is at most: (2e ln(2F + 1))(pj (u, v) cu costu(j) + sj (u, v) du costv(j) + 1 2 ) Since j was assigned to uv, we must have fj (u, v) > pj (u,v) cu costu(j)+sj (u,v) dv costv(j); from assumption 1 we also have fj (u, v) ≥ 1 so we can conclude that the increase in the righthand side is at most: (3e ln(2F + 1))fj (u, v) ≤ (3e ln(2F + 1))(A(j + 1) − A(j)) Now, we can bound the profit the optimum solution gets from tasks which we either fail to assign, or assign with a lower value of ft(u, v). The reason we did not assign these tasks was because the node costs were suitably high. Thus, we can bound the profit of tasks using the node costs. Lemma 10. Suppose the optimum solution assigned j to u, v, but the online algorithm either rejected j or assigned it to some u 0 , v0 with fj (u 0 , v0 ) < fj (u, v). Then pj (u,v) cu costu(n + 1) + sj (u,v) dv costv(n + 1) ≥ fj (u, v) Proof. When the algorithm considered j, it would find the u, v with maximum fj (u, v) satisfying Zjuv < fj (u, v). Since the algortihm either could not find such u, v or else selected u 0 , v0 with fj (u 0 , v0 ) < fj (u, v) it must be that Zjuv ≥ fj (u, v). The lemma then follows by inserting the definition of Zjuv and then observing that costu and costv only increase as the algorithm continues.Lemma 11. Let Q be the total value of tasks which the optimum offline algorithm assigns, but which Algorithm 1 either rejects or assigns to a uv with lower value of ft(u, v). Then Q ≤ Σu∈U costu(n + 1) + Σv∈V costv(n + 1). Proof. Consider any task q as described above. Suppose offline optimum assigns q to uq, vq. By applying lemma 10 we have: Q = Σqfq(uq, vq) ≤ Σq pq(uq, vq) cu costuq (n + 1) + sq(uq, vq) dv costq(n + 1) The lemma then follows from the fact that the offline algorithm must obey the capacity constraints. Finally, we can combine Lemmas 9 and 11 to bound our total profit. In particular, this shows that we are within a factor 3e ln(2F + 1) of the optimum offline solution, for an O(log F)-competitive algorithm. Theorem 11. Algorithm 1 never violates capacity constraints and is O(log F)- competitive. We can extend the result to k-sided placement, and can get a slight improvement in the required assumptions if we are willing to randomize. The results are given below: Theorem 12. For the k-sided placement problem, we can adapt algorithm 1 to be O(log kF)-competitive provided that assumption 2 is tightened to = min( 1 2 , 1 ln(kF +1) ). Proof. We must modify the definition of cost to: costu(j) = 1 k (e λu(j) ln(kF +1) 1− − 1) The rest of the proof will then go through. The intuition for the increase in competitive ratio is that we need to assign the first task to arrive (otherwise after this task our competitive ratio would be unbounded). This task potentially uses up space on k machines while obtaining a value of only 1. So as the value of k increases, the ratio of “best” to “worst” task effectively increases as well. Theorem 13. If we select a random power of two z ∈ [1, F] and then reject all placements with ft(u, v) < z or ft(u, v) > 2z, then we can obtain a competitive ratio of O(log F log k) while weakening assumption 2 to = min( 1 2 , 1 ln(2k+1) ). Note that in the specific case of two-sided placement this is O(log F)-competitive requiring only that no single job consumes more than a constant fraction of any machine. Proof. Once we make our random selection of z, we effectively have F = 2 and can apply the algorithm and analysis above. The selection of z causes us to lose (in expectation) all but 1 log F of the possible profit, so we have to multiply this into our competitive ratio. Collaboration in the Cloud at Google Yunting Sun, Diane Lambert, Makoto Uchida, Nicolas Remy Google Inc. January 8, 2014 Abstract Through a detailed analysis of logs of activity for all Google employees1 , this paper shows how the Google Docs suite (documents, spreadsheets and slides) enables and increases collaboration within Google. In particular, visualization and analysis of the evolution of Google’s collaboration network show that new employees2 , have started collaborating more quickly and with more people as usage of Docs has grown. Over the last two years, the percentage of new employees who collaborate on Docs per month has risen from 70% to 90% and the percentage who collaborate with more than two people has doubled from 35% to 70%. Moreover, the culture of collaboration has become more open, with public sharing within Google overtaking private sharing. 1 Introduction Google Docs is a cloud productivity suite and it is designed to make collaboration easy and natural, regardless of whether users are in the same or different locations, working at the same or different times, or working on desktops or mobile devices. Edits and comments on the document are displayed as they are made, even if many people are simultaneously writing and commenting on or viewing the document. Comments enable real-time discussion and feedback on the document, without changing the document itself. Authors are notified when a new comment is made or replied to, and authors can continue a conversation by replying to the comment, or end the discussion by resolving it, or re-start the discussion by re-opening a closed discussion stream. Because documents are stored in the cloud, users can access any document they own or that has been shared with them anywhere, any time and on any device. The question is whether this enriched model of collaboration matters? There have been a few previous qualitative analyses of the effects of Google Docs on collaboration. For example, the review of Google Docs in [1] suggested that its features should improve collaboration and productivity among college students. A technical report [2] from the University of Southern Queensland, Australia argued that Google Docs can overcome barriers to usability such as difficulty of installation and document version control and help resolve conflicts among co-authors of research papers. There has also been at least one rigorous study of the effect of Google Docs on collaboration. Blau and Caspi [3] ran a small experiment that was designed to compare collaboration on writing documents to merely sharing documents. In their experiment, 118 undergraduate students of the Open University of Israel were randomized to one of five groups in which they shared their written assignments and received feedback from other students to varying degrees, ranging from keeping texts 1Full-time Google employees, excluding interns, part-times, vendors, etc 2Full-time employees who have joined Google for less than 90 days 12 COLLABORATION VISUALIZATION private to allowing in-text suggestions or allowing in-text edits. None of the students had used Google Docs previously. The authors found that only students in the collaboration group perceived the quality of their final document to be higher after receiving feedback, and students in all groups thought that collaboration improves documents. This paper takes a different approach, and looks for the effects of collaboration on a large, diverse organization with thousands of users over a much longer period of time. The first part of the paper describes some of the contexts in which Google Docs is used for collaboration, and the second part analyzes how collaboration has evolved over the last two years. 2 Collaboration Visualization 2.1 The Data This section introduces a way to visualize the events during a collaboration and some simple statistics that summarize how widespread collaboration using Google Docs is at Google. The graphics and metrics are based on the view, edit and comment actions of all full-time employees on tens of thousands of documents created in April 2013. 2.2 A Simple Example To start, a document with three collaborators Adam (A), Bryant (B) and Catherine (C) is shown in Figure 1. The horizontal axis represents time during the collaboration. The vertical axis is broken into three regions representing viewing, editing and commenting. Each contributor is assigned a color. A box with the contributor’s color is drawn in any time interval in which the contributor was active, at a vertical position that indicates what the user was doing in that time interval. This allows us to see when contributors were active and how often they contributed to the document. Stacking the boxes allows us to show when contributors were acting at the same time. Only time intervals in which at least one contributor was active are shown, and gaps in time that are shorter than a threshold are ignored. Gray vertical bars of fixed width are used to represent periods of no activity that are longer than the threshold. In this paper, the threshold is set to be 12 hours in all examples. In Figure 1, an interval represents an hour. Adam and Bryant edited the document together during the hour of 10 AM May 4 and Bryant edited alone in the following hour. The collaboration paused for 8 days and resumed during the hour of 2 pm on May 12. Adam, Bryant and Catherine all viewed the document during that hour. Catherine commented on the document in the next hour. Altogether, the collaboration had two active sessions, with a pause of 8 days between them. Figure 1: This figure shows an example of the collaboration visualization technique. Each colored block except the gray one represents an hour and the gray one represents a period of no activity. The Y axis is the number of users for each action type. This document has three contributors, each assigned a different color. Although we have used color to represent collaborators here, we could instead use color to represent the locations of the collaborators, their organizations, or other variables. Examples with different colorings are given in Sections 2.5 and 2.6. 2 Google Inc.2 COLLABORATION VISUALIZATION 2.3 Collaboration Metrics 2.3 Collaboration Metrics To estimate the percentage of users who concurrently edit a document and the percentage of documents which had concurrent editing, we discretize the timestamps of editing actions into 15 minute intervals and consider editing actions by different contributors in the same 15 minute interval to be concurrent. Two users who edit the same document but always more than 15 minutes apart would not be considered as concurrent, although they would still be considered collaborators. Edge cases in which two collaborators edit the same document within 15 minutes of each other but in two adjacent 15 minute intervals would not be counted as concurrent events. The choice of 15 minutes is arbitrary; however, metrics based on a 15 minute discretization and a 5 minute discretization are little different. The choice of 15 minute intervals makes computation faster. A more accurate approach would be to look for sequences of editing actions by different users with gaps below 15 minutes, but that requires considerably more computing. 2.4 Collaborative Editing Collaborative editing is common at Google. 53% of the documents that were created and shared in April 2013 were edited by more than one employee, and half of those had at least one concurrent editing session in the following six months. Looking at employees instead of documents, 80% of the employees who edited any document contributed content to a document owned by others and 65% participated in at least one 15 minute concurrent editing session in April 2013. Concurrent editing is sticky, in the sense that 76% of the employees who participate in a 15 minute concurrent editing session in April will do so again the following month. There are many use cases for collaborative editing, including weekly reports, design documents, and coding interviews. The following three plots show an example of each of these use cases. Figure 2: Collaboration activity on a design document. The X axis is time in hours and the Y axis is the number of users for each action type. The document was mainly edited by 3 employees, commented on by 18 and viewed by 50+. Google Inc. 32.5 Commenting 2 COLLABORATION VISUALIZATION Figure 2 shows the life of a design document created by engineers. The X axis is time in hours and the Y axis is the number of employees working on the document for each action type. The document was mainly edited by three employees, commented on by 18 employees and viewed by more than 50 employees from three major locations. This document was completed within two weeks and viewed many times in the subsequent month. Design documents are common at Google, and they typically have many contributors. Figure 3 shows the life of a weekly report document. Each bar represents a day and the Y axis is the number of employees who edited and viewed the document in a day. This document has the following submission rules: • Wednesday, AM: Reminder for submissions • Wednesday, PM: All teams submit updates • Thursday, AM: Document is locked The activities on the document exhibit a pronounced weekly pattern that mirrors the submission rules. Weekly reports and meeting notes that are updated regularly are often used by employees to keep everyone up-to-date as projects progress. Figure 3: Collaboration on a weekly report. The X axis is time in days and the Y axis is the number of users for each action type. The activities exhibit a pronounced weekly pattern and reflect the submission rules of the document. Finally, Figure 4 shows the life of a document used in an interview. The X axis represents time in minutes. The document was prepared by a recruiter and then viewed by an engineer. At the beginning of the interview, the engineer edited the document and the candidate then wrote code in the document. The engineer was able to watch the candidate typing. At the end of the interview, the candidate’s access to the document was revoked so no further change could be made, and the document was reviewed by the engineer. Collaborative editing allows the coding interview to take place remotely, and it is an integral part of interviews for software engineers at Google. Figure 4: The activity on a phone interview document. The X axis is time in minutes and the Y axis is the number of users for each action type. The engineer was able to watch the candidate typing on the document during a remote interview. 2.5 Commenting Commenting is common at Google. 30% of the documents created in April 2013 that are shared received comments within six months of creation. 57% of the employees who used Google Docs in April commented at least once in April, and 80% of the users who commented in April commented again in the following month. 4 Google Inc.2 COLLABORATION VISUALIZATION 2.6 Collaboration Across Sites Figure 5: Commenting and editing on a design document. The X axis is time in hours and the Y axis is the number of user actions for each user location. There are four user actions, each assigned a different color. Timestamps are in Pacific time. Figure 5 shows the life of a design document. Here color represents the type of user action (create a comment, reply to a comment, resolve a comment and edit the document), and the Y axis is split into two locations. The document was written by one engineering team and reviewed by another. The review team used commenting to raise many questions, which the engineering team resolved over the next few days. Collaborators were located in London, UK and Mountain View, California, with a nine hour time zone difference, so the two teams were almost ”taking turns” working on the document (timestamps are in Pacific time). There are many similar communication patterns between engineers via commenting to ask questions, have discussions and suggest modifications. 2.6 Collaboration Across Sites Employees use the Docs suite to collaborate with colleagues across the world, as Figure 6 shows. In that figure, employees working from nine locations in eight countries across the globe contributed to a document that was written within a week. The document was either viewed or edited with gaps of less than 12 hours (the threshold for suppressing gaps in the plot) in the first seven days as people worked in their local timezones. After final changes were made to the document, it was reviewed by people in Dublin, Mountain View, and New York. Figure 7 shows one month of global collaborations for full-time employees using Google Docs. The blue dots show the locations of the employees and a line connects two locations if a document is created in one location and viewed in the other. The warmer the color of the line, moving from green to red, the more documents shared between the two locations. Google Inc. 52.6 Collaboration Across Sites 2 COLLABORATION VISUALIZATION Figure 6: Activity on a document. Each user location is assigned a different color. The X axis is time in hours and the Y axis is the number of locations for each action type. Users from nine different locations contributed to the document. Figure 7: Global collaboration on Docs. The blue dots are locations and the dots are connected if there is collaboration on Google Docs between the two locations. 6 Google Inc.3 THE EVOLUTION OF COLLABORATION 2.7 Cross Device Work 2.7 Cross Device Work The advantage of cloud-based software and storage is that a document can be accessed from any device. Figure 8 shows one employee’s visits to a document from multiple devices and locations. When the employee was in Paris, a desktop or laptop was used during working hours and a mobile device during non-working hours. Apparently, the employee traveled to Aix-En-Provence on August 18. On August 18 and the first part of August 19, the employee continued working on the same document from a mobile device while on the move. Figure 8: Visits to a document by one user working on multiple devices and from multiple locations. Not surprisingly, the pattern of working on desktops or laptops during working hours and on mobile devices out of business hours holds generally at Google, as Figure 9 shows. The day of week is shown on the X axis and hour of day in local time on the Y axis. Each pixel is colored according to the average number of employees working in Google Docs in a day of week and time of day slot, with brighter colors representing higher numbers. Pixel values are normalized within each plot separately. Desktop and laptop usage of Google Docs peaks during conventional working hours (9:00 AM to 11:00 AM and 1:00 PM to 5:00 PM), while mobile device usage peaks during conventional commuting and other out-of-office hours (7:00 AM to 9:00 AM and 6:00 PM to 8:00 PM). Figure 9: The average number of active users working in Google Docs in each day of week and time of day slot. The X axis is day of the week and the Y axis is time of the day in local time. Desktop/Laptop usage peaks during working hours while mobile usage peaks at out-of-office working hours. 3 The Evolution of Collaboration 3.1 The Data This section explores changes in the usage of Google Docs over time. Section 2 defined collaborators as users who edited or commented on the same document and used logs of employee editing, viewing and commenting actions to describe collaboration within Google. This section defines collaborators differently using metadata on documents. Metadata is much less rich than the event history logs used in Section 2, but metadata is retained for a much longer period of time. Document metadata includes the document creation time and the last time that the document Google Inc. 73.2 Collaboration for New Employees 3 THE EVOLUTION OF COLLABORATION was accessed, but no other information about its revision history. However, the metadata does include the identification numbers for employees who have subscribed to the document, where a subscriber is anyone who has permission to view, edit or comment on a document and who has viewed the document at least once. Here we use metadata on documents, slides and spreadsheets. We call two employees collaborators (or subscription collaborators to be clear) if one is a subscriber to a document owned by the other and has viewed the document at least once and the document has fewer than 20 subscribers. The owner of the document is said to have shared the document with the subscriber. The number of subscribers is capped at 20 to avoid overcounting collaborators. The more subscribers the document has, the less likely it is that all the subscribers contributed to the document. There is no timestamp for when the employee subscribed to the document in the metadata, so the exact time of the collaboration is not known. Instead, the document creation time, which is known, is taken to be the time of the collaboration. An analysis (not shown here) of the event history data discussed in Section 2 showed that most collaborators join a collaboration soon after a document is created, so taking collaboration time to be document creation time is not unreasonable. To make this assumption even more tenable, we exclude documents for which the time of the last view, comment or edit is more than six months after the document was created. This section uses metadata on documents created between January 1, 2011 and March 31, 2013. We say that two employees had a subscription collaboration in July if they collaborated on a document that was created in July. 3.2 Collaboration for New Employees Here we define the new employees for a given month to be all the employees who joined Google no more than 90 days before the beginning of the month and started using Google Docs in the given month. For example, employees called new in the month of January 2011 must have joined Google no more than 90 days before January 1, 2011 and used Google Docs in January 2011. Each month can include different employees. New employees are said to share a document if they own a document that someone else subscribed to, whether or not the person subscribed to the document is a new employee. Similarly, a new employee is counted as a subscriber, regardless of the tenure of the document creator. Figure 10 shows that collaboration among new employees has increased since 2011. Over the last two years, subscribing has risen from 55% to 85%, sharing has risen from 30% to 50%, and the fraction of users who either share or subscribe has risen from 70% to 90%. In other words, new employees are collaborating earlier in their career, so there is a faster ramp-up and easier access to collective knowledge. Figure 10: This figure shows the percentage of new employees who share, subscribe to others’ documents and either share or subscribe in each one-month period over the last two years. Not only do new employees start collaborating more often (as measured by subscription and sharing), they also collaborate with more people. Figure 11 shows the percentage of new employees with at least a given number of collaborators by month. For example, the percentage of 8 Google Inc.3 THE EVOLUTION OF COLLABORATION 3.3 Collaboration in Sales and Marketing new employees with at least three subscription collaborators was 35% in January 2011 (the bottom red curve) and 70% in March 2013 (the top blue curve), a doubling over two years. It is interesting that the curves hardly cross each other and the curves for the farthest back months lie below those for recent months, suggesting that there has been steady growth in the number of subscription collaborators per new employee over this period. Figure 11: This figure shows the proportion of new employees who have at least a given number of collaborators in each one-month period. Each period is assigned a different color. The cooler the color of the curve, moving from red to blue, the more recent the month. The legend only shows the labels for a subset of curves. The percentage of new employees who have at least three collaborators has doubled from 35% to 70%. To present the data in Figure 11 in another way, Table 1 shows percentiles of the distribution of the number of subscription collaborators per new employee using Google Docs in January 2011 and in January 2013. For example, the lowest 25% of new employees using Google Docs had no such collaborators in January 2011 and two such collaborators in January 2013. 25% 50% 75% 90% 95% January 2011 0 1 4 7 11 January 2013 2 5 10 17 22 Table 1: This table shows the percentile of number of collaborators a new employee have in January 2011 and January 2013. The entire distribution shifts to the right. 3.3 Collaboration in Sales and Marketing Section 3.2 compared new employees who joined Google in different months. This section follows current employees in Sales and Marketing who joined Google before January 1, 2011. That is, the previous section considered changes in new employee behavior over time and this section considers changes in behavior for a fixed set of employees over time. We only analyze subscription collaborations among this fixed set of employees and collaborations with employees not in this set are excluded. Figure 12: This figure shows the percentage of current employees in Sales and Marketing who have at least a given number of collaborators in each onemonth period. Figure 12 shows the percentage of current employees in Sales and Marketing who have at least Google Inc. 93.4 Collaboration Between Organizations 3 THE EVOLUTION OF COLLABORATION a given number of collaborators at several times in the past. There we see that more employees are sharing and subscribing over time because the fraction of the group with at least one subscription collaborator has increased from 80% to 95%. And the fraction of the group with at least three subscription collaborators has increased from 50% to 80%. It shows that many of the employees who used to have no or very few subscription collaborators have migrated to having multiple subscription collaborators. In other words, the distribution of number of subscription collaborators for employees who have been in Sales and Marketing since January 1, 2011 has shifted right over time, which implies that collaboration in that group of employees has increased over time. Finally, the number of documents shared by the employees who have been in Sales and Marketing at Google since January 1, 2011 has nearly doubled over the last two years. Figure 13 shows the number of shared documents normalized by the number of shared documents in January, 2011. Figure 13: This figure shows the number of shared documents created by employees in Sales and Marketing each month normalized by the number of shared documents in January 2011. The number has almost doubled over the last two years. 3.4 Collaboration Between Organizations Collaboration between organizations has increased over time. To show that, we consider hundreds of employees in nine teams within the Sales and Marketing group and the Engineering and Product Management group who joined Google before January 1, 2011, were still active in March 31, 2013 and used Google Docs in that period. Figure 14 represents the Engineering and Product Management employees as red dots and the Sales and Marketing employees as blue dots. The same dots are included in all three plots in Figure 14 because the employees included in this analysis do not change. A line connects two dots if the two employees had at least one subscription collaboration in the month shown. The denser the lines in the graph, the more collaboration, and the more lines connecting red and blue dots, the more collaboration between organizations. Clearly, subscription collaboration has increased both within and across organizations in the past two years. Moreover, the network shows more pronounced communities (groups of connected dots) over time. Although there are nine individual teams, there seems to be only three major communities in the network. Figure 14 indicates that teams can work closely with each other even though they belong to separate departments. We also sampled 187 teams within the Sales and Marketing group and the Engineering and Product Management group. Figure 15 represents teams in Engineering and Product Management as red dots and teams in Sales and Marketing as blue dots. Two dots are connected if the two teams had a least one subscription collaboration between their members in the month. Figure 15 shows that the collaboration between those teams has increased and the interaction between the two organizations has becomed stronger over the past two years. 10 Google Inc.3 THE EVOLUTION OF COLLABORATION 3.4 Collaboration Between Organizations Figure 14: An example of collaboration across organizations. Red dots represent employees in Engineering and Product Management and blue dots represent employees in Sales and Marketing Figure 15: An example of collaboration between teams. Red dots represent teams in Engineering and Product Management and blue dots represent teams in Sales and Marketing Google Inc. 113.5 Cultural Changes in Collaboration 4 CONCLUSIONS 3.5 Cultural Changes in Collaboration Google Docs allows users to specify the access level (visibility) of their documents. The default access level in Google Docs is private, which means that only the user who created the document or the current owner of the document can view it. Employees can change the access level on a document they own and allow more people to access it. For example, the document owner can specify particular employees who are allowed to access the document, or the owner can mark the document as public within Google, in which case any employee can access the document. Clearly, not all documents created in Google can be visible to everyone at Google, but the more documents are widely shared, the more open the environment is to collaboration. Figure 16: This figure shows the percentage of shared documents that are ”public within Google” created in each month. Public sharing is overtaking private sharing at Google. Figure 16 shows the percentage of shared documents in Google created each month between January 1, 2012 and March 31, 2013 that are public within Google. The red line, which is a curve fit to the data to smooth out variability, shows that the percentage has increased about 12% from 48% to 54% in the last year alone. In that sense, the culture of sharing is changing in Google from private sharing to public sharing. 4 Conclusions We have examined how Google employees collaborate with Docs and how that collaboration has evolved using logs of user activity and document metadata. To show the current usage of Docs in Google, we have developed a visualization technique for the revision history of a document and analyzed key features in Docs such as collaborative editing, commenting, access from anywhere and on any device. To show the evolution of collaboration in the cloud, we have analyzed new employees and a fixed group of employees in Sales and Marketing, and computed collaboration network statistics each month. We find that employees are engaged in using the Docs suite, and collaboration has grown rapidly over the last two years. It would also be interesting to conduct a similar analysis for other enterprises and see how long it would take them to reach the benchmark Google has set for collaboration on Docs. Not only has the collaboration on Docs changed at Google, the number of emails, comments on G+, calender meetings between people who work together has also had significant changes over the past few years. How those changes reinforce each other over time would also be an interesting topic to study. Acknowledgements We would like to thank Ariel Kern for her insights about collaboration on Google Docs, Penny Chu and Tony Fagan for their encouragement and support and many thanks to Jim Koehler for his constructive feedback. 12 Google Inc.REFERENCES REFERENCES References [1] Dan R. Herrick (2009). Google this!: using Google apps for collaboration and productivity. Proceeding of the ACM SIGUCCS fall conference (pp. 55-64). [2] Stijn Dekeyser, Richard Watson (2009). Extending Google Docs to Collaborate on Research Papers. Technical Report, The University of Southern Queensland, Australia. [3] Ina Blau, Avner Caspi (2009). What Type of Collaboration Helps? Psychological Ownership, Perceived Learning and Outcome Quality of Collaboration Using Google Docs. Learning in the technological era: Proceedings of the Chais conference on instructional technologies research (pp. 48-55). Google Inc. 13 Circulant Binary Embedding Felix X. Yu1 YUXINNAN@EE.COLUMBIA.EDU Sanjiv Kumar2 SANJIVK@GOOGLE.COM Yunchao Gong3 YUNCHAO@CS.UNC.EDU Shih-Fu Chang1 SFCHANG@EE.COLUMBIA.EDU 1Columbia University, New York, NY 10027 2Google Research, New York, NY 10011 3University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 Abstract Binary embedding of high-dimensional data requires long codes to preserve the discriminative power of the input space. Traditional binary coding methods often suffer from very high computation and storage costs in such a scenario. To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix. The circulant structure enables the use of Fast Fourier Transformation to speed up the computation. Compared to methods that use unstructured matrices, the proposed method improves the time complexity from O(d 2 ) to O(d log d), and the space complexity from O(d 2 ) to O(d) where d is the input dimensionality. We also propose a novel time-frequency alternating optimization to learn data-dependent circulant projections, which alternatively minimizes the objective in original and Fourier domains. We show by extensive experiments that the proposed approach gives much better performance than the state-of-the-art approaches for fixed time, and provides much faster computation with no performance degradation for fixed number of bits. 1. Introduction Embedding input data in binary spaces is becoming popular for efficient retrieval and learning on massive data sets (Li et al., 2011; Gong et al., 2013a; Raginsky & Lazebnik, 2009; Gong et al., 2012; Liu et al., 2011). Moreover, Proceedings of the 31 st International Conference on Machine Learning, Beijing, China, 2014. JMLR: W&CP volume 32. Copyright 2014 by the author(s). in a large number of application domains such as computer vision, biology and finance, data is typically highdimensional. When representing such high dimensional data by binary codes, it has been shown that long codes are required in order to achieve good performance. In fact, the required number of bits is O(d), where d is the input dimensionality (Li et al., 2011; Gong et al., 2013a; Sanchez ´ & Perronnin, 2011). The goal of binary embedding is to well approximate the input distance as Hamming distance so that efficient learning and retrieval can happen directly in the binary space. It is important to note that another related area called hashing is a special case with slightly different goal: creating hash tables such that points that are similar fall in the same (or nearby) bucket with high probability. In fact, even in hashing, if high accuracy is desired, one typically needs to use hundreds of hash tables involving tens of thousands of bits. Most of the existing linear binary coding approaches generate the binary code by applying a projection matrix, followed by a binarization step. Formally, given a data point, x ∈ R d , the k-bit binary code, h(x) ∈ {+1, −1} k is generated simply as h(x) = sign(Rx), (1) where R ∈ R k×d , and sign(·) is a binary map which returns element-wise sign1 . Several techniques have been proposed to generate the projection matrix randomly without taking into account the input data (Charikar, 2002; Raginsky & Lazebnik, 2009). These methods are very popular due to their simplicity but often fail to give the best performance due to their inability to adapt the codes with respect to the input data. Thus, a number of data-dependent techniques have been proposed with different optimization criteria such as reconstruction error (Kulis & Darrell, 2009), data dissimilarity (Norouzi & Fleet, 2012; Weiss et al., 1A few methods transform the linear projection via a nonlinear map before taking the sign (Weiss et al., 2008; Raginsky & Lazebnik, 2009).Circulant Binary Embedding 2008), ranking loss (Norouzi et al., 2012), quantization error after PCA (Gong et al., 2013b), and pairwise misclassification (Wang et al., 2010). These methods are shown to be effective for learning compact codes for relatively lowdimensional data. However, the O(d 2 ) computational and space costs prohibit them from being applied to learning long codes for high-dimensional data. For instance, to generate O(d)-bit binary codes for data with d ∼1M, a huge projection matrix will be required needing TBs of memory, which is not practical2 . In order to overcome these computational challenges, Gong et al. (2013a) proposed a bilinear projection based coding method for high-dimensional data. It reshapes the input vector x into a matrix Z, and applies a bilinear projection to get the binary code: h(x) = sign(RT 1 ZR2). (2) When the shapes of Z, R1, R2 are chosen appropriately, the method has time and space complexity of O(d 1.5 ) and O(d), respectively. Bilinear codes make it feasible to work with datasets with very high dimensionality and have shown good results in a variety of tasks. In this work, we propose a novel Circulant Binary Embedding (CBE) technique which is even faster than the bilinear coding. It is achieved by imposing a circulant structure on the projection matrix R in (1). This special structure allows us to use Fast Fourier Transformation (FFT) based techniques, which have been extensively used in signal processing. The proposed method further reduces the time complexity to O(d log d), enabling efficient binary embedding for very high-dimensional data3 . Table 1 compares the time and space complexity for different methods. This work makes the following contributions: • We propose the circulant binary embedding method, which has space complexity O(d) and time complexity O(d log d) (Section 2, 3). • We propose to learn the data-dependent circulant projection matrix by a novel and efficient time-frequency alternating optimization, which alternatively optimizes the objective in the original and frequency domains (Section 4). • Extensive experiments show that, compared to the state-of-the-art, the proposed method improves the result dramatically for a fixed time cost, and provides much faster computation with no performance degradation for a fixed number of bits (Section 5). 2 In principle, one can generate the random entries of the matrix on-the-fly (with fixed seeds) without needing to store the matrix. But this will increase the computational time even further. 3One could in principal use other structured matrices like Hadamard matrix along with a sparse random Gaussian matrix to achieve fast projection as was done in fast Johnson-Lindenstrauss transform(Ailon & Chazelle, 2006; Dasgupta et al., 2011), but it is still slower than circulant projection and needs more space. Method Time Space Time (Learning) Full projection O(d 2 ) O(d 2 ) O(nd3 ) Bilinear proj. O(d 1.5 ) O(d) O(nd1.5 ) Circulant proj. O(d log d) O(d) O(nd log d) Table 1. Comparison of the proposed method (Circulant proj.) with other methods for generating long codes (code dimension k comparable to input dimension d). n is the number of instances used for learning data-dependent projection matrices. 2. Circulant Binary Embedding (CBE) A circulant matrix R ∈ R d×d is a matrix defined by a vector r = (r0, r2, · · · , rd−1) T (Gray, 2006)4 . R = circ(r) :=         r0 rd−1 . . . r2 r1 r1 r0 rd−1 r2 . . . r1 r0 . . . . . . rd−2 . . . . . . rd−1 rd−1 rd−2 . . . r1 r0         . (3) Let D be a diagonal matrix with each diagonal entry being a Bernoulli variable (±1 with probability 1/2). For x ∈ R d , its d-bit Circulant Binary Embedding (CBE) with r ∈ R d is defined as: h(x) = sign(RDx), (4) where R = circ(r). The k-bit (k < d) CBE is defined as the first k elements of h(x). The need for such a D is discussed in Section 3. Note that applying D to x is equivalent to applying random sign flipping to each dimension of x. Since sign flipping can be carried out as a preprocessing step for each input x, here onwards for simplicity we will drop explicit mention of D. Hence the binary code is given as h(x) = sign(Rx). The main advantage of circulant binary embedding is its ability to use Fast Fourier Transformation (FFT) to speed up the computation. Proposition 1. For d-dimensional data, CBE has space complexity O(d), and time complexity O(d log d). Since a circulant matrix is defined by a single column/row, clearly the storage needed is O(d). Given a data point x, the d-bit CBE can be efficiently computed as follows. Denote ~ as operator of circulant convolution. Based on the definition of circulant matrix, Rx = r ~ x. (5) The above can be computed based on Discrete Fourier Transformation (DFT), for which fast algorithm (FFT) is available. The DFT of a vector t ∈ C d is a d-dimensional vector with each element defined as 4The circulant matrix is sometimes equivalently defined by “circulating” the rows instead of the columns.Circulant Binary Embedding F(t)l = X d−1 m=0 tn · e −i2πlm/d, l = 0, · · · , d − 1. (6) The above can be expressed equivalently in a matrix form as F(t) = Fdt, (7) where Fd is the d-dimensional DFT matrix. Let F H d be the conjugate transpose of Fd. It is easy to show that F −1 d = (1/d)F H d . Similarly, for any t ∈ C d , the Inverse Discrete Fourier Transformation (IDFT) is defined as F −1 (t) = (1/d)F H d t. (8) Since the convolution of two signals in their original domain is equivalent to the hadamard product in their frequency domain (Oppenheim et al., 1999), F(Rx) = F(r) ◦ F(x). (9) Therefore, h(x) = sign F −1 (F(r) ◦ F(x)) . (10) For k-bit CBE, k < d, we only need to pick the first k bits of h(x). As DFT and IDFT can be efficiently computed in O(d log d) with FFT (Oppenheim et al., 1999), generating CBE has time complexity O(d log d). 3. Randomized Circulant Binary Embedding A simple way to obtain CBE is by generating the elements of r in (3) independently from the standard normal distribution N (0, 1). We call this method randomized CBE (CBE-rand). A desirable property of any embedding method is its ability to approximate input distances in the embedded space. Suppose Hk(x1, x2) is the normalized Hamming distance between k-bit codes of a pair of points x1, x2 ∈ R d : Hk(x1, x2)= 1 k k X−1 i=0 sign(Ri·x1)−sign(Ri·x2) /2, (11) and Ri· is the i-th row of R, R = circ(r). If r is sampled from N (0, 1), from (Charikar, 2002), Pr sign(r T x1) 6= sign(r T x2) = θ/π, (12) where θ is the angle between x1 and x2. Since all the vectors that are circulant variants of r also follow the same distribution, it is easy to see that E(Hk(x1, x2)) = θ/π. (13) For the sake of discussion, if k projections, i.e., first k rows of R, were generated independently, it is easy to show that the variance of Hk(x1, x2) will be Var(Hk(x1, x2)) = θ(π − θ)/kπ2 . (14) 0 1 2 3 4 5 6 7 8 0 0.05 0.1 0.15 0.2 0.25 log k Variance Independent Circulant (a) θ = π/12 0 1 2 3 4 5 6 7 8 0 0.05 0.1 0.15 0.2 0.25 log k Variance Independent Circulant (b) θ = π/6 0 1 2 3 4 5 6 7 8 0 0.05 0.1 0.15 0.2 0.25 log k Variance Independent Circulant (c) θ = π/3 0 1 2 3 4 5 6 7 8 0 0.05 0.1 0.15 0.2 0.25 log k Variance Independent Circulant (d) θ = π/2 Figure 1. The analytical variance of normalized hamming distance of independent bits as in (14), and the sample variance of normalized hamming distance of circulant bits, as a function of angle between points (θ) and number of bits (k). The two curves overlap. Thus, with more bits (larger k), the normalized hamming distance will be close to the expected value, with lower variance. In other words, the normalized hamming distance approximately preserves the angle5 . Unfortunately in CBE, the projections are the rows of R = circ(r), which are not independent. This makes it hard to derive the variance analytically. To better understand CBE-rand, we run simulations to compare the analytical variance of normalized hamming distance of independent projections (14), and the sample variance of normalized hamming distance of circulant projections in Figure 1. For each θ and k, we randomly generate x1, x2 ∈ R d such that their angle is θ 6 . We then generate k-dimensional code with CBE-rand, and compute the hamming distance. The variance is estimated by applying CBE-rand 1,000 times. We repeat the whole process 1,000 times, and compute the averaged variance. Surprisingly, the curves of “Independent” and “Circulant” variances are almost indistinguishable. This means that bits generated by CBE-rand are generally as good as the independent bits for angle preservation. An intuitive explanation is that for the circulant matrix, though all the rows are dependent, circulant shifting one or multiple elements will in fact result in very different projections in most cases. We will later show in experiments on real-world data that CBE-rand and Locality Sensitive Hashing (LSH)7 has almost identical performance (yet CBE-rand is significantly faster) (Section 5). 5 In this paper, we consider the case that the data points are `2 normalized. Therefore the cosine distance, i.e., 1 - cos(θ), is equivalent to the l2 distance. 6This can be achieved by extending the 2D points (1, 0), (cos θ, sin θ) to d-dimension, and performing a random orthonormal rotation, which can be formed by the Gram-Schmidt process on random vectors. 7Here, by LSH we imply the binary embedding using R such that all the rows of R are sampled iid. With slight abuse of notation, we still call it “hashing” following (Charikar, 2002).Circulant Binary Embedding Note that the distortion in input distances after circulant binary embedding comes from two sources: circulant projection, and binarization. For the circulant projection step, recent works have shown that the Johnson-Lindenstrausstype lemma holds with a slightly worse bound on the number of projections needed to preserve the input distances with high probability (Hinrichs & Vyb´ıral, 2011; Zhang & Cheng, 2013; Vyb´ıral, 2011; Krahmer & Ward, 2011). These works also show that before applying the circulant projection, an additional step of randomly flipping the signs of input dimensions is necessary8 . To show why such a step is required, let us consider the special case when x is an allone vector, 1. The circulant projection with R = circ(r) will result in a vector with all elements to be r T 1. When r is independently drawn from N (0, 1), this will be close to 0, and the norm cannot be preserved. Unfortunately the Johnson-Lindenstrauss-type results do not generalize to the distortion caused by the binarization step. One problem with the randomized CBE method is that it does not utilize the underlying data distribution while generating the matrix R. In the next section, we propose to learn R in a data-dependent fashion, to minimize the distortions due to circulant projection and binarization. 4. Learning Circulant Binary Embedding We propose data-dependent CBE (CBE-opt), by optimizing the projection matrix with a novel time-frequency alternating optimization. We consider the following objective function in learning the d-bit CBE. The extension of learning k < d bits will be shown in Section 4.2. argmin B,r ||B − XRT ||2 F + λ||RRT − I||2 F (15) s.t. R = circ(r), where X ∈ R n×d , is the data matrix containing n training points: X = [x0, · · · , xn−1] T , and B ∈ {−1, 1} n×d is the corresponding binary code matrix.9 In the above optimization, the first term minimizes distortion due to binarization. The second term tries to make the projections (rows of R, and hence the corresponding bits) as uncorrelated as possible. In other words, this helps to reduce the redundancy in the learned code. If R were to be an orthogonal matrix, the second term will vanish and the optimization would find the best rotation such that the distortion due to binarization is minimized. However, when R is a circulant matrix, R, in general, will not be orthogonal. Similar objective has been used in previous works including (Gong et al., 2013b;a) and (Wang et al., 2010). 8 For each dimension, whether the sign needs to be flipped is predetermined by a (p = 0.5) Bernoulli variable. 9 If the data is `2 normalized, we can set B ∈ {−1/ √ d, 1/ √ d} n×d to make B and XRT more comparable. This does not empirically influence the performance. 4.1. The Time-Frequency Alternating Optimization The above is a combinatorial optimization problem, for which an optimal solution is hard to find. In this section we propose a novel approach to efficiently find a local solution. The idea is to alternatively optimize the objective by fixing r, and B, respectively. For a fixed r, optimizing B can be easily performed in the input domain (“time” as opposed to “frequency”). For a fixed B, the circulant structure of R makes it difficult to optimize the objective in the input domain. Hence we propose a novel method, by optimizing r in the frequency domain based on DFT. This leads to a very efficient procedure. For a fixed r. The objective is independent on each element of B. Denote Bij as the element of the i-th row and j-th column of B. It is easy to show that B can be updated as: Bij = ( 1 if Rj·xi ≥ 0 −1 if Rj·xi < 0 , (16) i = 0, · · · , n − 1. j = 0, · · · , d − 1. For a fixed B. Define ˜r as the DFT of the circulant vector ˜r := F(r). Instead of solving r directly, we propose to solve ˜r, from which r can be recovered by IDFT. Key to our derivation is the fact that DFT projects the signal to a set of orthogonal basis. Therefore the `2 norm can be preserved. Formally, according to Parseval’s theorem , for any t ∈ C d (Oppenheim et al., 1999), ||t||2 2 = (1/d)||F(t)||2 2 . Denote diag(·) as the diagonal matrix formed by a vector. Denote <(·) and =(·) as the real and imaginary parts, respectively. We use Bi· to denote the i-th row of B. With complex arithmetic, the first term in (15) can be expressed in the frequency domain as: ||B − XRT ||2 F = 1 d nX−1 i=0 ||F(B T i· − Rxi)||2 2 (17) = 1 d nX−1 i=0 ||F(B T i·)−˜r◦F(xi)||2 2 = 1 d nX−1 i=0 ||F(B T i·)−diag(F(xi))˜r||2 2 = 1 d nX−1 i=0 F(B T i·)−diag(F(xi))˜r T F(B T i·)−diag(F(xi))˜r = 1 d h <(˜r) TM<(˜r)+=(˜r) TM=(˜r)+<(˜r) T h+=(˜r) T g i +||B||2 F , where, M=diag nX−1 i=0 <(F(xi))◦<(F(xi))+=(F(xi))◦=(F(xi)) h = −2 nX−1 i=0 <(F(xi))◦<(F(B T i·))+=(F(xi)) ◦ =(F(B T i·)) g = 2 nX−1 i=0 =(F(xi)) ◦ <(F(B T i·)) − <(F(xi)) ◦ =(F(B T i·)).Circulant Binary Embedding For the second term in (15), we note that the circulant matrix can be diagonalized by DFT matrix Fd and its conjugate transpose F H d . Formally, for R = circ(r), r ∈ R d , R = (1/d)F H d diag(F(r))Fd. (18) Let Tr(·) be the trace of a matrix. Therefore, ||RRT − I||2 F = || 1 d F H d (diag(˜r) Hdiag(˜r) − I)Fd||2 F = Tr 1 d F H d (diag(˜r) Hdiag(˜r)−I) H(diag(˜r) Hdiag(˜r)−I)Fd = Tr (diag(˜r) Hdiag(˜r) − I) H(diag(˜r) Hdiag(˜r) − I) =||˜r H ◦ ˜r − 1||2 2 = ||<(˜r) 2 + =(˜r) 2 − 1||2 2 . (19) Furthermore, as r is real-valued, additional constraints on ˜r are needed. For any u ∈ C, denote u as the complex conjugate of u. We have the following result (Oppenheim et al., 1999): For any real-valued vector t ∈ C d , F(t)0 is real-valued, and F(t)d−i = F(t)i , i = 1, · · · , bd/2c. From (17) − (19), the problem of optimizing ˜r becomes argmin ˜r <(˜r) TM<(˜r) + =(˜r) TM=(˜r) + <(˜r) T h + =(˜r) T g + λd||<(˜r) 2 + =(˜r) 2 − 1||2 2 (20) s.t. =(˜r0) = 0 <(˜ri) = <(˜rd−i), i = 1, · · · , bd/2c =(˜ri) = −=(˜rd−i), i = 1, · · · , bd/2c. The above is non-convex. Fortunately, the objective function can be decomposed, such that we can solve two variables at a time. Denote the diagonal vector of the diagonal matrix M as m. The above optimization can then be decomposed to the following sets of optimizations. argmin r˜0 m0r˜ 2 0 + h0r˜0+ λd r˜ 2 0 − 1 2 , s.t. r˜0 = r˜0. (21) argmin r˜i (mi + md−i)(<(˜ri) 2 + =(˜ri) 2 ) (22) + 2λd <(˜ri) 2 + =(˜ri) 2 − 1 2 + (hi + hd−i)<(˜ri) + (gi − gd−i)=(˜ri), i = 1, · · · , bd/2c. In (21), we need to minimize a 4 th order polynomial with one variable, with the closed form solution readily available. In (22), we need to minimize a 4 th order polynomial with two variables. Though the closed form solution is hard (requiring solution of a cubic bivariate system), we can find local minima by gradient descent, which can be considered as having constant running time for such small-scale problems. The overall objective is guaranteed to be nonincreasing in each step. In practice, we can get a good solution with just 5-10 iterations. In summary, the proposed time-frequency alternating optimization procedure has running time O(nd log d). 4.2. Learning k < d Bits In the case of learning k < d bits, we need to solve the following optimization problem: argmin B,r ||BPk−XPT k RT ||2 F +λ||RPkP T k RT −I||2 F s.t. R = circ(r), (23) in which Pk = Ik O O Od−k , Ik is a k × k identity matrix, and Od−k is a (d − k) × (d − k) all-zero matrix. In fact, the right multiplication of Pk can be understood as a “temporal cut-off”, which is equivalent to a frequency domain convolution. This makes the optimization difficult, as the objective in frequency domain can no longer be decomposed. To address this issues, we propose a simple solution in which Bij = 0, i = 0, · · · , n − 1, j = k, · · · , d − 1 in (15). Thus, the optimization procedure remains the same, and the cost is also O(nd log d). We will show in experiments that this heuristic provides good performance in practice. 5. Experiments To compare the performance of the proposed circulant binary embedding technique, we conducted experiments on three real-world high-dimensional datasets used by the current state-of-the-art method for generating long binary codes (Gong et al., 2013a). The Flickr-25600 dataset contains 100K images sampled from a noisy Internet image collection. Each image is represented by a 25, 600 dimensional vector. The ImageNet-51200 contains 100k images sampled from 100 random classes from ImageNet (Deng et al., 2009), each represented by a 51, 200 dimensional vector. The third dataset (ImageNet-25600) is another random subset of ImageNet containing 100K images in 25, 600 dimensional space. All the vectors are normalized to be of unit norm. We compared the performance of the randomized (CBErand) and learned (CBE-opt) versions of our circulant embeddings with the current state-of-the-art for highdimensional data, i.e., bilinear embeddings. We use both the randomized (bilinear-rand) and learned (bilinear-opt) versions. Bilinear embeddings have been shown to perform similar or better than another promising technique called Product Quantization (Jegou et al., 2011). Finally, we also compare against the binary codes produced by the baseline LSH method (Charikar, 2002), which is still applicable to 25,600 and 51,200 dimensional feature but with much longer running time and much more space. We also show an experiment with relatively low-dimensional data in 2048 dimensional space using Flickr data to compare against techniques that perform well for low-dimensional data but do not scale to high-dimensional scenario. Exam- C/C++ Thread Safety Analysis DeLesley Hutchins Google Inc. Email: delesley@google.com Aaron Ballman CERT/SEI Email: aballman@cert.org Dean Sutherland Email: dfsuther@cs.cmu.edu Abstract—Writing multithreaded programs is hard. Static analysis tools can help developers by allowing threading policies to be formally specified and mechanically checked. They essentially provide a static type system for threads, and can detect potential race conditions and deadlocks. This paper describes Clang Thread Safety Analysis, a tool which uses annotations to declare and enforce thread safety policies in C and C++ programs. Clang is a production-quality C++ compiler which is available on most platforms, and the analysis can be enabled for any build with a simple warning flag: −Wthread−safety. The analysis is deployed on a large scale at Google, where it has provided sufficient value in practice to drive widespread voluntary adoption. Contrary to popular belief, the need for annotations has not been a liability, and even confers some benefits with respect to software evolution and maintenance. I. INTRODUCTION Writing multithreaded programs is hard, because developers must consider the potential interactions between concurrently executing threads. Experience has shown that developers need help using concurrency correctly [1]. Many frameworks and libraries impose thread-related policies on their clients, but they often lack explicit documentation of those policies. Where such policies are clearly documented, that documentation frequently takes the form of explanatory prose rather than a checkable specification. Static analysis tools can help developers by allowing threading policies to be formally specified and mechanically checked. Examples of threading policies are: “the mutex mu should always be locked before reading or writing variable accountBalance” and “the draw() method should only be invoked from the GUI thread.” Formal specification of policies provides two main benefits. First, the compiler can issue warnings on policy violations. Finding potential bugs at compile time is much less expensive in terms of engineer time than debugging failed unit tests, or worse, having subtle threading bugs hit production. Second, specifications serve as a form of machine-checked documentation. Such documentation is especially important for software libraries and APIs, because engineers need to know the threading policy to correctly use them. Although documentation can be put in comments, our experience shows that comments quickly “rot” because they are not updated when variables are renamed or code is refactored. This paper describes thread safety analysis for Clang. The analysis was originally implemented in GCC [2], but the GCC version is no longer being maintained. Clang is a productionquality C++ compiler, which is available on most platforms, including MacOS, Linux, and Windows. The analysis is currently implemented as a compiler warning. It has been deployed on a large scale at Google; all C++ code at Google is now compiled with thread safety analysis enabled by default. II. OVERVIEW OF THE ANALYSIS Thread safety analysis works very much like a type system for multithreaded programs. It is based on theoretical work on race-free type systems [3]. In addition to declaring the type of data (int , float , etc.), the programmer may optionally declare how access to that data is controlled in a multithreaded environment. Clang thread safety analysis uses annotations to declare threading policies. The annotations can be written using either GNU-style attributes (e.g., attribute ((...))) or C++11- style attributes (e.g., [[...]] ). For portability, the attributes are typically hidden behind macros that are disabled when not compiling with Clang. Examples in this paper assume the use of macros; actual attribute names, along with a complete list of all attributes, can be found in the Clang documentation [4]. Figure 1 demonstrates the basic concepts behind the analysis, using the classic bank account example. The GUARDED BY attribute declares that a thread must lock mu before it can read or write to balance, thus ensuring that the increment and decrement operations are atomic. Similarly, REQUIRES declares that the calling thread must lock mu before calling withdrawImpl. Because the caller is assumed to have locked mu, it is safe to modify balance within the body of the method. In the example, the depositImpl() method lacks a REQUIRES clause, so the analysis issues a warning. Thread safety analysis is not interprocedural, so caller requirements must be explicitly declared. There is also a warning in transferFrom(), because it fails to lock b.mu even though it correctly locks this−>mu. The analysis understands that these are two separate mutexes in two different objects. Finally, there is a warning in the withdraw() method, because it fails to unlock mu. Every lock must have a corresponding unlock; the analysis detects both double locks and double unlocks. A function may acquire a lock without releasing it (or vice versa), but it must be annotated to specify this behavior. A. Running the Analysis To run the analysis, first download and install Clang [5]. Then, compile with the −Wthread−safety flag: clang −c −Wthread−s af et y example . cpp#include ” mutex . h ” class BankAcct { Mutex mu; i n t balance GUARDED BY(mu ) ; void d e p o s itIm p l ( i n t amount ) { / / WARNING! Must l o c k mu. balance += amount ; } void withd rawImpl ( i n t amount ) REQUIRES (mu) { / / OK. C a l l e r must have lo c ked mu. balance −= amount ; } public : void withd raw ( i n t amount ) { mu. l o c k ( ) ; / / OK. We ’ ve lo c ked mu. withd rawImpl ( amount ) ; / / WARNING! F a i l e d t o unlo c k mu. } void t r a n sf e rF r om ( BankAcct& b , i n t amount ) { mu. l o c k ( ) ; / / WARNING! Must l o c k b .mu. b . withd rawImpl ( amount ) ; / / OK. d e p o s itIm p l ( ) has no requi rement s . d e p o s itIm p l ( amount ) ; mu. unlo c k ( ) ; } } ; Fig. 1. Thread Safety Annotations Note that this example assumes the presence of a suitably annotated mutex.h [4] that declares which methods perform locking and unlocking. B. Thread Roles Thread safety analysis was originally designed to enforce locking policies such as the one previously described, but locks are not the only way to ensure safety. Another common pattern in many systems is to assign different roles to different threads, such as “worker thread” or “GUI thread” [6]. The same concepts used for mutexes and locking can also be used for thread roles, as shown in Figure 2. Here, a widget library has two threads, one to handle user input, like mouse clicks, and one to handle rendering. It also enforces a constraint: the draw() method should only be invoked only by the GUI thread. The analysis will warn if draw() is invoked directly from onClick(). The rest of this paper will focus discussion on mutexes in the interest of brevity, but there are analogous examples for thread roles. III. BASIC CONCEPTS Clang thread safety analysis is based on a calculus of capabilities [7] [8]. To read or write to a particular location in memory, a thread must have the capability, or permission, to do so. A capability can be thought of as an unforgeable key, #include ” ThreadRole . h ” ThreadRole Input Th read ; ThreadRole GUI Thread ; class Widget { public : v i r t u a l void o nC l i c k ( ) REQUIRES ( Input Th read ) ; v i r t u a l void draw ( ) REQUIRES ( GUI Thread ) ; } ; class Button : public Widget { public : void o nC l i c k ( ) o v e r r i d e { depressed = t rue ; draw ( ) ; / / WARNING! } } ; Fig. 2. Thread Roles or token, which the thread must present to perform the read or write. Capabilities can be either unique or shared. A unique capability cannot be copied, so only one thread can hold the capability at any one time. A shared capability may have multiple copies that are shared among multiple threads. Uniqueness is enforced by a linear type system [9]. The analysis enforces a single-writer/multiple-reader discipline. Writing to a guarded location requires a unique capability, and reading from a guarded location requires either a unique or a shared capability. In other words, many threads can read from a location at the same time because they can share the capability, but only one thread can write to it. Moreover, a thread cannot write to a memory location at the same time that another thread is reading from it, because a capability cannot be both shared and unique at the same time. This discipline ensures that programs are free of data races, where a data race is defined as a situation that occurs when multiple threads attempt to access the same location in memory at the same time, and at least one of the accesses is a write [10]. Because write operations require a unique capability, no other thread can access the memory location at that time. A. Background: Uniqueness and Linear Logic Linear logic is a formal theory for reasoning about resources; it can be used to express logical statements like: “You cannot have your cake and eat it too” [9]. A unique, or linear, variable must be used exactly once; it cannot be duplicated (used multiple times) or forgotten (not used). A unique object is produced at one point in the program, and then later consumed. Functions that use the object without consuming it must be written using a hand-off protocol. The caller hands the object to the function, thus relinquishing control of it; the function hands the object back to the caller when it returns. For example, if std :: stringstream were a linear type, stream programs would be written as follows:st d : : st r i n g st r e am ss ; / / produce ss auto& ss2 = ss << ” H e l l o ” ; / / consume ss auto& ss3 = ss2 << ” World . ” ; / / consume ss2 re tu rn ss3 . s t r ( ) ; / / consume ss3 Notice that each stream variable is used exactly once. A linear type system is unaware that ss and ss2 refer to the same stream; the calls to << conceptually consume one stream and produce another with a different name. Attempting to use ss a second time would be flagged as a use-after-consume error. Failure to call ss3. str () before returning would also be an error because ss3 would then be unused. B. Naming of Capabilities Passing unique capabilities explicitly, following the pattern described previously, would be needlessly tedious, because every read or write operation would introduce a new name. Instead, Clang thread safety analysis tracks capabilities as unnamed objects that are passed implicitly. The resulting type system is formally equivalent to linear logic but is easier to use in practical programming. Each capability is associated with a named C++ object, which identifies the capability and provides operations to produce and consume it. The C++ object itself is not unique. For example, if mu is a mutex, mu.lock() produces a unique, but unnamed, capability of type Cap (a dependent type). Similarly, mu.unlock() consumes an implicit parameter of type Cap. Operations that read or write to data that is guarded by mu follow a hand-off protocol: they consume an implicit parameter of type Cap and produce an implicit result of type Cap. C. Erasure Semantics Because capabilities are implicit and are used only for typechecking purposes, they have no run time effect. As a result, capabilities can be fully erased from an annotated program, yielding an unannoted program with identical behavior. In Clang, this erasure property is expressed in two ways. First, recommended practice is to hide the annotations behind macros, where they can be literally erased by redefining the macros to be empty. However, literal erasure is unnecessary. The analysis is entirely static and is implemented as a compile time warning; it cannot affect Clang code generation in any way. IV. THREAD SAFETY ANNOTATIONS This section provides a brief overview of the main annotations that are supported by the analysis. The full list can be found in the Clang documentation [4]. GUARDED BY(...) and PT GUARDED BY(...) GUARDED BY is an attribute on a data member; it declares that the data is protected by the given capability. Read operations on the data require at least a shared capability; write operations require a unique capability. PT GUARDED BY is similar but is intended for use on pointers and smart pointers. There is no constraint on the data member itself; rather, the data it points to is protected by the given capability. Mutex mu; i n t ∗p2 PT GUARDED BY(mu ) ; void t e s t ( ) { ∗p2 = 42; / / Warning ! p2 = new i n t ; / / OK ( no GUARDED BY ) . } REQUIRES(...) and REQUIRES SHARED(...) REQUIRES is an attribute on functions; it declares that the calling thread must have unique possession of the given capability. More than one capability may be specified, and a function may have multiple REQUIRES attributes. REQUIRES SHARED is similar, but the specified capabilities may be either shared or unique. Formally, the REQUIRES clause states that a function takes the given capability as an implicit argument and hands it back to the caller when it returns, as an implicit result. Thus, the caller must hold the capability on entry to the function and will still hold it on exit. Mutex mu; i n t a GUARDED BY(mu ) ; void foo ( ) REQUIRES (mu) { a = 0; / / OK. } void t e s t ( ) { foo ( ) ; / / Warning ! Requi res mu. } ACQUIRE(...) and RELEASE(...) The ACQUIRE attribute annotates a function that produces a unique capability (or capabilities), for example, by acquiring it from some other thread. The caller must not-hold the given capability on entry, and will hold the capability on exit. RELEASE annotates a function that consumes a unique capability, (e.g., by handing it off to some other thread). The caller must hold the given capability on entry, and will nothold it on exit. ACQUIRE SHARED and RELEASE SHARED are similar, but produce and consume shared capabilities. Formally, the ACQUIRE clause states that the function produces and returns a unique capability as an implicit result; RELEASE states that the function takes the capability as an implicit argument and consumes it. Attempts to acquire a capability that is already held or to release a capability that is not held are diagnosed with a compile time warning. CAPABILITY(...) The CAPABILITY attribute is placed on a struct, class or a typedef; it specifies that objects of that type can be used to identify a capability. For example, the threading libraries at Google define the Mutex class as follows:class CAPABILITY ( ” mutex ” ) Mutex { public : void l o c k ( ) ACQUIRE ( t hi s ) ; void reade rLock ( ) ACQUIRE SHARED( t hi s ) ; void unlo c k ( ) RELEASE( t hi s ) ; void reade rUnlock ( ) RELEASE SHARED( t hi s ) ; } ; Mutexes are ordinary C++ objects. However, each mutex object has a capability associated with it; the lock () and unlock() methods acquire and release that capability. Note that Clang thread safety analysis makes no attempt to verify the correctness of the underlying Mutex implementation. Rather, the annotations allow the interface of Mutex to be expressed in terms of capabilities. We assume that the underlying code implements that interface correctly, e.g., by ensuring that only one thread can hold the mutex at any one time. TRY ACQUIRE(b, ...) and TRY ACQUIRE SHARED(b, ...) These are attributes on a function or method that attempts to acquire the given capability and returns a boolean value indicating success or failure. The argument b must be true or false, to specify which return value indicates success. NO THREAD SAFETY ANALYSIS NO THREAD SAFETY ANALYSIS is an attribute on functions that turns off thread safety checking for the annotated function. It provides a means for programmers to opt out of the analysis for functions that either (a) are deliberately thread-unsafe, or (b) are thread-safe, but too complicated for the analysis to understand. Negative Requirements All of the previously described requirements discussed are positive requirements, where a function requires that certain capabilities be held on entry. However, the analysis can also track negative requirements, where a function requires that a capability be not-held on entry. Positive requirements are used to prevent race conditions. Negative requirements are used to prevent deadlock. Many mutex implementations are not reentrant, because making them reentrant entails a significant performance cost. Attempting to acquire a non-reentrant mutex that is already held will deadlock the program. To avoid deadlock, acquiring a capability requires a proof that the capability is not currently held. The analysis represents this proof as a negative capability, which is expressed using the ! negation operator: Mutex mu; i n t a GUARDED BY(mu ) ; void c l e a r ( ) REQUIRES ( !mu) { mu. l o c k ( ) ; a = 0; mu. unlo c k ( ) ; } void r e s et ( ) { mu. l o c k ( ) ; / / Warning ! C a l l e r cannot hold ’mu ’ . c l e a r ( ) ; mu. unlo c k ( ) ; } Negative capabilities are tracked in much the same way as positive capabilities, but there is a bit of extra subtlety. Positive requirements are typically confined within the class or the module in which they are declared. For example, if a thread-safe class declares a private mutex, and does all locking and unlocking of that mutex internally, then there is no reason clients of the class need to know that the mutex exists. Negative requirements lack this property. If a class declares a private mutex mu, and locks mu internally, then clients should theoretically have to provide proof that they have not locked mu before calling any methods of the class. Moreover, there is no way for a client function to prove that it does not hold mu, except by adding REQUIRES(!mu) to the function definition. As a result, negative requirements tend to propagate throughout the code base, which breaks encapsulation. To avoid such propagation, the analysis restricts the visibility of negative capabilities. The analyzer assumes that it holds a negative capability for any object that is not defined within the current lexical scope. The scope of a class member is assumed to be its enclosing class, while the scope of a global variable is the translation unit in which it is defined. Unfortunately, this visibility-based assumption is unsound. For example, a class with a private mutex may lock the mutex, and then call some external routine, which calls a method in the original class that attempts to lock the mutex a second time. The analysis will generate a false negative in this case. Based on our experience in deploying thread safety analysis at Google, we believe this to be a minor problem. It is relatively easy to avoid this situation by following good software design principles and maintaining proper separation of concerns. Moreover, when compiled in debug mode, the Google mutex implementation does a run time check to see if the mutex is already held, so this particular error can be caught by unit tests at run time. V. IMPLEMENTATION The Clang C++ compiler provides a sophisticated infrastructure for implementing warnings and static analysis. Clang initially parses a C++ input file to an abstract syntax tree (AST), which is an accurate representation of the original source code, down to the location of parentheses. In contrast, many compilers, including GCC, lower to an intermediate language during parsing. The accuracy of the AST makes it easier to emit quality diagnostics, but complicates the analysis in other respects. The Clang semantic analyzer (Sema) decorates the AST with semantic information. Name lookup, function overloading, operator overloading, template instantiation, and type checking are all performed by Sema when constructing the AST. Clang inserts special AST nodes for implicit C++ operations, such as automatic casts, LValue-to-RValue conversions,implicit destructor calls, and so on, so the AST provides an accurate model of C++ program semantics. Finally, the Clang analysis infrastructure constructs a control flow graph (CFG) for each function in the AST. This is not a lowering step; each statement in the CFG points back to the AST node that created it. The CFG is shared infrastructure; the thread safety analysis is only one of its many clients. A. Analysis Algorithm The thread safety analysis algorithm is flow-sensitive, but not path-sensitive. It starts by performing a topological sort of the CFG, and identifying back edges. It then walks the CFG in topological order, and computes the set of capabilities that are known to be held, or known not to be held, at every program point. When the analyzer encounters a call to a function that is annotated with ACQUIRE, it adds a capability to the set; when it encounters a call to a function that is annotated with RELEASE, it removes it from the set. Similarly, it looks for REQUIRES attributes on function calls, and GUARDED BY on loads or stores to variables. It checks that the appropriate capability is in the current set, and issues a warning if it is not. When the analyzer encounters a join point in the CFG, it checks to confirm that every predecessor basic block has the same set of capabilities on exit. Back edges are handled similarly: a loop must have the same set of capabilities on entry to and exit from the loop. Because the analysis is not path-sensitive, it cannot handle control-flow situations in which a mutex might or might not be held, depending on which branch was taken. For example: void foo ( ) { i f ( b ) mutex . l o c k ( ) ; / / Warning : b may o r may not be held here . doSomething ( ) ; i f ( b ) mutex . unlo c k ( ) ; } void l o c k A l l ( ) { / / Warning : c a p a b i l i t y s et s do not match / / at s t a r t and end of loop . fo r ( unsigned i =0; i < n ; ++ i ) mutexArray [ i ] . l o c k ( ) ; } Although this seems like a serious limitation, we have found that conditionally held locks are relatively unimportant in practical programming. Reading or writing to a guarded location in memory requires that the mutex be held unconditionally, so attempting to track locks that might be held has little benefit in practice, and usually indicates overly complex or poor-quality code. Requiring that capability sets be the same at join points also speeds up the algorithm considerably. The analyzer need not iterate to a fixpoint; thus it traverses every statement in the program exactly once. Consequently, the computational complexity of the analysis is O(n) with respect to code size. The compile time overhead of the warning is minimal. B. Intermediate Representation Each capability is associated with a C++ object. C++ objects are run time entities, that are identified by C++ expressions. The same object may be identified by different expressions in different scopes. For example: class Foo { Mutex mu; bool compare ( const Foo& ot h e r ) REQUIRES ( this−>mu, ot h e r .mu ) ; } void ba r ( ) { Foo a ; Foo ∗b ; . . . a .mu. l o c k ( ) ; b−>mu. l o c k ( ) ; / / REQUIRES (&a)−>mu, (∗ b ) .mu a . compare (∗ b ) ; . . . } Clang thread safety analysis is dependently typed: note that the REQUIRES clause depends on both this and other, which are parameters to compare. The analyzer performs variable substitution to obtain the appropriate expressions within bar(); it substitutes &a for this and ∗b for other. Recall, however, that the Clang AST does not lower C++ expressions to an intermediate language; rather, it stores them in a format that accurately represents the original source code. Consequently, (&a)−>mu and a.mu are different expressions. A dependent type system must be able to compare expressions for semantic (not syntactic) equality. The analyzer implements a simple compiler intermediate representation (IR), and lowers Clang expressions to the IR for comparison. It also converts the Clang CFG into single static assignment (SSA) form so that the analyzer will not be confused by local variables that take on different values in different places. C. Limitations Clang thread safety analysis has a number of limitations. The three major ones are: No attributes on types. Thread safety attributes are attached to declarations rather than types. For example, it is not possible to write vector, or ( int GUARDED BY(mu))[10]. If attributes could be attached to types, PT GUARDED BY would be unnecessary. Attaching attributes to types would result in a better and more accurate analysis. However, it was deemed infeasible for C++ because it would require invasive changes to the C++ type system that could potentially affect core C++ semantics in subtle ways, such as template instantiation and function overloading. No dependent type parameters. Race-free type systems as described in the literature often allow classes to be parameterized by the objects that are responsible for controlling access. [11] [3] For example, assume a Graph class has a list of nodes, and a single mutex that protects all of them. In this case, theNode class should technically be parameterized by the graph object that guards it (similar to inner classes in Java), but that relationship cannot be easily expressed with attributes. No alias analysis. C++ programs typically make heavy use of pointer aliasing; we currently lack an alias analysis. This can occasionally cause false positives, such as when a program locks a mutex using one alias, but the GUARDED BY attribute refers to the same mutex using a different alias. VI. EXPERIMENTAL RESULTS AND CONCLUSION Clang thread safety analysis is currently deployed on a wide scale at Google. The analysis is turned on by default, across the company, for every C++ build. Over 20,000 C++ files are currently annotated, with more than 140,000 annotations, and those numbers are increasing every day. The annotated code spans a wide range of projects, including many of Google’s core services. Use of the annotations at Google is entirely voluntary, so the high level of adoption suggests that engineering teams at Google have found the annotations to be useful. Because race conditions are insidious, Google uses both static analysis and dynamic analysis tools such as Thread Sanitizer [12]. We have found that these tools complement each other. Dynamic analysis operates without annotations and thus can be applied more widely. However, dynamic analysis can only detect race conditions in the subset of program executions that occur in test code. As a result, effective dynamic analysis requires good test coverage, and cannot report potential bugs until test time. Static analysis is less flexible, but covers all possible program executions; it also reports errors earlier, at compile time. Although the need for handwritten annotations may appear to be a disadvantage, we have found that the annotations confer significant benefits with respect to software evolution and maintenance. Thread safety annotations are widely used in Google’s core libraries and APIs. Annotating libraries has proven to be particularly important, because the annotations serve as a form of machine-checked documentation. The developers of a library and the clients of that library are usually different engineering teams. As a result, the client teams often do not fully understand the locking protocol employed by the library. Other documentation is usually out of date or nonexistent, so it is easy to make mistakes. By using annotations, the locking protocol becomes part of the published API, and the compiler will warn about incorrect usage. Annotations have also proven useful for enforcing internal design constraints as software evolves over time. For example, the initial design of a thread-safe class must establish certain constraints: locks are used in a particular way to protect private data. Over time, however, that class will be read and modified by many different engineers. Not only may the initial constraints be forgotten, they may change when code is refactored. When examining change logs, we found several cases in which an engineer added a new method to a class, forgot to acquire the appropriate locks, and consequently had to debug the resulting race condition by hand. When the constraints are explicitly specified with annotations, the compiler can prevent such bugs by mechanically checking new code for consistency with existing annotations. The use of annotations does entail costs beyond the effort required to write the annotations. In particular, we have found that about 50% of the warnings produced by the analysis are caused not by incorrect code but rather by incorrect or missing annotations, such as failure to put a REQUIRES attribute on getter and setter methods. Thread safety annotations are roughly analogous to the C++ const qualifier in this regard. Whether such warnings are false positives depends on your point of view. Google’s philosophy is that incorrect annotations are “bugs in the documentation.” Because APIs are read many times by many engineers, it is important that the public interfaces be accurately documented. Excluding cases in which the annotations were clearly wrong, the false positive rate is otherwise quite low: less than 5%. Most false positives are caused by either (a) pointer aliasing, (b) conditionally acquired mutexes, or (c) initialization code that does not need to acquire a mutex. Conclusion Type systems for thread safety have previously been implemented for other languages, most notably Java [3] [11]. Clang thread safety analysis brings the benefit of such systems to C++. The analysis has been implemented in a production C++ compiler, tested in a production environment, and adopted internally by one of the world’s largest software companies. REFERENCES [1] K. Asanovic et al., “A view of the parallel computing landscape,” Communications of the ACM, vol. 52, no. 10, 2009. [2] L.-C. Wu, “C/C++ thread safety annotations,” 2008. [Online]. Available: https://docs.google.com/a/google.com/document/d/1 d9MvYX3LpjTk 3nlubM5LE4dFmU91SDabVdWp9-VDxc [3] M. Abadi, C. Flanagan, and S. N. Freund, “Types for safe locking: Static race detection for java,” ACM Transactions on Programming Languages and Systems, vol. 28, no. 2, 2006. [4] “Clang thread safety analysis documentation.” [Online]. Available: http://clang.llvm.org/docs/ThreadSafetyAnalysis.html [5] “Clang: A c-language front-end for llvm.” [Online]. Available: http://clang.llvm.org [6] D. F. Sutherland and W. L. Scherlis, “Composable thread coloring,” PPoPP ’10: Proceedings of the ACM Symposium on Principles and Practice of Parallel Programming, 2010. [7] K. Crary, D. Walker, and G. Morrisett, “Typed memory management in a calculus of capabilities,” Proceedings of POPL, 1999. [8] J. Boyland, J. Noble, and W. Retert, “Capabilities for sharing,” Proceedings of ECOOP, 2001. [9] J.-Y. Girard, “Linear logic,” Theoretical computer science, vol. 50, no. 1, pp. 1–101, 1987. [10] S. Savage, M. Burrows, G. Nelson, P. Sobalvarro, and T. Anderson, “Eraser: A dynamic data race detector for multithreaded programs.” ACM Transactions on Computer Systems (TOCS), vol. 15, no. 4, 1997. [11] C. Boyapati and M. Rinard, “A parameterized type system for race-free Java programs,” Proceedings of OOPSLA, 2001. [12] K. Serebryany and T. Iskhodzhanov, “Threadsanitizer: data race detection in practice,” Workshop on Binary Instrumentation and Applications, 2009. An Optimized Template Matching Approach to Intra Coding in Video/Image Compression Hui Su, Jingning Han, and Yaowu Xu Chrome Media, Google Inc., 1950 Charleston Road, Mountain View, CA 94043 ABSTRACT The template matching prediction is an established approach to intra-frame coding that makes use of previously coded pixels in the same frame for reference. It compares the previously reconstructed upper and left boundaries in searching from the reference area the best matched block for prediction, and hence eliminates the need of sending additional information to reproduce the same prediction at decoder. In viewing the image signal as an auto-regressive model, this work is premised on the fact that pixels closer to the known block boundary are better predicted than those far apart. It significantly extends the scope of the template matching approach, which is typically followed by a conventional discrete cosine transform (DCT) for the prediction residuals, by employing an asymmetric discrete sine transform (ADST), whose basis functions vanish at the prediction boundary and reach maximum magnitude at far end, to fully exploit statistics of the residual signals. It was experimentally shown that the proposed scheme provides substantial coding performance gains on top of the conventional template matching method over the baseline. Keywords: Template matching, Intra prediction, Transform coding, Asymmetric discrete sine transform 1. INTRODUCTION Intra-frame coding is a key component in video/image compression system. It predicts from previously reconstructed neighboring pixels to largely remove spatial redundancies. A codec typically allows various prediction directions1–3 , and the encoder selects the one that best describes the texture patterns (and hence rendering minimal rate-distortion cost) for block coding. Such boundary extrapolation based prediction is efficient when the image signals are well modeled by a first-order Markovian process. In practice, however, image signals might contain certain complicated patterns repeatedly appearing, which the boundary prediction approach can not effectively capture. This motivates the initial block matching prediction, that searches in the previously reconstructed frame area for reference, as an additional mode.4 A displacement vector per block is hence needed to inform decoder to reproduce the prediction, akin the motion vector for inter-frame motion compensation. To overcome such overhead cost that diminishes the performance gains, a template matching prediction (TMP) approach was developed5 that employs the available neighboring pixels of a block as a template, measures the template similarity between the block of interest and the candidate references, and chooses the most “similar” one as the prediction. Clearly the decoder is able to repeat the same process without recourse to further information, which further allows the TMP to operate in smaller block size for more precise prediction at no additional cost. A conventional 2D-DCT is then applied to the prediction residuals, followed by quantization and entropy coding, to encode the block. Certain coding performance gains were obtained by integrating the TMP in a regular intra coder. In viewing the image signals as an auto-regressive process, pixels close to the block boundaries are more correlated to the template pixels, and hence are better predicted by the matched reference, than those sitting at far end. Therefore, the residuals ought to exhibit smaller variance at the known boundaries and gradually increased energy to the opposite end, which makes the efficacy of the use of DCT questionable due to the fact that its basis functions get to the maximal magnitude at both ends and are agnostic to the statistical characteristics of the residuals. This work addresses this issue by incorporating the ADST,6, 7 whose basis functions possess the desired asymmetric properties, as an alternative to the TMP residuals for optimal coding performance. A complementary similarity measurement based on weighted template matching, in recognition of the statistical E-mails: {huisu, jingning, yaowu}@google.com. Visual Information Processing and Communication V, edited by Amir Said, Onur G. Guleryuz, Robert L. Stevenson, Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 9029, 902904 © 2014 SPIE-IS&T · CCC code: 0277-786X/14/$18 · doi: 10.1117/12.2040890 SPIE-IS&T/ Vol. 9029 902904-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 01/05/2015 Terms of Use: http://spiedl.org/termsReconstructed Pixels To-be-coded Pixels Target Block Prediction Block Target Template Candidate Template Figure 1. Template matching intra prediction. variations across the block, was also proposed to improve the search quality. The scheme was implemented in the VP9 framework, in conjunction with other boundary prediction based intra coding modes. Experiments demonstrated remarkable performance advantages over conventional TMP as well as the baseline codec. The rest of the paper is organized as follows: Sec. 2 presents a brief review on the template matching approach. In Sec. 3, we describe the proposed techniques in details. Experimenting results are presented in Sec. 4, and Sec. 5 concludes the paper. 2. REVISITING MATCHING PREDICTION We provide a brief review on the TMP approach5 in this section. As shown in Fig. 1, the TMP employs the pixels in the adjacent upper rows and left columns of a block as its template. Every template in the reconstructed area of the frame is considered as a reference template, and the template of the block to be encoded is the target template. The similarity between the target template and the reference templates is then evaluated in terms of sum of absolute/squared difference. The encoder selects amongst the reference templates the one that best resembles the target template as the candidate template, and the block corresponding to this candidate template is used as the prediction for the target block. Since it only involves comparing reconstructed pixels, the same operations can be repeated at the decoder side without any additional side information sent, resulting in higher compression efficiency than the direct block matching approach.4 As a consequence, the decoding process gets more computationally loaded. The TMP approach was shown to be particularly efficient in the scenarios where certain complicated texture patterns, that cannot be captured by the conventional directional intra prediction modes, appear repeatedly in the image/frame. Recent research efforts have been devoted to further improve the TMP scheme, including combining multiple candidates with top similarity scores,8 using hybrid TMP and block matching (with displacement vector sent explicitly),9 etc. This work is focused on optimizing the original TMP approach by observing and exploiting the statistical property of the TMP residual signals. It is noteworthy that the proposed principles are generally applicable to other advanced variants as well. 3. PROPOSED TECHNIQUES We view the image signals as an auto-regressive model, which implies that two nearby pixels are more correlated than those far apart. Since the template of a matched reference block closely resembles that of the block of TEMPLATE SPIE-IS&T/ Vol. 9029 902904-2 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 01/05/2015 Terms of Use: http://spiedl.org/termsTarget Block Pixels Template Pixels with Larger Weight Template Pixels with Smaller Weight Figure 2. The proposed weighted template matching scheme. The pixels that are closer to the target block are assigned with larger weight. interest, the pixels sitting close to the known boundaries of the two blocks are element-wisely more correlated, than those at the opposite end. Hence the pixels near the top/left boundaries are better predicted by the matched reference block, which translates into a key observation that the variance of prediction residuals tends to vanish at the known boundaries and gradually increase towards the far end. This inspires that unlike the discrete cosine transform (DCT) whose basis functions get maximum magnitude at both ends, the (near) optimal spatial transform for the TMP residuals should possess such asymmetric properties. We hence propose to employ the asymmetric discrete sine transform (ADST)6, 7 for transform coding of the TMP residuals. A complementary matching approach that expands the template to multiple boundary rows and columns, and uses a weighted sum of difference measurement is first developed for more precise referencing. A statistical study of the TMP residuals, followed by the detailed discussion of ADST will be provided next. 3.1 Weighted Template Matching In order to obtain reliable template matching, it is reasonable to define multiple layers of boundary pixels as the template of a block. In our study, we have observed that the prediction accuracy can be improved when the number of rows and columns in the template increases. However, it is not wise to adopt too many layers, as the gain in matching accuracy becomes saturated and the computational complexity explodes. In our implementation, the template consists of the pixels in the 2 rows and 2 columns above and to the left of the given block, which gives a good tradeoff between accuracy and computation complexity. The similarity between the target templates and reference templates can be measured by the sum of absolute difference (SAD). Along the line of recognizing the variations in statistics, the template pixels closer to the block are highly correlated to the block content, and hence should be more weighted in the SAD calculation than those distant ones. This idea is illustrated in Fig. 2. A weight ratio of 3:2 for the inner row/column versus the outer row/column is used in this work. 3.2 Spatial Transformation In video/image compression, the prediction residuals are typically processed via transformation to further remove the remaining spatial redundancy, before the quantization and entropy coding modules. The Karhunen-Loeve transform (KLT) is considered as the optimal spatial transform in terms of energy compaction. However, KLT SPIE-IS&T/ Vol. 9029 902904-3 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 01/05/2015 Terms of Use: http://spiedl.org/termsis rarely used in practical coding system due to its high computation complexity. The DCT has long been a popular substitute due to its good tradeoff between energy compaction and complexity. The basis functions of the DCT are as follows: [TC ]j,i = α cos π(j − 1)(2i − 1) 2N , (1) where N is the block size, i, j ∈ {1, 2, · · · , N} denote the space and frequency indexes, respectively; and α =    q 1 N , if j = 1 q 2 N , otherwise It is easy to see that the basis functions of the DCT achieve their maximum energy at both ends (i.e., i = 1 or i = N). Assuming the template of a matched reference block closely approximates that of the block of interest, it is highly likely that pixels close to these known boundaries are also well predicted, while those distant pixels are less correlated, which results in a relatively higher residual variance. This postulation is verified by the following experimental study. We collected the absolute values of the TMP prediction residues element-wisely over 8000 blocks (of dimension 4 × 4) from the foreman sequence, and the average of the residue signal at each pixel location was calculated, as shown below:   4.05 4.37 4.51 5.13 4.72 5.48 5.50 6.60 5.04 5.95 6.12 7.32 5.50 6.28 6.97 8.20   As can be seen from the matrix, the variance of the prediction residue signal indeed increases along both the horizontal and vertical directions. As abovementioned, the basis functions of the conventional DCT achieve their maximum energy at both ends and are therefore agnostic to the statistical patterns of the prediction residuals. As an alternative, the ADST6, 7 has basis functions of form: [TS]j,i = 2 √ 2N + 1 sin (2j − 1)iπ 2N + 1 , (2) where N is the block size, i, j ∈ {1, 2, · · · , N} denote the space and frequency indexes, respectively. It is shown6, 7 that the ADST is a better approximation of the optimal KLT than the DCT when the partial boundary information is available. Clearly, the basis functions of ADST vanishes at the known prediction boundary (i = 1) and maximizes at the far end (i = N), and therefore matches well with the statistical patterns of the TMP residuals. We hence propose to employ the ADST as the spatial transform for the TMP residuals. It is experimentally shown in the next section that the use of ADST provides substantial performance improvement over the TMP followed by the conventional DCT. 4. EXPERIMENT RESULTS The proposed scheme was tested in the VP9 framework.1 We verified its efficacy in a relatively simplified setting, where the block size was fixed as 8 × 8. There are 10 intra prediction modes in VP9, including vertical prediction, horizontal prediction, 8 angular prediction modes, and a “true motion” mode that utilizes the left, above and corner pixels simultaneously. The TMP scheme was implemented as an additional mode to the 10 existing ones. The selection among the 11 modes is based on rate-distortion optimization. In the TMP mode, the 8 × 8 block is further partitioned into four 4 × 4 blocks, each of which is predicted via template matching, followed by the 2D-ADST transform, quantization, and reconstruction, in a raster scan order. The template consists of pixels from 2 rows and 2 columns above and to the left of the given block. For the weighted template matching, we use a weight ratio 3:2 for the inner row/column versus the outer row/column, as shown in Fig. 2. SPIE-IS&T/ Vol. 9029 902904-4 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 01/05/2015 Terms of Use: http://spiedl.org/terms2 3 4 5 6 x 104 36 37 38 39 40 41 42 43 44 Bits per frame PSNR VP9 Baseline Template Matching Proposed Scheme 5 6 7 8 9 10 11 12 x 104 36 37 38 39 40 41 42 Bits per frame PSNR VP9 Baseline Template Matching Proposed Scheme Figure 3. Rate-Distortion curves of the Ice (upper) and Foreman (lower) test sequences. Several test video clips were used to compare the coding efficiency, including the Ice, Foreman, and Carphone sequence. For every test sequence, the first 75 frames were coded as key-frame (i.e., all blocks were coded in intra modes), at various bit-rates. The coding performance gains of the conventional TMP and the proposed method over the reference codec, measured by the Bjontegaard metric, are shown in Table 1. Clearly, the proposed approach that optimizes the transformation for prediction residual significantly improves the performance of TMP, and both outperform the reference VP9 baseline. The rate-distortion curves of the ice and foreman sequence are also provided in Fig. 3. It can be seen from the figure that the proposed techniques boost the coding efficiency of the conventional TMP consistently. 5. CONCLUSIONS AND FUTURE WORK This work proposed a novel approach that incorporated the ADST for TMP prediction residuals as an additional mode for intra-frame coding. A complementary template matching method along the lines of recognizing the SPIE-IS&T/ Vol. 9029 902904-5 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 01/05/2015 Terms of Use: http://spiedl.org/termsTable 1. Coding performance gains over VP9 baseline in terms of bit-rate reduction percentage. Sequences Conventional TMP Proposed Method Ice 2.89 3.78 Foreman 2.88 3.33 Carphone 1.05 1.35 statistical variations across block was also provided for more precise reference search. The scheme implemented in the VP9 framework demonstrated substantial performance improvements over the conventional TMP as well as the reference codec. The TMP approach can also be applied to inter-frame prediction.10 The template of a block is defined as the pixels in the adjacent upper rows and left columns, in the same way as in the case of intra prediction. The optimal template which is best matched to that of the block of interest is found in a previously encoded reference frame, and the block to be encoded is filled in by copying the block corresponding the optimal template. By the same principles in this work, the residue signal of the template matching inter prediction should also present asymmetric statistical property across the block. We thus expect the ADST to be more efficient than the conventional DCT for the transform coding of the template matching inter prediction, and are currently working along this direction. REFERENCES [1] VP9 Video Codec , http://www.webmproject.org/vp9/. [2] Wiegand, T., Sullivan, G. J., Bjontegaard, G., and Luthra, A., “Overview of the H.264/avc video coding standard,” IEEE Trans. Circuits and Systems for Video Technology 13, 560–576 (July 2003). [3] Sullivan, G., J. Ohm, W. H., and Wiegand, T., “Overview of the high effciency video coding (HEVC) standard,” IEEE Trans. Circuits and Systems for Video Technology 22, 1649–1668 (Dec. 2012). [4] Yu, S. and Chrysafis, C., “New intra prediction using intra-macroblock motion compensation,” Tech. Rep. JVT-C151 (2002). [5] Tan, T., Boon, C., and Suzuki, Y., “Intra prediction by template matching,” IEEE Proc. ICIP , 1693–1696 (2006). [6] Han, J., Saxena, A., and Rose, K., “Towards jointly optimal spatial prediction and adaptive transform in video/image coding,” IEEE Proc. ICASSP , 726–729 (2010). [7] Han, J., Saxena, A., Melkote, V., and Rose, K., “Jointly optimized spatial prediction and block transform for video and image coding,” IEEE Trans. on Image Processing 21, 1874–1884 (2012). [8] Tan, T., Boon, C., and Suzuki, Y., “Intra prediction by averaged template matching predictors,” IEEE Proc. CCNC (2007). [9] Cherigui, S., Thoreau, D., Guillotel, P., and Perez, P., “Hybrid template and block matching algorithm for image intra prediction,” IEEE Proc. ICASSP , 781–784 (2012). [10] Sugimoto, K., Kobayashi, M., Suzuki, Y., Kato, S., and Boon, C. S., “Inter frame coding with template matching spatio-temporal prediction,” IEEE Proc. ICIP (2004). SPIE-IS&T/ Vol. 9029 902904-6 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 01/05/2015 Terms of Use: http://spiedl.org/terms How Many People Visit YouTube? Imputing Missing Events in Panels With Excess Zeros Georg M. Goerg, Yuxue Jin, Nicolas Remy, Jim Koehler1 1 Google, Inc.; United States E-mail for correspondence: gmg@google.com Abstract: Media-metering panels track TV and online usage of people to analyze viewing behavior. However, panel data is often incomplete due to nonregistered devices, non-compliant panelists, or work usage. We thus propose a probabilistic model to impute missing events in data with excess zeros using a negative-binomial hurdle model for the unobserved events and beta-binomial sub-sampling to account for missingness. We then use the presented models to estimate the number of people in Germany who visit YouTube. Keywords: imputation; missing data; zero inflation; panel data. 1 Introduction Media panels (GfK Consumer Panels, 2013) are used by advertisers to estimate reach and frequency of a campaign: reach is the fraction of the population that has seen an ad, frequency tells us how often they have seen it (on average). It is important to get good estimates from panel data, as they largely determine the cost of an ad spot on TV or a website. Na¨ıvely, one would use a sample fraction of the number of non-zero events (website visits, TV spots watched, etc.) per unit time to estimate reach; similarly, for frequency. This, however, suffers from underestimation as panels often only record a fraction of all events due to e.g., non-compliance or work usage. Correcting this bias and imputing missing events has been studied previously (Fader and Hardie, 2000; Yang et al., 2010). In this work we i) extend the beta-binomial negative-binomial (BBNB) model (Hofler and Scrogin, 2008) with a hurdle component to improve modeling excess zeros in panel data (§2); ii) present the maximum likelihood estimator (MLE) and also add prior information on missingness (§3); and iii) use the methodology to estimate – from online media panels and internal YouTube log files – how many people in Germany visit YouTube (§4). The proposed methodology can be applied to a great variety of situations where events have been counted – but some are known to be missing.2 How Many People Visit YouTube? 2 Hierarchical Event Imputation Let Ni ∈ {0, 1, 2, . . .} count the true (but unobserved) number of visits by panelist i. The population consists of people who do not visit YouTube at all (with probability q0 ∈ [0, 1]), and those who visit at least once. If she visits (overcoming the “hurdle” with probability 1 − q0), we assume that Ni is distributed according to a shifted Poisson distribution (starting at n = 1) with rate λi . For model heterogeneity among the population we use a Gamma r, q1 1−q1 prior for λi , with r > 0 and q1 ∈ (0, 1). Overall, this yields a shifted negative binomial hurdle (NBH) distribution P (N = n; q0, q1, r) = ( q0, if n = 0, (1 − q0) · Γ(n+r−1) Γ(r)Γ(n) · (1 − q1) r q n−1 1 , if n ≥ 1. (1) We choose a hurdle, rather than a mixture, model for the excess zeros (Hu et al., 2011), since 1 − q0 can be directly interpreted as the true – but unobserved – 1+ reach: if an advertiser shows an ad on YouTube they can expect that a fraction of 1 − q0 of the population sees it at least once. Let pi be the probability a visit of user i is recorded in the panel. Assuming independence across visits the total number of recorded panel events, Ki ∈ {0, 1, 2, . . .}, thus follows a binomial distribution, Ki ∼ Bin(Ni , pi). To account for heterogeneity across the population we assume pi ∼ Beta(µ, φ), with mean µ and precision φ (Ferrari and Cribari-Neto, 2004). Here µ represents the expected non-missing rate and φ the (inverse) variation across the population. Integrating out pi gives a Beta-Binomial (BB) distribution, Ki | Ni ∼ BB(Ni ; µ, φ). (2) Combining (1) and (2) yields a hierarchical beta-binomial negative-binomial hurdle (BBNBH) imputation model with parameter vector θ = (µ, φ, q0, r, q1): Ni ∼ NBH(N; q0, r, q1) and Ki | Ni ∼ BB(K | Ni ; µ, φ). (3) 2.1 Joint Distribution The pdf of (2) can be written as g(k | n; µ, φ) = n k Γ(k + φµ)Γ(n − k + (1 − µ)φ) Γ(n + φ) Γ(φ) Γ(µφ)Γ(φ(1 − µ)) . For k = 0 this reduces to P (K = 0 | N, µ, φ) = Γ(n + (1 − µ)φ) Γ(n + φ) × Γ(φ) Γ(φ(1 − µ)) . (4)Goerg et al. 3 Due to the zero hurdle it is useful to treat N = 0 and N > 0 separately: P (N, K) = P (K | N) · P (N) = BB(k | n; µ, φ) · NBH(n; q0, q1, r) (5) For n = 0, (5) is non-zero only for k = 0, P (N = 0, K = 0) = q0, since P (K > N) = 0. For n > 0, P (N = n, K = k) =(1 − q0) 1 B(φµ, φ(1 − µ)) (1 − q1) r Γ(r) × Γ(k + φµ) Γ(k + 1) × Γ(n − k + φ(1 − µ)) Γ(n − k + 1) Γ(n + r − 1) Γ(n + φ) q n−1 1 × Γ(n + 1) Γ(n) . (6) 2.2 Conditional Predictive Distribution For Imputation The panel records ki events for panelist i, but we want to know how many events truly occurred. That is, we are interested in (dropping subscript i) P (N = n | K = k) = P (K = k | N = n) P (N = n) P (K = k) , (7) To obtain analytical expressions we consider k = 0 and k > 0 separately: k = 0: Either none truly happened (n = 0) or a panelist visited at least once (n > 0), but none were recorded. n = 0: P (N = 0 | K = 0) = q0 P (K = 0). (8) n > 0: P (N = n | K = 0) = 1 P (K = 0) × Γ(n + φ(1 − µ)) Γ(n + φ) Γ(φ) Γ(φ(1 − µ)) × (1 − q0) Γ(n + r − 1) Γ(n) (1 − q1) r Γ(r) q n−1 1 , where the second term comes from (4). k > 0: The zero “hurdle” for N has been surpassed for sure. n < k : By construction of Binomial subsampling P (N = n | K = k) = 0 for all n < k. (9) n ≥ k: Here P (N = n | K = k) = n · q n−1 1 Γ(n − k + (1 − µ)φ) Γ(n − k + 1)Γ(n + φ) Γ(n + r − 1)× X∞ m=0 (m + k) Γ(m + φ(1 − µ)) Γ(m + 1) Γ(m + k + r − 1) Γ(m + k + φ) q m+k−1 1 !−1 .4 How Many People Visit YouTube? Estimate Std. Err. t value P r(> |t|) µ 0.272 q0 0.641 0.016 38.858 0.000 q1 0.982 0.002 494.105 0.000 r 0.252 0.021 11.811 0.000 φ 2.320 0.594 3.907 0.000 TABLE 1: MLE for θ for panel data on YouTube visits in Germany. 3 Parameter Estimation Let k = {k1, . . . , kP } be the number of observed events for all P panelist. Each panelist also has socio-economic indicators such as gender, age, and income. These attributes determine their demographic weight ˜wi , which equals the number of people in the entire population that panelist i represents. Finally, let wi = ˜wi · P/PP i=1 w˜i be re-scaled weight of panelist i such that PP i=1 wi equals sample size P. We estimate θ using maximum likelihood (MLE), θb = arg maxθ∈Θ `(θ; x), where the log-likelihood `(θ; x) = X {k|xk>0} xk · log P (K = k; θ), (10) and x = {xk | k = 0, 1, . . . , max (k)}, where xk = P {i|ki=k} wi is the total weight of all panelists with k visits. For deriving closed form expressions of P (K = k) = P∞ n=0 P (N = n, K = k) it is simpler to consider k = 0 and k > 0 separately: P (K = 0) = q0 + (1 − q0) × Γ(φ) Γ(φ(1 − µ)) (1 − q1) r Γ(r) × X∞ n=0 Γ(n + 1 + φ(1 − µ)) Γ(n + 1) Γ(n + r) Γ(n + 1 + φ) q n 1 , (11) and for k > 0, P (K = k) =(1 − q0)(1 − q1) r Γ(φ) Γ(µφ)Γ(φ(1 − µ)) 1 Γ(r) × Γ(k + µφ) Γ(k + 1) × X∞ m=0 (m + k) Γ(m + φ(1 − µ)) Γ(m + 1) Γ(m + k + r − 1) Γ(m + k + φ) q m+k−1 1 . (12)Goerg et al. 5 0 4 8 12 17 22 cdf 0.65 0.80 P(N <= n; r = 0.25, q1 = 0.98, q0 = 0.64) true counts (N) q0 = 64 % 0.0 0.2 0.4 0.6 0.8 1.0 0 1 2 3 4 5 pdf Beta(p; µ = 0.27, φ = 2.3) non−missingness rate α = 0.63, β = 1.7 0 5 10 15 20 25 0.80 cdf 0.90 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● P(K <= k; θ) observed counts (K) ● empirical model Log−likelihood: −6466.69 0 2 4 6 8 10 K = 0 K = 2 pmf 0.0 0.4 0.8 P(N = n | K = k; θ) true counts (N) E(N|K=0) = 1.02 E(N|K=2) = 13.12 79.5% FIGURE 1: Model estimates for: (top left) true counts Ni ; (top right) nonmissing rate pi ; (bottom left) empirical count frequency and model fit; (bottom right) conditional predictive distributions and expectations. 3.1 Fix expected non-missing rate µ Usually, researchers must estimate all 5 parameters from panel data. For our application, though, we can estimate (and fix) the non-missing rate µ a-priori as we have access to internal YouTube log files. Let ¯kW˜ = PP i=1 w˜iki be the observed panel visits projected to the entire population. Analogously, let N¯W˜ = PP i=1 w˜iNi be the panel projections of the number of true YouTube visits. While any single Ni is unobservable, we can estimate N¯W˜ by simply counting all YouTube homepage views in Germany from our YouTube log files, yielding Nb¯W˜ . We herewith obtain a plug-in estimate of the non-missing rate, µbLogs = ¯kW˜ /Nb¯W˜ . The remaining 4 parameters, θ(−µ) = (φ, q0, r, q1), can be obtained by MLE, θb (−µ) = arg maxθ(−µ) `((µbLogs, θ(−µ)); x). The overall estimate is θb = (µbLogs, θb (−µ)). 4 Estimating YouTube Audience in Germany Here we use data from a German online panel (GfK Consumer Panels, 2013), which monitors web usage of P = 6, 545 individuals in October, 2013 (31 days). In particular, we are interested in the probability that an adult in Germany visited the YouTube homepage www.youtube.de. Empirically,Pb (K = 0) = 0.81, yielding 19% observed 1+ reach. However, we know by comparison to YouTube log files that the panel only recorded 27.2% of all impressions. We fix the expected non-missing rate at µb = 0.272 and obtain the remaining parameters via MLE (Table 1): Figure 1 shows the model fit for the true, observed, and predictive distribution. In particular, the true 1+ reach is 36% (qb0 = 0.64), not 19% as the na¨ıve estimate suggests. 5 Discussion We introduce a probabilistic framework to impute missing events in count data, including a hurdle component for more flexibility to model lots of zeros. Researchers can use our models to obtain accurate probabilistic predictions of the number of true, unobserved events. We apply our methodology to accurately estimate how many people in Germany visit YouTube. Acknowledgments: We want to thank Christoph Best, Penny Chu, Tony Fagan, Yijia Feng, Oli Gaymond, Simon Morris, Raimundo Mirisola, Andras Orban, Simon Rowe, Sheethal Shobowale, Yunting Sun, Wiesner Vos, Xiaojing Wang, and Fan Zhang for constructive discussions and feedback. References Fader, P. and Hardie, B. (2000). A note on modelling underreported Poisson counts. Journal of Applied Statistics, 27(8):953–964. Ferrari, S. and Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7):799–815. GfK Consumer Panels (2013). Media Efficiency Panel. Hofler, R. A. and Scrogin, D. (2008). A count data frontier model. Technical report, University of Central Florida. Hu, M., Pavlicova, M., and Nunes, E. (2011). Zero-inflated and hurdle models of count data with extra zeros: examples from an HIV-risk reduction intervention trial. Am J Drug Alcohol Abuse, 37(5):367–75. Rose, C., Martin, S., Wannemuehler, K., and Plikaytis, B. (2006). On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data. J Biopharm Stat, 16(4):463–81. Schmittlein, D. C., Bemmaor, A. C., and Morrison, D. G. (1985). Why Does the NBD Model Work? Robustness in Representing Product Purchases, Brand Purchases and Imperfectly Recorded Purchases. Marketing Science, 4(3):255–266. Yang, S., Zhao, Y., and Dhar, R. (2010). Modeling the underreporting bias in panel survey data. Marketing Science, 29(3):525–539. 38 COMMUNICATIONS OF THE ACM | SEPTEMBER 2014 | VOL. 57 | NO. 9 practice DOI:10.1145/2643134 Article development led by queue.acm.org Preventing script injection vulnerabilities through software design. BY CHRISTOPH KERN SCRIPT INJECTION VULNERABILITIES are a bane of Web application development: deceptively simple in cause and remedy, they are nevertheless surprisingly difficult to prevent in large-scale Web development. Cross-site scripting (XSS)2,7,8 arises when insufficient data validation, sanitization, or escaping within a Web application allow an attacker to cause browser-side execution of malicious JavaScript in the application’s context. This injected code can then do whatever the attacker wants, using the privileges of the victim. Exploitation of XSS bugs results in complete (though not necessarily persistent) compromise of the victim’s session with the vulnerable application. This article provides an overview of how XSS vulnerabilities arise and why it is so difficult to avoid them in real-world Web application software development. Software design patterns developed at Google to address the problem are then described. A key goal of these design patterns Securing the Tangled WebSEPTEMBER 2014 | VOL. 57 | NO. 9 | COMMUNICATIONS OF THE ACM 39 IMAGE BY PHOTOBANK GALLERY is to confine the potential for XSS bugs to a small fraction of an application’s code base, significantly improving one’s ability to reason about the absence of this class of security bugs. In several software projects within Google, this approach has resulted in a substantial reduction in the incidence of XSS vulnerabilities. Most commonly, XSS vulnerabilities result from insufficiently validating, sanitizing, or escaping strings that are derived from an untrusted source and passed along to a sink that interprets them in a way that may result in script execution. Common sources of untrustworthy data include HTTP request parameters, as well as user-controlled data located in persistent data stores. Strings are often concatenated with or interpolated into larger strings before assignment to a sink. The most frequently encountered sinks relevant to XSS vulnerabilities are those that interpret the assigned value as HTML markup, which includes server-side HTTP responses of MIME-type text/html, and the Element.prototype.innerHTML Document Object Model (DOM)8 property in browser-side JavaScript code. Figure 1a shows a slice of vulnerable code from a hypothetical photosharing application. Like many modern Web applications, much of its user-interface logic is implemented in browser-side JavaScript code, but the observations made in this article transfer readily to applications whose UI is implemented via traditional serverside HTML rendering. In code snippet (1) in the figure, the application generates HTML markup for a notification to be shown to a user when another user invites the former to view a photo album. The generated markup is assigned to the innerHTML property of a DOM 40 COMMUNICATIONS OF THE ACM | SEPTEMBER 2014 | VOL. 57 | NO. 9 practice main page. If the login resulted from a session time-out, however, the app navigates back to the URL the user had visited before the time-out. Using a common technique for short-term state storage in Web applications, this URL is encoded in a parameter of the current URL. The page navigation is implemented via assignment to the window.location.href DOM property, which browsers interpret as instruction to navigate the current window to the provided URL. Unfortunately, navigating a browser to a URL of the form javascript:attackScript causes execution of the URL’s body as Java Script. In this scenario, the target URL is extracted from a parameter of the current URL, which is generally under attacker control (a malicious page visited by a victim can instruct the browser to navigate to an attacker-chosen URL). Thus, this code is also vulnerable to XSS. To fix the bug, it is necessary to validate that the URL will not result in script execution when dereferenced, by ensuring that its scheme is benign— for example, https. Why Is XSS So Difficult to Avoid? Avoiding the introduction of XSS into nontrivial applications is a difficult problem in practice: XSS remains among the top vulnerabilities in Web applications, according to the Open Web Application Security Project (OWASP);4 within Google it is the most common class of Web application vulnerabilities among those reported under Google’s Vulnerability Reward Program (https://goo.gl/82zcPK). Traditionally, advice (including my own) on how to prevent XSS has largely focused on: ˲ Training developers how to treat (by sanitization, validation, and/or escaping) untrustworthy values interpolated into HTML markup.2,5 ˲ Security-reviewing and/or testing code for adherence to such guidance. In our experience at Google, this approach certainly helps reduce the incidence of XSS, but for even moderately complex Web applications, it does not prevent introduction of XSS to a reasonably high degree of confidence. We see a combination of factors leading to this situation. element (a node in the hierarchical object representation of UI elements in a browser window), resulting in its evaluation and rendering. The notification contains the album’s title, chosen by the second user. A malicious user can create an album titled: Since no escaping or validation is applied, this attacker-chosen HTML is interpolated as-is into the markup generated in code snippet (1). This markup is assigned to the innerHTML sink, and hence evaluated in the context of the victim’s session, executing the attacker-chosen JavaScript code. To fix this bug, the album’s title must be HTML-escaped before use in markup, ensuring that it is interpreted as plain text, not markup. HTMLescaping replaces HTML metacharacters such as <, >, ", ', and & with corresponding character entity references or numeric character references: <, >, ", ', and &. The result will then be parsed as a substring in a text node or attribute value and will not introduce element or attribute boundaries. As noted, most data flows with a potential for XSS are into sinks that interpret data as HTML markup. But other types of sinks can result in XSS bugs as well: Figure 1b shows another slice of the previously mentioned photo-sharing application, responsible for navigating the user interface after a login operation. After a fresh login, the app navigates to a preconfigured URL for the application’s The following code snippet intends to populate a DOM element with markup for a hyperlink (an HTML anchor element): var escapedCat = goog.string.htmlEscape(category); var jsEscapedCat = goog.string.escapeString(escapedCat); catElem.innerHTML = '' + escapedCat + ''; The anchor element’s click-event handler, which is invoked by the browser when a user clicks on this UI element, is set up to call a JavaScript function with the value of category as an argument. Before interpolation into the HTML markup, the value of category is HTML-escaped using an escaping function from the JavaScript Closure Library. Furthermore, it is JavaScript-string-literal-escaped (replacing ' with \' and so forth) before interpolation into the string literal within the onclick handler’s JavaScript expression. As intended, for a value of Flowers & Plants for variable category, the resulting HTML markup is: Flowers & Plants So where’s the bug? Consider a value for category of: ');attackScript();// Passing this value through htmlEscape results in: ');attackScript();// because htmlEscape escapes the single quote into an HTML character reference. After this, JavaScript-string-literal escaping is a no-op, since the single quote at the beginning of the page is already HTML-escaped. As such, the resulting markup becomes: ');attackScript();// When evaluating this markup, a browser will first HTML-unescape the value of the onclick attribute before evaluation as a JavaScript expression. Hence, the JavaScript expression that is evaluated results in execution of the attacker’s script: createCategoryList('');attackScript();//') Thus, the underlying bug is quite subtle: the programmer invoked the appropriate escaping functions, but in the wrong order. A Subtle XSS BugSEPTEMBER 2014 | VOL. 57 | NO. 9 | COMMUNICATIONS OF THE ACM 41 practice Subtle security considerations. As seen, the requirements for secure handling of an untrustworthy value depend on the context in which the value is used. The most commonly encountered context is string interpolation within the content of HTML markup elements; here, simple HTML-escaping suffices to prevent XSS bugs. Several special contexts, however, apply to various DOM elements and within certain kinds of markup, where embedded strings are interpreted as URLs, Cascading Style Sheets (CSS) expressions, or JavaScript code. To avoid XSS bugs, each of these contexts requires specific validation or escaping, or a combination of the two.2,5 The accompanying sidebar, “A Subtle XSS Bug,” shows this can be quite tricky to get right. Complex, difficult-to-reason-about data flows. Recall that XSS arises from flows of untrustworthy, unvalidated/escaped data into injection-prone sinks. To assert the absence of XSS bugs in an application, a security reviewer must first find all such data sinks, and then inspect the surrounding code for context-appropriate validation and escaping of data transferred to the sink. When encountering an assignment that lacks validation and escaping, the reviewer must backward-trace this data flow until one of the following situations can be determined: ˲ The value is entirely under application control and hence cannot result in attacker-controlled injection. ˲ The value is validated, escaped, or otherwise safely constructed somewhere along the way. ˲ The value is in fact not correctly validated and escaped, and an XSS vulnerability is likely present. Let’s inspect the data flow into the innerHTML sink in code snippet (1) in Figure 1a. For illustration purposes, code snippets and data flows that require investigation are shown in red. Since no escaping is applied to sharedAlbum.title, we trace its origin to the albums entity (4) in persistent storage, via Web front-end code (2). This is, however, not the data’s ultimate origin—the album name was previously entered by a different user (that is, originated in a different time context). Since no escaping was applied to this value anywhere along its flow from an ultimately untrusted source, an XSS vulnerability arises. Similar considerations apply to the data flows in Figure 1b: no validation occurs immediately prior to the assignment to window.location.href in (5), so back-tracing is necessary. In code snippet (6), the code exploration branches: in the true branch, the value originates in a configuration entity in the data store (3) via the Web front end (8); this value can be assumed application-controlled and trustworthy and is safe to use without further validation. It is noteworthy that the persistent storage contains both trustworthy and untrustworthy data in different entities of the same schema—no blanket assumptions can be made about the provenance of stored data. In the else-branch, the URL originates from a parameter of the current URL, obtained from window.location.href, which is an attacker-controlled source (7). Since there is no validation, this code path results in an XSS vulnerability. Many opportunities for mistakes. Figures 1a and 1b show only two small slices of a hypothetical Web application. In reality, a large, nontrivial Web application will have hundreds if not thousands of branching and merging data flows into injection-prone sinks. Each such flow can potentially result in an XSS bug if a developer makes a mistake related to validation or escaping. Exploring all these data flows and asserting absence of XSS is a monumental task for a security reviewer, especially considering an ever-changing code base of a project under active development. Automated tools that employ heuristics to statically analyze data flows in a code base can help. In our experience at Google, however, they do not substantially increase confidence in review-based assessments, since they are necessarily incomplete in their reasoning and subject to both false positives and false negatives. Furthermore, they have similar difficulties as human reviewers with reasoning about whole-system data flows across multiple system components, using a variety of programming languages, RPC (remote procedure call) mechanisms, and so forth, and involving flows traversing multiple time contexts across data stores. The primary goal of this approach is to limit code that could potentially give rise to XSS vulnerabilities to a very small fraction of an application’s code base.42 COMMUNICATIONS OF THE ACM | SEPTEMBER 2014 | VOL. 57 | NO. 9 practice user-profile field). Unfortunately, there is an XSS bug: the markup in profile.aboutHtml ultimately originates in a rich-text editor implemented in browser-side code, but there is no server-side enforcement preventing an attacker from injecting malicious markup using a tampered-with client. This bug could arise in practice from a misunderstanding between front-end and back-end developers regarding responsibilities for data validation and sanitization. Reliably Preventing the Introduction of XSS Bugs In our experience in Google’s security team, code inspection and testing do not ensure, to a reasonably high degree of confidence, the absence of XSS bugs in large Web applications. Of course, both inspection and testing provide tremendous value and will typically find some bugs in an application (perhaps even most of the bugs), but it is difficult to be sure whether or not they discovered all the bugs (or even almost all of them). The primary goal of this approach is to limit code that could potentially give rise to XSS vulnerabilities to a very small fraction of an application’s code base. A key goal of this approach is to drastically reduce the fraction of code that could potentially give rise to XSS bugs. In particular, with this approach, an application is structured such that most of its code cannot be responsible for XSS bugs. The potential for vulnerabilities is therefore confined to infrastructure code such as Web application frameworks and HTML templating engines, as well as small, self-contained applicationspecific utility modules. A second, equally important goal is to provide a developer experience that does not add an unacceptable degree of friction as compared with existing developer workflows. Key components of this approach are: ˲ Inherently safe APIs. Injection-prone Web-platform and HTML-rendering APIs are encapsulated in wrapper APIs designed to be inherently safe against XSS in the sense that no use of such APIs can result in XSS vulnerabilities. ˲ Security type contracts. Special types are defined with contracts stipuSimilar limitations apply to dynamic testing approaches: it is difficult to ascertain whether test suites provide adequate coverage for whole-system data flows. Templates to the rescue? In practice, HTML markup, and interpolation points therein, are often specified using HTML templates. Template systems expose domain-specific languages for rendering HTML markup. An HTML markup template induces a function from template variables into strings of HTML markup. Figure 1c illustrates the use of an HTML markup template (9): this example renders a user profile in the photo-sharing application, including the user’s name, a hyperlink to a personal blog site, as well as free-form text allowing the user to express any special interests. Some template engines support automatic escaping, where escaping operations are automatically inserted around each interpolation point into the template. Most template engines’ auto-escape facilities are noncontextual and indiscriminately apply HTML escaping operations, but do not account for special HTML contexts such as URLs, CSS, and JavaScript. Contextually auto-escaping template engines6 infer the necessary validation and escaping operations required for the context of each template substitution, and therefore account for such special contexts. Use of contextually auto-escaping template systems dramatically reduces the potential for XSS vulnerabilities: in (9), the substitution of untrustworthy values profile.name and profile. blogUrl into the resulting markup cannot result in XSS—the template system automatically infers the required HTML-escaping and URL-validation. XSS bugs can still arise, however, in code that does not make use of templates, as in Figure 1a (1), or that involves non-HTML sinks, as in Figure 1b (5). Furthermore, developers occasionally need to exempt certain substitutions from automatic escaping: in Figure 1c (9), escaping of profile.aboutHtml is explicitly suppressed because that field is assumed to contain a user-supplied message with simple, safe HTML markup (to support use of fonts, colors, and hyperlinks in the “about myself” lating that their values are safe to use in specific contexts without further escaping and validation. ˲ Coding guidelines. Coding guidelines restrict direct use of injectionprone APIs, and ensure security review of certain security-sensitive APIs. Adherence to these guidelines can be enforced through simple static checks. Inherently safe APIs. Our goal is to provide inherently safe wrapper APIs for injection-prone browser-side Web platform API sinks, as well as for server- and client-side HTML markup rendering. For some APIs, this is straightforward. For example, the vulnerable assignment in Figure 1b (5) can be replaced with the use of an inherently safe wrapper API, provided by the JavaScript Closure Library, as shown in Figure 2b (5’). The wrapper API validates at runtime that the supplied URL represents either a scheme-less URL or one with a known benign scheme. Using the safe wrapper API ensures this code will not result in an XSS vulnerability, regardless of the provenance of the assigned URL. Crucially, none of the code in (5’) nor its fan-in in (6-8) needs to be inspected for XSS bugs. This benefit comes at the very small cost of a runtime validation that is technically unnecessary if (and only if) the first branch is taken—the URL obtained from the configuration store is validated even though it is actually a trustworthy value. In some special scenarios, the runtime validation imposed by an inherently safe API may be too strict. Such cases are accommodated via variants of inherently safe APIs that accept types with a security contract appropriate for the desired use context. Based on their contract, such values are exempt from runtime validation. This approach is discussed in more detail in the next section. Strictly contextually auto-escaping template engines. Designing an inherently safe API for HTML rendering is more challenging. The goal is to devise APIs that guarantee that at each substitution point of data into a particular context within trusted HTML markup, data is appropriately validated, sanitized, and/or escaped, unless it can be demonstrated that a specific data item is safe to use in that context based on SEPTEMBER 2014 | VOL. 57 | NO. 9 | COMMUNICATIONS OF THE ACM 43 practice Figure 1. XSS vulnerabilities in a hypothetical Web application. Browser Web-App Frontend Application Backends (4) (3) (1) Application data store (2) Browser Web-App Frontend Application Backends (4) (3) (5) (6) (7) Application data store (8) Browser Web-App Frontend Application Backends (12) (13) (9) (10) Profile Store (11) (a) Vulnerable code of a hypothetical photo-sharing application. (b) Another slice of the photo-sharing application. (c) Using an HTML markup template. 44 COMMUNICATIONS OF THE ACM | SEPTEMBER 2014 | VOL. 57 | NO. 9 practice sanitizer to remove any markup that may result in script execution renders it safe to use in HTML context and thus produces a value that satisfies the SafeHtml type contract. To actually create values of these types, unchecked conversion factory methods are provided that consume an arbitrary string and return an instance of a given wrapper type (for example, SafeHtml or SafeUrl) without applying any runtime sanitization or escaping. Every use of such unchecked conversions must be carefully security reviewed to ensure that in all possible program states, strings passed to the conversion satisfy the resulting type’s contract, based on context-specific processing or construction. As such, unchecked conversions should be used as rarely as possible, and only in scenarios where their use is readily reasoned about for security-review purposes. For example, in Figure 2c, the unchecked conversion is encapsulated in a library (12’’) along with the HTML sanitizer implementation on whose correctness its use depends, permitting security review and testing in isolation. Coding guidelines. For this approach to be effective, it must ensure developers never write application code that directly calls potentially injection-prone sinks, and that they instead use the corresponding safe wrapper API. Furthermore, it must ensure uses of unchecked conversions are designed with reviewability in mind, and are in fact security reviewed. Both constraints represent coding guidelines with which all of an application’s code base must comply. In our experience, automated enforcement of coding guidelines is necessary even in moderate-size projects—otherwise, violations are bound to creep in over time. At Google we use the open source error-prone static checker1 (https:// goo.gl/SQXCvw), which is integrated into Google’s Java tool chain, and a feature of Google’s open source Closure Compiler (https://goo.gl/UyMVzp) to whitelist uses of specific methods and properties in JavaScript. Errors arising from use of a “banned” API include references to documentation for the corresponding safe API, advising developers on how to address its provenance or prior validation, sanitization, or escaping. These inherently safe APIs are created by strengthening the concept of contextually auto-escaping template engines6 into SCAETEs (strictly contextually auto-escaping template engines). Essentially, a SCAETE places two additional constraints on template code: ˲ Directives that disable or modify the automatically inferred contextual escaping and validation are not permitted. ˲ A template may use only sub-templates that recursively adhere to the same constraint. Security type contracts. In the form just described, SCAETEs do not account for scenarios where template parameters are intended to be used without validation or escaping, such as aboutHtml in Figure 1c—the SCAETE unconditionally validates and escapes all template parameters, and disallows directives to disable the auto-escaping mechanism. Such use cases are accommodated through types whose contracts stipulate their values are safe to use in corresponding HTML contexts, such as “inner HTML,” hyperlink URLs, executable resource URLs, and so forth. Type contracts are informal: a value satisfies a given type contract if it is known that it has been validated, sanitized, escaped, or constructed in a way that guarantees its use in the type’s target context will not result in attackercontrolled script execution. Whether or not this is indeed the case is established by expert reasoning about code that creates values of such types, based on expert knowledge of the relevant behaviors of the Web platform.8 As will be seen, such security-sensitive code is encapsulated in a small number of special-purpose libraries; application code uses those libraries but is itself not relied upon to correctly create instances of such types and hence does not need to be security-reviewed. The following are examples of types and type contracts in use: ˲ SafeHtml. A value of type SafeHtml, converted to string, will not result in attacker-controlled script execution when used as HTML markup. ˲ SafeUrl. Values of this type will not result in attacker-controlled script execution when dereferenced as hyperlink URLs. ˲ TrustedResourceUrl. Values of this type are safe to use as the URL of an executable or “control” resource, such as the src attribute of a

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MOLENE MOUETTES HENANT KERANGOC TRESIGNEAU MUR NOGUELLOU NOISETIERS NOISETIERS KEREZ OUESSANT PACIFIQUE PARC AR CASTEL PARC BRIS PARC C HOESSANT PARC MARCHE PARC MINE PARK DOUAR BERGE PARK MINN PARK ROZ SERUZIER PEINTRES PEMPOUL PEN PEN AN ENEZ PEN AR C HOAT MOUSTERLIN PEN AR C HRA PEN AR HOAT PEN AR PARC PEN AR PAVE PEN AR STER PENFOENNEC PENFRAT PENHOAT SALAUN PENN AR VUR PENQUER PERCHE FONTAINE SUISSE PHARE TRAIN PEUPLIERS PIGEONNIER PINS PLAGE PLAGE GRISE POINTE POINTE PRIMEL POINTE GLUGEAU ALIBEN AR BLEIZ AR CHLAN PONTI PONTUAL VIAN PORS AR VILIN VRAS PORS CAVE PORSMELLEC PORZ OLIER POUL POUL ALEN POULALLANICK POUL AR COQUET POUL AR GOQUET POUL FANG POULGIGOU POULL CALLAC POULLOGODEN PRAIRIE PRAT CUIC PRAT HIR PRES PRESBYTERE PUITS QUISTILLIC REA RELIQUES RELUYEN RESTAURANT KEROUDOT RESTIC VIAN RIVIERE ROC AR C HAD ROCHES ROCH YAN ROSCAROC ROSCOLER ROUDOUIC ROZ AR GRILLET ROZBEG ROZ DANIELOU ROZ GLAZ RUBIERN RUISSEAU SAINTE CHRISTINE RUMORVAN RUN AN ILIZ RUN AR C HAD RUN AR HAD 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COMMERCIALE COSQUER VIAN CREAC AL LEO CREACH AR LEO CREACH MICKAEL CRINOU CROAS AR BEUZ CROAS AR GARREC CROAS HIR CROIX MISSIONS CROIX ROUGE D ANTIN BELLEVUE KER ANNA KER ELO KERGUELEN KERMENGUY KERSAUX KROAS SALIOU PAIX RUCHE TERRE NOIRE L ODET PORS MOELAN AJONCS D OR ANCIENS BRUYERES CHARDONS BLEUS FLEURS GENETS HORTENSIAS MARRONNIERS PINS RAMIERS ROSIERS TY BOURHIS DUTERQUE DOURIC CHANOINE CHAPALAIN CROIZIOU GUESCLIN MENEZ BIROU PICHERI 18 ALEZ AR GOSKER PICHERI 19 ALEZ AR GOSKER EGLANTIERS EGLANTIERS NIZON ERNEST RENAN AIRIAU FAO FONTAINE FONTAINES FONTAINE FREDLAND FRELAND FRIEDLAND GARO GARVAN GARZABIC GENETS GENETS D OR GLYCINES GOLLEN GORREKEAR GRATZ GUENAN GUENANS GUENON GUEVEN GUILLY GWEL KAER HENT COZ HIRONDELLES HLM KERHALLON VIHAN HORTENSIAS ILES IRLANDE IZELLA JARDINS ASSOLANT JAURES JEANNE D ARC JONQUILLES JULES DUTERQUE JULIA KER ABARDAEZ KERADENNEC KERALGUY PORSMILIN KER ANNA KERARZANT KERAVEL KERAZAN KERBINIOU KERBLEUNIOU KERBRUG KER COADIC KERDOUSSAL KEREAN KERELLEAU KER ELLEZ KERENTRECH KER EOL KERESPERN KEREVER KERFEUNTEN KERFEUNTEUN KERGALL KERGANAVAL KERGAUTHIER KERGAUTIER KERGOFF KERGUEN KERHEUN KERHORNOU KERIBIN KERIEQUEL KERIFAOUEN KERILIS KERINCUFF KERIVARCH KER IZELLA KERJACOB KERJEAN KERLAOUEN KERLEVEN HUELLA KERLOCH KERLOQUIC KERMARIA KERMILIAU KERMILLAU KERMILLEAU KERMILLIAU KERMOOR KERNAOGUER KERNEVEZ KEROHOU KEROZA KERRENTRECH KERRIOU KERROZA KERSABILIC KERSIOUL KERVAO KERVENNEC KERVEUR KERVILAR KERVIZIGOU KERVOAZEC KERYEQUEL LAENNEC LANNIEN LANN ROHOU LANORGANT LAURIERS LESSARD LILAS LOCH POAS MADELEINE MAHE MATHIEU DONNARD MATHIEU DONNART MEIL COAT BIHAN MENEZ BIHAN MENEZ GOUERON MENEZ HOM MESANGES MESMEUR MIMOSAS MONTGOLFIER MOULIN VENT NEVEZ KERBRAT NOTRE DAME ODET ORATOIRE PARC AN DOSSEN PARC EN DOSSEN PARK AR ROZ PARK BRAS PARK FEUNTEUN PARMENTIER PAU PENAMPRAT PEN AR DORGUEN PEN KERNEVEZ PENMEUR PENN KERNEVEZ PESMARCH PHARES PICHERI 15 ALEZ AR GOSKER BENOIT PINS POL AURELIEN POMMIERS PONANT AR MANACH GUEN ILIS PER PORS NEVEZ 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VILLA KERJANYVIERE EOLE EPERVIERS ERABLES ERIC TABARLY ERMITAGE ERNEST RENAN ESTIENNE D ORVES E TAL ARMOR ETANG EUGENE BELEGUIC EUGENE BERTHOU EUGENE GONIDEC FALC HUN FAUVETTES FERNAND GUEY FEUNTEUN AR ROZEN FEUNTEUN BOL FEUNTEUNIC AR LEZ FEUNTEUNIGOU FEUNTEUN VENELLE FEUNTEUN VERO FIGUIERS FLEURS FLEURUS FONTAINE FOUGERES FOYER 1 ER CUEFF DUAULT LALAISSE VILLON FRANKLIN ROOSEVELT FREDERIC GUYADER FREDERIC MISTRAL FREGATE DECOUVERTE FREGATE LAPLACE FRENES FROMVEUR GABRIEL SIGNE GABRIEL LIPPMANN GAGARINE GALETS GAMBETTA GARENNE GAUGUIN GENETS GENETTES GEORGE SAND BIZET BRASSENS BAIL GERARD NERVAL GIROFLEES GLAIEULS GLENAN GLINEC GLYCINES GOAREM HUEL GOAREM IZELLA GOAREM PERZEL GOARIVEN GOLF GORLANIC GORREQUEAR GOUELET AR LEN GOUEROU GOULET GOULITQUER GOURANOU GRADLON TERRE VENELLE LARGE GREEN GRILLONS GUEL GUELEDIGOU CALVEZ JAN GUIP GUSTAVE FLAUBERT GUSTAVE LOISEAU GUYNEMER GWALARN GWAREMM AR GWELTOC GWEZ KIGNEZ HARAS HAUTS GUERN HAUTS TERENEZ HELENE BOUCHER HENRI BECQUEREL HENRI CARIO 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KERDUDY KERDUEL KERDURANT KEREBARS KEREDEC KEREDERN KEREDOL KEREGARD KERELIE KERELLE KERELLEC KERELLER KERELLOT TREFLEZ KERELLOU KEREOL KEREON KEREOZEN KERERVEN KERESCAR KERESSEIS KEREUNEUD HUELLA KEREUNEUD IZELLA KEREUZEN KEREVEN KEREVER KEREZELLEC KEREZELOU KEREZOUN KERFANY KERFAVEN KERFESAN KERFESTOUR KERFEUNTEUN KERFEUNTEUNIOU KERFICHAUT KERFILY KERFLEURY KERFLOUS KERFONTAINE KERFORN KERFRAVAL KERFURUST KERGABEL KER GABRIELLE KERGADAVARN KERGADIEN KERGADORET KERGAELE KERGALET KERGALET HUELLA KERGALL KERGALLEC KERGALVEZOC KERGAMET KERGAOUEN KERGARIOU KERGARO KERGARVAN KERGAZEGAN KERGLANCHARD KERGLEUHAN KERGLEUZ KERGLIEN KERGLINTIN TREBOUL KERGOALER KERGOAT KERGOAT HUELLA KERGOAT IZELLA KERGOFF KERGOFF VIAN KERGOLEC KERGOLEZEC KERGOLLE KERGOLVEN KERGOMPEZ KERGONAN KERGONAN DINAN KERGONAN ROSTUDEL KERGONFORT KERGONGAR KERGONNEC KERGONVEL KERGORNEC BIHAN KERGORNEC NEVEZ KERGOSTIOU KERGOTTER KERGOULOUET KERGOUROUN KERGOUSTANCE KERGOZ KERGREACH KERGREGUEN KERGRENN KERGREVEN KERGROAC VIAN KERGROAS KERGROES KERGUELEN KERGUEN KERGUENTRAT KERGUEN VIAN KERGUEO KERGUILLAOUET KERGUILLE KERGUILLO KERGUINIOU KERGUNIC KERGUNUS KERGUS KERHABO KERHALLON KERHALS DAMANY KERHALVEZ KERHAMON KERHARO KERHARO VIAN KERHAT KERHEROU KERHERVE KERHIR KERHOM KERHOR KERHORNOU KERHUEL KERHUELLA KER HUELLA KERHUITEN KERHUON KERIAGU KERIBIN KERICHARD KERICHEN KERICUFF KERIDA KERIDAOUEN KERIEGU KERIEL KERIEZEGAN KERIFIN KERIGEANT KERIGOU KERIGOUALCH KERILLIEN KERIMEL KERINGARS KERINIZAN KERINTIN KERINVAL KERIOGAN KERIOLET KERIONOC KERIONQUER KERIORET KERIOUAL KERIOUALEN KERIQUEL KER ISCHIA KERISPERN KERISTIN KERIVIN KERIVIN VAO KERIVOAL KERIVOAS KERIZELLA KERJAFFRES KERJEAN KERJEGOU KERJEGU KERJEZEGOU KERKARN KERSECOL KERLAERON KERLAN KERLANN KERLAOUARN KERLAOUEN KERLARAN KERLARY KERLASSET KERLASTRE KERLASTREN KERLAURENT KERLEDAN KERLEN KERLEO KERLEOGUY KERLERCUN KERLEVIC KERLIDEC KERLIDIEN KERLIGUET KERLILY KERLIN KERLIVIOU KERLIZIC KERLOA KERLOAI KERLOCH KERLOGODEN KERLOHIC KERLOHOU KERLOIS KERLOQUIN KERLORET KERLORETTA KERLOSQUEN KERLOSQUET KERLOUANTEC KERLOUET KER LOUIS KERLOUSSOUARN KERLUANDRE KERLUBRIDIC KERLYS KERMABIVEN KERMADEC KERMALGUEN KERMAO KERMARC KERMARC H KERMARE KERMARIA KERMAVEZAN KERMAZEAS KERMEN KERMERRIEN KERMEUR KERMEUR BIHAN KERMEURBRAS KERMEURZACH KERMINAOUET KERMINGANT KERMINGUY KERMOAL KERMOALIC KERMOGUER KERMORVAN KERNABAT KERNALLEC KERNAOUR KERNAVENO KERNEACH KERNEAC GUIDADOU KERNEAS KERNEGUEZ KERNERZIC KERNESCOP KERNEVEZ KERNEVEZ AL LAN KERNEVEZ COZ KERNEVEZ GUITRE KERNEVEZ HUELLA KERNEVEZ IZELLA KERNEZEN KERNIE KERNIGUEZ KERNIJEANNE NEVEZ KERNINON KERNISI KERNIZAN KERNOAS KERNON ARGOAT KERNON ARMOR KERNONEN KERNU KEROHAN KEROIGNANT KEROLZEC KEROLZIC KERONDO KERORIOU KEROTER KEROUAL KEROUALAR KEROUANNEC KEROUDERN KEROUDOT KEROUEZEL KEROUGAR KEROUGOUN KEROUHANT KEROUINY KEROULAR KEROULDRY KEROULEDIC KEROULLE KEROURIN KEROUZE KEROZAR KERPAUL KERPONT KERPORZIOU KERPRIGENT KERRET KERRIOU KERROCH KERROS KERROUALLON KERROUER KERROUES KERROUS KERROUX KERROZEC KERSALAUN KERSALAUN BIHAN KERSALAUN BRAS KERSALIOU KERSALOMON KERSANIOU KERSAO KERSAOS KERSAUX KERSCAO KERSCAO LOUARN KERSCOAZEC KERSCOFF KERSCOURIC KERSECOL KERSEGALOU KERSEL KERSERHO KERSEVINIEN KERSIDAN KERSIOCH KERSIVINY KERSKAO KERSOCH KERSOLF KERSTRAT KERSUET KERSULEC KERTALG KERTANGUY KERUDALAR BIHAN KERUGUEL KERUN KERUZAL VIHAN KERUZANVAL KERUZAVEL KERUZORE KERUZUNEL KERVAGOR KERVAILLANT KERVALGUEN KERVALLAN KERVALY KERVANNES KERVAO KERVAOGON KERVAON KERVARDEL KERVARVAIL KERVASDOUE KERVAVEON KERVAY KERVAZE KERVAZIOU KERVEGANT KERVEGUEN KERVELIGEN KERVELT KERVENEURE KERVENNI VIHAN KERVENNI VRAZ KERVENNOU KERVER KERVERET KERVEREZ KERVERIEN KERVERN KERVERN TALAOURON KERVERON KERVERRET KERVERZET KERVESQUEN KERVETOT KERVEUR KERVEZINGAR IZELLA KERVICHEN KERVIDOT KERVIGNAC KERVIGNES KERVIL AVEL KERVILIEN KERVILINER KERVILLERM KERVINIEC KERVINIOU VIAN KERVIR KERVIRZIC KERVOANEC KERVOASCLET KERVON KERVOUEC KERVOURIC KERVOUYEN GLAS KERVRAN KERVREN KERVROUYEC KERVUNUS KERYON VIAN KERYOUEN KER YVES KER YVETTE KERYVON KERZAO KERZERE KERZESPES KERZEVEN KERZINAOU KERZIOU KERZOURAT KERZU BOISSIERE BONNE RENCONTRE CORNICHE CROIX CROIX NEUVE CROIX ROUGE FONDERIE FONTAINE FONTAINE BLANCHE FORGE NEUVE GARENNE GARENNE BOURG LAGAT YAR GARENNE METAIRIE PALUD GREVE BLANCHE GREVE HAIE HAUTE VALLEE HAYE MADELEINE LAMBALEZ LAMBELL LAMBERT LAMBOEZER METAIRIE METAIRIE NEUVE MINOTERIE LAMLIEU DIT LAMBELL MOTTE LAN AR C HOAT LANDAOUDEC LANDAURE LANDUC LANENOS LANGONIANT LANGOULIAN LANGOULOUARN LANGOUROUGAN LANGROAS LANGROES LANHIR LANHUS LANNEGUEL LANNENORET LANNIC LANNON LANRIOT LANRIVAN LANRIVOAS LANVAGEN LANVALOU LANVAON LANVARGON LANVEN LANVENAEL LANVEUR LANVIAN LANVINTIN GARENNE POTERIE QUINQUIS LARLACH HUELLA LARLARCH IZELLA LARMOR L ARMORIQUE ROCHE SALETTE TOUR LAVALLOT LAVALLOT VIAN VENELLE VIEILLE VALLEE VIERGE NOIRE VILLENEUVE BEAUBOIS BELON BINIGOU BONNOU BOUIS BOULLACH BOURG BUIS CALVAIRE CANARDIC CANN CARBON CARO CERF CLERCH BURTULL CLEUN CLOASTRE CLOS HERRY COMTE COSQUER CRANN CREAC H CREACH CROISSANT CROIZIOU CUN DORGUEN DOURIC DREFF DRENNEC FESSIOU FOZOU FRESQ FRET FUME GARO GARZON GOLLEN GUELLEC GUENDON GUERN LAUNAY GUILLOC GUIP HAFFOND HELLES LEIGNOU LEIGN ROUX JOA LANNOU LAUNAY LEING LEURE LICHERN LOURCH MANOIR MENDY MENHIR MERDY MESTO MEZOU L ENCLOS NEC HOAT PALAIS PARADIS PARCOU PEDEL PEULIOU PILLION PLESSIS NEUF PONTOIS POTEAU VERT QUEAU QUINQUIS RADEN RELAIS RELUET RESTAURANT REUN RHUN ROHOU ROSIER ROUDOUR ROUDOUS LESCAOUIDIC LESCOAT EOZEN SCRAIGN SEQUER NEVEZ FRIANTIS GENETS HARAS HAUTS PESMARCH HAUTS DOURDUFF LESIVY BRAS LESMEL LESMOUALCH BRAS LESNON SPINS PLANTS LESPLOMEUR ROCAILLES SALLES LESSOUGAR STANG LESTARIDEC LESTEMBERT LESTEVEN LESTIALA LESTRAOUEN STREAT STREJOU LESTREMINOU LESTREVIGNON LESTRIOUAL LESVINGANT TEMPLE TRAON TREFF TREILLIC TY ZONT LEUR AR MARCHE LEUR AR MORIS LEURIOU VAL ROUGE VAL VERT VERGEZ VERN VEZEC VIBEN VIEUX HAFFOND VIZIER VRENNIT ZABRENN LEZARZOU LEZ ELORN LEZERDOT L HERMITAGE L HOSPITALOU LIENEN L ILE LINSPERN LIORZOU LISLOC H LOCHRIST LOCMELAR LOCMELAR IZELLA LODOEN LOGE BROUT LOGE GAOR LOGE PHILIPPE LOGUELLOU LOJOU LOMBARDY LOMOGAN LORMEAU LOROZAN LOSTALLEN LOST AN AOD LOST AR HOAT LOSTENNIC LOSTMARCH LOSTROUC H LOUARGAT LOUZAOUEN LUZUGUENTER LUZURIA MALACHAPPE MALVEZAN MANER COAT CLEVAREC MANER GOFF MANER SOUL MANOIR RESTAURANT MECHOU LELAS MEJOU TREOUGUY MELCHENNEC MENAN MENEMARZIN MENESGUEN MENEZ BIAN MENEZ CRESSALIC MENEZ KERHORS MENEZ KEROUIL MENEZ KERZON MENEZ MAURICE MENEZ MEUR MENEZ TORALAN MENHIR MEOT MERRIEN MESCAM MESCLEO MESCOAT MESCOUEZ MESCOUEZEL MESDOUROC MESGALL MESGALON MESGRALL MESGUEN MESIVEN MESLEAN MESPANT MESPRAT MESQUINIEC MESSIOU AR GUERENT MESTIRGAC MEZANLEZ MEZANTELLOU MEZAR FEUNTEUN MEZ BERNARD MEZEOZEN MEZEVIN MEZOUT MILIN AN AOD MILINIC MILIN NEVEZ MINE BRENELIO MINE GROAS NE MINE GUERVEUR MINE KERVIR MINE MAES MINE TREOUZAL MINTRIC MOGUENNOU MOGUERAN MOGUERAN IZELLA MONTOURGARD MORBIC MORGAT KERIGOU MORGAT ROSTUDEL AUX PRETRES CREACH GUIAL KERBALANEC KERDREIN KERDUFF KERGOALER KERMADEC PENGUILLY JUSTICES LIEU DIT KERANDREAU KERRY NEUF MOUSTERPAUL MOUSTREN NIVIRIT NOADEGALET NOMBRAT ODEVEUR PARC AN COAT PARC AUTRET PARC HUELLA PARC KEROUZIC PARC MON BAIL PARC MORVAN PARCOU PARCOU ROYAL PARC VENARCH PARK AR MENEZ PEN ALLEN PENALLIORZOU PENANDREFF PEN AN ERO PENANGUER PENANGUER VIAN PENANPRAT PENANRUN PENANSTER PEN AN TRAON PEN AR C HOAT PEN AR COAT PEN AR CREACH PEN AR DORGUEN PEN AR FOREST PEN AR GUEAR PENARGUER PEN AR GUER PEN AR HOAS PEN AR HOAT PEN AR MENEZ PEN AR PEN AR PRAT PEN AR QUINQUIS PEN AR ROZ PEN AR STRET PEN AR VALY PEN AR VANEL PEN AR VERN PEN AR YED PENBIS PEN BOUILLEN PENBRAN PEN COAT MEUR PENCREAC H PEN DIRY PENDREFF VIAN PENDRUC PENFELD PENFOND PENFRAT PENGOAZIOU PENGUEREC PENGUILLY PENHER PENHOAT PENHOAT AR VILIN PENHOAT BIAN PENHOAT BRAS PENHOET PENI CONAN PEN ILLIS PEN KEAR PENKER PENLAN PENLOCH PENMARCH PEN MEZEN PENNANC PENNANEAC H PENNANEACH PENNANEAC ROSEGAT PENN AR STREJOU PENNKEAR PENN KER LAE PEN PRAT PENQUER PENTRAON PENTREFF PENVERN PENZORN PERENNOU PERNAMAN PERROS PERROS TREBERON LANDE PETITES SALLES KERVAO LEZ PARIS ELOI PILODEYER PIPIBOL PLACAMEN POINTE JUMENT POLOTREZEN AMIS AR BELLEC AR BILOU AR BLED AR LAER AR QUINQUIS AR ZALL CABIOCH CALLEC CHRIST CROIX KERLEO HUEL PONTIC HERRY MELAN MESGRALL MINAOUET POL PRENN PONTROUFF PORS AR GLOUET PORS AR GROAS PORS BRAS PORS CARN PORS KERC HOANT PORSMEAN PORSQUELEN POSTOLONNEC POUL AR FEUNTEUN POUL AR VELIN POUL AR VRAN POULAZEN POULBIDER POULBROEN POULCARADEC POULCOQ POULDOHAN POULDOUR POULDREZ POULDU POULELEST POULEZOU POULFANC POULFEUNTEUN POULGUINAN POULGUIOCHET POULHOAS POULL AR MARANT POULMARCH POUL MEAN POULRAN POULVEZ PRADENNOU PRADIGOU PRAD LEDAN PRAD MENAN PRAT AR GOFF PRAT AR GUIB PRAT AR VOT PRAT CREIS PRAT GOUZIEN PRAT GUEN PRAT LEDAN PRAT LIDEC PRAT PIP PRIMEL QUELARNOU QUELEN QUELENNOC QUELERON QUELOURN QUEROU QUESTEL QUEVREL QUILIHOUARN QUILILEN QUILLIMERRIEN QUILLIVANT QUILLIVOUDEN QUINQUIS QUINQUIS MARC QUINQUIS MEUR RAGLANN RAGUENES RANVEDAN RESCREN RESTAMBERN RESTAURANT VEZ REUNIOU RHEUN DAOULIN RICHOU ROC IRVIN ROCH AN DOUR ROCH AR BRINI ROCH CREIS ROCH DUFF ROCHE BLANCHE ROCHE PLATE ROCH FILY ROCH GLAS ROCH HIGOU ROCH MELAR ROSCANVEL ROSCARVEN ROSNEVEZ ROSPLUEN ROSSIMON ROSTUDEL ROUDOUIC ROUDOULEVRY ROUZPLEIN ROZ AN TREMEN ROZ AR BANNIEL ROZ AR BROC CREACH ROZ AR HOAT ROZ AR ROZ AR YAR PELLA ROZ AR YAR TOSTA ROZ AVEL ROZ COZ ROZ LAZ ROZOS ROZ PENANGUER RUAT RUBRAN RUFILY RUGORN RUGUELLOU RULAN RUMEIN RUMENGOL KERLAVAREC RUMIGOU RUNAHER RUNAVEL RUNAVOT RUNCADIC RUNDAOULIN RUN GUEN RUN POULZIC RUPLOUENAN RUPONT RUQUELEN RUSALIOU ADRIEN CADO CAVA CLAUDE GUENOLE HERNOT MICHEL THAMEC TREMEUR SALUT SECHOUER SPERN AR BIC STANCOU STANG BALARY STANG BIHAN STANG BOUDILIN STANG COADIGOU STANG CREMOREN STANG DOUR STANG GUEGUEN STANG MARTIN STANGOLCH STANG QUELFEN STANG ROZOS STANG SANT LAORANS STEIRVIAN STE MARINE 13 HENT AR VORCH VR STERVEN STER VIAN STEUD STREAT GOZ STREJOU SUSCINIO TAL AR GROAS TI AN NEC ROCH AR HEZEC TI PRAT TOLL AVEL TORALAN TOUL AL LAN TOUL AR CHIST TOUL AR C HOAT TOUL AR GROAS TOUL AR HOAT TOULBALENA TOULL TREAZ TOULTREIZ TRAON BEUZIT TRAON DAVID TRAON ELORN TRAON KERRET TRAON LOUARN TRAON MENHIR TRAPIC KERVASDOUE TREANTON TREASTEL TREBERON TREBOMPE TREDALOU TREFARN TREFASCOET TREGANNE TREGASTEL TREGONAL TREGUE TREGUESTAN TREGUNVEZ TREHUBERT TRELANNEC TRELAZEC TRELLER TREMAIDIC TREMEOU TREMOT TRENEZ TREONGAR TREONVEL TREOUDAL TREOURON TREOUZAL TREVARN TREVIC TREVOAZEC TRIDOUR TROGAN TROGUEROT TROHOAT TROLOUC H TROMATHIOU TROMEAL TROMELIN TRONOEN TUNISIE TY ALLAIN TY AR MENEZ TY AR MOAL TY COZ TY COZ PENVERN TY COZ TALAOURON TY CROAS TY DOUR TY DREN TY FLOCH TY FORN COZ TY GARDE TY GLAS ROSNABAT TY GLOANEC TY GOEL YAN VRAS TY GUEN TY MEAN TY MEN TY MENEZ TY MOTTER VRAS TY NEVEZ TY NEVEZ KERSTRAT TY NOD TY POUL TY PRI ROSNABAT TY RADEN TY ROBE TY ROUZ TY VALORDY VALY FAO VALY NEVEZ VERNHIR PELLA VIEILLE ROCHE VILLA FLEURIE VILLENEUVE VORLEN VRONEC LIGNOUDREIN LIGOLENNEC LIGOUFFEN LIGOUFFIN LIJOU MOELAN LIJOU TREBELLEC L ILE LILEYVON LILIA 1051 REUN LILIA 1114 REUN LILIA 1123 REUN LILIA 1280 REUN LILIA 135 CAVA LILIA 152 KERSKAO LILIA 155 KERVENNI VRAZ LILIA 19 MECHOU KERVENNI VIHAN LILIA 1 MEZNEN LILIA 227 KERSKAO LILIA 244 REUN LILIA 24 MECHOU KERVENNI VRAZ LILIA LILIA 29 REUN LILIA 566 CAVA LILIA 56 MECHOU KERVENNI VRAZ LILIA 604 KERSKAO LILIA 627 REUN LILIA 6 MECHOU KERVENNI VIHAN LILIA 710 REUN LILIA 879 KERSKAO LILIA BILOU LILIA KERAZAN LILIA KERAZAN BRAS LILIA KERIDAOUEN LILIA KERJEGU LILIA KERMAREGUES LILIA KERNEVEZ LILIA KEROUGOUN LILIA LOSTROUC H LILIA MENEZ PERROS LILIA PERROS LILIA PRAT KEROUGOUN LILIA ANTOINE LILIA THEVEZAN LILIA YULOC LILIOUARN LILLACH LILLOUET LILYVON L IMAGE LIMBAHU LIMBEHU LIMBEHU IZELLA LIMBEUZ LIMINEC LIMITE LINCOSPER LINDOUR LINEO LINGANAP LINGAOULOU LINGLAS LINGLAS HUELLA LINGLAS IZELLA LINGOGUET LINGOUAL LINGUE LINGUENNEC LINGUENNEC VIHAN LINGUER LINGUEZ LINGUINOU LIN HOZ LINIHOUARN LINLOUET LINOUARN 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GORRE AR MENEZ CAVAREC MENEZ CLAUDE MENEZ CLEGUER MENEZ CLOEDER MENEZ CLOST MENEZ CLOZ MENEZ CORAY MENEZ COZ MENEZ CRANN MENEZ CRECH MENEZ CREIS MENEZ CREN MENEZ CRENN MENEZ CREQUIN MENEZ CROAS MENEZ CUZ MENEZ DAVID MENEZ DEVET MENEZ DINEAULT MENEZ MENEZ ERVEN MENEZ FOLLEZOU MENEZ FONTEYOU MENEZ GOUERON MENEZ GOURET MENEZ GOZ VEIL MENEZ GROAS MENEZ GROAS LESCORS MENEZ GUEGUEN MENEZ GUEN MENEZ GULVAIN MENEZ GURE MENEZ GURET MENEZ GWE FAO MENEZ GWEN MENEZ HAIE MENEZ HARS MENEZ HIR MENEZ HUELLA MENEZ HUELOU MENEZIC GUEN MENEZ JESUS MENEZ JUSTICE MENEZ KAM MENEZ KERDEVOT MENEZ KERDREANTON MENEZ KERGABET MENEZ KERGADET MENEZ KERGARIDIC MENEZ KERGOFF MENEZ KERGOS MENEZ KERGREACH MENEZ KERGREIS MENEZ KERGROAS MENEZ KERGUELEN MENEZ KERGUEN MENEZ KERGUS MENEZ KERHERVE MENEZ KERHUON MENEZ KERINEC MENEZ KERINEG MENEZ KERISLAY MENEZ KERIVIN MENEZ KERIVOAL MENEZ KERJEGU MENEZ KERLAERON MENEZ KERLAN MENEZ KERLAOUARN MENEZ KERLAZ MENEZ KERLEGAN MENEZ KERLEGANT MENEZ KERLOSQUER MENEZ KERMADEC MENEZ 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MENEZ REUN MENEZ RHEUN MENEZ RIOU MENEZ RIOU BIHAN MENEZ RIOU BRAS MENEZ ROCH CRENN MENEZ ROCH MEUR MENEZ ROCHTOURMENT MENEZ ROHOU MENEZ ROSPOAS MENEZ ROST MENEZ ROTULEN MENEZ ROUE MONTAGNE ROI MENEZ ROUS MENEZ ROUX MENEZ ROUZ MENEZ ROZ BIHAN MENEZ ROZ COZ MENEZ ROZ YAN MENEZ RUGUEL MENEZ RUIEN MENEZ MENEZ SALUDEN MENEZ SIZUN MENEZ STANG MENEZ STEUD MENEZ TREBREVAN MENEZ TREVIGODOU MENEZ TROMILLOU MENEZ TROPIC MENEZ TY AR GALL MENEZ TY COZ MENEZ VEIL AVEL MENEZVEN SEACH MENEZ VERIENEC MENEZ VERIENNEC MENEZ VIAN MENEZ VRAS MENEZ VRIZED MENEZ VUR MENEZ YANN MENEZ YEUN MENFAOUTET MENFILOUS MENFRANCAN MENGAOULOU MEN GLAZ MENGLAZOU MENGLEUFF MENGLEUX MENGLEUZ MENGLEUZ AR MENGUEN MENGUY MENHARS MENHIR MENHIR KERVIGOT MENIC MENLAOUER MENMEUR MENN BREACH MENNIC AN TRI PERSON MENOC MENOIGNON MENPEVAL MENSAVET MENTAFFRET MENTAVA MENTOUL MENUCH MEN ZAO MEOT MEOUET MEOUET VIAN MEOUET VRAS MEOUT MEOUT VRAS MERDI MERDIEZ MERDY MERDY BIAN MERDY BURUNOU MERDY VIHAN MERICLEN MERIENEN MEROS MERRIEN 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COAT CANTON COATSQUIRIOU DOULVEN FONTEYOU GLUJEAU GOASMOAL GUENGAT GUERVENNEC GUINIGOU KERANGOC KERANROCH KERBIRIOU KERDU KEREFFREN KERFRES KERGAERIC KERGONAN KERGONVAN KERGUELEN KERHUON KERLEVENE KERLOAS KERLOSTREC KERMARGUIN KERMEUR KERMORVAN KERNAULT KERNEVEZ KERNONGAL KEROLVEN KEROUALLON KERROCH KERSTRAD KERVELLA KERVERNIOU KERVOUSTER L ABBE BOISSIERE BOULAIE CHAUSSEE HAIE MER LESTREVET MOTHE MOTTE DELANGUIONAS LANHOULOU LANNIDY LANNIEC PALUE ROCHE TOUR LAUNAY VILLENEUVE LESCUZ LESVOYEN LEZAFF LEZERAZIEN L HERMITAGE LIVINOT LOSSULIEN LOSTEVIR D ENEZ D EN HAUT PENLANN PENQUELEN AR MARC HAT PORSMOGUER QUINQUIS QUISTINIC ROGNAN ROUDOUMEUR ROZVEUR ALOUARN CLOUD MOULIN PRES TREMADUR TRESSEOL TREVIEN TROAGUILLY TROANEZ TROYON TY VARLEN DEUFFIC DONAN DONANT DOURGUEN DREZEC BUIS BEG AEL DUC CAM CANAL CHANTRE CHAT CRANN CRECQ DUC FAOU FOLGOET GUERN HILGUY HILGUY TY CHASS JET JUCH LAY LAZ PENHOAT PLESSIX MOULIN PRE PRIEURE QUINQUIS ROI ROY ROZ ROZOS RUFFA SEQUER STALE STANG TEMPLE VARCH ENFER ETANG FOLGOET FONTAINE BLANCHE FOURDEN GARDES GARDEZ GARO GAUCHE GILLETTA GOASSELEN GOEL GOFF GOUENNOU GOUEZ GOUZIOU PALUD GUILER HAIE HASCOET HAUT HAUT HELOU HENRY HENVEZ HILBARS HILVERN JET JEUNE JOANET JOANNET KASTELL BOC H KERALLE KERAMBELLEC KERANFLEC H KERANGOC KERANGOMAR KERANY KERBEOC H KERBERN KERBIZIEN KERDREIN KERDUNIC KERELLEC KERELLOU KERENIEL KEREON KEREUZEN KERFEUTEUNIOU KERGAER KERGANAPE KERGARAOC KERGARAOC NORD KERGOAT KERGOFF KERGONADEACH KERGONIOU KERGONVAN KERGOULOUARN KERGUESTEN KERGUNUS KERGUS KERHALS KERHAO PENHARN KERHAS KERHINE KERIAR KERIBER KERIBERT KERINGARD KERIVARCH KERLEAU KERLEO KERLIGONAN KERLOCH KERNABAT KERNAVENO KERNO KEROCUN KEROMNES KEROUE KERRIEGARS KERROT KERROUANT KERSABIEC KERSANDY KERSCOULET KERSTRAD KERSTRAT KERVAO KERVASTAL KERVEN KERVIZIN KERZUOT KOZ KASTEL L ABBE LABBE LANNIEC LANNOGAT LANNOUEDIC LANORGARD LANSOLOT LANVERN LAUNAY LESCOAT LESCOP LESPOUL LIDIEN MANACH MARANT MARCHE MAREGUEZ MER MEUR MOALHIC MOELLIEN MOGUEL MOGUEROU MOTTE NARCH NEUF NEUF QUILLIEN NEUF MATHIEU NEZET NOHENNEC PAPIER PARLAVANT PELL PEN AR GUER PENARUN PEN AR VERN PEN ENEZ PENFROUT PENGUERN PENHOAT PENLAN PENMARC H PENNAYEUN PERAN PLOMB PLUFERN POAS PONCHOU MOULIN PONTALENNEC CARADEC COUALCH CROACH GARVAN HERE LEY MORVAN OURS QUEAU RHEUN PORS MILIN VALLY PORSMOGUER PORS MORO POUL BLEIS POULFANC PRE PROVOST PRY QUELENNEC QUENIC BEUZEC QUENICTUEC QUESTEL QUIDAN QUILLIEN QUILLIOU QUINIOU QUISTILLIC RABAT RAGOT RICHET RIVE RIVOALEN ROCHE ROGUAN RONVARCH ROSQUIJEAN ROSQUIJEAU ROSSIOU ROSTIC ROUAL ROUDOU GLAZ ROUDOUGOALEN ROUDOUS ROUX ROY ROZOS CLOUD CLOUE ELOI MOULIN NORGARD OUARNEAU SINAN SOUL SQUIRRIOU STANG STANG ZU SUGUENSOU MOULINS VERTS TALARN PENFELD TANNE TARIEC TERRE TOULGOAT TRAON TRAOUNEVEZEC TREANNA TREAS TREDIEC TREGALET TREGONGUEN TREGUIER TREOURON TREUSCOAT TREVAN TREVILIS TROGOFF TROMELIN TY MEN TY VARLEN VERGRAON VERN VERT VEYER VIEUX MOUSTANGUERN MOUSTARGOLL MOUST AR GUERN MOUSTER MOUSTERLANN MOUSTERLIN 13 METAIRIE MOUSTERLIN 17 GARN GLAZ MOUSTERLIN 19 LEN C HANKED MOUSTERLIN 27 KARN STER MOUSTERLIN 4 HENT KROAS KERNEING MOUSTERLIN 9 METAIRIE MOUSTERLIN HENT CLEUT ROUZ MOUSTERLIN KARN MENEZ KERBADER MOUSTEROU MOUSTOIR MOUSTOIR MENEC MOUSTOIR CADO MOUSTOIR VIHAN MOUSTOULGOAT MOUSTREN MOUSTRENN MUNUGUIC MUNUT MUR MUR HUELLA MURIOU MUR MESGOUEZ MUR NEUF NAMOUHIC NANCH NANCLIC NANGELOU NAONT NAOT HIR NAOUGOAD NAOUNT NAOUT NARC GWEN NARGOAT NARRET NARTOUS NATELLIOU NATOR NAVALENNOU NAVALHARS NAVALIC NAVAN NAVANT NAVARO KERASCOET NEC HOAT NEDER NEHELEN NEILLOU NEISCAOUEN NEIS VRAN NEIZ VRAN NELEHEN NELLACH NELOHAN NEMBLACH NENCHES NENEZ NENEZ NEVEZ NENEZ TREFLADUS NENGOAT NENVEZ NEOLENNOU NEONEC NERGOAT NERGOZ NERIZELEC NERODINE NESLARCH HUELLA NESLARCH IZELLA NESTAVEL BIAN NESTAVEL BRAS NESTOU NEUVE NEUZIOU NEVARS NEVARS BIHAN NEVARS HUELLA NEVARS IZELLA NEVEIC NEVEIT NEVEN NEVEZ CREACH NEZ AR C HANT NEZARD NEZERDY NEZERS NEZERT NIADEC QUILLOUARN NIGOLOU NILIZIC NILVIT NIOU NIOU HUELLA NIOU IZELLA NIQUELVEZ NIVEN NIVER NIVERN NIVERNIC NIVERROT NIVIEZ NIVINEN NIVIRIT BRAS NIZERDY NIZON CROISSANT ANDRE NIZON KERAMBEUX NIZON KERANGALL NIZON KERANGOFF NIZON KERCAUDAN KERGORNET NIZON KERFUR NIZON KERGANET NIZON KERGAZUEL NIZON KERGOADIC NIZON KERGOADIC VRAS NIZON KERHUIL NIZON KERLARET NIZON KERLARRET NIZON KERMANN NIZON KERMOAL NIZON KERNONEN NIZON LANDEDEO NIZON LUZUEN NIZON ANDRE NIZON TREMALO NIZON VILLENEUVE NODET NODEVEN NOGUELLOU NOHENNEC NOISERAIE NONAOU NONEN NONNAT HUELLA NONNAT IZELLA NONNOT NORMANDIE NORMANDY NORVENNIC NORVEZ NOTERIC NOTERIOU NOUARNEC NOUSTE POULFANC NOUVELLE COTE NOYELLOU NUOC ODE AR GLOUET ODEBLEIS ODE BLEIZ ODEGALET ODEGAR ODE LOUC H ODEVEN ODEVEUR ODE VEUR ODE VRAZ ORATOIRE OREE OROBANCHES OUENNEC PAGOTAIE PALAE PALAIS PALUD PALUD AN HIR PALUD BIAN PALUD BRAS PALUD CREMUNY PALUD CRUGOU PALUD CRUMUNI PALUD DOURDUFF PALUDEN PALUDEN BEL ABRI PALUD GORREBEUZEC PALUD GOURINET PALUD GRONDVAL PALUD KEREMMA PALUD KERGUELLEC PALUD KERGURUN PALUD KERISTENVET PALUD KERISTEVET PALUD PELLAN PALUD PENLAN PALUD TREBANEC PALUD TREBANNEC PALUE PALUE KERGUELLEC PALUE KERSUGAL PANT PAOTR TREOURE PAOU PAPEREZ PAPETERIE MAUDIT KERISOLE PAPIOLET PARADEN PARADEN TY POAS PARADIS PARALUCHEN PAR AR VEROURI PARC AN AGNIEL PARC AN ALE PARC AN AOD BRAZ KERLIOU PARC AN AOT PARC AN AUTROU PARC AN AVEL QUELVEZ PARC AN BROC PARC AN DOSSEN PARC AN HIVER PARC AN ILLIS PARC AN IVERN PARC AN NOAN PARC AN OACH PARC AN OGUEN PARC AN PICARD PARC AN TRAON PARC AN ZANT PARC AR BARENNOU PARC AR BARNOU PARC AR BARRES PARC AR BASTARD PARC AR BELLEC PARC AR BEYER MENEZ NOAS PARC AR BOURG PARC AR BREACH PARC AR BREUR PARC AR BROC PARC AR CHAEL PARC AR CHAPEL PARC AR C HOAT PARC AR CHOAT PARC AR FAVEN PARC AR FEUNTEUN PARC AR GLOAN PARC AR GO PARC AR GROAS PARC AR HAD PARC AR HEZEC PARC AR HOAT PARC AR JOL PARC AR LAND PARC AR LANDE PARC AR LANN PARC AR MEIN GUEN PARC AR MENEZ PARC AR MENGUEN PARC AR MENHIR PARC AR MILINER PARC AR PARQUER PARC AR PONTIC PARC AR PORS PARC AR PORS KERNEAVAL PARC AR PRAT PARC AR RHEUN TREGONDERN PARC AR ROUE PARC AR STANG PARC AR TRAON LEZABANNEC PARC AR VALIS PARC AR VALLY PARC AR VEIL PARC AR VEROURI PARC AR VUR PARC AR YAR PARC AU DUC PARC VEROURI PARC BALAN PARC BANALOU PARC BEAUSEJOUR PARC BIHAN PARC BLEUD PARC BRAS PARC COAT PARC COLIN PARC COSQUER PARC COZ PARC CREIS PARC D ARVOR PARC DAVID PARC EXPOSITIONS LANGOLVAS PARC FANQUIC PARC FEUNTEUN PARC FOEN PARC FOENNEC PARC FRIC PARC FROST PARCFUR PARC GALLO PARC GUEOT KERNAVENO PARC GWEN PARC HAUT PARC HIR PARC HOAT BEO PARC HUELLA PARC HUELTREC PARC ILIS PARC IZELLA PARC JAFFRE PARC KERHAPPS PARC KERINGARD PARC KERISTIN PARC KERIZIT PARC KEROHAN PARC KERVEGUEN PARCKIGOU PARC KROAS PARC LAER PARC LAGADEC PARC MAIS BLANCHE M LANINON PARC LAND PARC LANGOLEN PARC LANN PARC LANN ROHOU PARC LEC PARC LEONNEC PARC LEUR PARC LEVENEZ PARC LOCH GUEN PARC MARC PARC MARCHE PARC MARCHE COAT BEUZ PARC MARCHE SEACH PARC MARVAIL PARC MAURICE PARC MEIL TRESSEOL PARC MELINER PARC MENEZ PARC MENEZ KERSABY PARC MESNIGOALEN PARC MINE PARC MINOR KERDAENES PARC MOROS BIHAN PARC MORVAN PARC NEVEZ PARCOAM PARC OLLIVIER PARCOSQUER PARCOU PARCOU BIHAN PARCOU BRAS PARCOU BRAZ PARCOU BRIS PARCOU GORREKEAR PARCOU JACQ PARCOU LAND PARCOU LEUHAN PARCOU MENEZ PARCOU MENGLAZ PARCOU PENHOAT SALAUN PARCOU ROYAL PARCOU SEGAL PARCOUYARD PARC PALUD TY PALUD PARC PELL KEREVEN PARC POST PARC QUESTEN PARC QUISTINIC PARC ROCH PARC ROUTE PENMARCH PARC RULAN PARC SCOUDOU PARC SEVEAN PARC STANG PARC STER PARC STER CAP COZ PARC TAL AN ILIS BOURG PARC THOMAS PARC TOUL AR BLEIS PARC TREHERN KERLEAN PARC TRI HORN PARC TY LAI PARC VEIL PARC VENARCH PARC VEROURY PARC VIAN PARC YANN PARC YDER PARC ZALE PARGAMOU PARIAL PARICHARD PARIOU PARK LEUR PARK AL LEUR PARK AN AVEL PARK AN DOUR PARK AN DREZEG PARK AN HIVER PARK AN TIEZ PARK AR BELEG PARK AR BELEG LANVISCAR PARK AR C HIVIGER PARK AR C HOAT PARK AR GOZHKER PARK AR GROAZ PARK AR GWIADER PARK AR LEUR PARK AR MENHIR PARK AR ROZ KERHARO PARK AR RUN BOURG PARK AR STANKOU PARK AR STREAT BOURG PARK AVALOU PARK BALAN PARK BRAS PARK BRAZ PARK CLOS PARK L ISLE PARK FORN PARK GOUEL Y 28 HENT KERBORC H PARK GUEN KERSTRAT PARK HENT GLAZ PARK HUELLA PARK HUELLA KERMARIA PARKING INTERMARCHE KERCHAPALAIN PARK KERCO PARK KERGREAC H PARK KREIS AR VOURC H PARK KREIZ PARK LAE PARK LANIG PERES PARK LEUR PARK LEURIOU PARK MARGOD PARK MENN MEUR PARK MERVENT PARK MEUR PARK MONTOUARC H PARK N ALLE PARK NEVEZ PARKOU PARK PEN DUICK PARK ROUZIG PARK VRAS PARLUCHEN PARLU HEN PAROU KERMAVIOU PARVIS BLAISE PASCAL PASSAGE PASSAGE AUGUSTE BRIZEUX PASSAGE BOBBY SANDS PASSAGE PASSAGE CORENTIN CELTON PASSAGE CORNOUAILLES PASSAGE KERGUELEN PASSAGE LEVEE PASSAGE POSTE PASSAGE L ARMEN PASSAGE FEUX FOLLETS PASSAGE FRENES PASSAGE LAVANDIERES PASSAGE LUTINS PASSAGE ONDINES PASSAGE SIRENES PASSAGE DOMREMY PASSAGE DOURIC COZ PASSAGE FOUR PASSAGE HENAN PASSAGE BART PASSAGE KERGUELEN PASSAGE LUCIEN PICARD PASSAGE PRAD AR ZANT PASSAGE HERLE PASSAGE SIRENES PASSAGE SURCOUF PASTOURON PAVILLON 2 COSQUER PAVILLON ALBERT LARHER PAVILLON LOCH PAYAOU PAYS FRUIT PEDEL PELEUZ PELHET PELLAE PELLAIN PELLAN PELLAN BIHAN PELLAND PELLAVON PELLAY PELLEM PELLEN PELLEOC PEMBRAT PEMP HENT PEMPHENT PEMPIC PEMPOUL PEMPRAT PEN PENAHARS PEN HOAT PENAHOAT PENALAN PEN ALAN PEN LAN PEN LAND PENALAND MEROS PENALEIN PENALEN PEN LEN PEN AL LAN PENALLAN PEN ALLAN GARO PEN AL LAN KERIZUR PEN AL LANN PEN AL LAN VIAN PENALLEN PEN ALLEN 2 HENT MENEZ BRIS PENALLEURGUER PEN ALLEUR GUER PENALLEURGUEUR PENAMENEZ PENAMPRAT PEN AN PEN AN DAIL PEN AN DALLAR PENANDAOUENT PEN AN DELCH PEN AN DOUR PENANDOUR PENANDREFF PENANDREFF BIAN PENANEAC H PEN ANEACH PENANEACH TREANNA PENANECH PEN AN ERO PENANGUER PEN AN HEDER PEN AN ILIS KERVILLIC PEN AN NATIL PEN AN NEACH PEN AN NEAC H PEN AN NEC H PEN AN PRAT PENANREST PENANROS PENANROS PHILIBERT PENANROUS PENANROZ PEN AN ROZ BODIVIT PEN AN ROZ KERGOFF PENANRUN PEN AN TEVEN PEN AN THEVEN PEN AN TRAON PENANTRAON PENANVERN PEN AN VERN PENANVEUR PENANVOAS 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GUER PENNARGUER PENN AR HOAT IZELLA PENN AR LANN PENN AR LEUR GER PENN AR MAEZOU PENN AR MEN PENN AR MENE PENN AR MENEZ PENNARMENEZ PENNAROLLAND PENNAROS PENNAROS ROSTEGOFF PENNAROUS MENGLAZ PENNAROUS ROCH TOUL PENNAROUZ PENNAROZ PENN AR PRAD PENNARPRAT PENN AR PRAT PENN AR REUN PENN AR RHEUN PENN AR ROS PENN AR ROZ PENN AR ROZ TREMELE PENN AR RUN PENNARSTANG PENN AR STANG PENN AR STANK PENN AR STER PENNARU PENNARUE PENNARUN PENNARUN BIHAN PENNARUN D ANTRAON PENNARUN KERANDON BEUZEC PENNARUN VIAN PENNARUN VRAS PENN AR VERN PENNARVOAS PENN AR YEUN PENNAVERN PENN AVERN PENNAVERN AR GORRE PENNAVERN KERLIVER PENNAVERN MOGUEDET PENNAVERN SAINTE MARGUERITE PENNAVERN SULIAN PENNAVERN SULIAU PENNAVERN TRESIGUIDY PENNAVOAS PENNAYEN PENNAYEUN PENNAYUN PENNEACH PENNEGOAT PENNENEZ PENN ENEZ PENNENGOAT HUELLA PENN FEUNTEUN PENN FOUL PENNIG AR VERN PENNISQUIN PENN KEAR PENNKER PENN KER PENN MENE AR ZOZ PENNOCH PENNOD PENNOU CREAC H PENNTI RUZ PENORS PEN OUEZ PEN PARCOU PENPRADOU PENPRAT PEN PRAT PEN 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AUVERGNE VICTOIRE L L HOUARDON L ENFER L EUROPE L EVECHE L HOTEL L ILE AUX MOUTONS L ILE PENFRET LOCRONAN LOURDES METZ MEZDOUN PENHELEN PENVILLERS QUILBIGNON ROSCANVEL AGAPANTHES ALIZES AMANDIERS AMERICAINS ANCIENS HARAS BOUVREUILS BRUYERES CARMES CHENES COLOMBES CYPRES DEPORTES DROITS L HOMME ECOLES ERABLES FFI FRENES FRERES CAVAREC FTPF FUSILLES HETRES JACOBINS MIMOSAS NOISETIERS ORMES OTAGES PECHEURS PHARES ROSIERS ROUGETS SALAGANES SANGLIERS TONNELIERS STRASBOURG TREBEHORET VERDUN VIARMES D IROISE DIXMUDE CAMUS CARAES LOUDOUX ODEYE 118 EME REGIMENT INFANTERIE 11 NOVEMBRE 18 JUIN 1940 19 EME REGIMENT INFANTERIE 19 MARS 1962 8 MAI 1945 BEL AIR BOT BOULOUARD BOURG CALVAIRE CAPITAINE GUYNEMER CHAMP BATAILLE CHAMP FOIRE PLACE DUCHESSE ANNE COMBAT TRENTE COMMERCE COSMOS DOSSEN DOURIC COZ FROMVEUR GENERAL GENERAL GUERDY DUGUESCLIN LYCEE BRIS MARCHALLAC H MARCHE PARVIS PERE MAUNOIR PLACE GUERN POULIET DUQUESNE ROI ARTHUR ROUZ STIVEL VEROURY VILLERS D YS PLACE EDOUARD VAILLANT PLACE NIZON 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LYAUTEY MARCEL STEPHANT MARCHE MARE MARECHAL D LATTRE TASSIGNY MARECHAL FOCH MARECHAL CURIE MARRONNIERS MARTRAY MASSENA MAURICE GILLET MEDARD MELLAC MENDES FRANCE MENEYER MESGLOAGUEN MEUR MICHEL COLOMB MICHEL LAREUR MIMOSAS MONSEIGNEUR FREPPEL MONSEIGNEUR TESTAR COSQUER MONTS D ARREES MOREAU 4 HENT AR VEIL MOUETTES PLACENAN PLACENANT NAPOLEON III APPERT NORMANDIE OCEAN ODET ONTARIO OTAGES PAIX PARC AN TRAON PARK PASCAL GAUGUIN FLEM SERUSIER STEPHAN PENTHIEVRE PENVERN PER JAKEZ HELIAS PERROS AUGUSTE RENOIR BARRE RONSARD LOTI PLASENN AR BRUN PORS CLOS PORT PORZ GUEREC POSTE POUL AR STANG PRAT AR C HREN PUITS QUARTIER MAITRE LANNUZEL QUATRE POMPES RACINE RENARD RENE CASSIN REPUBLIQUE RESISTANCE RESISTANTS RIMBAUD ROBERT GOFF ROCHEBRUNE ROHAN ROSIERS ROSVILY ALOUARN ANTOINE CORENTIN DIDIER SAINTE BARBE SAINTE HAUDE EUTROPE FIACRE GUENOLE HERBOT HUBERT PLACE JOSEPH LAURENT LOUIS MARTIN MATHIEU MELAR MICHEL MONNA PLACE REMY TANGUY THOMAS TREMEUR YVES SALVATOR ALLENDE SANE SANQUER SAUX SCATTERY SEMAPHORE SIMON STERNES SUFFREN TACHEN MORCH TANGUY MALMANCHE TANGUY PRIGENT TERRE AU DUC TEVEN AR REUT TOUL AL LAER TOUR TANGUY TRAOULEN TREZ HIR TROIS COANT TROISE TROIS ORMES PLACETTE KERNEGUEZ PLACETTE MONSEIGNEUR PERSON TY AN HOLL VAUBAN VERCINGETORIX VERDUN VICTOR HUGO VICTOR ROSSEL VICTOR SALES VICTOR SEGALEN VIEUX CHENE VIEUX MARCHE VINET WAGRAM XAVIER GRALL YANN LANDEVENNEG YEALMPTON YS YVES KERBOUL YVES LAVIEC YVES TANGUY PLADOUCEN PLAGE BERTHEAUME PLAGE CHARDONS BLEUS PLAGE KERVEL KERVEL PLAGE GOULIEN KERNAVENO PLAGE ILLIEN PLAGE KERHORNOU PLAGE KERSIDAN PLAGE KERSINY PLAGE KERVEL PLAGE KERVEL KERVEL PLAGE KERVILZIC PLAGE LESTREVET PLAGE MINOU PLAGE PARADIS PLAGE PORSMOGUER PLAGE POULDOHAN PLAGE TREIZMALAOUEN PLAGE TRENEZ PLAGE TREZ BELLEC PED PLAINE PLAISANCE PLANT AR VOAZ PLANTEN PLAS AN DANS PLAS AR GUER PLAS AR SAOUT PLASCAER PLASENN AN ILIZ PLASENN AR C HOEL PLASENN KERGOFF PLASEN PEN AR C HRA PLEEN PLEG AR PLEG AR VOAS PLEG ER LANN PLEGOUBER PLESQUIVIT 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TREANTON PLOUJEAN TROIS VALLEES PLOUJEAN TROUGOUAR PLOUJEAN TUNISIE PLOUJEAN TY NOD PLOUSTERN PLOUZORNOU PLTE MONSEIGNEUR PERSON PLUFERN PN 498 STANG COAT MORN PN 525 KERDRIOLET PN NUMERO 9 POBLEIS PODER PODER BIHAN POINT VUE POINTE BEG TANGUY POINTE BILOU POINTE CORSEN POINTE TREVIGNON POINTE TREVIGNON CORNICHE POINTE TREVIGNON CURIOU POINTE TREVIGNON KERLIN POINTE TREVIGNON PENLOCH POINTE MILLIER POINTE POSTILLON POINTE VAN TROUGUER POINTE GRIGNALLOU POINTE KERVIGORN POINTE JUMENT POINTE DREFF POINTE MOUSTERLIN 14 METAIRIE POINTE PEN HIR POINTE POSTILLON POINTE COULITZ POINTE GILLES POINTE TOULINQUET POINTE TREVIGNON POINTE TREVIGNON FEUNTEUNODOU POINTE TREVIGNON HENT MEN POINTE TREVIGNON KERLEO POINTE TREVIGNON STANG QUELFEN POLARD POLBOGER POLHOAT 14 TAL AR HOAD POLHOAT 1 ALEZ VIHAN POLHOAT 23 ALEZ GLAZ POLHOAT 2 ALEZ VIHAN POLHOAT 4 ALEZ VIHAN POLHOAT 5 ALEZ VIHAN POLHOAT 6 ALEZ VIHAN POLHOAT 7 ALEZ HIR POLHOAT PENKER POLOTREZEN POMMERAIE POMPILLAU POMPOULOUDOU POMTPLINCOAT 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ROSMAGUER ROSMELLEC ROSMELLIC ROSMENGLAS ROSMEUR ROSMORDUC ROSNEVINEN ROSNIVINEN ROSNOEN ROSNONEN ROSONOUAL ROSPEILLET ROSPER ROSPERNES ROSPICO ROSPICO KERANGLAS ROSPICO KERASCOET ROSPICO KERDAVID ROSPIRIOU ROSPLEN ROSQUELEN ROSQUERNO ROSQUEROU ROSQUERVEL ROSQUIJEAN ROSQUIJEAU ROSQUILIOROC ROSQUILL ROSQUILLEC ROSQUILLIOROC ROSSERMEUR ROSSOLIC ROSTAND ROSTEGOFF ROSTELLEC ROSTERNIC ROSTEVEN ROSTIEGUEN ROSTIVIEC ROSTOMIC VRAS ROSTRENNEC ROSTUDEL ROSTURIEN ROSVALLEN ROSVEGUEN ROSVEIGN ROSVERN ROSVEUR ROS VIHAN ROSVOAL ROSVOALIC ROTUDAL ROUAL ROUAL BIAN ROUAL HUELLA ROUALLOU ROUALOU ROUANTELEZ ROUAOU ROUAS ROUDAOU ROUDERRIEN ROUD GUEN ROUDOU ROUDOUAN ROUDOUARN ROUDOUDERC H ROUDOUDERC CADOU ROUDOUFILY ROUDOU FLOCH ROUDOUFRANC ROUDOUGOALEN ROUDOUGUENVEZ ROUDOUHIR ROUDOUIC ROUDOUIC VIHAN ROUDOUIG ROUDOUIG VIHAN ROUDOULEVRY ROUDOUMEUR ROUDOUR ROUDOURIC ROUDOUROU ROUDOUROUS ROUDOUROUX ROUDOUS ROUDOUS AN AOUR ROUDOUS HIR ROUDOUSHIR ROUDOUS MEAN ROUDOUZIC ROUE ROUENNOU ROUGE ROUISTIN ROUMOULOUARN 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ROUTE KERBRIANT ROUTE KERBRIS ROUTE KERBULIC ROUTE KERBUNCOU ROUTE KERCHAPALAIN ROUTE KERC HEN ROUTE KERDANIEL ROUTE KERDANIOU ROUTE KERDENIEL ROUTE KERDEO ROUTE KERDIGAZUL ROUTE KERDOSTIN ROUTE KERDRES ROUTE KERDRUC ROUTE KERENOT ROUTE KEREUNEUT UHELLA ROUTE KEREVER ROUTE KERFEUNTEUN ROUTE KERFEURGUEL ROUTE KERFILY ROUTE KERFLOUZ ROUTE KERGABET ROUTE KERGADEL ROUTE KERGADIOU ROUTE KERGANAVAL ROUTE KERGARADEC ROUTE KERGARANTEZ ROUTE KERGARIOU ROUTE KERGOADIC ROUTE KERGOAT ROUTE KERGONDA ROUTE KERGONIOU ROUTE KERGONNIOU KERIZEL ROUTE KERGORS LESTREVET ROUTE KERGOUTOIS GOARIVA ROUTE KER GOZ ROUTE KERGUELL ROUTE KERGUERAL ROUTE KERGUIDAL ROUTE KERHALL ROUTE KERHAMON ROUTE KERHENOR ROUTE KERHORNOU ROUTE KERHUEL ROUTE KERIAZO ROUTE KERIDER ROUTE KERIFORN ROUTE KERINCUFF ROUTE KERINEC ROUTE KERINER ROUTE KERIVES ROUTE KERIVOAL ROUTE KERIZELLA ROUTE KERJEAN ROUTE KERJEGU ROUTE KERJEZEQUEL ROUTE KERLANN ROUTE KERLANOU ROUTE KERLAUDY ROUTE KERLEAO STANG VENELLE ROUTE KERLECUN ROUTE KERLEGUER ROUTE KERLEO VIHAN ROUTE KERLEVEN ROUTE KERLIVER ROUTE KERLOC H ROUTE KERLOM ROUTE KERLOSQUET ROUTE KERLOU ROUTE KERLOUAN ROUTE KERMADORET ROUTE KERMARIA ROUTE KERMARZIN ROUTE KERMAZEGAN ROUTE KERMEBEL ROUTE KERMENGUY ROUTE KERMENHIR ROUTE KERMERRIEN ROUTE KERMEUR ROUTE KERMEUR BRAS KERVIGODES ROUTE KERMINE ROUTE KERMORVAN ROUTE KERMORVAN BLANCS SABLONS ROUTE KERNAOUR ROUTE KERNEGUES ROUTE KERNEOST ROUTE KERNEVEZ ROUTE KERNEYET ROUTE KERNILIS ROUTE KERNIOC ROUTE KERNIOUAL ROUTE KERNIVINEN ROUTE KERNIZAN ROUTE KERNIZAN KERBERNES ROUTE KERNON ROUTE KERODORET ROUTE KEROHANT ROUTE KEROLLAND ROUTE KEROUANNEN TREZ HIR ROUTE KEROUGUEN ROUTE KEROUMAN ROUTE KEROUTER ROUTE KERPHILLIPOT ROUTE KERRETEUR ROUTE KERRIEN ROUTE KERRIOU ROUTE KERROM ROUTE KERSCOFF ROUTE KERSCOULET ROUTE KERSEHEN ROUTE KERSENVAL ROUTE KERSERVEN ROUTE KERSIVIANT ROUTE KERSONIS ROUTE KERSPRIGENT ROUTE KERSTEPHAN ROUTE KERSTRAD ROUTE KERTANGUY TALAOURON ROUTE KERTHOMAS ROUTE KERUGUEN ROUTE KERURUS ROUTE KERUZOU ROUTE KERVAEZ ROUTE KERVANQUEN ROUTE KERVAZAEN ROUTE KERVEAN ROUTE KERVEDAL ROUTE KERVEGUEN ROUTE KERVELLIC ROUTE KERVENNEC ROUTE KERVENNI ROUTE KERVENNOU ROUTE KERVENNY ROUTE KERVEOC H ROUTE KERVEOCH ROUTE KERVERRY ROUTE KERVESCONTOU ROUTE KERVIAN ROUTE KERVIC ROUTE KERVIGEN ROUTE KERVIGORN ROUTE KERVILIGEN ROUTE KERVILLO ROUTE KERVILLOU ROUTE KERVILON ROUTE KERVINIC ROUTE KERVIZIEN ROUTE KERVIZIN ROUTE KERVOAL ROUTE KERVOAZEC ROUTE KERVOERET ROUTE KERVOEZEC ROUTE KERYANN ROUTE KERZEHEN ROUTE KERZERVEN ROUTE KERZEST ROUTE LABABAN ROUTE LABER ROUTE LACROIX ROUTE FEUILLEE ROUTE FEUILLEE BOURG ROUTE FEUILLEE KERHORNOU ROUTE HAUT ROUTE MARTYRE ROUTE LAMBERVEZ ROUTE LAMPAUL ROUTE LAMPAUL GUIMILIAU ROUTE LAMPAUL GUIMILIAU KERMAT ROUTE LAMZOZ ROUTE LAN ROUTE LANARVILY ROUTE LANDERNEAU ROUTE LANDEVENNEC ROUTE LANDIVISIAU ROUTE LAND KERGUIPP ROUTE LANDOUZEN ROUTE LANDUDAL ROUTE LANGOLEN ROUTE LANGONERY ROUTE LANGURU ROUTE LANHOULOU ROUTE LANHURON ROUTE LANLEYA ROUTE LANMEUR ROUTE LANNEDERN ROUTE LANNEGUIC ROUTE LANNIEN ROUTE LANNILIS ROUTE LANNILIS KERRENVEL ROUTE LANNOUENNEC ROUTE LANNOURZEL IZELLA ROUTE LANNOURZEL UHELLA ROUTE LANN ROSTED ROUTE LANRIVOARE ROUTE LANVENAEL BEUZEC CAP CAVAL ROUTE LANVOY ROUTE LAOUAL ROUTE LARAON ROUTE ROCHE MAURICE ROUTE LARRET ROUTE LARVOR ROUTE LAUBERLAC H ROUTE LAUNAY ROUTE LAUNAY VERN ROUTE LAVOIR ROUTE FOGOT ROUTE LEGUERRIOU ROUTE LEINEURET IZELLA ROUTE LEINGUERN ROUTE LEON ROUTE LESCOAT ROUTE LESCOGAN ROUTE LESCONIL ROUTE LESCUZ ROUTE LESMENGUY ROUTE LESMINILY ROUTE LESNEVEN ROUTE LESQUIDIC ROUTE LESTREVET ROUTE LESTREVET MENEZ NOREN ROUTE LESVEZ ROUTE LETTY ROUTE LEUR AR HARDIS ROUTE LEUR GOZ ROUTE LEURVEAN ROUTE LEZ ROUTE LEZAOUVREGUEN ROUTE LIGEN ROUTE LINIOU ROUTE LIVIDIC ROUTE LOCAL AMAND ROUTE LOCH AR BIG ROUTE LOCH AR VATEN ROUTE LOCHRIST ROUTE LOCQUILLEC ROUTE LOCQUIREC ROUTE LOCRONAN ROUTE LOCRONAN TY PLANCHE ROUTE LOCRONAN VORCH HUELLA ROUTE LOCTUDY ROUTE LODONNEC ROUTE LODONNEC EZER ROUTE LOGONNA ROUTE LOGONNA DAOULAS ROUTE LOGONNA KERSANTON ROUTE LOPEREC ROUTE LOPERHET ROUTE LOQUEFFRET ROUTE LORIENT ROUTE LOSMARC H ROUTE MADELEINE ROUTE MAEL CARHAIX ROUTE MALANDIN ROUTE MANOIR BEULIEC ROUTE MANOIR GOANDOUR ROUTE MARCHE ROUTE MARTYRE ROUTE MEAN ROUTE MEAN KADOR ROUTE MECHOU ROUTE MECHOU BALAENNOU ROUTE MEIL DREAU ROUTE MELON ROUTE MENDY ROUTE MENEZ ROUTE MENEZ AR VEL CERTAU ROUTE MENEZ HAIE ROUTE MENEZ MEUR ROUTE MENEZ ROHOU ROUTE MENGLEUS ROUTE MENHIR ROUTE MER ROUTE MESCADOROC ROUTE MESCREN ROUTE MESLAN ROUTE MESLAND ROUTE MESPAUL ROUTE MESSELLOU ROUTE MESTREZEC ROUTE MEZANCOU ROUTE MEZANGOU ROUTE MEZ LAN ROUTE MEZ PRIZ ROUTE MILIEU ROUTE MILINOU ROUTE MILIZAC ROUTE MOELAN ROUTE MONTAGNE ROUTE MONT MICHEL ROUTE MONTS D ARREE ROUTE MORLAIX ROUTE ROUTE KERNABAT ROUTE CHANTRE ROUTE ROUTE KERAVEN ROUTE MER ROUTE MEUR ROUTE NEUF ROUTE PROVOST ROUTE MOULINS KERMARIA ROUTE TREVIEN ROUTE VERT ROUTE MOUSTER ROUTE MOUSTERCOAT ROUTE MOUSTERLIN ROUTE NATIONALE ROUTE NEUVE ROUTE NEVEZ KEROREN ROUTE NOBLESSA ROUTE NODEVEN ROUTE NORS VRAS ROUTE ODET ROUTE PALUD ROUTE PALUD GOURINET ROUTE PANORAMA KERNISI ROUTE PARC AN AUTROU ROUTE PARC LAND ROUTE PARC PELLA ROUTE PARIS ROUTE PARK FLOUREN ROUTE PASSAGE ROUTE PATRONAGE ROUTE PE AR ROUTE PELEUZ ROUTE PENALLEE ROUTE PEN AN DOUAROU ROUTE PEN AN DREFF ROUTE PENANTREIN ROUTE PENANVEUR ROUTE PEN AR C HLEUZ ROUTE PEN AR GUEAR ROUTE PEN AR MENEZ ROUTE PENAROS ROUTE PEN AR ROUTE PEN AR PRAT ROUTE PENARSTANG ROUTE PEN AR VALY ROUTE PENDREFF ROUTE PENDRUC POULDOHAN ROUTE PENFOUL ROUTE PENFRAT ROUTE PENHER ROUTE PENHIR ROUTE PENKEAR ROUTE PENMARCH PENFOND VENELLE ROUTE PENNALE ROUTE PENN AN DIGUE ROUTE PENN AR C HLEUZ ROUTE PENN AR LANN ROUTE PENNAYEN ROUTE PENQUER ROUTE PENTREZ ROUTE PENZE ROUTE PERGUET ROUTE PERHARIDY ROUTE PERHEREL ROUTE PERROS ROUTE ROUTE NICE ROUTE PEULLIOU ROUTE PHARE ROUTE PHARE TREZIEN ROUTE PLABENNEC ROUTE PLACAMEN KERANDREGE ROUTE PLAGE ROUTE PLAGE GRISE KERMEN ROUTE PLAGE KERIZELLA GUERN ROUTE PLAGE CAON ROUTE PLATEAU ROUTE PLEGAVERN ROUTE PLESTIN ROUTE PLEYBEN ROUTE PLOENEZ ROUTE PLOEVEN ROUTE PLOGASTEL ROUTE PLOMEUR ROUTE PLONEIS ROUTE PLONEVEZ ROUTE PLONEVEZ FAOU ROUTE PLONEVEZ PORZAY ROUTE PLONIVEL ROUTE PLOUAY ROUTE PLOUDALMEZEAU ROUTE PLOUDINER ROUTE PLOUDIRY ROUTE PLOUESCAT ROUTE PLOUEZOCH ROUTE PLOUGASNOU LAND KERVERN ROUTE PLOUGRAS ROUTE PLOUGUERNEAU ROUTE PLOUIDER ROUTE PLOUMOGUER ROUTE PLOUNEOUR MENEZ ROUTE PLOURIN ROUTE PLOUVIEN ROUTE PLOUYE ROUTE PLOUZANE ROUTE POINTE ROUTE POINTE RAZ ROUTE POINTE JUMENT ROUTE POINTE LAURENT ROUTE AR BIS ROUTE AR FLOC H ROUTE AR GUIP ROUTE AR STANG ROUTE AVEN ROUTE AVEN PENQUER ROUTE BODIVY ROUTE CROIX ROUTE BUIS ROUTE EON ROUTE PONTEREC ROUTE GONAN ROUTE PONTHOU ROUTE PONTIC ROUTE L ABBE ROUTE L ABBE GUIB ROUTE L ABBE KERANDRO ROUTE LORBIC ROUTE MENOU LOVROU ROUTE MEUR ROUTE PONTOULLEC ROUTE SCORFF ROUTE TALEC ROUTE TOULLEC ROUTE PORSACH ROUTE PORSAC KERVELAN ROUTE PORSACH CROAS AR GALL ROUTE PORSACH LANGLAZIK ROUTE PORS AR VIR KERURUS ROUTE PORS CARN ROUTE PORSCAVE ROUTE PORSCAVE ROHENNIC ROUTE PORS CREGUEN ROUTE PORSFEUNTEUN ROUTE PORSGUEN ROUTE PORS GUEN ROUTE PORS KARO ROUTE PORS KERAIGN ROUTE PORSKY ROUTE PORS MEILLOU ROUTE PORSMOGUER ROUTE PORSMORIC ROUTE PORSPAUL ROUTE PORS PERON ROUTE PORSPODER ROUTE PORS POULHAN ROUTE PORT ROUTE PORT BLANC ROUTE PORT BLANC DIBEN ROUTE PORT CARHAIX ROUTE PORTEZ ROUTE PORT FORET ROUTE PORT LAUNAY BEURRERIE ROUTE PORT SEVIGNE ROUTE PORTZIC ROUTE PORZ AR VIR ROUTE PORZ AR VIR KERURUS ROUTE PORZAY ROUTE PORZ DON ROUTE PORZ MOELAN ROUTE POUL AR BORN ROUTE POUL AR MARCHE ROUTE POULDERGAT ROUTE POULDREUZIC ROUTE POULDREUZIC KERHOALER ROUTE POULDREUZIC KERVEN VIHAN ROUTE POULDU ROUTE POULFOEN ROUTE POULGIGOU ROUTE POULHY ROUTE POULLAN ROUTE POUL MAURICE ROUTE POULMAVIC ROUTE POULMIC ROUTE POULPRY ROUTE PRAT ROUTE PRAT AR COUM ROUTE PRAT AR GUIP ROUTE PRAT AR MEN ROUTE PRAT AR ZANT ROUTE PRAT JOULOU ROUTE PRAT LEDAN ROUTE PRAT ROUTE PRESQU ILE VIVIER ROUTE PRIMEL ROUTE PUITS KERZIOU ROUTE QUATRE ROUTE QUATRE POMPES ROUTE QUELEREC ROUTE QUELERN ROUTE QUELORDAN ROUTE QUENEQUEN ROUTE QUESTEL ROUTE QUILVIT ROUTE QUIMERCH ROUTE QUIMPER ROUTE QUIMPERES ROUTE QUIMPERLE ROUTE QUIMPERLE NIVINEN ROUTE QUINQUIS ROUTE RADE ROUTE RADEN ROUTE RADIO ROUTE RAGUEN CELAN ROUTE RAGUENES ROUTE RAGUENES CELAN ROUTE RAKER ROUTE RELECQ ROUTE RESTAURANT ROUTE RESTGOALER ROUTE RESTRENOT ROUTE RHUNENEZ ROUTE RICK ROUTE RIEC ROUTE RIEC SUR BELON ROUTE RIVIERES ROUTE ROC HANOU ROUTE ROCHARD ROUTE ROCHE ROUTE ROCHES ROUTE ROCHES BLANCHES ROUTE ROCHES JAUNES ROUTE ROI GRADLON ROUTE ROSCAO ROUTE ROSCARIOU ROUTE ROSCONNEC ROUTE ROSNOEN ROUTE ROSPICO KERASCOET ROUTE ROSPORDEN ROUTE ROSTELLEC ROUTE ROSTIVIEC ROUTE ROUDOUALLEC PINS ROUTE ROUDOU HIR ROUTE ROUDOVEN ROUTE ROZ AR BELLEC ROUTE RUDOLOC ROUTE RUGLEIS ROUTE RUGOLVA ROUTE RUGUELLOU CROAS HIR ROUTE RULAN ROUTE RULAND ROUTE RULIVER ROUTE RUMENGOL ROUTE RUNIOU ROUTE RUNVEGUEN ROUTE RUSCUMUNOC ROUTE SACRE COEUR ROUTE ALBIN ROUTE ALOUR ROUTE ANDRE ROUTE AUGUSTIN ROUTE DERRIEN ROUTE DIDY ROUTE DIVY ROUTE SAINTE ANNE ROUTE SAINTE ANNE PORTZIC ROUTE SAINTE BARBE ROUTE ELOI ROUTE SAINTE MARGUERITE ROUTE GILLES ROUTE HERNIN ROUTE ILEC ROUTE JACOB ROUTE JACQUES ROUTE ROUTE TROLIMON ROUTE TROLIMON KERALUIC ROUTE TROLIMON TY PIN ROUTE JOSEPH ROUTE JULIEN ROUTE MADY LANGLAZIK ROUTE MATHIEU ROUTE MEEN ROUTE OURZAL ROUTE POL ROUTE RENAN ROUTE SAMSON ROUTE SAUVEUR ROUTE THEGONNEC ROUTE THEODORE ROUTE THOMAS ROUTE THURIEN LOGE TAERON ROUTE TUDY ROUTE TUGEN ROUTE URBAIN ROUTE YVI ROUTE SALLE ROUTE SALLES ROUTE SANSPE ROUTE SANSPEZ ROUTE SANTEC ROUTE SCAER ROUTE SCAER MENEZ BRAS ROUTE SCRIGNAC ROUTE SEIGNEUR GRALON ROUTE SEMAPHORE ROUTE SIECK ROUTE SIZUN ROUTE SOURCE ROUTE SPEZET ROUTE SQUIVIDAN ROUTE STADE ROUTE STALAS ROUTE STALAZ ROUTE STANG ROUTE STANGALARD ROUTE STER ROUTE STIFF ROUTE STREAT AR BARA PENIGUET ROUTE STUM ROUTE TACHEN LANGOLEN ROUTE TAL AR C HOAT ROUTE TAL AR GROAS ROUTE TAL AR HOAT ROUTE TANGUY CHASTEL ROUTE TERENEZ ROUTE TERRAIN SPORTS ROUTE TERRAIN SPORTS ROUTE THEVEN ROUTE THEVEN COZ ROUTE THEVEN PRAT ROUTE TIBIDY ROUTE TORCHE ROUTE TOUL AR HOAT ROUTE TOULBROCH ROUTE TOUR ROUTE TOURCH ROUTE TOURIGOU ROUTE TOURISTIQUE ROUTE TOUR NOIRE ROUTE TRAON ROUTE TRAON ELORN ROUTE TRAONNAZEN ROUTE TRECHOU ROUTE TREFFELEN ROUTE TREFFIAGAT ROUTE TREFLAOUENAN ROUTE TREFLEVENEZ QUILLEVENEC ROUTE TREGANA ROUTE TREGASTEL ROUTE TREGLONOU ROUTE TREGOUDAN ROUTE TREGOUREZ ROUTE TREGUENNEC ROUTE TREGUENNEC KERSULEC ROUTE TREGUNC ROUTE TREHOU ROUTE TREMAZAN ROUTE TREMEOC ROUTE TREMEOC STANGOULINET ROUTE TREMORE ROUTE TREOGAN ROUTE TREOGAN KERANGUEN ROUTE TREOGAN TY MAHE ROUTE TREOGAT ROUTE TREOUERGAT ROUTE TREVARGUEN ROUTE TREVIGNON ROUTE TREVILIS HUELLA ROUTE TREVOURDA ROUTE TREVOUX ROUTE TREZIEN ROUTE TREZMALAOUEN ROUTE TRIMEAN ROUTE TROAON ROUTE TROGOFF ROUTE TROHARE ROUTE TROIS PRATS ROUTE TY AR ZANT ROUTE TY COAT ROUTE TY CROAS ROUTE TY DOUAR ROUTE TY FLEHAN ROUTE TY JOPIC ROUTE TY NENEZ ROUTE TYNEVEZ ROUTE TY PIN ROUTE TY SOUL ROUTE VALLEE ROUTE VALLON ROUTE VALY PERROS ROUTE VEILLANEC ROUTE VENEC ROUTE VENIEC ROUTE VERGRAON ROUTE VERLEN ROUTE VERN ROUTE VERONIQUE ROUTE VIADUC ROUTE VIEUX BOURG ROUTE VOAS VENELLE ROUTE VOUGOT ROUTE VRAY SECOURS ROUTE YELEN ROUTOU YARIC ROUTOU YARRICK ROUZIC ROUZIC VERN ROUZOUCON ROUZ PLEIN ROUZ TREMORVEZEN ROYAL ROYAL AR HOAT ROZ ROZ LAE ROZALGUEN ROZ AN AVEL ROZ AN AVEL GOAREM EDERN ROZ AN CLOAREC ROZANCOAT ROZ AN DACHEN ROZANDON ROZ AN DOUR ROZANDUC ROZ AN DUCHEN ROZANEACH ROZ AN EOL ROZANGALL ROZ AN GALL ROZANGUILLOU ROZ AN ILIS ROZ AN TACHEN ROZ AN TREMEN ROUS ROZ AR BASTARD ROZ AR BELLEC ROZ AR BIC ROZ AR BOURC HIS ROZ AR BRUG ROZ AR CHAD ROZ AR CHALLEZ ROZ AR C HOEL CADOU ROZ AR CREAC H ROZ AR FEUNTEUN ROZ AR GALL ROZ AR GAOUENN ROZ AR GARREC ROZ AR GAVED ROZ AR GRILLET ROZARGUER ROZ AR GWIN ROZ AR HAN ROZ AR HAON ROZ AR HARO ROZ AR HASTEL ROZ AR HOAT ROZ AR HOEL ROZ AR LUZ ROZ AR NEVET ROZARNOU ROZ AR PAOU ROZ AR PILLAT ROZARPOTEN ROZ AR POULLOU ROZ AR SCOUR ROZ AR VEIL ROZ AR YAR ROZ AVEL ROZAVEL ROZ AVEL BRIGNOLEC ROZ AVEL KERVEN ROZ AVEL LESCORRE ROZ BALAN HENT KEROUZIEN ROZ BRAS ROZ BRASS RUAT ROZ BREIZELLOU ROZBRULLU ROZ COAT ROZ COAT DELLEC ROZ COAT KERJOA ROZ ROZELLEC ROZELLIG ROZEMBELLEC ROZENN AN AVEL ROZERNIC ROZERVILLE ROZ FEUNTEUN ROZ GARNOEN ROZ GLAS ROZ GLAZ ROZ GODEN ROZ GUIN ROZ GWEN ROZ HIR ROZHUEL ROZ HUELLA ROZIC ROZIC AN ARCH ROZIC AR PINS ROZIG ROZIGOU ROZIOU ROZIVIN ROZKANNOU ROZ KERBERON ROZ KEREON ROZ KERGREACH ROZ KERGUIAN ROZ KERNARET ROZ KERVINIOU ROZ KERVOUYEN ROZ LANGUIDOU ROZ LANVAIDIC ROZ LEURIOU ROZ LEZONGARD ROZLINOU ROZ LODOU ROZ MEIN GLAS POULDOHAN ROZMEL ROZMEUR ROZ MOGUEROU ROZ NAOUR ROZ NEVEZ ROZON ROZONNEC ROZPLA ROZ QUELEN ROZ QUIMERCH ROZ RAGN GUILLY VIAN ROZ RHUN ROZ SIMON ROZ SPERNEZ ROZUC ROZUEL ROZ VALAN ROZVEGUEN ROZ VEIN ROZVENNI ROZ VEUR ROZ VIAN ROZ VIHAN ROZ WENN RTE BENODET RTE GUILVINEC 29 ROBINER RTE PLOUIDER RTE FOUESNANT RTE HUELGOAT 5 RTE NEVEZ RTE PORT FORET RTE QUIMPER RTE ROSCOFF RUAT RUBANAL RUBARDIRI RUBEO RUBETEREL RUBIAN RUBINOU RUBIOU RUBRAN RUBRAT RUBUEN RUBUSCUFF RUBUSKU RUBUZAOUEN RUCARADEC RUCAT KERAVEL RUDENEZ RUDEVAL RUDEVEL RUDOLOC RUDONGE RUDULLACH 11 NOVEMBRE 1918 11 NOVEMBRE 12 KERMABON 12 SILLONS 14 JUILLET 16 AOUT 1944 17 KERMABON 18 JUIN 1940 18 JUIN 19 MARS 1962 19 MARS 62 1 ERE DFL 2 DIVISION BLINDEE 3 3 EME RAP 3 FRERES DARIDON 3 FRERES LEJEUNE 3 FRERES LEROY 3 FRERES VIENNE 4 SAISONS 4 VENTS 5 AOUT 1944 7 AOUT 1944 8 MAI 1945 8 MAI 45 9 AOUT ABALOR ABBADIE ABBAYE ABBE CADIOU ABBE CONAN ABBE L EPEE ABBE JOSEPH TANGUY ABBE BORGNE ABBE GALL ABBE GUEN ABBE LETTY ABBE LUGUERN ABBE QUEAU ABBE RICOU ABBE ROUDOT ABBES PETTON HABASQUE ABBESSE ABBES TANGUY ABBE TANGUY ABEILLES ABER ABER BENOIT ABER BENOUIC ABER ILDUT ABERS ABER WRACH ABRI MARIN ACACIAS ADIGARD ADOLPHE ALAVOINE ADOLPHE ASSI ADOLPHE GOAZIOU ADOLPHE PORQUIER PLOUJEAN AIGRETTES AIME COTTON AIMEE TALLEC AIME TALLEC AIRE L EVEQUE AIRELLES AJONCS AJONCS D OR ALAIN ALAIN BARBE TORTE ALAIN BERTHOU ALAIN BOUCHARD ALAIN CANIART ALAIN CAP ALAIN COLAS ALAIN DANIEL ALAIN BERGEVIN ALAIN KERGRIST ALAIN FERGENT ALAIN FOURNIER ALAIN GERBAULT ALAIN ALAIN LAY ALAIN MARTRET PREVILLE ALAIN QUINIOU ALAIN SIGNOR LAURENT JUSSIEU ALAVOINE ALBATROS ALBENIZ ALBERT ANDRIEUX ALBERT CAILLAREC ALBERT CAMUS ALBERT MUN ALBERT EINSTEIN ALBERT BRUN ALBERT ALBERT LOUPPE ALBERT POCHAT ALBERT ROLLAND ALBERT ROUSSEL ALBERT SAMAIN ALBERT SCHWEITZER ALBERT THIBAUDET ALBERT THOMAS ALBERT YVINEC ALBINONI ALDERIC LECOMTE GUEN ALENO ALOUARN ALEXANDRE BALEY ALEXANDRE BRETHEL ALEXANDRE DUMAS ALEXANDRE FLEMING ALEXANDRE LEMONNIER ALEXANDRE MASSERON ALEXANDRE MILLERAND ALEXANDRE PRADERE NIQUET ALEXANDRE RIBOT ALEXANDRE TRAUNER ALEX INIZAN ALEXIS CARREL ALEXIS CLAIRAUT ALEXIS MOIGNE ALEXIS OLIVIER VERCHIN BINET COURCY MUSSET VIGNY DODDS JARRY RAY MUSSET NOBEL ALGESIRAS ALGUES ALICE COUDOL ALIZES ALLAE ALLAIN AL LANN VERTE ALLENDE ALLIES ALOUETTES ALPHONSE DAUDET ALPHONSE LAMARTINE ALPHONSE RAZER ALPHONSE SALAUN ALSACE ALSACE LORRAINE AMBROISE CROIZAT AMBROISE PARE AMBROISE THOMAS AMEDEE BELHOMMET AMEDEE MENARD AMEDEE ROLLAND AMERIGO VESPUCCI AMERS AMIRAL BAUDIN AMIRAL BAUGUEN AMIRAL CHARNER AMIRAL COUEDIC AMIRAL COURBET AMIRAL CRAS AMIRAL COATLOGON AMIRAL COUEDIC AMIRAL GUEYDON AMIRAL KERSAINT AMIRAL GRANDIERE AMIRAL D ESTAING AMIRAL D ESTREES AMIRAL D ORVILLIERS AMIRAL COUEDIC AMIRAL EMERIAU AMIRAL GRIVEL AMIRAL GUEPRATTE AMIRAL JAUREGUIBERY AMIRAL CRAS AMIRAL JULIEN COSMAO AMIRAL JURIEN GRAVIERE AMIRAL LABROUSSE AMIRAL LACAZE AMIRAL QUERRE AMIRAL LINOIS AMIRAL MORARD GALLE AMIRAL MUSELIER AMIRAL NICOL AMIRAL NIELLY AMIRAL REVEILLERE AMIRAL ROMAIN DESFOSSES AMIRAL RONARC H AMIRAL RONARCH AMIRAL SALAUN AMIRAL THIERRY D ARGENLIEU AMIRAL TROUDE AMIRAL VALLON AMIRAL VRIGNAUD AMIRAL ZEDE AMIRAUX AMPERE AMUNDSEN AN AOD AN AOD VENELLE AN AOD WENN ANATOLE FRANCE ANATOLE BRAS ANATOLE BRAZ ANCIENNE VOIE FERREE ANCIENS COMBATTANTS ANCIEN VOIE FERREE ANDRE ABIVEN ANDRE ANTOINE ANDRE BERGER ANDRE BRETON ANDRE CHENIER ANDRE CHEVRILLON ANDRE COLIN ANDRE CORVEZ ANDRE DERAIN ANDRE EVEN ANDRE FOY ANDRE GARREC ANDRE GIDE ANDRE GUILCHER ANDRE NOTRE ANDRE L HELGOUACH ANDRE MAGINOT ANDRE MALRAUX ANDRE MAUROIS ANDRE MERIEL BUSSY ANDRE MESSAGER ANDRE MILLOUR ANDRE PORTAIL ANDRE SIEGFRIED ANDRE THEURIET ANDRE TREVIDIC ANEMONES AN ESTREVET VOAN ANGE GUERNISAC ANGELA DUVAL ANGEL DOLCI ANGELE VANNIER AN HENT HOUARN ANITA CONTI ANJELA DUVAL ANNA BIZIEN ANNALOUSTEN ANNE BRETAGNE ANNE MESMEUR ANNE GALL ANNE JAVOUHEY ANNE SELLE ANSE ANTER HENT ANTOINE CARIOU ANTOINE HUBAUDIERE ANTOINE EXUPERY ANTOINE LAVOISIER ANTOINE NAU ANTOINE PARMENTIER ANTOINE RABY ANTOINE VITEZ AN TOUL DON ANTRE KEAR APOLLINAIRE ARAGO AR BALUSSEN AR BARZH KADIOU AR BRIEC ARCHIMEDE AR C HLOZIG AR DOUR VIAN AR FEUNTEUN AR FOENNEG VRAS AR GOAS VIAN ARGOAT AR GORE AR GROAS COZ AR GROAS VERR AR HOAJOU ARISTIDE BERGES ARISTIDE BRIAND ARISTIDE LUCAS ARLETTY ARMAND CARVAL ARMAND CHATELLIER ARMAND FALLIERES ARMAND PEUGEOT ARMAND ROBIN ARMAND ROUSSEAU ARMAND SEGUIN AR MARQUIS AR MEINEIER AR MENHIR LEHAN ARMISTICE AR MOOR ARMOR AR MOR ARMORIQUE ARMOR PORSGUEN ARNAUD THOYA ARNOULT AR PARK AR AR POUL GLEUT VIAN AR PRAT AR PUNS AR PUSSOU ARSENE D ARSONVAL ARSENE KERSAUDY AR SONER AR STEIR AR STER AR STREAT NEVEZ ARTHUR HENDRYCKS ARTHUR HONEGGER ARTHUR RIMBAUD ARTIMON AR VEIL AR VEIL VOURCH AR VENNIG AR VERET AR VIGOUDEN AR VILIN GOZ ARVOR ASCOET ASTOR ATHABASKAN ATHABASKAN PORSMEUR ATLANTIQUE AUBEPINES AUDIERNE AUDRAN AUDREN KERDREL AU FIL AUGUSTE ANTOINE THOMAZI AUGUSTE AVRIAL AUGUSTE BERGOT AUGUSTE BRIZEUX AUGUSTE CAROFF AUGUSTE COMTE AUGUSTE DELAUNE AUGUSTE DUPOUY AUGUSTE MAYER AUGUSTE GOY AUGUSTE KERNEIS AUGUSTE KERVERN AUGUSTE MERCEUR AUGUSTE PERRET AUGUSTE RENOIR AUGUSTE RICHARD AUGUSTE RODIN AUGUSTE SALAUN AUGUSTE TERTU AUGUSTE VERMOREL AUGUSTIN CAUCHY AUGUSTIN BOISANGER AUGUSTIN FRESNEL AUGUSTIN MORVAN AUGUSTIN THIERRY AU LIN AULNE AUX EAUX AUX OEUFS AVEL AR BENN AVEL DRO AVEL HEVRED AVEL HUEL AVEL MOOR AVEL MOR AVEL STERENN AVEL VOR AVEL WALARN AVENIR AVIATEUR BRIX AVIATION AYMARD BLOIS AZALEES BADEL BAIE BAILLY BAIL MEIGNANT BALANEC BALTZER BALY PLUFERN BALYSAN BALZAC BANNALEC BARA BARADOZIC BAR AL LANN BARBES BARBIER LESCOAT BARZAZ BREIZ BASSE BATZ BAUDELAIRE BAYARD BEARN BEAUMANOIR BEAUMARCHAIS BEAUPRE BEAU RIVAGE BEAUSEJOUR BECASSINES BECQUEREL BEDERNAU BEETHOVEN BEG AR COMPAZ BEG AR GROAS BEG AR GROGN BEG AR RESTAURANT BEG AR ROZ BEG AR VECHEN BEG AR VIR BEG AVEL BEG FOZ BEG MENEZ BEG MINEZ BEG TAL GWARD BEL AIR BELERIT BELETTE BELLA BARTOK BELLE ANGELE BELLE RIVE BELLEVUE BELLE VUE BELLONTE BELON BELVEDERE BENIGUET BENJAMIN CONSTANT BENJAMIN DELESSERT BENJAMIN FEBVRIER BENJAMIN FRANKLIN BENNIGET BENOIT MALON BENOIT NORMANT BENVENUTO CELLINI BERANGER BERENES BEREVEN BERGE BERGERE BERGSON BERLIOZ BERNANOS BERNARD ANSQUER BERNARD MOELAN BERNARD PALISSY BERNARD SCHEIDAUER BERRIEN BERTHEAUME BERTHELOT BERTHE SYLVA BERTHOLLET BERTRAND D ARGENTRE BERTRAND FRECHE BERTRAND RUSSEL BERVEN BESQUIEN BETHANIE BETHEREL BEUZIT HUELLA BEUZIT IZELLA BIDERGUEN BIENVENUE BIGOT MOROGUES BIHAN BILOU BIR HAKEIM BISSON BLAISE CENDRARS BLAISE PASCAL BLAVEAU BLERIOT BLES D OR BLEUETS BLEUN BRUG BOBBY SANDS BOCAGE BOD ILIO BOD ILLIO BODINIO BODONOU BOENNEC BOHARS AR COAT BOIELDIEU BOILEAU BOIS AMOUR D AMOUR KERESTAT LAMBEZELLEC PIN KERESTAT NEUF BOISSIERE BON COIN BONNE NOUVELLE BONNE RENCONTRE BONNETS ROUGES BORDA BORIS VIAN BOSCO BOSQUET BOSSUET BOT ON BOT QUELEN BOTREL PORSGUEN BOTSCO BOUCHERIES BOUDOULEC BOUESTARD TOUCHE BOUET BOUGAINVILLE BOUILLON BOUILLOUR BOULOUARN BOURDELLE BOURDONNAIS BOURG BOURG BOURGS BOURGNEUF BOURHIOL BOURHIOL VRAS BOURLA BOURNAZOU BOURRELIER BOURVIL BOUS BOUSSINGAULT BOUT BOUTINOU BOUVET BRAHMS BRAILLE BRANDA BRANLY BRASPARTS BREBIS BREHAT BREIZ IZEL BREMODER BREMOND D ARS BRENILOUR BREST BREST BEURRERIE BRETAGNE BREZEHAN BRIEC BRISCOUL BRIZEUX BROCELIANDE BRODEUSES BROSSOLETTE BROUAN BROUSSAIS BROUZIC BRUAT BRUNETIERE BRUXELLES BRUYERE BRUYERES BUDOU BUFFON BUGEAUD BURDEAU BUTOU CABESTAN CABLES SOUS MARINS CADIOU CADORET CADOUDAL CAFFARELLI CAFFIERI CALE CALMETTE CALMETTE GUERIN CALONNEC CALVAIRE CAMBRY CAMELIAS CAMELINAT CAMILLE BERNIER CAMILLE CLAUDEL CAMILLE DESMOULINS CAMILLE FLAMMARION CAMILLE GUERIN CAMILLE LANGEVIN CAMILLE PELLETAN CAMILLE SAENS CAMILLE VALLAUX CANCOURT CANDY CANNEBIERE CAP CHEVRE CAP MENOU CAP HORN CAPITAINE BLANCHARD CAPITAINE COADOU CAPITAINE LARMINAT CAPITAINE MENOU CAPITAINE ESVAN CAPITAINE DREZEN CAPITAINE LELIEVRE CAPITAINE MENOU CAPITAINE VAISSEAU RENON CAPUCINES CAPUCINS CARDINAL COATIVY CARDINAL RICHELIEU CARELLOU CARHAIX CARMES CARN AR RESTAURANT CARN AR SCAO CARN AR SKAO CARN GUILLERMIC CARNOT CARN TY ROZ CARPEAUX CARPONT CARRIERE CASABIANCA CASSE FOI CASSINO CASTEL AN DOUR CASTELMEUR CASTEL MEUR CASTEL PHARAMUS CAVARDY CAWSAND CAWSAND KINGSAND CAYENNE CAZUGUEL CECILE RAVALLEC CEDRE CELESTIN FREINET CELESTIN SEITE CELTES CENDRES CENTRALE CENTRE BOURG CERISIER CERISIERS CERVANTES CESAR FRANCK CEZANNE CHAISE CHAISE CURE CHALONIC CHALUTIERS CHALUTIERS LANDMEUR CHAMPLAIN CHAMPS CHAMPS FOUR CHANOINE ABGRALL CHANOINE BELLEC CHANOINE BOSSENEC CHANOINE FALC HUN CHANOINE GUERMEUR CHANOINE KERBRAT CHANOINE CALVEZ CHANOINE RANNOU CHANOINE RANOU CHANOINE SALUDEN CHAP CHAPELLE CHAPELLE PRAT COULM CHAPELLENDY CHAPELLE CHAPELLE YVES CHAPENLLENDY CHAPTAL CHARCOT CHARDIN CHARDON COURCELLES CHARDONNERETS CHARDONS BLEUS BARBAROUX BARROIS BAUDELAIRE BERNARD BERTHELOT CHASSE CORCUFF COTTET BORTOLI COULOMB FOUCAULD FOUCAULT CHARLES GOFFIC D ORLEANS DRAPIER QUELENNEC EDOUARD FILIGER FOULON GAMBON GERARDIN GOUNOD GOUX HERNU JOURDE LAUTROU BASTARD GOFF GOFFIC GROS GUEN HIR MOULT LEVENEZ LINDBERGH LONGUET NODIER PEGUY ROLLAND TILLON VANEL VUILLEMIN CHARLIE CHAPLIN CHARLOTTE BRISSIEUX CHARRETTES CHATAIGNIERS CHATEAUBRIAND CHATEAUBRIANT D EAU GAILLARDIN CHATEAULIN CHATEAUNEUF CHATEAURENAULT CHATEAUX CHAUMIERES CH EDOUARD DESERTS DAMES VERT CHENES CHENES VERTS C HENRY SIMON CHERCHEUR CALMETTE GUERIN CHERE CHERTEMPS SEUIL CHEVALIER D ASSAS CHEVALIER PERCEVAL CHEVREUILS CHEVREUL CHOISEUL CHOPIN CHOQUET LINDU CHOULDRY CHRISTOPHE COLOMB CHRISTOPHE MORVAN CIEUX CINQ CROIX CITE KERMARIA CITERNE CLARTE CLAUDE BERNARD CLAUDE BOURGELAT CLAUDE DEBUSSY CLAUDE FARRERE CLAUDE FORBIN CLAUDE GOASDOUE CLAUDE LANCELOT CLAUDE LAE CLAUDE PRAT CLAUDE MONET CLAUDE PERRAULT CLAUDE POUILLET CLEDER CLEGUER CLEGUER BIHAN CLEGUER TREZ HIR CLEMENT ADER CLEMENT MAROT CLEMEUR CLOCHETTES CLOITRE CLOS CLOSMEUR CLOUET CLOZ NEVEZ COAT AN ABAT COAT AN ED COAT AR BOURG COAT AR FAOU COAT AR GUEVEN COAT AR MAILLOUX COAT AR PIQUET COAT BIAN COAT BIHAN COAT BRAS COATDIC COAT FAO COATIGARIOU COATILOUARN COAT IVIN COAT KAER COAT KEAR COAT KERROUS COAT KERVEAN COAT LOSQUET COAT MEZ COAT ONN COAT PIN COATUDAL COATUDAVEL COATUDUAL COETIVY COETLOGON COIN COLIN COLAS COLBERT COLETTE COLLEGE COLLINE COLONEL BERTHAUD COLONEL BERTHAUD BOURRIERES COLONEL CURIE COLONEL SOYER COLONEL DRIANT COLONEL FABIEN COLONEL FAUCHER COLONEL FONTFERRIER COLONEL PICOT COLONEL RAOUL LANCIEN COLONEL SICAUD COLONNES JUSTICE COLS VERT COLS VERTS COMBATTANTS U F COMBOUT COMMANDANT AVRIL COMMANDANT CARFORT COMMANDANT CHARCOT COMMANDANT COUSTEAU COMMANDANT EDMOND CARON COMMANDANT FERNAND COMMANDANT LAMY COMMANDANT JEUNE COMMANDANT THUILLIEZ COMMANDANT L HERMINIER COMMANDANT LOUIS FRIANT COMMANDANT LUCAS COMMANDANT MAILLOUX COMMANDANT MALBERT COMMANDANT MAURICE CHALLE COMMANDANT MOGUEROU COMMANDANT NATALINI COMMANDANT SOMME PY COMMANDANT TISSOT COMMANDANT TOUL COMMANDANT VIOLANT COMORANS COMTESSE BLANCHE COMTESSE CARBONNIERES COMTESSE NOAILLES COMTESSE SEGUR COMTESSE RODELLEC PORTZIC CONCARNEAU CONDE CONDILLAC CONDORCET CONEN LUC CONNETABLE RICHEMONT CONSEIL CONSEILLEROU CONSTANT LANCIEN CONSTANT ROUDAUT COQ HARDI COQUELICOTS CORENTIN BARON CORENTIN BOZEC CORENTIN CARIOU CORENTIN CARRE CORENTIN CELTON CORENTIN GUILLOU CORENTIN BERRE CORENTIN FLOCH CORENTIN PERENNES CORMORANS CORN AR GAZEL CORNEILLE CORNIC CORNIC DUCHENE CORNICHE CORNICHE PORS POULLAN CORN LAN CORNOUAILLE CORNOUAILLES CORN YAR COROT CORRE CORSAIRES CORVETTE PRIMAUGUET COSMAO DUMANOIR COSMAO PRETOT COSQUER COSQUER LARVOR COSQUEROU COSQUIES COSTES COTEAU COTEAU KERANGLIEN COTEAU TREZ HIR COTE LEGENDES COTTIN COULOMB COURBET COURCY COURLIS COURTE COURTILS COUSTEAU COUVENT COZ CASTEL COZ LANNOC CRAN CRANIC CRANN CRANTOCK CRANTOK CRAONENIC CRAZ CREAC H CREACH CREACH ALLAN CREAC AVEL CREAC AVEL VRAZ CREAC QUELEN CREACH AR BOTRED CREACH AVEL CREACH DUAN CREACH HUELLEN CREACH JOLY CREACH MEAN CREACH MOYEC CREIZ KERIGOU CREOLE CREVETTES CRINOU CROAS AN DOFFEN CROAS AN TOUR CROAS AR BLEON CROAS AR GARREC CROAS AR GOAS CROAS AR GORET CROAS AR GORRET CROAS AR POULLOU CROAS AR RHEUN CROAS AR RHEUN RIDINY CROAS AR SKRILL CROAS AR VEYER CROAS BOULIC CROAS CAER CROAS HIR CROAS LOHOU CROAS MALO CROAS MAZO CROAS MEN CROAS PRENN CROAS STANG VENELLE CROAS TALUD CROAS TOR CROAS VER CROAZ NEVEZ CROAZOU CROEZIOU CROISEUR EMILE BERTIN CROISSANT CROIX CROIX AR POULLOU CROIX AU LIN CROIX BLANCHE CROIX MISSION CROIX FLEURS CROIX MISSIONS CROIX DONNARD CROIX DONNART CROIX FLEURS CROIX MISSION CROIX ROUGE CRUGUEL CUIRASSE BRETAGNE CULLOMPTON CURIE CYGNES CYPRES D ABOVILLE D AHES DAHUT D AIGUILLON D ALEMBERT D ALGERIE D ALSACE DAMANY D ANAUROT DANIELE CASANOVA DANIEL FERY DANIELLE CASANOVA DANIEL FLANCHEC DANIEL MESSIA DANIELOU DANIEL TRELLU D ANJOU DANTE ALIGHIERI DANTON D AQUITAINE D ARGOAT D ARMAGNAC D ARMOR D ARMORIQUE D ARSONVAL DARTMOOR D ARTOIS D ARVOR D ARZANO D AUDIERNE DAUMESNIL DAUMIER DAUPHINE DAURAT D AUVERGNE DAVID D AVRANCHES BALANEC BANNEC BAZEILLES BEARN BEG AR FRY BEG AVEL BEL AIR BELIZAL BELLE ILE EN MER BENIGUET BEREVEN BIR HAKEIM BIROU BODMIN BOHARS BOTLAN BOUGAINVILLE BOURGOGNE BRASPARTS BRECON BREHAT BREHOULOU BREST BRINGALL BRINGALL HUELLA BROCELIANDE BRUGES BRUXELLES DEBUSSY CAEN CALLAC CAMARET CASTELNAU CHAMPAGNE CHARENTE CHATEAULIN CHATELLIER CHERBOURG CHINON CLOHARS CLONAKILTY COADIC MEZ COAT AN ABAD COATANER COAT AR GUEOT COAT COAT CHAPEL COAT CONGAR COAT DERO COAT EDERN COAT FAOU COATIDREUX COATIGOFF COATIGRACH COAT KUELEN COAT LAND COAT MANACH COAT MEZ COAT OMNES COAT QUESTEN COAT TAN COAT TANGUY COATUELEN COATUFAL COETLOGON COIGN AR YAR COLGUEN COLMAR COMMANA CONCARNEAU CORAY CORNOUAILLE CREAC AL LAN CREAC CADIC CREAC MARIA CROAS AMANDY CROAS AR GAC CROAS TEO CROAZ CHUZ DAWLISH DENVER DEPOT DINAN DIXMUDE DOUARNENEZ DUNKERQUE DEESSE BRIGITTE ETANG FALKIRK FLANDRE FLESCOU FLEURUS FORN AR ZALL FOUGERES FRANCE KERBRAT FRANCHE COMTE GAND GARENNE AR GROAS VAR GARIGLIANO GARIGLIANO COQUILLAGES GARLODIC DEGAS GASCOGNE GASTE GLASGOW GOAREM GLAZ GOAREM GOZ GOAREM GUEON GORREQUER GOUESNACH NEVEZ GOUESNOU GRASSE GRISOLLES GUEBRIANT GUERNEVEZ GUILERS GUIMILIAU GUINGAMP GUISSENY GUYENNE HAUTE SAVOIE HIRGUER HUNFELD JERUSALEM KERABEC KERABECAM KERADENNEC KERADRIEN KERAFILY KERAGUEN KERALAS KERALIO KERALIOU KERALLAN KERAMBEC KERAMBELLEC KERAMBRIEC KERAMPENSAL KERAMPOIX KERAMPROVOST KERAMPUIL KERANCALVEZ KERANDON KERANDRAON KERANFURUST KERANGALL KERANGOFF KERANGUEO KER AN ILIS KERANMOULIN KERANQUERE KERANROUX KERANSIGNOUR KERARBIZOU KERARBLEIS KERARGALET KERARGAOUYAT KERARGROAS KERARMAZE KERASCOET KERATRY KERAVEL KER AVEL KERAVELLOC KERAVILIN KERBASGUEN KERBENOEN KERBERTRAND KERBERVET KERBIGUET KERBLOAS KERBONNEVEZ KERBORZOC KERBRAT KERBRIANT KERBRIZILLIC KERCADORET KERDANIEL KERDANNE KERDAVID KERDEVOT KERDIDRUN KERDUFF KEREDEC KEREDERN KERELAN KERELIE KERELISA KER ELISE KERELLE KERELLOU KEREMBLEIS KERENEC KERENTRE KERENTREE KERFEUNTEN KERFEUNTEUN KERFRAVAL KERGADIEN KERGANTEN KERGAOUEN KERGARET KERGARIOU KERGESTIN KERGLAS KERGLEUZ KERGOADIG KERGOAT KERGOFF KERGOMPEZ KERGONAN KERGONIAM KERGORENTIN KERGOURY KERGRACH KERGREACH KERGROAS KERGUELEN KERGUEN KERGUEREC KERGUIP KERHELENE KERHUEL KERHUELLA KERHUN KERICHEN KERIDREUX KERIGUEL KERIGUY KERILIS KERINAOUEN KERIOLET KERIONOC KERISTIN KERIVARCH KERIVIN KERIVOAL KERIVOAS KERIZAC VIAN KERJAOUEN KERJEAN KERJEGU KERJOAIC KERJOUANNEAU KERLAN VIAN KERLECU 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CITADELLE LA COLLINE COMMUNAUTE COOPERATIVE CORDELIERE CORNICHE CORVETTE CREOLE CROIX DELACROIX CROIX FLEURS CROIX NEUVE CROIX ROUGE DIGUE DOUFFINE DUCHESSE ANNE L FEE MORGANE FONTAINE FONTAINE AU LAIT FONTAINE AUX LOUPS FONTAINE BLANCHE FONTAINE FONTENELLE FORET FORGE FOSSE AUX LOUPS FOUASSERIE FRANCE COMBATTANTE FRATERNITE GALICE GARENNE GROTTE HALLE HAUTIERE JOIE LANDE LANDELLE LIBERATION LIBERTE L ALMA LOUISIANE MADELEINE MAISON BLANCHE MAISON PAILLE MARINE MARNE LAMARTINE MARTINIQUE LAMBOUR MEDEE MELPOMENE METAIRIE METAIRIE NEUVE MONTAGNE MOTTE LAMPAUL LANADAN HUELLA L ANCIENNE FORGE L ANCIENNE LANDEVENNEC LANDIVISIAU L ANJOU LANNILIS LANNINGUER LANNION NOUVELLE CALEDONIE LANRIEC LANRIVINEC L ANSE SAUPIN LANVEN LANVEOC LANVIAN PAIX PALESTINE PAPETERIE PASSERELLE PECHERIE PENFELD CHESNAIE MONTAGNE PALUD PISCINE PLAGE PLAINE POINTE PORTE PORTE ROUGE POSTE PRAIRIE PROVIDENCE PSALETTE L AQUEDUC ROMAIN RADE REPUBLIQUE RESISTANCE L ARGOAD L ARGOAT L ARGONNE RIVE L ARMORIQUE SABLIERE SALETTE SALLE SOMME SOURCE L ASTROLABE SURVEILLANTE TANNERIE TERRE NOIRE L ATLANTIQUE TOUR TOUR AUVERGNE TOUR D AUVERGNE TOURELLE TRINITE TROMENIE LATTRE TASSIGNY L AUBEPINE LAUBERLACH L AULNE L AUNIS LAUTREAMONT VALLEE VENDEE L VICTOIRE VIERGE NOIRE D YS VILLEMARQUE VILLENEUVE L EAU BLANCHE L L VOILE L ECOSSE LEDENES L EGALITE L L ELLE L ELORN L ENFER LEN GOZ LESCAO LESCORS LESNEVEN L ESPOIR LESQUIFFINEC L EST LESTONAN VIAN L ESTUAIRE L ETAIN L ETANG LEUR AR VEIL LEURGUERIC L EUROPE L EXODE L HARTELOIRE L HIPPODROME L HIRONDELLE L HOPITAL L HOPITAL FREMEUR L HORN L HOSPICE L HYERES LIEGE L ILE AUX MOINES L ILE AUX MOUTONS L ILE BENIGUET L ILE CALLOT L ILE D ARZ L ILE BALANEG L ILE BATZ L ILE BREHAT L ILE GROIX L ILE DESIRADE L ILE SEIN L ILE D HOEDIC L ILE D HOUAT L ILE LOCH L ILE GARO L ILE LONGUE L ILE GALANTE L ILE MAYOTTE L ILE MOLENE L ILE RONDE LILLE L IROISE LISKEARD L ISOLE LISTROUARN LITTIRY D ELLIANT L OBSERVATOIRE L OCEAN LOCRONAN LOCTUDY L ODET LOHIGOU L ORATOIRE L OREE LORIENT LORIOU L ORPHELINAT LORRAINE LOSCOAT LOST LENN LOSTALLEN L OUEST LUDUDU LUDUGRIS L UNIVERSITE LYON L YSER MADAGASCAR MAISSIN MANER AR C HOAT MARREGUES MEERBUSCH MEJOU BRAZ MELGVEN MELLAC MEN CREN MENEZ BIHAN MENEZ GAD MENEZ GROAS MENEZ KERGOFF MENEZ NOAS MENEZ MENEZ TY AR GALL MEN FALL MERVILLE MESCOUEZEL MESCOUEZEL VIAN MESDOUN MESGALON MESLOAGUEN MESMERRIEN MESNIGOALEN MESNOS MESSIDOU MESTRIDEN MESTUAL METZ MEZANTELLOU MILIZAC MINVEN DEMI QUARTIER MISSILIEN MOELAN MOGUERIEC DEMOISELLES MOLENE MORLAIX MOSTAGANEM NANCY NARVIK NEUVILLE NEVARS DENIS PAPIN NORMANDIE DENTELIERES DENTELLES DENTELLIERES DENTELLIERES LECHIAGAT PARC AR GROAS PARC AR HOAT PARC STEIR PARIS PARK AR MANER PARK MENEZ DEPARTOUT PENANGUER PEN AN PEN AN TRAON PEN AR C HOAT PEN AR CREACH PEN AR GARENNE PEN AR LIORZOU PEN AR MENEZ PEN AR PEN AR QUINQUIS PEN AR RUN PEN AR STANG PEN AR STEIR PEN AR STREAT PEN AR VALY PEN CARN PENFRET PENGUEREC PENHARS PENHOAT PENMARCH PENNANROZ PENNARUN PENQUEAR PENTHIEVRE 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BRIEUC ELOI EXUPERY GERMANS GUENOLE JALMES NAZAIRE SAINTONGE PHILIBERT POL LEON RIOU SAUVEUR THEGONNEC SAISY AJONCS AJONCS D OR ALBATROS ALGUES ALLIES SALONIQUE ALOUETTES SALTASH ANCIENS ABATTOIRS ANEMONES SANTEC ANTILLES ARCHIVES ARTIFICIERS AUBEPINES AUGUSTINS AULNES SAVOIE AZALEES BAIGNEURS BECASSINES BENEDICTINES BERGERONNETTES BLES D OR BLEUETS BOLINCHEURS BONNETS ROUGES BOUCHERIES BOUCHERS BOULEAUX BOULISTES BOUTONS D OR BOUVREUILS BREBIS BRODEUSES BRUYERES BUTINEUSES CABLES SOUS MARINS CAMELIAS CAPUCINES CAPUCINS CARMES DESCARTES CASTORS CEDRES CELTES CERISIERS CHAMPS CHARDONNERETS CHARDONS BLEUS CHARMES CHATAIGNIERS CHENES CHEVALIERS CLOCHETTES DESCLOUZEAUX COLIBRIS COLOMBES COLS VERTS COLVERTS COOPERATIVES COQUELICOTS COQUILLAGES CORDIERS CORMORANS CORSAIRES COURBES COURLIS CYGNES CYPRES DAHLIAS DARDANELLES DEMINEURS DENTELLIERES DEPORTES DOUVES DUNES ECOLES ECOSSAIS ECUREUILS EGLANTIERS EGLANTINES ELFES EMBRUNS ERABLES FAUVETTES FLANDRES FLEURS FONTAINES FORGERONS FOUGERES FRANCAIS LIBRES FREESIAS FREGATES FRENES FRERES GONCOURT FRERES FAUCHEUR FRERES LUMIERE FRERES MAZEAS FRERES PENNAMEN FRERES RANNOU FUSAINS FUSILLES 1944 FUSILLES GALETS GEMEAUX GENETS GENTILSHOMMES GERANIUMS GIROFLEES GLAIEULS GLENAN GLENANS GLYCINES GOELANDS GOEMONIERS GORGENNES GRANDES LANDES GRILLONS GRIVES GUETTEURS HALLES HERMINES HETRES HIRONDELLES HORTENSIAS HOSPITALIERS SIAM ILES ILES LOYAUTE ILES MARQUISES SILGUY DESIRE GRANET DESIRE LUCAS IRIS SIZUN JARDINIERS JARDINS JONQUILLES KORRIGANS LANDES LAURIERS LAURIERS PALMES LAVANDIERES LAVOIRS LILAS LUCIOLES LYS MACAREUX MAGNOLIAS MARAICHERS MARCHES MARGUERITES MARINS MARRONNIERS MARSOUINS MARTINETS MARTINS PECHEURS MARTYRS MENHIRS MESANGES MESANGES BLEUES MEUNIERS MIMOSAS MINIMES MOINEAUX MORTS MOUETTES MOULINS MOUSSES MYOSOTIS MYRTILLES NEREIDES NOISETIERS OEILLETS OISEAUX SOLIDARITE SOLOGNE ORFEVRES ORMES OUVRIERS PORT PAPILLONS PAQUERETTES PARTISANS PECHEURS PECHEURS KERHORRES PELICANS PERDRIX PERVENCHES PETITES SALLES PETRELS PEUPLIERS DESPIAU PIETONS PINS PINSONS PIVERTS PIVOINES PLAGES PLATANES PLOMARCH POMMIERS POUDRIERS PRAIRIES PRES PRES VERTS PRIMEVERES PROFESSEURS CURIE PUFFINS RECOLLETS REGUAIRES REMOULEURS REMPARTS RENARDS RENONCULES RESEDAS RESERVOIRS ROCHERS ROCHES BLANCHES ROITELETS ROMAINS ROSEAUX ROSES ROSIERS ROSSIGNOLS ROUGES GORGES SABLES BLANCS SABLES BLANCS PHARE SABOTIERS SAPINS SARCELLES SAULES SENNEURS SITTELLES SORBIERS SOURCES SPORTS STERNES SUREAUX SYCOMORES TAILLANDIERS STALINGRAD TAMARIS STANG AR C HOAT STANG ARGANT STANG AR LIN STANG BIHAN STANG COULZ STANG KUZET STANG RADEN TANNERIES TANNEURS TEMPETES TEMPLIERS TERRES NEUVAS STER VAD THUYAS D ESTIENNE D ORVES D ESTIENNES D ORVES TILLEULS TISSERANDS STRAJA STRASBOURG STREAT AR VEUR TROENES TULIPES URSULINES VAGUES VANNEAUX VANNIERS VENELLES VENETES VERDIERS VIEILLES MURAILLES VIEILLES URSULINES VIGNES VIOLETTES VIRE COURT TAHITI TALAROU DETENTE TERRE ADELIE TI HOUARN TOUBALAN TOUL AN AON TOULMENGLEUZ TOULON TOURAINE 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DONIZETTI D ORADOUR SUR GLANE D ORLEANS DOSSEN DOUAR AR DOUARLINEC DOUARNENEZ DOUAUMONT DOUE D OUESSANT DOUR BIHAN DOURDU DOURDUFF DOURDY DOURIC DOURIC AR GUEBEN DOURLOGOT DOURMEUR DOURVEIL DOUVES DOUZE SILLONS DRENIT DREON AN ILIS DR MME MARNIERE DROITS L HOMME 11 NOVEMBRE 1918 11 NOVEMBRE 18 JUIN 1940 19E REGIMENT D INFANTERIE 19 MARS 1962 2 EME RIC 4 AOUT 1944 8 MAI 1945 BAC BARADOS BARADOZ BARDE CUEFF BARON LARREY BATEAU TREMINTIN BEARN BEAUBOIS BEL AIR BELON BERRY BIGOUDIC BLAVET BOCAGE BODAN BOIS BLANC D AMOUR SAPINS BOT BOULOUARD BOURBONNAIS BOURGNEUF BOUT DU BRIGADIER CANN BUDOU BUGN CACATOIS CALVAIRE CAP CHEVRE CAPITAINE FERDINAND FERBER CAPITAINE GUYNEMER CAPITAINE PEZENNEC CAPRICORNE CARDINAL LAVIGERIE CARO CARPON CARPONT CARREFOUR CASTORET DUC D AUMALE DUCHAFFAULT CHAMP FOIRE CHAMP GOFF CHAMP MORVAN CHANOINE GRALL CHANOINE MOREAU CHANOINE SALUDEN CHANT OISEAUX CHAPEAU ROUGE CHATEAU D EAU DUCHEL FER DAMES CHERCHE MIDI DUCHESSE ANNE DUCHESSE ANNE BRETAGNE CHEVALIER KERMELEC 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GENERAL GIRAUD GENERAL GOURY GENERAL LAMBERT GENERAL LAMORICIERE GENERAL GENERAL FLO GENERAL MANGIN GENERAL MORVAN GENERAL MUSSAT GENERAL PAULET GENERAL WEYGAND GENOIS DUGESCLIN GORET GORREQUER GOUELOU GOULET BOURG FOC KERZU LARGE LAUNAY PORT DUGUAY TROUIN GUE FLEURI GUELMEUR GUEODET GUESCLIN DUGUESCLIN GUET GUILLY GUILVINEC GUIRIC GWELMEUR GYMNASE HAVRE HENT COZ JARLOT JUSTISSOU KEFF LAC LANGUEDOC LANNOC LANTEL LARGE LAVOIR LEGUER LEON LEONARD LEVANT LEZARDEAU LIA LIMOUSIN LIN LOCH LOUIS D OR LTDV PARIS LYCEE MAINE MANOIR MANOIR KERBEZEC MAQUIS MARECHAL VILLARS MARECHAL FOCH MARECHAL JOFFRE MARECHAL JUIN MARECHAL MARECHAL LYAUTEY MARECHAL MAUNOURY MARECHAL NEY MARECHAL VALEE MAROC DUMAS BERGER MEJOU MENEZ MENEZ GORRE MENHIR MENHIR COUCHE MENHIR PORZIC MENIC AN TRI PERSON MERDY MERLE BLANC MIDI MILLIER DUMONT D URVILLE MONT VALERIEN MORBIC MOROS MOULIN PAPIER POUDRE AUX COULEURS VENT BLANC KERNIGUEZ CHEVRE SUETTE MELGVEN MARANT DENIS VERT MOUSTIER MUGUET MUR MUSEE PREHISTORIQUE NECKAR 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TIREURS FRANKLIN ROOSEVELT FREAU FREDERIC CHOPIN FREDERIC JOLIOT FREDERIC JOLIOT CURIE FREDERIC GUYADER FREDERIC MISTRAL FREDERIC PLESSIS FREDERIC SAUVAGE FREDERIC THEISZ FREGATE BELLE CORDELIERE FREGATE BELLE POULE FREGATE BOUSSOLE FREGATE FLORE FREGATE LAPLACE FREGATE RENOMMEE FREGATE L ARETHUSE FREGATE L ASTROLABE FREGATE THETIS FREGATE VENGEUR FREGATE L INCOMPRISE FREGATES FREGATE SURVEILLANTE FREHEL FREMINVILLE F RENE CHATEAUBRIAND FRERE GELEBART FRERES BOURHIS FRERES CHARRETEUR FRERES FLOCH FRERES JACOB FRERES JAMET FRERES GAC FRERES LEJEUNE FRERES L F TEMPLE FRERES OLLIVIER FRERES PENNEC FRERES ROGEL FRERES SCIALLOUX FRESNEL FRET FREZIER FROMVEUR FROSTIC FROUT FRUITIERS FUCHSIAS FULGENCE BIENVENUE FULTON FUR FUSILIERS MARINS FUSILLERS MARINS FUSILLES FUSILLES POULGUEN FUSILLES POULGUEN FUSILLES POULGUEN FUSTEL COULANGES GABARES GABRIEL BIZIEN GABRIEL CASTREC GABRIEL FAURE GABRIEL LAURENT GABRIEL SIGNE GABRIEL LIPPMANN GABRIEL PERI GABRIEL PIERNE GABY MORLAY GAI LOGIS GALAAD GALICE GALILEE GALISSONIERE GALLIENI GAMBETTA GANDHI GARAN AN AOD GARAN AN AOT GARCHINE GARD SIGN GARE NORD GARENN AR LOUATEZ GARENNE GARENNE AN AOD GARENNE AN DALAR GARENNE AR MANER GARENNE DREON GARENNE DREON KER GARENNE LOUP GARENNES NORD GAREN ZU GARO GARRECH GLAZ GARREC VENELLE GARROS GARS MARIA GASMARIA GASPARD COLIGNY GASPARD MAUVIEL GASTON CHABAL GASTON L HOPITAL GASTON DOUMERGUE GASTON ESNAULT GASTON RAMON GASTON VIARON GAUGUIN GAULOIS GAVARNI GAY LUSSAC GENERAL ARCHINARD GENERAL BARATIER GENERAL BETHOUART GENERAL BORGNIS DESBORDES GENERAL CHANGARNIER GENERAL CLEMENT HUNTZIGER GENERAL COLLIOU GENERAL DAMREMONT GENERAL CASTELNAU GENERAL GENERAL MAPA GENERAL PENFENTENYO GENERAL REALS GENERAL SONIS GENERAL TROBRIAND GENERAL DIEGO BROSSET GENERAL ELY GENERAL FAIDHERBE GENERAL GENERAL GOURAUD GENERAL GOURY GENERAL LAPERRINE GENERAL GENERAL FLO GENERAL MARCHAND GENERAL MARGUERITTE GENERAL PENFENTENYO GENERAL PIGEAUD GENERAL TROMELIN GENETS GENETS D OR GENEVE GENEVIEVE D HAUCOURT GENOT GENTILHOMMES GENTILSHOMMES GEOFFROY HILAIRE GEORGE GERHSWIN ADAM GEORGE SAND AUFFRET BERNANOS BERNARD BIZET BOULLET BRAQUE BRASSENS CADOUDAL CLEMENCEAU COURTELINE CUVIER DIDAILLER GUYNEMER HAMON KERBRAT BAIL MERDY NAELOU LEYGUES MANDEL MELIES MELOU MILINEAU PERROS POLITZER POMPIDOU ROUAULT ROUDAUT GEORGE WASHINGTON GERANIUMS GERARD NERVAL GERARD PHILIPE GERARD PONDAVEN GERMAIN MARTIN GICQUEL DESTOUCHES GILBERT RENAULT GLAIEULS GLEDIG GLENAN GLENANS GLENMOR GLEVIAN GLYCINES GOARDON GOAREM GOAREM AN AOD GOAREM BIAN GOAREM COZ GOAREM CREIS GOAREM DIHAN GOAREM DRO GOAREM GOZ GOAREM KERINOU GOAREM LAOUIC GOAREM MHEIN GOAREM NEVEZ GOAREM VEGUEN GOAREM VIAN GOARIMIC GOASANEYEC GOASANNEYEC GOASSANEYEC GOAZANNEYEC GOELANDS GOELETTES GOELO GOEMONIERS GOETHE GOLF GONIDEC GORRE AR C HOAT GORREQUEAR GORREQUER GORRET GORZ GOUDOUL GOUEROU GOUIN GOULVEN GOUNOD GOURANOU GOURIN GOYA GOYEN GOZ GRADLON GREVE HERMINE GRANDES ROCHES GRANDIERE LARGE GRANDS CHENES GRANDS SABLES GRANDS VIVIERS GRAVERAN GREAT TORRINGTON GREBES GREGOIRE CAM GRETRY GREUZE GREVE GREVE BLANCHE GRILLONS GRIMINEL GRIVART GRIVES GROAS PRENN GROIX GROUANNOC H GUELEBARA GUELMEUR GUENGAT GUENIOC GUEPIN GUERIN GUERLAC H GUERNEVEZ GUERRANNIC GUERVEUR GUERVIAN GUERZIT GUESNO GUEVEN GUEVEUR GUEVREN GUEZNO GUEZNO BOTSEY GUIC GUICHEN GUILBAUD GUILHEM GUILIGUI APOLLINAIRE BALAY BALEY BOUGEANT CHATEL KERSIMON KERSIMONT DUPUYTREN KERAUDY BRUN TOSCER GUILLEC GUILLEMOTS GUILVINEC DROITE GUIZIOU GUIZOT GULF STREAM GUSTAVE CHARPENTIER GUSTAVE CLUZERET GUSTAVE COURBET GUSTAVE EIFFEL GUSTAVE FLAUBERT GUSTAVE FRANCAIS GUSTAVE THOUDOUZE GUSTAVE TOUDOUZE GUSTAVE ZEDE GUSTAV MAHLER GUTENBERG GUY AUTRET GUY MAUPASSANT GUY LAOT GUY GUEN KERANGALL GUY NORMAND GUY MOCQUET GUY MOQUET GUYNEMER GUY PERON GUY RAOUL GUY ROLLAND GUY ROPARS GUY ROPARTZ GUY ROPARZ GWELL KAER GYMNASE HAENDEL HAIE HAIE BLANCHE HALTE TREBEUZEC HAOR HARAS HARTZ HAUTE HAUTE FONTAINE HAUTS L ETANG HAUTS RESTIC HECTOR AUDREN HECTOR BERLIOZ HELENE BOUCHER HELENE HASCOET HELLEN HELLES HEMON ROPARZ HENAN HENANT HENRI BARBUSSE HENRI BATAILLE HENRI BECQUE HENRI BECQUEREL HENRI BILLANT HENRI BORDEAUX HENRI BOURHIS HENRI CARIO HENRI CEVAER HENRI CROISSANT HENRI BOURNAZEL HENRI DELAVALEE HENRI DELAVALLEE HENRI LECLUSE HENRI MONTHERLANT HENRI REGNIER HENRI DUNANT HENRIETTE DAGORN HENRI FARMAN HENRI GOURMELEN HENRI GOURMELIN HENRI GUEVEL HENRI IV HENRI JACQUELIN HENRI LAUTREDOU HENRI HENRI GOFF HENRI SIDANER HENRI MATISSE HENRI MOISSAN HENRI MOREAU HENRI POINCARE HENRI PROVOSTIC HENRI QUEFFELEC HENRI ROE HENRI ROYER HENRI SALAUN HENRI SERGENT HENRI WAQUET HENRY CHEFFER HENRY GOURMELIN HENRY LAPERRINE HENRY MORET HENT HENT AR BROCH HENT AR C HOAT HENT AR FEUNTEUN HENT AR FIEAZEN HENT AR GRILLED HENT AR GROAS HENT AR LANN HENT AR VOURCH VRAZ HENT BEG AR FRI HENT BEG AR FRI DOUR HEOL HENT BIHAN HENT COAT PER HENT COZ HENT DON HENT GLAZ HENT GWEN HENT KAMM HENT KER HENT KOZ HENT PENN AR GUER HENT ROAZHON HENT SABL HENT SANT YANN HENT TOUL MOGER HENT TY 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JAURES LESCONIL JAURES GUENOLE JESTIN JEZEQUEL JULIEN JULIEN CUMIN JULIEN LEMORDANT LANCIEN LAOT PEROUSE LAUTREDOU BERRE BRUSQ COZ DAIN DUFF GALL LE GUEN GUEN KERANGALL GUIFF MENN LEMEUNIER LEOQUET LOUIS HENNOT LOUIS DELMOTTE LOUIS DIROU LOUIS GOARNISSON LOUIS GOFF LOUIS ROLLAND MACE MARC BIRRIEN CARER LAMENNAIS GELEBART GUILLOU KERMAREC BEC BRIS GALL SKOURR PEDEL MASSON MENEZ MERMOZ MERRIEN MICHEL CARADEC MONFORT MONNET MONTFORT MOREAU MORVAN JEANNE D ARC JEANNE BEAUREGARD JEANNE BELLEVILLE JEANNE FLAMME JEANNE MAISTRE OBERLE JAFFRES MARAT SARTRE PERRIN PHILIPPE RAMEAU PIAGET PICHAVANT BARBE CALLOCH DANIGO DEUNFF TIMBAUD QUEGUINER RACINE RAMEAU RENE MORVAN RICHEPIN RIOU ROBIC ROSPART ROSTAND SALIOU SAVINA SEBASTIEN BACH SEBASTIEN CORVELLEC TANGUY TEURROC TIRILLY TOTH TROMELIN V VICTOR RIOU VILAR XXIII YVES GUILLARD ZAY JEFF PENVEN JEHAN BAZIN JEHAN TOUR JEHAN LAGADEUC JEROME BERTHOMME JEROME PETION J BARCHOU PENHOEN JIM SEVELLEC J L GOFF J M HUON KERMADEC JOACHIM BELLAY JOBBE DUVAL 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PELLAY MARCEL PIROU MARCEL POTIN MARCEL PREVOST MARCEL PROUST MARCEL SALEUN MARCEL SCUILLER MARCEL SEMBAT MARCHANDS MARC HARID FULUP MARCHE MARCHE DELATTRE MARC SANGNIER MARC SCOUARNEC MARC SEGUIN MARECHAL CLAUZEL MARECHAL LATTRE MARECHAL LATTRE TASSIGNY MARECHAL FOCH MARECHAL FRANCHET D ESPEREY MARECHAL JOFFRE MARECHAL JUIN MARECHAL KOENIG MARECHAL MARECHAL PELISSIER MARENGO MARGUERITE DURAS MARGUERITES MARGUERITE YOURCENAR MARIA CHAPDELAINE MARIANO ANNE STEPHAN CHAPALAIN CURIE KERSTRAT CORDELIERE JACQUELINE DESOUCHES LENERU LITTRE LOUIS CLOAREC LOUISE BERNARD MILIN MARIGNAN MARINE MARINS FL MARINS PECHEURS MARION FAOUET MARIOTTE MARK TWAIN MARNE MARQUISE KERGARIOU MARTHE MAISONFORT MARTIN LUTHER KING MARTYRS MARX DORMOY MARYAN DESCHART MARYSE BASTIE MASSENA MASSENET MASSILLON MATELOT POCHIC MATHIEU DONNART MATHILDE DELAPORTE MATHILIN AN DALL MATHURIN MEHEUT MATHURIN THOMAS MAUDUIT DUPLESSIS MAUPERTUIS MAURICE BARRES MAURICE BLONDEL MAURICE BON MAURICE BROGLIE MAURICE DENIS MAURICE TRESIGUIDY MAURICE DONNAY MAURICE DUHAMEL MAURICE GENEVOIX MAURICE GUERIN MAURICE FLEM MAURICE LUC MAURICE SCOUARZEC MAURICE SCOUEZEC MAURICE MATHEY MAURICE NOGUES MAURICE MAURICE PIQUEMAL MAURICE RAVEL MAURICE STERVINOU MAX FAUCHON MAXIME CAMP MAXIME MAUFRA MAX JACOB MAX POL FOUCHET MAX RADIGUET MEANTOUL MECHOU MECHOUROUX MECHOUROUX PELLA MEDECIN GENERAL KERAUDREN MEDECIN GENERAL BERRE MEGALITHES MEIN TORET MEIN TORRET MEJOU MEJOU AN ILIS MEJOU BIHAN MEJOU GUEN MEJOU KER MEJOU VEIL MELANIE ROUAT MELLAC MENAIS MEN AN HID MEN CREN MEN CRENN MENDELSSOHN MENDES FRANCE MEN DON MEN MENE BARRIERE MENEC MENEYER MENEZ MENEZ AR C HLAN MENEZ AR PIQUET MENEZ AR VOURCH MENEZ BERROU MENEZ BIAN MENEZ BIHAN MENEZ BONIDON MENEZ BONIDOU MENEZ BOSS MENEZ BRAS MENEZ DREGAN MENEZ FROST MENEZ GORED MENEZ GOUYEN MENEZ HOM MENEZ HUELLA MENEZIC MENEZ IZELLA MENEZ KERGREACH MENEZ KERMADEC MENEZ KERSUGARD MENEZ KERVEADY MENEZ LUZ MENEZ MOOR MENEZ PLAINE MENEZ PLEN MENEZ ROUZ MENEZ ROZ MENEZ MENEZ TY AR GALL MENEZ TY LOR MENEZ VEIL MENFIG MENGLENOT MEN GUEN MENGUEN MENHIR MENHIR COUCHE MENHIRS MEN HOLLO MEN HOLO MENIC MEN MEUR MEN PLAD MER MERENCE LISRIM MERENCE LISRIN MERENCE LISRINS MERLIN MERMOZ MERMOZ CADOL MESANGES MESARNOU MESCOTY MESCURUNEC MESCURUNEC PENZE MESDOUN MESLE MESMENIOU MESMEROT MESNY MESPANT MESPERLEUC MESRENCE LISRIN MESSIBIOC MESSILIO MESSINOU MESSIRE KEREBEL MESTIOUAL METAIRIE METAIRIE KERNEVEL MEUNIERES MEZARNOU MEZ CREIS MEZEOZEN MEZMORVAN MEZOU AR MEIN MEZOU BRAZ MICHEL MICHEL ANGE MICHEL BAKOUNINE MICHEL BALTAS MICHEL BEHIC MICHEL CALVEZ MICHEL COLOMBE MICHEL CREACH MICHEL CORNOUAILLE MICHELET MICHEL FLOCH MICHEL HENRY MICHEL HERVE MICHEL KERBIRIOU MICHEL BARS MICHEL GARS MICHEL NOBLETZ MICHEL SAOUT MICHEL MARION MICHEL MORAND MICHEL OGEE MICHEL OLIVIER MICHEL YVONNOU MIDI MILIN MILIN AVEL MILLET MIMOSAS MIMOSAS TREZ HIR MINES MINEZ BERROU MINOTERIE MIORCEC KERDANET MIQUEL MIRABEAU MOABREN MOAL MOELAN MOGUEROU MOGUEROUX MOLE MOLENE MOLIERE MONET MONEZ AR MOR MONGE MON REPOS MONSEIGNEUR MARCHE MONSEIGNEUR MARHALLACH MONSEIGNEUR DUPARC MONSEIGNEUR GRAVERAN MONSEIGNEUR JOLIVET MONSEIGNEUR MARHALLAC H MONSEIGNEUR RAOUL MONSEIGNEUR ROLLAND MONTAGNARDS MONTAGNE MONTAIGNE MONTCALM MONTE AU CIEL MONTESQUIEU MONTFORT MONTMARIN MONTS ARREE MONTS D ARREE MONTS D ARREES MORGAN MORICE PARC MORLAIX MORVAN LEBESQUE MOTTE MOUCHEZ MOUDENNOU MOUETTES MOUETTES LESTRIVIN MOUETTES TREZ HIR MOUETTES TY PAVEC MOULIN CARN DOELAN D OR CHAT ROY KERAUDIERNE LANORGANT MER NEUF ROZHUEL MOULINS TREGUIER VERT MOUSTOIR MOUTONS MOZART MUR MURS MUSEE MYOSOTIS MYRDHINE MYRTILLES NAOT HIR NAPOLEON III NARCISSE QUELLIEN NARVAL NATHALIE MEL NATIONALE NATTIER NAUFRAGES 23 MAI 1925 NAVARIN NAVARROU NECKER NEHRU NEPTUNE NERHU NEUVE NEVARS NICEPHORE NIEPCE NICOLAS APPERT BOILEAU COPERNIC MALEBRANCHE LABAT LAINE POUSSIN NIFRAN NIOU NIZON NOBEL NODEVEN NOEL KERDRAON NOEL LERRANT NOEL L HERRANT NOEL ROQUEVERT NOIRE NOISETIERS NOMINOE NORBERT WIENER NORMANDIE NIEMEN NOROIT NOTRE DAME NOTRE DAME KERNITRON NOTRE DAME LORETTE NOTRE DAME BON PORT NOUARN NUNGESSER OBERHAUSEN OBSCURE OCEAN OCEANIDE OCEANIDES OCEAN LECHIAGAT OCTAVE AUBRY OCTAVE FEUILLET ODE AN DENVED ODET OEUFS OISEAUX OLIVIER CLISSON OLIVIER GOURIOU OLIVIER HENRY OLIVIER MERCELLE OLIVIER PELLAN OLIVIER PERRIN OLIVIERS OLLIVIER HENRI OLLIVIER PELLAN ONZE MARTYRS ONZE NOVEMBRE ORADOUR SUR GLANE ORATOIRE ORCHIDEES ORMES ORMES TREGANA OUESSANT OYATS PABLO NERUDA PABLO PICASSO PAIN BENI PAIX PALAIS PALERS PALESTRINA PALUD PALUD KERFRIANT PALUD COSQUER PALUE PALUE COSQUER PALUE KERFRIAN PALUE COSQUER PALUE KERFRIANT PAOTR TREOURE PAPE LEON XIII PAQUERETTES PARADIS PARC PARC ALLEC PARC AR PARC AR FEUNTEUN PARC AR HOAT PARC AR LEUR PARC AR ROUZIC PARC AR ROZ PARC BIHAN PARC BOREDEN PARC BRIEC PARC COZ PARC HUELLA PARCK LANN PARC LAN PARC LANN PARC MAITRE PARC MARCHE PARC MORVAN PARC PRIGENT PARC TY NEVEZ PARC ZALE PARC ZALEE PARIS PARK PARK AN DOURIC PARK AN GWINIZ PARK AR C HAVAREZ PARK AR FEUNTEUN PARK AR FOENN PARK AR HOAT PARK AR LOUARN PARK AR ROZ PARK AR VENGLEUZ PARK BRAS PARK BRAZ PARK BRUG PARK HIR PARK LANN PARK MENEZ PARK MENN MEUR PARK NONN PARK TRELLER PARK TROALEN PARMENTIER PAROU BRAS PARTISANS PASCAL PASCAL KERANVEYER PASCAL KERANVEYERE PASSAGE PASSEREAUX PASSERELLE PASTEUR PASTEUR W J JONES PASTORALE PATRIOTES PATRONAGE ALEXIS ROBIC AR PALUD BERNARD BERT BOROSSI BORROSSI BOURGET BURCKEL CEZANNE CLAUDEL FLOTTE DOUMER DUKAS ELUARD EMILE VICTOR PAULET FEVAL PAUL GAUGUIN GOARDON GOASGLAS HELIES HERVIEU LADMIRAULT LANGEVIN FLEM LEONNEC TALLEC MARZIN MASSON PHELEP POCHARD POURHIET SERUSIER TREGUER VALERY VERLAINE PAVILLONS P BROSSOLETTE P BROSSOLETTE BIGORN P BROSSOLETTE CROAS P BROSSOLETTE CROAS AVEL P BROSSOLETTE LESTRIVIN P BROSSOLETTE YVES PECHEURS PEISEY NANCROIX PELICAN PELLETIER PELLETIER D OISY PEMP HENT PEMP POULL PEN PEN AL LANN PEN AN NEAC H PENANROS PENANROZ PEN AN PEN AN THEVEN PEN AR BED PEN AR BUTTE PEN AR CHLEUZ PEN AR CREACH PEN AR GUEAR PEN AR GUER PEN AR HOAT PEN AR MEAN PEN AR MEAN TREZ HIR PEN AR MEEN PEN AR MENEZ PEN AR PARK PEN AR PEN AR PRAT PEN AR ROZ PEN AR STER PEN AR STREAT PEN AR THEVEN PEN AR VALY PEN AR VERN PEN AR VEUR PEN AR VIR PENDOULLIC PEN DUICK PEN ENEZ PENFELD PENFELD HUELLA PENFELD IZELLA PENFEUNTEN PENFEUNTEUN PENFOUL PENFOULIC PENFRAT PENFRET PENHADOR PEN HAT PENHOAT LANN PENKEAR PENKER PENKER POAZ PENKER TRAON PEN LAN PENLAND PEN LANN PENMARCH PEN MEN PENMOR PEN MORVAN PENNALE PENN AR LENN PENN AR PRAT PENN AR ROZ PENNECT PENN LAND PEN PAVE PENQUER PENTEVEN PENTY PENVEN PENVERN PENZANCE PENZE PERCEVAL PERDRIEL VAISSIERE PERDRIX PERE BENOIT PERE GELEBART PERE MAUNOIR PERENTE PER JAKES HELIAS PER JAKEZ HELIAS PERONNELLE ROCHEFORT PERONNIC PERROS PERROT PERRUGANT PERRUGAUT PERVENCHES PERZEL PETIT HAIE ROCHE VENELLE PORT PETRELS PEUPLIERS PHARE PHARE LOCHRIST PHILIPPE LEBON PHILOSOPHE ALAIN PICASSO PIC MIRANDOLE PICHODOU PICPUS PIE ABELARD ALLIER BELBEOCH BONNARD BRELIVET BRETONNEAU BRIAND BROSSOLETTE BROSSOLETTE BOURG BROSSOLETTE YVES CAPITAINE CARDUNER CAUSSY CORLE CORNEILLE CORRE CROC CURIE DANTEC BEAUMARCHAIS BELAY COUBERTIN DREUX FERMAT RONSARD DORNIC DOUILLARD DUPOUY CURIE GESTIN GOURLAOUEN GUEGUEN JAFFRET JEFFROY JESTIN JULIEN GILBERT KERNEIS LAROUSSE DAUX GUEN LEOSTIC LEROUX L HOSTIS LOHEAC LOSTIS LOTI MAC ORLAN TOUBOULIC OZANNE PATEROUR PELISSIER PENDELIO PERNES PERON PICARD POSTOLLEC PUGET QUILLEC RENE FELIX DAGORNE RICHARD RIQUET SANQUER SEMARD SEVRE TALCOAT TANNEAU TERMIER TREPOS VELLY VOLANT ZACCONE PILLOT PILOTE TREMINTIN PINS PINSONS PIRANDELLO PISCINE PITI GUEGUEN PITRE CHEVALIER PLACITRE PLAGE PLAGES PLAGES KERLURON PLAINE PLAN D EAU PLATEAU PLESTIN PLOMEUR PLONEVEZ FAOU PLOUEGAT PLOUESCAT PLOUEZOCH PLOUIGNEAU PLOURIN PLOZEVET PLUVIERS PLYMOUTH POINTE POINTE LECHIAGAT POINTE JULIEN POITOU POL AURELIEN POL COURCY POLITZER POL PASQUET POMMIERS PONANT PONCELET PONCELIN PONCHOU PONTADIG LOUET AR AR GROAS AR GUIP AR RESTAURANT AVEN BELLEC CALLEC COAGUEL COZ D ARM BUIS DINOU DU CHATEL DUET PONTIGOU ILIS JEGU L ABBE LEN LENN MENOU NELIS NELLIS NEUF NEVEZ PER PERONIC PONT POL ROHEL PONTRUCHE SAINTE MARTHE PONTSCORFF POPULAIRE PORIGUENOR PORLAZOU PORS AN TOUCH PORS AR BAYEC PORS AR MARC H PORS AR STREAT PORS AR VILLIEC PORS BOURDEL PORS BRAS PORSCAVE PORSCAVE LAMPAUL PORSCAVEN PORS CLOS PORSCUIDIC PORS EGRAS PORS GROUEN PORSGUEN PORSGUERN PORS HAOR PORS KERVEN PORS KORENTIN PORS LAOUEN PORS LEON PORS LOUARN PORS MANOIR PORSMILIN PORS MISCLIC PORSMOGUER PORSPAUL PORSPODERIOU PORSPODIROU PORSPOL PORS POULHAN PORS RIAGAT PORSTREIN LAPIERRE PORT PORT LARVOR PORTE PORTE VEZINS PORTEZ PORT FORET PORT LARVOR PORT POULDU PORT MANECH PORT PORTSALL PORTSALL GOZ PORTZMOGUER PORZ AR VAG PORZAY PORZ CASTEL POSTE POTERIE POUL AN OD POUL AN ODD POUL AN VERN POUL AR POUL AR BLOUCH POUL AR GANOL POUL AR GOASY POUL AR GOAZY POUL AR GRAZ POUL AR MANCHEC POUL AR PALUD POUL AR RANET POUL AR SAF POUL AR STANG POUL BLEIZ POUL BRIANT POULBROHOU POUL CAILLO POULDREUZIC POULDU POULFANC POUL FOEN POULFOS POULGUEGUEN POULHERVE POULHI POULIE POULIZAN POUL LAND POULLAOUEC POULLEACH POULLEN POULLIC AL LOR POULLOU POULLOU AOT POULLOUROU POULLUEN POULMALOTTEN POULMARC H POULMIC POULMOAN POULOU POULOUPRY POULPEYE POULPRY POULQUER POUL RHU POUL ROARCH POULY POULZERBE POULZERBE TREZ HIR POUNT AR C HANTEL PRAD PRAD KELENN PRADOC PRAIRIE PRAIRIES PRAJOU GWEN PRAT PRAT AN AOD PRAT AN ASQUEL PRAT AN EOL PRATANIROU PRAT AR CHARMILLE PRAT AR C HREN PRATAREUN PRAT AR FEUNTEUN PRAT COULM PRAT CUIC PRAT LAOUALC H PRAT LOUALC H PRAT MENOC PRAT MENOCH PRAT MEUR PRAT PANN PRAT PER PRE PRE BAS PREFET ALDERIC LECOMTE PREFET COLLIGNON PREFONTAINE PRES PRESBYTERE PRESIDENT ALLENDE PRESIDENT KENNEDY PRESIDENT SADATE PRES J F KENNEDY PRESQU ILE PRES TREVIDY MAISON PRIEURE PRIMAUGUET PRIMEL PRIMEVERES PRINCE JOINVILLE PRINCESSE DAHUT PRINCIPALE PRISON PROFESSEUR ALAIN PROFESSEUR CHRETIEN PROFESSEUR LANGEVIN PROFESSEUR LERICHE PROFESSEURS CURIE PROGRES PROGRES POULGOAZEC PROMENADE PROSPER MERIMEE PROSPER PROUX PROSPER SALAUN PROUST PROVENCE PROVIDENCE PUEBLA PUIG RITALONGI PUIT PUITS PUVIS CHAVANNES PYRAMIDE PYRAMIDES QIMPER QUAI QUARTIER GOLVES QUARTIER MAITRE BONDON QUARTIER NEUF QUATRE BRAS QUATRE VENTS QUEBEC QUEGUINER QUELARGUY QUELERN QUEMENES QUEMEURLANEC QUERE QUERRIEN QUESTEL QUILINEN QUILLIEN QUILLIMERIEN QUILLIVIC QUIMPER QUIMPER KERVERN QUIMPERLE QUINQUIS RABELAIS RACINE RADENOC RAGUENES RAIL RAMEAU RAMPE RAMZ KOZ RANDON RANTERBOUL RAOUL ANTHONY RAOUL DAUTRY RAOUL DUFY RAPHAEL RAPHAEL XAVIER QUIDEAU RAVAGNON RAYMOND ARON RAYMOND COUILLANDRE RAYMOND GUENET RAYMOND JEZEQUEL RAYMOND CORRE RAYMOND PALU RAYMOND POINCARE RAYMOND STEPHAN REAUMUR RECTEUR MOAL REFUGE REGIMENT NORMANDIE NIEMEN REIMS REMBRANDT REMOULOUARN REMPARTS REMPARTS HAUTS QUELERN REMY BELLEAU REMY COPPIN REMY LAY RENANGUIP RENE AUTRET RENE BARJAVEL RENE BAZIN RENE BERTHELOT RENE BIHANNIC RENE CAILLE RENE CASSIN RENE CLAIR RENE CLEMENT RENE COADOU RENE COGUEN RENE DANIEL RENE CHATEAUBRIANT RENE KERALLAIN RENE DESCARTES RENE GAUBIN RENE GOUBIN RENE GUILLAMOT RENE GUY CADOU RENE GUY KADOU RENE HUIBAN RENE LAENNEC RENE BERRE RENE BOMIN RENE HAMP RENE MAO RENE MADEC RENE QUILLIVIC RENE QUINTRIC RENE TURQUET RENE YVES CRESTON RENE ZAZZO RENOIR REPUBLIQUE RESISTANCE RESKEN RESTAURANT RESTIGOU RETALAIRE REUN AR CHOAT REUNION REVELOUR REVEREND PERE MASSE RHEUN RHODODENDRONS RHU BIAN RHUN AR VUGALE RHUN PREDOU RICHELIEU RICHER RICOU RIDINY RIDOU RIVE RIVERIEUX RIVOLI RUERNO ROBERT KERGROADES ROBERT DOISNEAU ROBERT FROGER ROBERT JOUBIN ROBERT JOURDREN ROBERT GUINER ROBERT MAO ROBERT PLANQUETTE ROBERT RICCO ROBERT SCHUMAN ROBERT SCHUMANN ROBERVAL ROBESPIERRE ROBINSON ROCHAMBEAU ROC AVEL ROC EOL ROC MELEN ROC VENN ROCH AR RUGUEL ROCH AVEL ROCH ROCHE ROCHE CINTREE ROC HELLOU ROCH EOL ROCHERS ROCHES ROCHES BLANCHES ROCHES DIABLE ROCHE TREMBLANTE ROCH GLAZ ROCH KROUM ROCHON ROCH VENELLE RODIN RODOLPHE KOECHLIN ROGER DIQUELOU ROGER MARTIN GARD ROGER PENEAU ROGER QUINIOU ROGER SALENGRO ROGER SIGNOR ROGER THOMAS ROGER VERCEL ROHENNIC ROI GRADLON ROI MARC H ROITELETS ROLAND DORE ROLAND GARROS ROLZAC H ROMAIN DERRIEN ROMAIN DESFOSSES ROMAIN GARY ROMAIN MALASSIS ROMAIN ROLLAND ROMAIN ROLLAND LESCONIL ROMARIN ROMY SCHNEIDER RONSARD ROPARTZ MORVAN ROQUEFEUIL ROQUEFEUILLE ROSAMPONT ROSAMPOUL ROSBIGOT ROSCAROC ROSCOGOZ ROSEAUX ROSE VENTS ROSE VENTS LANMEUR ROSEMONDE GERARD ROSENBERG ROSERAIE ROSES ROSES VENTS ROSIERE ROSIERS ROSIERS BOURG ROSILY MESROS ROSMADEC ROSNOEN ROSPORDEN ROSSINI ROSTIVIEC ROSVILY ROUDOUALLEC ROUDOUR ROUDOUS ROUEDOU ROUERGUE ROUGET L ISLE ROUGET LISLE ROUZ ROZ ROZAMBIDOU ROZ AN DOUR ROZ AON ROZAON ROZ AR GALL ROZ AR GOFF ROZ ARM ROZ AR ROZ AR VERN ROZAVEL ROZ AVEL ROZAVOT ROZENDERO ROZEVEN ROZIC ROZIERE ROZILIO RUBALAN RUBEREL RUBIAN RUDYARD KIPLING RUELLOU RUERNO RUFA RUFOLIGOU RUGOLVA RUGUNAY RUJEAN RUKERDREIN RULAZ RULIANEC RUMEUR RUN AR GROAS RUNAVALEN RUNESCOP RUSTEPHAN RUVEIL SABATIER SABLE BLANC SABLES BLANCS SABLES BLANCS LARVOR SABOTIER SABOTIERS SACRE COEUR SADI CARNOT SAINT ALOR ALPHONSE ANNE ANTOINE AUGUSTIN BREVALAIRE BRIEUC BUDOC CHRISTOPHE CONOGAN CORENTIN DEMET DOMINIQUE DREYER SAINTE ANNE SAINTE ANNE EUTROPE SAINTE AZENOR SAINTE BARBE SAINTE BERNADETTE SAINTE BEUVE SAINTE BRIGIDE SAINTE CATHERINE SAINTE EVETTE EGAREC SAINTE GENEVIEVE SAINTE GWEN SAINTE MARGUERITE SAINTE MARINE SAINTE MARTHE SAINTE MERE ERGAT ESPRIT SAINTE THERESE EVETTE EXUPERY FIACRE GERMAIN GILDAS GILLES GUENAL GUENOLE GUETHENOC GURLOES HERVE IDUNET JACOB JACQUES SAINT BAPTISTE SALLE JEROME JOSEPH JULIEN JUST KE LAURENT LEGER LEONARD LO LUC LUCAS LUC QUIMERCH MALO MARC MARTIN MATHIEU MAUDET MAURICE MELAINE MICHEL PATERNE ROUX PHILIBERT SAINT PIRIC POL POL LEON POL ROUX QUENTIN QUIJEAU RATIAN RENAN RIOC ROCH SACREMENT SAENS SALOMON SANE SAUVEUR SEBASTIEN SESNY SEVERIN SEVERIN TY NEVEZ SIMON TELO THEGONNEC THELAU THELO THIVISIAU THOMAS THUAL THUDEG THUDON THURIEN TREVEL TUGDUAL VALENTIN VINCENT VINOC YVES YVES TREZ HIR SALETTE SALOMON ROUX SALOU SALUT AR VERHEZ SALVADOR ALLENDE SANE SANT DIVY SANTEC SANTIK SANT KIREG SANTOS DUMONT SAO HEOL SAPINIERE SAPINS SARAGOZ SARAH BERNHARDT SARDINIERS SAULES SAVARY SAVOIE SAVORGNAN BRAZZA SCAER SCARS SCHILLER SCHUBERT SCIERIE SCOEN SCRAFIC SEBASTIEN AR BALP SEBASTIEN BALP SEBASTIEN DURAND SEBASTIEN GUIZIOU SEBASTIEN BALP SEBASTIEN VELLY SEBASTOPOL SEDILLOT SEIGNEUR GALON SEIZ AVEL SELL WAR KER SEMAPHORE SEPT ILES SERGENT BAILLANCE SEVERN SEVIGNE SHAKESPEARE SHERIDAN SIBIRIL SIECK SILINOU SIMON SIMON NANTUA SIMONE SIGNORET SIMONE WEIL SIMON GARENGEAU SIRENE SIROCCO SISLEY SOEUR SOEUR PAULINE SOLDAT DUVAL SOLDAT DUVAL LESCOFF SOLFERINO SOMME SORBIERS SOURCE SOURCES SOUS MARIN EURYDICE SOUTH BRENT SPERNOC SPHINX SPORTS SQUIRIOU SQUIRIOU HUELLA SQUIRIOU HUELLA KERHEOL SQUIVIT STADE STADE LECHIAGAT STADES STALINGRAD STANCOU STANCOULINE STANG STANG ALLESTREC STANGALON STANG AN DOUR STANG AR DOUR STANG AR GUER STANG AR LIN BEUZEC CONQ STANG AR ROZEN STANG REO STANG TREMEUR STANG VENELLE STANG YEN STAOL STEIR STELLAC H STENDHAL STEPHANE MALLARME STER STER AN ALLE STER GOR STERNES STERNES TREZ HIR STEROUDOU STERVINOU STIFF STIVEL STIVELL STRASBOURG STRAUSS STREAT AL LANN STREAT AN AOD STREAT AN DREZ STREAT AR ARVORIZ STREAT AR BOULAC H STREAT AR GOZ STREAT AR RELEGOU STREAT BALAN STREAT DOUN STREAT GLAZ STREAT HIR STREAT JOLY STREAT KAN AN AVEL STREAT NEVEZ STREJOU STYVEL SUFFREN SULER SULLY SULLY PRUDHOMME SURCOUF SURCOUFF SUZANNE PARCEVAUX SUZANNE PARCEVEAUX SUZANNE GUIGANTON SUZANNE POUMEROL SUZANNE VALADON SYNDICAT AGRICOLE TACHEN AR CHOAT TACHEN FOIRE TACHEN GUEN TACHEN MORCH TACHEN PONTIC TAILLEURS TAINE TALAMON TAL AR C HEF TAL AR GROAS TALARMAIN TAL AR VEIL TAL AR VILIEN TALBOTEC TAL LENN TAMARIS TANGUY CHATEL TANGUY CHATEL KERSAINT TANGUY JACOB TANGUY MALMANCHE TANGUY PHILIP TANGUY PRIGENT TANNEUR TANTE YVONNE TARIEC TARPORLEY TEILHARD CHARDIN TELEGRAPHE TELEPHERIQUE TEMPLE TEMPLIERS TERRAIN SPORTS TERRAIN SPORTS TERRAINS SPORTS TERRE VANNES TERRE NEUVAS TERRES NEUVAS TEVEN AR REUT 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TREMENARCH TREMEUR TREMINTIN TREMOR TRENINEZ TREOMPAN TREUSCOAT TREVAREZ TREVOEDAL TREVOEDAL LOCQUERAN TREVORC H TREVOUX TREZ TREZIEN TRIELEN TRINITE TRISTAN TRISTAN CORBIERE TRISTAN CORBIERES TRISTAN ISEULT TROADEC TROALEN TROENES TROERIN TROEUJEUR TROFEUNTEUN TROHOAT TROIS FERMES TROIS FORGET TROIS FRERES CHARETTEUR TROIS FRERES DARIDON TROIS FRERES LEJEUNE TROIS FRERES LEROY TROIS FRERES TANGUY TROIS GUENNEC TROIS ROIS TROMENIE TROMEUR TROUGOUREZOU TROUIDY TROUZ AN DOUR TULIPES TULIPES LODONNEC TUMULUS TUMULUS POULGUEN TURANCHOU TURENNE TURGOT TY ARVOR TY BAUL TY BEUZ TY BRAS TY COAT TY COZ TY DEVET TY DEVET TY DON TY DOUAR TY DOUR TY FORN TY FORUM TY GARDE TY GLAZ TY GUEN TY JOPIC TY LAE TY LANN TY LAPIN TY LOSQUET TY MELAR TY MEUR TY NEVEZ TY NEZ TY PELLA TY PIC TY PISTEL TY PRY TYRIEN GLAS TY RUS TY VARLAES URBAIN QUELEN USINE UTRILLO VAD VALANEC VALENTIN VALENTIN HAUY VAL FLEURI VALLEE VALLON VALMY VALORY VALY VALY COZ VAN GOGH VANNEAUX VANNIERS VAN PARYS VARCQ VARQUEZ VASCO 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CAM VOELAZ VOIE FERREE VOILIERS VOLNEY VOLTAIRE VORC LAE VORCH LAE VORLEN VOURCH VUILLEMIN VULCAIN WALDECK ROUSSEAU WALLIS FUTUNA WATTEAU WINSTON CHURCHILL WOAS KAM XAVIER BICHAT XAVIER LANGLAIS XAVIER GRALL YAN D ARGENT YAN NAOD YANN D ARGENT YANN LUIS DOORNIK YED YEUN YOUEN DREZEN YOUENN DREZEN YS YSER YSEULT YVES ANTOINETTE YVES AUBERT YVES BAILLERGEAU YVES BERTH YVES BIGNON YVES COLLET YVES CORDENNER YVES KERGUELEN YVES ANTOINETTE YVES EVENAT YVES FLOCH YVES BODENEZ YVES FRELAUD YVES FRELAUT YVES GILOUX YVES GUEGUEN YVES GUILLOU YVES HALL YVES HERVE YVES HERVIOU YVES HILY YVES JACOB YVES JADE YVES JANNES YVES JAOUEN YVES LAGATHU YVES LAGATU YVES LANDOUAR YVES BOT YVES BRIX YVES FLOCH YVES GALL YVES GUEN YVES HENAFF YVES LANN YVES MANCHEC YVES MORVAN YVES MOAL YVES MONTAND YVES OMNES YVES PHILIPPE YVES QUEIGNEC YVES QUINQUIS YVES SELLIN YVES TALARMAIN YVES TALARMIN YVES TANGUY YVES TREGUER YVES TRICHARD YVES WOLFARTH YVES YANNES YVON BENOIT YVON DONNARD YVON BERRE YVON LUCAS ZALOU RUFFELIC RUFILY RUFOLIGOU RUGAIN RUGAOUDAL RUGERE RUGLASTEN RUGLEIS RUGOLENNEC RUGOLOEN RUGOLVA RUGORNOU BRAS RUGREGUEN RUGUEL RUGUELLOU RUGUELVEN RUGUIC RUHEN RUHOLLO RUIEN RUJEAN RUKERDREIN RULAN RULAND RULAROU RULEA RULEN RULLAND NEVEZ RULLANN RULOGOT RULOSKET RULOSQUET RULOSQUET VIHAN RULUDU 18 PORS AN EIS VINIS RULUDU RUMADEC RUMAIN RUMALIC RUMAOUT RUMEAN RUMEIN RUMEN RUMENGOL COSQUER RUMENGOL GORREGUER RUMENGOL GUERVENNEC RUMENGOL KERASEAS RUMENGOL KERDREIN RUMENGOL KERGADIOU RUMENGOL KERGARADEC RUMENGOL KERGARREC BIAN RUMENGOL KERGROAS RUMENGOL KER LAURENT RUMENGOL KERLAVAREC RUMENGOL KERMOAL RUMENGOL KERNEVEZ RUMENGOL LANNERVEL RUMENGOL LINCOSPER RUMENGOL PEN AR MENEZ RUMENGOL PEN AR PRAT RUMENGOL PEN AR VOAS RUMENGOL REUN AR BEUZIT RUMENGOL RULAM RUMENGOL STUM RUMENGOL TOULLOUDU RUMENGOL TY NEVEZ RUMENGUY RUMENOU RUMERDY RUMERIEN RUMEROU RUMEUR RUMEUR MESGUEN RUMIADOU RUMINIOU RUMIQUEAL RUMOAL RUMOLOU RUMORVAN RUMORVANIC RUMORZOL RUMPOULZIC RUN RUNABAT RUNAGAVET RUNAHER RUNALEN RUNAMBEZIT RUNANDOL RUNAOUEN RUN AR BEUZIT RUN AR C HAD RUN AR CHOAT RUN AR C HRAJOU RUNARC HY RUN AR GOIC RUN AR PUNS RUN AR ROUE RUN AVAL RUNAVANEL RUN AVEL RUNAVEL RUNAVOT RUNCADIC RUNDAVID RUN RUNDU RUNDUIC RUNEN RUNEON RUNERVI RUNERVY RUNEVEN RUNEVEZ RUNEVEZ VIAN RUNGLAS RUNGLEO RUNGROAS RUNGUEN RUNGUENNOU RUNHUEL RUNIC RUNIC ROSTIVIEC RUNIGEN RUNIGOU RUNIGOU VIHAN RUNIOU RUNIZIN RUNJOAIC RUN KERHERVE RUNNAVANEL RUNNIAT RUNNIC RUNOBLE RUNON RUNORVEN RUNOTER RUNTAN RUNTANNIC RUN VIAN RUN VRAZ RUOT RUOT NEVEZ RUOT VRAS RUPAPE RUPELETER RUPIQUET RUPLOUENAN RUPODOU RUPONT RUPULVEN RUSCUMMUNOC RUSCUMUNOC RUSCUMUNOC PEN AN DREFF RUSCUMUNOC TREZIEN RUSILY RUSQUEC RUSQUEC VIAN RUSQUEC VRAS RUSTEPHAN RUSTIN RUSUL RUSULAN RUSUNAN RUTAN RUVARCQ RUVARNISON RUVARQ RUVAS RUVEIC RUVELEC RUVERNISON RUVISCOU RUYEN RUZADEN RUZUILLEC RUZULIEC RY HUELLA RY IZELLA SAB AR HELEN SABEROUS SABLIERE SABREG ADRIEN ALBIN ALBIN MENEZ BIHAN ALOUARN ALOUR ALPHONSE AMBROISE ANDRE ANTOINE ANTOINE ABERWRACH ANTOINE PERROS LILIA AOUEN AUBIN IZELLA AVIT BUDOC LESVIGNAN CADO CADO TY NEVEZ KERBELEGOU CADOU CADOU GRINEC CADOU GUIDELLOU CADOU HENGOAT CADOU KERGREAC H CADOU KERGUDON CADOU KERJEAN CADOU KERMARQUER CADOU LESTREMELARD CADOU PENHOAT CADOU ROUDOUDERC H CADUAN CARADEC CAVA CLAIR CLAUDE COAL COME COMTE COULEO DAVID DEC DEMET DENEC DENIS DEVAL DIDY DIDY KERSECH DONAT DRAFF DREG VRAS DREZOUARN DRIDAN DRIEC DRIGENT DRIONE DUREC SAINTE AGNES SAINTE AGNES IZELLA SAINTE ANASTASE SAINTE ANNE SAINTE ANNE PORTZIC SAINTE ANNE PALUD BON COIN SAINTE BARBE SAINTE BRIGITTE SAINTE CATHERINE SAINTE CECILE SAINTE CHRISTINE EDEN SAINTE EDEN SAINTE ELISABETH EFFLEZ EGANNEC EGAREC EGAREC MECHOU MEZ AN AOD SAINTE GERTRUDE ELOI ELOY ELOY KERVEUZEN ELVEN SAINTE MARGUERITE SAINTE MENEZ HOM SAINTE MARINE 1 SAINTE MARINE 3 HENT KERNAVRIOU SAINTE MARINE 5 HENT AR STANKOU SAINTE MARINE 6 HENT AR STANKOU SAINTE MARINE KERANGUEL SAINTE MARINE KERGUILLEC ENER SAINTE PETRONILLE EREP SAINTE ROSE EUTRE KEROMNES EUTROPE CORVEOU EUTROPE KERADRAON EUTROPE KERMORVAN EUTROPE KERNIVINEN EUTROPE KERSTRAD EUTROPE LESTIANTEL EUTROPE MENGLEUZ EUTROPE SPERNEN FIACRE FIACRE BELLEVUE GERMAIN GILDAS GILLES GONVAC H GONVARCH GONVEL GONVELD GONVEN GUENAL GUENOLE GUENOLE BURDUEL GUENOLE FERMOU GUINEC HERBOT HERNOT HILAIRE HONORE HONORE BIHAN HUEL JACOB JACQUES JAOUA SAINT BALANAN LEYDEZ JOACHIM JOSEPH JULIEN KONNEG LAURENT LAURENT CONVENANT CAER LAURENT KERDIDREUX LAURENT ROUDOUR LEGER LEUFFROY LUCAS MADY MAGLOIRE MARZIN MATHIEU MAUDE MAUDET MAUDETZ MAUDEZ MAURICE MEEN MELAIR MELAIRE MICHEL MICHEL BEG AR SPINS MICHEL KERGOFF MICHEL KERVOANEC MICHEL PEN AR STREJOU MICHEL STREJOU MODEZ MOGOT MOLIEN NEP NICOLAS PYROTECHNIE NOGOT NORGARD OUERNEAU PERAN PHILIBERT PHILIBERT HENT KERIKEL PHILIBERT KERJEAN PHILIBERT PENANROS PHILIBERT STANG QUELFEN PHILIBERT TREBEROUANT PHILIBERT TROBIDAN SAINT PRETRE QUENAN QUIJEAU QUINIDIC ROCH SAMSON SAUVEUR SEBASTIEN SERVAIS SEVERIN SOURON SULA THAMEC THELAU THEODORE THEY THOMAS THUDEC THUDON THUGEN TREMEUR TUGDUAL TUGEN URFOL VELTAS VENAL VENEC VENNEC VIO VIZIAS YDUNET YVES YVES KEROZAL SALLE SALLE BURUNOU SALLE VENTES CRIEE SALLE GRANET SALLE PEN HOAT HUON SALLE POULFONNEC SALLES SALOU SALOU COATIVY SALOU TRENZOC SANEIS SAN NEVEN SANSPEZ SANT ALC HOUEN KERONAN SANTEZ VENTROC KERJANE SANT GWENOLE SANT VAODEZ SAO PENNANGHER SAO PENNANGUER SAOUDUA SAOULEC SAOUTENET SAPINIERE SAPIN VERT SAVARDIRY SCALENNOU SCANTOUREC SCAO SCARABIN SCARS SCARS KERNONEN SCAVEN SCAVENNOU SCAVIT SCOET SCOLDY SCOLDY GULVAIN SCOLMARCH SCOS BRAS SCOUERIC SCOZ BIHAN SCOZ BRAS SCOZ VIAN SCRAP AR MOAL SCRAP AR YARD SCUEIR SEACH SEGAL SEDER SEGUINIERE TY NEVEZ KERLAGADIC SEIES GROAS SEILLOU SEITER BIAN SEITER BRAS SELLIOU SELL VAR KER TY BLOCOU SEMAPHORE SEMAPHORE CAP CHEVRE SENANCHOU SENTIER COTIER SENTIER TRAIN SENTIER PLOMARC H SEQUER NEVEZ SEVELEDER SEVEN SEVENE SEVIGNON SEZNEC SILINOU SINAN SITCH DEROZ BAEL GRIGNALLOU SKLERIJENN AR MOR VEILLANEC SKOL AR GROAZ SOULOUGAN SPARLE SPERN AR BLEIZ SPERNEC SPERNEGER SPERNEN SPERNIT SPERNOT SPERNOU SPINS SQUARE ABERIC SQUARE ANATOLE BRAZ SQUARE BALLYHAUNIS SQUARE BOTHOREL SQUARE BOTREL SQUARE BOULACH SQUARE CAPUCINES SQUARE CORNOUAILLE SQUARE CAMARET SQUARE KERELAN SQUARE L ALBATROS SQUARE RESISTANCE SQUARE TOUR D AUVERGNE SQUARE L EIDER SQUARE LEZEBEL SQUARE MENEZ SQUARE MORGAT SQUARE AIRELLES SQUARE AJONCS SQUARE CHENES VERTS SQUARE HORTENSIAS SQUARE LUCES SQUARE MORETS SQUARE PELICANS SQUARE SANDERLINGS SQUARE STERNES SQUARE SWANSEA SQUARE COMMANDANT L HERMINIER SQUARE POITOU SQUARE ROUSSILLON SQUARE GLAZIKS SQUARE JAKEZ RIOU SQUARE JOEL MOIGNE SQUARE BAS SQUARE BOULACH SQUARE MADELEINE SAVARY SQUARE MARECHAL LATTRE TASSIGNY SQUARE THERESE DELALANDE SQUARE MAX JACOB SQUARE MONSEIGNEUR ROULL SQUARE CORNEILLE SQUARE PROFESSEUR RIEUX SQUARE REGINE TANNEAU SQUARE RENE JAMAULT SQUARE ROCHE SQUARE TRISTAN CORBIERE SQUARE VICTOR HUGO SQUARE VICTOR JARA SQUARE VICTOR JARRA SQUAVEN SQUERNEC SQUERNEC NEVEZ SQUIBARVEUR SQUIFIEC SQUILLIOU SQUILLOU SQUIRIOU SQUIRRIOU SQUIVIDAN SQUIVIT STADE GILDAS STALAS STALAZ ST ALBIN 15 HENT AR MENEZ ST ALBIN 9 HENT AR MENEZ STALL STANC AR BIDER STANCOU STANCOU VIAN STANCOU VRAS STANC VRAS STAND AR VOGUER STANENNIC STANG STANG ALAN STANGALARD STANGALIJOU STANGALINET STANG AL LAN STANGANABAT STANG AN AMAN STANG AN AMAND STANG AN ARBEL STANG AN AUTROU STANGANAY STANG AN EOL STANGANNAY STANG AN OL STANG AR STANG AR BACOL STANG AR BESQ STANG AR BEUZ STANG AR C HARO STANG AR CHOAT STANG AR FLOC H STANG AR GARRONT STANG AR GLAN STANG AR GLOANEC STANG AR GOFF STANG AR GROAS STANG AR GUEL STANG AR HOAT STANG AR LABOU STANG AR LENN STANG AR LIJOU STANG AR LIOU STANG AR MANER KERGABEN STANG AR RAZ STANG AR REO STANG AR RHEUN STANG AR ROUDAOU STANG AR VEIL STANG AR VEZEN STANG AR VOGUER STANG AR VORGUER STANG AR VOUD STANG AR WENNEG STANG ASQUEL STANG BEULIEC STANG BIHAN STANG BOUDILIN STANG CARIOU KERDALIDEC STANG CHRENN STANG COADIGOU STANG COAT STANG COAT 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LANNERGAT TAL AR HOAD TAL AR HOAT TAL AR HOAT BIHAN TAL AR MOR CAMEZEN TAL AR MOR LESTREVET TAL AR ROZ TAL AR VEIL TALAVERN TALAVERN CARN LOUARN TALGUEBES TALGUEBEZ TALHIARN TALHOAT TALINGOAT TAL MOOR TANVAI TARAGNON TARAIGNON TARIEC TARROS TAULE TAVARN TY ELISE TELARGALET TERENEZ TERNANT TERRE TERRE PLEIN PORT TERRIEN TERROHANT TERROHAY THEOLEN THEORES THEVEN THEVEN AR ROUANES THEVEN AR ROUANEZ THEVEN KERBRAT THEVEN MEUR THEVEZAN BIHAN THEVEZAN BRAS THEVEZAN VIAN THEVEZAN VIAN LILIA THEVEZAN VRAS LILIA THUIAS TI AL LEVENEZ 9 HENT AR STER TI AN DEURGED TI AN TI AR CHOAD TI AR FOENNEG KERHENRY TI AR GROAS TI AR GWENNELI KERHOAZOC TI AR LOUARN GOAREM EDERN TI AR NEVEZ KREC KELLIG TI ARVOR PEN AR VALY TI AR YEUN TIBIDOUR TIBIDY TI BLAISE TI BRUG TI BUGALE KERLOUGA TI CARIOU TI COZ KERBERNARD TI CRAPON TI DOUAR TI DUCHEN TIER NEVEZ TIERS COZ TIEZ MEIN TIEZ NEVEZ TI FAOU TI FLOCH TI FORN TI GARDE TI GOIG TI GONAN TIGWEN TI GWEN TI GWENN TI GWENN KERNAVENO TI ISELLA TI KELEN TI KERMANAC KERASCOET TI KOAD TI KORN TI LABOUSED KERICUFF TI LANN TILIBRENNOU TILIDRENNOU TI LIPIG 10 ALEZ UHELA TI LIPIG 11 ALEZ UHELA TI LIPIG 17 ALEZ UHELA TI LIPIG 19 ALEZ UHELA TI LIPIG TILLEULS KERARTHUR TI MENN TI MEUR TINDUFF 101 KERMUTIL TINDUFF 1 LESTRAOUEN TINDUFF TI NEVEZ KERGOGANT TI NEVEZ KERNEVEZ TI NEVEZ KOMBRENN TI NEVEZ LESVEL TI NEVEZ MEILH CORNIGELL TI NEVEZ MOGUEROU TI NOD TI OLENT PENHOAT TI PIN TIRILI TIRINOU TI ROUE TIRRIENNOU BIHAN TI RUZ TI VOLIEN TOARE TOLLAN TORAMUR TORANEAC H TORANGANT TOR AR HOAT TORCH AR YEUN TORC HOAD TORREYEUN TOSSEN AR MOIGN TOSSENOU MELEN TOUARC HEN TOUBRAN TOUINEL TOULABOUDOU TOULALAN TOULALAND TOUL LANN TOUL AL LAER TOUL AL LAN TOUL AL LANN TOUL AN AEL TOULANAIE TOULANAY TOUL NAY TOULANAYE TOUL AN CAVEL TOULANCOAT TOUL AN COAT BIHAN TOUL AN COAT BRAS TOUL AN DOUR TOUL AN DOUR TREZIEN TOULANER TOULANER PENQUER TOUL AN HAYE TOULANNAY TOUL AN PORZ TOUL AN TERRIENNOU TOUL PIC TOUL AR TOUL AR BLEIS TOUL AR BLEIZ TOUL AR BLEIZ BIHAN TOUL AR BORN TOUL AR BROCHET TOUL AR C HAON TOUL AR C HARRONT TOUL AR C HAZ TOUL AR C HOAT TOULARCUS TOUL AR GARONT TOUL AR GARRONT TOUL AR GLOET TOUL AR GOUER TOUL AR GROAS TOUL AR GROAZ TOUL AR GUER TOUL AR HAIL TOUL AR HARONT TOUL AR HOAT TOUL AR LUTIN TOUL AR PESKED TOUL AR PIC TOUL AR PORS TOUL AR RAN TOUL AR RANNIC TOUL AR ROGNON TOUL AR ROHOU TOUL AR ROUDOU TOUL AR STIFF TOUL AR ZAOUT TOUL BELENAN TOULBELORY TOULBEUZ TOUL BLANC TOUL BLAYE TOUL BLEIS TOULBOHEN TOUL BONDE TOULBOUILLEN TOUL BOUT TOULBROCH TOUL BROEN TOUL BROYET TOULCARFURIC TOULCARFURIQUET TOUL C HRAON TOULCOAT TOUL COZ TOULCUZ TOUL DIVEON TOUL DIWEON TOULDON TOUL DOUAR TOULDOUAR TOUL DOUR TOULDREIZ TOULDREZEN TOULDU TOUL EFFLAM TREGONDERN TOULEGRAS TOULENCAVEL TOUL EN COAT TOUL EN PORZ TOULFEUNTEUN TOUL FIL TOULGOAT TOULGOAT PENDRUC TOUL GOLO TOULGUICH TOULHOAT TOULHUILET TOULIBIL TOULIGUIN TOULILOUT TOULIVIN TOULIVINEN TOUL JAOUEN TOULLAERON TOULLAN TOUL LAND TOULL AR C HARN TOULL AR CHARRONT TOULL DIVREACH TOULLOUARN TOULLOUARNEC TOULLOUDU TOULL RUZ TOULL TREAZ TOUL MELEN TOULMINOU TOUL MOUDED TOULON TOULOULAN TOULOULERN TOULOUPRY TOUL PEB TOUL PEP TOUL PLAD TOULPRI TOUL PRIE TOUL PRIS TOULQUELEN TOULRAN TOUL RAN TOUL REO TOUL RIPP TOUL ROBIN TOUL SABLE TOULSAC H TOULTACHOU TOULTOSSEC TOUL TRAP TOUL TRING TOUL TRINK TOUL ZABREN TOUPET TOURAN TOUR AN ARVOR TOUR BIAN TOUR BLANCHE TOURELLE TOURELLES TOURELLOU TOURIGOU TOUR D ARGENT TOURNE ICI TOURNE BAS TOUR NOIRE TOURNUS TOURON TOUROUS VIAN TOUROUS VRAS TOUR QUELENEC TOURQUELENNEC TOURROUSSEL TOUR TAN TOUZ BANNEL TOYAL TRANSVAAL TRANVOUEZ TRAON TRAON AN DOUR TRAON AR TRAON AR GUIRIN TRAON AR LIN TRAON AR RHUN TRAON AR ROZ TRAON AR VENIEC TRAON AR VILIN TRAON AR VOAS TRAON AR VORCH TRAON AR VOUC H TRAON BELEC TRAON BELLEC VIAN TRAON BERDER TRAONBEZEDEN TRAON BIAN TRAON BIAN PENMESMADEC TRAON BIHAN TRAON BILZIC TRAON BIZIN TRAON BRAS TRAONBROUEN TRAON COURANT TRAON DAVID TRAONDIDU TRAON DOUR TRAON DOUR BRAZ TRAONDOUR VIAN TRAON ELORN TRAONEVEZEC TRAONFEUNTEUN TRAON FEUNTEUNIOU TRAON FOENNEC TRAON GALL TRAON GLEZOUN TRAONGOFF TRAON GOUEZ TRAON GOUZIEN TRAON GUILLY TRAONGURUN TRAON HIR TRAONHUEL TRAON HUEL TRAON HUELLA TRAONIEN KERNE TRAON ILDIG TRAON IVERN TRAON IZELLA TRAON JULIEN TRAON KER TRAON KEROMNES TRAONLAN TRAONLEN TRAONLIORS TRAON MENEZ BIAN TRAON MEUR TRAONMEUR TRAON MILIN TRAON NAZEN TRAON NEVEZ KERNEVEZ TRAON PAOL TRAON PERENEZ TRAON RENARD TRAON RIOU TRAON RIVIN TRAON ROUDOUR TRAONRUVILY TRAONSOULEN TRAON STANG TRAONSTANG TRAON TEUCHER TRAONVELAR TRAON VILIN TRAON VISTINIC TRAONVOAS TRAONVOAS KERHALLON TRAONVOAS QUINQUIS TRAONVOEZ TRAON VOUEZ TRAOUALAN TRAOUDON TRAOUEN TRAOUIDAN TRAOU JOIE TRAOULE TRAOULEN TRAOULER TRAPIC TRAPIC 2 BRELEIS TRAVERSE TY NAY TRAVERSE ECOLIERS TREAMBON TREANNA TREANTON TREAS TREASTEL TREBALAY TREBANEC TREBANEC HUELLA TREBANEC IZELA TREBAOC TREBAOL TREBAOL IZELLA TREBE TREBELLEC TREBEOLIN TREBERN TREBERON TREBEROUANT TREBERRE TREBEUZEC TREBIC TREBIOU TREBODEN TREBODENNIC TREBOMPE TREBON VEL TREBREVAN TREBUON TREBUON VIHAN TREDAOU TREDERN TREDIERN TREDUDON TREFALEGAN TREFALEGAN DIDREUX TREFELAGAN TREFERNIC TREFEST TREFEUNTEC TREFFIEC TREFFRY TREFILY TREFLEACH TREFLES TREFLEZ TREFRANC TREFREIN TREFRY TREGAGUE TREGAGUE NEVEZ TREGALET TREGANA TREGANO TREGANO NEVEZ TREGARN TREGOARANT TREGODALEN TREGOEN TREGOLE TREGOLLE TREGOLLE BRAS TREGOMEZ TREGONAL TACHENN TREGONDA TREGONDERN TREGONDERN LANDONNIC TREGONETER TREGONEVEL TREGONGUEN TREGONGUEN NEVEZ TREGONNEC TREGOR TREGORF TREGORFF TREGOR MESMEUR TREGOUAR TREGUEILLER TREGUENNOC TREGUENOC TREGUER TREGUER CREIS TREGUERNE TREGUER VIHAN TREGUESTAN TREGUESTAN LILIA TREGUEUSTAN LILIA TREGUIDO TREGUINTIN TREGUISTAN TREGULLIER TREGUNVEZ TREGURON TREGUY BIAN TREHENVEL TREINVEL TREIZ COZ TREIZ YVES TRELAN TRELANNEC TRELAN VIHAN TRELAZ TRELEON TRELESQUIN TRELEUN TRELEVER TRELIVALAIRE TRELLER TRELLU TRELLUAN TRELLUANT TREMADUR TREMAEC TREMAGON TREMAIDIC TREMALO TREMAREC TREMARIA TREMASCLOET TREMATOUARN TREMAZAN TREMEAL TREMEAL BIAN TREMEAL BRAZ TREMEAL LILIA TREMEDERN TREMELAOUEN TREMELE TREMELEC TREMELLEC TREMEN TREMENEC TREMENEC KERELLOU TREMENGON TREMEUR TREMEUR BIHAN TREMEUR BRAS TREMEUR HENT STER TREMIC TREMILLAN TREMILLEC TREMILLIEN TREMILLIO TREMILLIO IZELLA TREMILLOUR TREMINOU TREMOAN TREMOAN VIAN TREMOBIAN TREMOGUER TREMOR TREMORE TREMORGAT TREMORVEZEN TREMORVEZEN KERVAILLET TRENAOURET TRENVEL TRENVER TREOFFRET TREOGUER TREOLFEN TREOMPAN TREONGAR TREORDO TREOTAT TREOUALEN TREOUARC H TREOUELAN TREOULAN TREOULLAN TREOURE TREOURON TREOUZAL TREPANNEC TRESCAO TRESIGNEAU TRESSEOL TREST TREST FOEN TREUHEN TREUNOT TREUSCLEUS TREUS CLEUZ TREUSCOAT TREUSCOAT BIHAN TREUSCOAT IZELLA TREUSCOAT VIHAN TREUSCOAT VRAS TREUSKER TREUSQUILLY TREUSTEL GOZ TREUSTEL NEVEZ TREUZEULOM TREVALAN TREVALUIC TREVAN TREVANNEC TREVARGUEN TREVARHA TREVARN TREVARS TREVEDEC TREVEGOT TREVEIL TREVEL TREVELLER TREVELOP TREVELOPP TREVELVEN TREVENAN TREVENNEC TREVENNEC CREIS TREVENNOU TREVENOUEN TREVEOC TREVEOC BRAS TREVEON TREVEON VIHAN TREVER TREVERN TREVEROC TREVERREC TREVERROC TREVEUGANT TREVEUGANT BIAN TREVEUGANT BRAS TREVEUR TREVEZER TREVIAN TREVIANOU TREVIDEC TREVIDERN TREVIDIC TREVIDIERN TREVIDY TREVIGNON TREVIGNON CURIOU TREVIGNON GOELAN TREVIGNON HENT AN DACHENN TREVIGNON HENT AR STANKENNIG TREVIGNON HENT AVEL DRO TREVIGNON HENT MEN TREVIGNON HENT MOR BRAS TREVIGNON KERLEO TREVIGNON KERLIN TREVIGNON PENLOCH TREVIGNY TREVIGODOU TREVIGUER TREVILIS TREVILIT TREVILLY TREVILLY HUELLA TREVILY TREVILY HUELLA TREVILY IZELLA TREVIN AR LOUZ TREVINEC BIHAN TREVINEC BRAS TREVINILY TREVINOU TREVIN VRAS TREVISQUIN TREVOAL TREVOALLEC TREVOAN TREVOAZEC TREVODOU TREVODU TREVOEDAL TREVOELET TREVOEN TREVOURDA TREVOUREC TREVOURIET TREVOYEN TREVOYEN VIHAN TREVULE TREYOUT TREZEGUER TREZEL TREZEN TREZENT TREZENVEL TREZENVIT TREZER TREZERVAN TREZ FOEN TREZ GOAREM TREZIEN TREZIEN KER EOL TREZIEN KERGADOR TREZIEN PENANDREFF TREZIEN RUSCUMUNOC TREZ KERMALERO TREZ KEROUER TREZ KEROUER IZELLA TREZMALAOUEN TREZ ROUZ TREZURON TRICORN TRIDOUR TRIEVIN TRIGUEN TRIGUER TRI GUESTEN KERLOET TRINITE TRINIVEL TRISTINN TRIVILY TROAON TROBALO TROBARA TROBARA HUELLA TROBARREG TROBAY TROBELLEC TROBESCOND TROBEU TROBIDAN TROBILY TROBIROU TROBOA TROBRIAND TROCHOADOUR TROC HULIER TRODIBON TRODON TROEL TROELLO TROEOC TROERIN TROFAGAN TROFEUNTEUN TROGALVEZ TROGANVEL TROGLAZ TROGOR TROGOUR TROGRIFFON TROGUENEC TROGUEROT TROGUERVERN HUELLA TROGUERVERN IZELLA TROGUIC TROGUION TROGUYVEN TROHALU TROHANET TROHARE TROHEOL TROHINEL TROHOAT TROHOET TROHOM TROHOM BIAN TROHON TROHONAN TROILLO TROINGUY TROIS CANARDS TROIS CANARDS RUN BIHAN TROIS CURES TROIS FONTAINES TROIS TROIS CROIX TROIS VALLEES TROJOA TROLAND TROLERON TROLEVEN TROLEZ TROLLEO TROLOAN TROLOGOT TROLOUCH TROMAO TROMARCH TROMARC VIAN TROMARC VRAS TROMARO TROMARZIN TROMATHIOU HUELLA TROMATIOU TROMATIOU HUELLA TROMBARS VIHAN TROMBARS VRAZ TROMBOZ TROMEAL TROMEAL HUELLA TROMEL TROMELIN TROMENDY TROMENEC TROMENEC HUELLA TROMER VIHAN TROMER VRAZ TROMEUR TROMILIN TROMILLOU TROMORVAN TROMOULOUARN TRONC TRONEOLY TRONGAL TRONIN TRON JOLY TRONJOLY TRON TRONVAL TRONVAL IZELLA TROPARDEC TROP CHER TROPOSTEC TROQUERVERN HUELLA TROREON TROSANG TROUBIROU TROUEC TROUERENNEC TROUGOUAR TROUGUENNOUR TROUGUER TROUIDY TROUJOULEN TROULOUCH MICHEL TROUS AR C HANT TROUZ AN DOUR KERNAVENO TROUZ AR MOOR KERMEN TROUZ AR MOR TROUZ ARNAEL TY MEN TROUZENT TROUZOULEN TROVELIEC TROVEOC TROVERN TROVILIEC TROVOAS TROVREACH D ALLAE TROVREACH D AN TRAON TROYALACH TROYELLOU TROYON TROYSOL TRUDUJOU TRUEL TRUHEN TRUNVEL TRUSQUENNEC TUCHENNOU TUDAVAL TUNES TUSCAOL TUSCAOUL TY AEL KERNIOU TY AN AOT TY AN AVEL CORN AR PALUD TY AN DANTEC TY AN DEVET TY AN DIGOR TY AN DISTRO TY AN DOA TY AN DOSSEN TY AN DOUY TY ANDRE TY AN DREAU TY ANDRE BIHAN TY AN TY AN DUCHEN TY ANGLAIS TY ANQUER TY ANQUER KER YS TY AN TAMIC TY AN TARO TY AN TEUREC TY AN TRAON TY AN TRAON PENBIS TY AR TY AR BLEIS GWENN TY AR C HANT TY AR CHOAT TY AR C HOAT LESTRIVIN TY AR C HOST TY AR CROAS LAVALLOT TY AR FAOC TY AR GALANT TY AR GALL TY AR GANET TY AR GARREC TY AR GLAS TY AR GOAZIC TY AR GOFF LOGONNA QUIMERC TY AR GUEN TY AR HANT TY AR HOAT TY AR HOENT TY AR MANACH TY AR MENEZ TY AR MEN KERTREGUIER TY AR MOAL TY ARMOR 13 HENT BEG CROASSEN TY ARMOR KERNEIS TY AR MOR KERVOAL TY AR MORTOLOD KERHOAZOC TY AR MUNUT TY AR NEVEZ TY AR POES TY AR RODOU PENQUER TY AR SEVEN TY AR STANG DOURGUEN TY AR VEIL TY AR VELEYEN TY AR VERET TY AR VERRET TY AR VUGALE TY AR VUR TY AR YEUN TY AR ZANT TY AUDREN TY AVALOU CARIC TY AVALOU QUILIOU TY AVEL TY AVELEC TY BALAN TY BALLON TY BANAL TY BAOL TY BARIOU TY BASTILLON TY BAUL TY BEO TY BERTHOU TY BIAN TY BIAN AR TY BIANET TY BIAN KERROLACH TY BIDAN TY BIDEAU TY BIHAN TY BIHAN KERVAY TY BIHEN KERLEOL TY BIRIOU TY BISSON TY BLAISE TY BLANCOU TY BLEIS TY BLOCOU TY BOCOUR TY BODEL TY BODIOU TY BOL TY BOL BROEZ TY BOL STUM TY BORDEAUX TY BOS TY BOSS TY BOUD TY BOUILLEN TY TY BOULIC TY BOURVIC TY BOUSSARD TY BOUT TY BOUTIC 12 TY BOUTIC TY BOUTIC TY BOZEC TY BRABANT TY BRAS TY BRAS KERGENETAL TY BRAS KERGUENNEC TY BRAS KERSAINT TY BRAS SAINTE CATHERINE TY BREIZ KERIVEL TY BRID TY BRIT TY BROCH TY BRUG TY BUREAU TY CAM TY CANEVET TY CARO TY CARRE TY CHALONIC TY CHANU TY CHAPEL TY CHAPELL TY CHAP TALAOURON TY C HOAT RUN AR C HAD TY CLOZ TY COAT TY COAT LESTONAN TY COAT PLANQUEN TY COLIN TY COLLET TY COLO TY CONAN TY CORN TY CORNICHE TY COZ TY COZ BODONNOU TY COZ GUERAND BIAN TY COZ KERADENNEC TY COZ KERANGOMAR TY COZ KERGADORET TY COZ KERMEN TY COZ LANDAVEN TY COZ RHU TY COZ TALAOURON TY CRAN TY CRANN TY CRANO TY CRAS TY CREIS TY CREIS LANNEUSFELD TY CREN COATPLOHOU VRAS TY CRENN TY CROAS TY CROAZ TY CROM TY DANIELOU TY DANIGOU TY DANS TY DAOUDAL TY DEVET TY DIDROUZ TY DIDROUZ AR GORRE BODIVIT TY DON TY DORGUEN TRAON BEUZIT TY DOUAR TY DOUR TY DREN TY DRENN TY DUCHENE TY DUFF TY LOPREDEN TY ELVENN 51 AR ZAL TY EOL KUZET KERVRAN TYER DOUAR TYES MEIN TY ES MEIN TY EUGENE TY FAMILHOU KERDIOUZET TY FAO TY FAOU TY FEUNTEUN TY FLATRES TY FLEHAN TY FLOCH TY FLOUEREN LANRIOT TY FLOUR TY FOENNEC TY FORN TY FORN COZ TY FORN KERESTOU TY FOURN KERGENETAL TY FOURN LOPREDEN TY FOURN TRAON HIR TY FUR TY GAEL GOARBIC TY GARDE TY GARDE KERJACOB TY GARDE MESQUEON TY GARDE TREFRY TY GARDIEN TY GARD KERUSCAR TY GEAN TY GLAS TY GLAS KERGALET TY GLASS TY GLAZ TY GLAZ 29 PENLAN TY GLAZICK TY GLAZ LESTREZEC TY GLOANEC TY GOAREM TY GOASKET TY GOEL YAN VRAS TY GOFF TY GOUEL YAN TY GOUEL YANN TY GOUZOU TY GREAN TY GRIS FER KERCANIC TY GROAS TY GROES TY GUEN TY GUEN CREACHQUELLIC TY GUEN KERGOGAN TY GUEN KERGOGANT TY GUEN KERLESQUIN TY GUEN LEONGARD TY GUENN TY GUEN QUELLERET TY GUEN TY PRI TY GUICHET TY GUICHET VIHAN TY GUIP TY GWASKELL KERAUTRET TY GWEN TY GWENN TY HEMON TY HERE TY HIR TY HIR LOST AR HOAT TY HOANT TY HOCHEC TY HOLLIER TY HOR TY HORN TY HORNEC VRAS TY HOR VIAN TY HOS TY HOURN TY HOURRET TY HOUYEN TY HUEL TY HUELLA TY HUR TY IZELLA TY IZELLA KEROUZERN TY JACQ TY JACQUES TY JAFFRE TY JAFFRES TY JAOUEN TY JARDIN TY JOB TY JOLLEC TY JOPIC TY JOS KERTREGUIER TY JOS MORGAT KERBASGUEN TY JULIEN TY KELES TY KENTA 1 STREAT AN ABER TY KER TY KERAVEC TY KERJORE TY KERLORET TY KERLOU TY KERNEIS TY KERREG KERCOULIOU TY KOLLET TY KREIS AR LAND TY KUZET TY KUZ TAOL KERNEVEZ TY LABOUS TY LAGALA TY LAN TY LANN TY LANN CREIS TY LANNEC TY LAOUEN LANEVRY TY LAURENT TY LEN TY LEOC TY LIPIC 10 ALEZ IZELA TY LOBA CINQ TY LOSKET TY LOSQUET TY LOUET TY LOUIS TY LUDU TY LUTEN TY MABIK KERARIOU TY MAB URVOAS TY MAHE TY MANUELINE HENT MEN TY MAO TY TY MARK TY MARRANT TY MARTELOT TY CRAPON TY MEAN TY MEAN BRAZ TY MEIL TY MEILH TY MEIL KERONDO TY MEL TY MELEN TY MELEN LANGUIEN TY MEN TY MEN BRAS TY MEN COZ KERLIN TY MENEZ TYMEN KERDELAND TY MENN TY MENS TY MEUR TY MEUR KERMOBEREZ TY MIGNON TY MIKAEL TY MILIN TY MOAL TY MOGUELL TY MOTER VRAS TY MOTTER VIAN TYMOTTER VRAS TY MOULLEC TY MOULLEC VIAN TY MOUSTOIR GOAREMMOU TY NADAN TY NAEL TY NAFFRET TY NAIR TY NANCIEN TY NAOD TY NAOT TY NAOU TY NAOUR TY NAST TY NATUR TY NATUR BIHAN TY NEARCH LOGONNA TY NEC H TY NENEZ TY NEOL TY NEVEZ TY NEVEZ AR BRIEC TY NEVEZ BARRE TY NEVEZ BASCAM TY NEVEZ BEROT TY NEVEZ BOUTEFELEC TY NEVEZ CAMASQUEL TY NEVEZ COAT MORVAN TY NEVEZ COBALAN TY NEVEZ CORTIOU TY NEVEZ DOURYED TY NEVEZ GARNILIS TY NEVEZ GORREQUER TY NEVEZ GUENANE TY NEVEZ GUERNEZ TY NEVEZ GURVALE TY NEVEZ GURVENNOU TY NEVEZ KERAMBOYEC TY NEVEZ KERANROUX TY NEVEZ KERBERNEZ TY NEVEZ KERDALAE TY NEVEZ KERDANIEL TY NEVEZ KERDELLIOU TY NEVEZ KERELLOU TY NEVEZ KERFORT TY NEVEZ KERGUINOU TY NEVEZ KERHERVE TY NEVEZ KERHIER TY NEVEZ KERHOANTIEN TY NEVEZ KERHUA TY NEVEZ KERICUN TY NEVEZ KERIOU KERRIOU TY NEVEZ KERLAGADIC TY NEVEZ KERLESTREC TY NEVEZ KERLEVIC TY NEVEZ KERLEVOT TY NEVEZ KERLIES TY NEVEZ KERLONIC TY NEVEZ KERLORET TY NEVEZ KERMADEC TY NEVEZ KERMADORET TY NEVEZ KERMOAL TY NEVEZ KERMOBEREZ TY NEVEZ KERNESCOP TY NEVEZ KERNON TY NEVEZ KEROMNES TY NEVEZ KEROUARCH TY NEVEZ KERPUNZ TY NEVEZ KERREST TY NEVEZ KERSABIEC TY NEVEZ KERTANGUY TY NEVEZ KERUSTANS TY NEVEZ KERVAO TY NEVEZ KERVEGUEN TY NEVEZ KERVENAL TY NEVEZ KERVEVIN TY NEVEZ KERVIEL TY NEVEZ KERVOUELET TY NEVEZ KERYANNIC TY NEVEZ KERYOT TY NEVEZ LANNEC TY NEVEZ LESPLOMEUR TY NEVEZ LESPRITEN TY NEVEZ LEUSTEC TY NEVEZ LEZOUANACH TY NEVEZ LIGEN TY NEVEZ LIORZOU TY NEVEZ LOPEAU TY NEVEZ LOPREDEN TY NEVEZ LORETTE MENEZ LORETTE TY NEVEZ LOUEDEC TY NEVEZ MAILLE TY NEVEZ MEIL CORNIGUEL TY NEVEZ MEIL KERROC H TY NEVEZ MESMEUR TY NEVEZ MEZANLEZ TY NEVEZ MOUSTERCOAT TY NEVEZ MOUSTERLIN HENT KERLER TY NEVEZ PENHUEL TY NEVEZ PENNANEACH TY NEVEZ PENN AR LANN TY NEVEZ PENPRAT TY NEVEZ PEREN TY NEVEZ PERN TY NEVEZ PRATARE BIHAN TY NEVEZ QUILLAVOGUEN TY NEVEZ ROSBRIANT TY NEVEZ ROZHUEL TY NEVEZ RUGUEL TY NEVEZ RUMAIN TY NEVEZ RUPIQUET TY NEVEZ CADO TY NEVEZ JILI TY NEVEZ SCUILLER TY NEVEZ TRAON HIR TYNEVEZ TRAON RUVILY TY NEVEZ TREDIEC TY NEVEZ VARIA TY NEVEZ VEIL GOZ TY NOD TY NOT TY NOUET TY OURONT TY OURTES TY PAGE TY PALMER TY PALUD TY PARC TY PATRICK TY PAVEC TY PELLO TY PENFRAT TY PENGLEO TY PER LOUIS TY PHIOU TY PIC TY PIN 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CARABASSE CARABASSE TERRASSES DAC LESCOT PINEDES PINS PLAGE PLAGNOL PLANETE MERCURE PLANETE SATURNE PLATANES PLEIN SUD AVEN FER PORT PONANT PORTANEL POURQUIE PREVOT PROVENCE RAYMOND BUSSIERES RENE DESCARTES RIEU POURQUIE RIVELIN ROC ROGER SALENGRO ROMARINS ROUGETTE RUBENS RUTEBEUF RUTHENES SABLES SACHA GUITRY SADI CARNOT AUGUSTIN ETIENNE CAVALL CAVALLE SAINTE AGNES SAINTE BAUME SALOMON BROSSE SEMAILLETS SEMILLON SENIBELETS SERVANT SIDON SIMON VOUET SIMONE BEAUVOIR SOEUR CHAGNY SOURCE SOURCES SURCOUF SYRAH TEILHARD CHARDIN TERRES ROUGES THEODORE BEZE THYM TILLEUL TILLEULS TONNELIERS TREILLES TREMBLE TURENNE ULYSSE VACARES VAL OLIVIERS VAL GARRIGUES VERRERIE VICTOR SCHOELCHER VOLTA WILHELM ROENTGEN YVES NAT YVES STOURDZE YVES TANGUY ALLEES REPUBLIQUE ALLEES CYPRES ALLEES ALLEES CHABLIS ALLEES RIQUET ALOUETTE AMANDIERS ANANDA PESTOUS PESTOUS ANCIEN ANCIENNE COOPERATIVE ANCIENNE ANCIENNE CAILHO ANCIENNE VILLAGE ANCIENNE FORGE ANCIENNE ANCIENNE ANCIENNE POSTE ANDABRE ANEXE ARAMON 40 ST FONTAURY ARAMONS ARCADE JACQUES COEUR ARDIDE BASSE ARDOUANE ARGELIERS ARGILAS ARIFAT ARNET ARTE BEDARIEUX ARTE MONTPELLIER ASPES ASPRES AU PRADAL AUBERGE AUMET AUPIGNO AUREILHE AUTHEZE AUTIGNAGUET AUTOROUTE CA AUTOROUTE CARNON AUTOROUTE PEAGE BEZIERS EST AUZIALE 1 ER MAI 11 NOVEMBRE 11 NOVEMBRE 1918 17 AOUT 19 MARS 1962 2 FONTAINES 22 AOUT 1944 8 MAI 8 MAI 1945 8 MAI 45 BRIAND ABBE BROUILLET ABBE PARGUEL ABBE SAGNES ABBE TARROUX ABRIVADOS ACACIAS ACCACIAS ACHILLE LEVERE AGAU AGDE AGELLIERS AGROPOLIS AIME TREBOULON AIRES ALBERT 1 ER ALBERT CAMUS ALBERT DUBOUT ALBERT EINSTEIN ALBERT LAURENS ALBERT LAURENT ALBERT SCHWEITZER ALEXANDRE LAVAL ALEXANDRE PIQUET BOAT BOUAT ALIGNAN ALIZES ALPHONSE DAUDET ALPHONSE GRANIER ALPHONSE JUIN ALPHONSE MAS ALSACE ALTAIR AMANDIERS AMANTS AMBROISE PARE AMEDEE BORREL AMIRAL COURBET AMPHITRITE ANATOLE FRANCE ANCIENS COMBATTANTS ANDRE AMPERE ANDRE BRINGUIER ANDRE CHASSEFIERE ANDRE MALRAUX ANDRE PALMADE ANIANE ANTOINE D ARGENVILLE ANTOINE EXUPERY APOLLON APOTHICAIRES ARGELIERS ARGELLIERS ARISTIDE BRIAND ARMAND DANEY ARNAUD PLESSIS ARTHUR RIMBAUD ASPHODELES ASSAS ASSIGNAN ASSIGNAN 6 MARTINET AUBE ROUGE AUGUSTE ALBERTINI AUGUSTE COTE AUGUSTIN FLICHE AUMES AURELLE AZILLANET BACCHUS BAINS BALAMAN BALAUSSAN BARCELONE BARDEJEAN BAS RHONE BASSAGET BASSAGUET BEDARIEUX BEL AIR BELARGA BENJAMIN GAUZY BERGERIE BERNARD CABANE BERNARD CABANES BESSAN BEZIERS BIGOS BILLIERE BLAISE PASCAL BLANQUI BOULOGNE 34 COUCHANT BOISSIER BOSQUET BOUISSON BERTRAND BOUTONNET BOYNE BUTTE CABRIERES CALMETTE CALMETTES CALVAIRE CAMBACERES CAMILLE DESMOULINS CAMILLE MONDOU CAMILLE SAENS CAMPAGNAN CAMPING CAMPINGS CANCEL CANET CANTAGRIL CANTINIERES CAPESTANG CAPESTANG AUBERGES CAPIMONT CAPITAINE BONNET CAPITAINE FULCRAN CAPUS CARDINAL FLEURY CARIGNAN CARLET CARNOT CAROUX CARRIERES CARRION NIZAS CASINO CASSAN CASSIOPEE CASSIOPEE BERGERIE CASTELLAS CASTELNAU CAUSSE CAUSSES CAUX CAVE CAVE COOPERATIVE CAVE PARC CAZEDARNES CAZILHAC CAZOULS CELESTIN ARNAUD CENTURIONS CERISIERS CESSE CESSENON CESSERAS CEVENNES CEVENNES 6 LAZIOLS CH CLAIRVILLE CHAMP L HORTE CHAMP HORTS CHAMP MOULINS CHAMP MOULINS CHAMPS CHAMPS BATAILLE CHAMPS HORTS CHAMPS MOULINS CHAMPS MOULINS CHANCEL CHAPELLE CHARCOT CAUQUIL CORBIERE CROS FOUCAULD CHARLES FLAHAULT FLAHAUT GOUNOD CHASSEFIERE CHASSEURS CHATEAU D O NEUF CHEVALIER D ALPHONSE CIBRAN VIEUX CISTES CITES CLAPAS CLAUDE BERNARD CLAVOUX CLEMENCEAU CLEMENT ADER CLEMENT CUGNENC CLERMONT CLERMONT L HERAULT CLOS BERGERONNETTE CLOS GREC COLOMBES COLONEL D ORNANO COLONEL PAVELET COLONEL SIMON COMBARELLES COMBATTANTS COMTES MELGUEIL COMTES MELGUEIL CONDAMINE CONDAMINES CONDORCET CONSEIL GENERAL COOPERATIVE CORNICHE NEUBURG COSTIERES COTEAUX MONTFERRAND COULOBRES COULOUMA COURNONTERRAL COURNONTERRAL 3 CROIX MOUNIE CROIX CAPITAINE CROIX SUD D ABEILHAN D AGDE D AIGUES D AIGUES MORTES D ALIGNAN VENT D ANDALOUSIE D ASSAS D ESPAGNE D ESPONDEILHAN D ESTIENNE D ORVES D HEIDELBERG D INGRIL D ISSANKA D ITALIE D OC D OCCITANIE D UNTERSCHLEISSHEIM DANTON DEMAR BADONES BARCELONE BEAULIEU BEDARIEUX BEZIERS BOIRARGUES BOUJAN BOUZENAC CAMPAGNAN CARNON CASTELNAU CASTELNAU GUERS CASTRIES CLERMONT COULOBRES COURNONTERRAL DOSCARES FLORENSAC FREJORGUES FUMEL GOLFY GRAMMONT L ABRIVADO L AGAU L AIGOUAL L ANCIENNE L AUBE ROUGE L ESCANDORGUE L ESCANDOUNE L ESPINOUSE L ESPLANADE L ESTABEL L ETANG GREC L EUROPE L INDUSTRIE BARRIERE BOUVINE CADOLE CADOULE CAVE COOPERATIVE CLASTRE CONDAMINE COOPERATIVE CROIX MOUNIE CROIX MOUNIER CROIX CAPITAINE DEVEZE FABRIQUE FONTAINE NEUVE FONTASSE FONTVIN FRIGOULE GAILLARDE GALINE GARDIOLE GARE 11 MIDI GARRIGUE JETEE JOLIETTE LIBERATION LIBERTE LIRONDE MARNE MEDITERRANEE MEDITERRANNEE MER MIRANDE MISSION MOLIERE MONTAGNE MOSSON MOUTTE PAIX MOTTE PINEDE PISCINE PLAGE PLEIADE POMPIGNANE PRADELLE PROMENADE REINE D ITALIE REP MONTFERRAND REPUBLIQUE RESISTANCE RIVIERE ROYALE SAUVAGINE SERRE SOURCE TANNE TONNELLE TOUR TRAMONTANE TREMOULETTE TUILERIE VOIE DOMITIENNE VOLHE LATTARA LIBRILLA LIEURAN LODEVE LOUISVILLE LUNEL 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MICOCOULIERS MOULINS MURIERS MUTINS NOUVEAUX THERMES OLYMPIADES ORCHIDEES PINS PLAGES PLATANES PORTES MER PRES D ARENES PRUNUS QUATRE SAISONS SABINES SALADELLES SERGENTS SERRES SOPHORAS THERMES ATHENA THERMES ROMAINS TREILLES TREILLES FALGOUS VACANCES VENUS VERGERS VIGNERONS DEUX PONTS DISTILLERIE JACQUES FOURCADE JOSEPH BOUDOURESQUE MENARD TABOURIECH DOYEN G GIRAUD DOYEN GASTON GIRAUD DOYEN RENE GOSSE DROITS L HOMME 11 NOVEMBRE 11 NOVEMBRE 1918 22 AOUT 1944 8 MAI 8 MAI 1945 BAGNAS BASSIN THAU BELVEDERE BERANGE BITERROIS BOULOGNE COUCHANT CAPISCOL CAPITAINE FULCRAND CHATEAU D EAU D O NEUF CLAPAS COLONEL ANDRE PAVELET COLONEL BRES COLONEL GUIZARD COLONEL SIMON COMTE NICE CONSUL COUCHANT DEVOIS PEZET DOYEN G GIRAUD DOYEN GASTON GIRAUD GENERAL GENERAL JUIN GENERAL GENERAL MALAFOSSE GENERAL SARRAIL GINESTIER GOLF CHENE LARGE GRENACHE JEU MAIL LAC LANGUEDOC LAURAGAIS LEZ LITTORAL MAJOR FLANDRE MALUZAN MARCHE MARECHAL BUGEAUD MARECHAL FOCH MARECHAL JUIN MARECHAL KOENIG MARECHAL MARTINET MAS ARGELLIERS MAS D OLIVET MAS BARON MAS CLERGUES MAS FINET MAS QUARANTE MAS SAPTE MAS FAUGERE MAS MAS ROUGE MEJEAN MIRADOU MISTRAL MONDIAL 98 MONTOULIERS MOULIN VENT JASSE MUSCAT PARC PASSEUR CHALLIES PECH GALINIERE PERE PREVOST PERE SOULAS BARD NICE TRAIN PIC LOUP PICHAGRET PIN PARASOL PIREE PLAN PLAN D AILAS POILU DU JUVENAL TRINQUAT VIEUX PORT PORT NOTRE DAME PR ANTONELLI PRADO PRESIDENT WILSON PROFESSEUR EMILE JEANBRAU PROFESSEUR GRASSET PROFESSEUR LOUIS VIALA PROFESSEUR LOUIS RAVAS PUECH MASSANE ROC FERLUS ROSAIRE ROUARGUES SALAISON SERPENTIN STADE STADE MUNICIPAL TENNIS TOURTOUREL VAL MONTFERRAND VERT VIDOURLE VIGUIER D AGRICULTURE ECOLES EDMOND ROSTAND EDOUARD BONNAFE EGALITE ELYSEES EMBRUSCALES EMBRUSCALLES EMILE BERTIN SANS EMILE CABROL EMILE CLAPAREDE EMILE COMBES EMILE DIACON EMILE JEANBRAU EMILE LOSCOS EMILE MERCIER EMILE ZOLA EMMANUEL ARNAUD ENSEIGNE ALBERTINI ESPONDEILHAN ETANG ETANG GREC ETATS LANGUEDOC ETATS LANGUEDOC FRED BOUISSON MEHUL ETTORE BUGATTI EUGENE DELACROIX EUROPE EUROPE CLAPIERS EVECHE MAGUELONE EVECHE MAGUELONE EVECHE MAGUELONNE F LESSEPS F MAS F SABATIER D ESPEYRAN FABRE MORLHON FABRICOLIS FANFONNE GUILLERME FAUGERES FAUGERES EMPRADES FAUVILLE EN FAUVILLE EN CAUX FAUVILLE ST CLOTILDE CAUX FERDINAND LESSEPS FERDINAND LESSEPS FERDINAND PALOC FERME FERNAND SCHMIDT FERNAND STHMIDT FIGUIERE FIGUIERES FIGUIERS FLEMING FLEURY FLORENSAC FOCH FOLCO BARONCELLI FONTAINE MOSSON FONTCAUDE FONTCERISE FONTES FONTREGEIRE FOUZILLON AMADOU BOISSIER CUREE HUE MAS MIOCH MITTERAND MITTERRAND FREDERIC MISTRAL FRERES BOYER FRERES LUMIERE FRIGOULET G CLEMENCEAU GABRIEL ALDIE GABRIEL FAURE GABRIEL IMBERT GABRIEL MAZEL GABRIEL PERI GAILLARDE GALERES GALINE GAMBETTA GARBIE GARDIE GARDIOLE GARE DUBOUT ALBERT DUBOUT MIDI ONGLOUS MAGELLAN VILLENEUVE BEZIERS GARENNE GARRIGUES GASTON BAISSETTE GASTON BRES GASTON DEFFERRE GAVE GEN LATTRE TASSIGNY GEN GAULE HT FABREGUES GEN GENERAL BALAMAN GENERAL GENERAL GENERAL GROLIER GENERAL GROLLIER GENERAL GENERAL MONTAGNE GENERAL SARRAIL GENETS GENEVRIERS BIZET BRASSENS CHARPENTIER CLEMENCEAU GUYNEMER MELIES MILHAU MILHAUD POMPIDOU GERMAINE RICHIER GIGNAC GOLF GOLF 106 GOLF MASSANE GOURGAN GOURGAU GOURGOU GRACION CIBRAN GRACION CIBRAND CHENE JEU SOLEIL GARRIGUE MAIRE GRANGE ROUGE GRASSION CIBRAN GRASSION CIBRAND GREEN GRENACHE GROTTE GROUPE SCOLAIRE GUILHEM POITIERS PELLICIER HAMEAUX GOLF HAROLD KLINE HAUTS FONCAUDE HAUTS FONTCAUDE HAUTS FONCAUDE HECTOR BERLIOZ HEIDELBERG HELENE MAINGUIN TOUS HENRI GALINIER HENRI GOUROU HENRI MARES HENRI MAS HENRY BRISSAC HESPERIDES HOMPS HUSSICHES ILE ILE AMERIQUE ILE D AMERIQUE ILE D AMERIQUE CAP 2000 INDUSTRIE INGARRIGUES INGRIL INTERSCHLEISSHEIM ISSANKA ISSENSAC J FOUCAULT BOITE POSTALE JACQUES CARTIER JACQUES GAFFINEL JARDINS JASSE BAPTISTE CLEMENT BAPTISTE SOLIGNAC BATISTE SOLIGNAC BENE BOREL BRINGER CONSTANS FOUCAULT FOUCAULT MARCHE GAU JAURES LAURES MANZANERA MEJANEL MERMOZ MONET MONET 8 MONNET SEBASTIEN BACH STER JEANNE D ARC JETEE JEU BALLON JEU MAIL JOCKEYS 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MAR FOCH MAR MARCEL DASSAULT MARCEL MAJUREL MARCEL PAGNOL MARCELIN ALBERT MARCELLIN ALBERT MARCHE MARCHE MARECHAL D LATTRE TASSIGNY MARECHAL LATTRE T MARECHAL LATTRE TASSIGNY MARECHAL FOCH MARECHAL JOFFRE MARECHAL JUIN MARECHAL KOENIG MARECHAL LATTRE TASSIGNY MARGON MONTPELLIER MARIUS ALES MARIUS REDONNEL MARJOLAINE MARRONNIERS MARSEILLAN MARTYRS RESISTANCE MAS D AFFRE MAS CHAVE MAS SAPTE MAS FAUGERE MAS OLIVET MAS SALAT MAUREILHAN MAURICE PLANES MAURIN MAURIN P 3 MAX DORMOY MEDITERRANEE MEDITERRANEE BELLE ETOILE MELGUEIL MELUSINE MENARD MENDES FRANCE MER MER CENTAURE MER SOLIS ELYTIS MEZE MICHEL CHEVALIER MICHEL L HOSPITAL MIDI MILLAU MIMOSAS MINERVOIS MOLIERE MONDIAL 98 CS MONSIEUR TESTE MONT AIGOUAL MONT CAMP MONTADY MONTAGNAC MONTAGNETTE MONTAURIE MONTBLANC MONTOULIERS MONTPELLIER MONTS RAMUS MONUMENT MOUGERE MOUGERES MOULIN TOURTOUREL MOULINS MUOIS MURIERS MURIERS PORTIRAGNES PLAGE MURVIEL MUSCAT MUTINS 17 EME RI NAFFRIE NARBONNE NATHAN MURAT NAVIGARDE NEFFIES NEUBURG NIMES NISSAN NOEMIE BERTHOMIEU NOTRE DAME NOTRE DAME PINS NOTRE DAME ROUVIERE NOUVEAU MONDE OCCITANIA OCCITANIE OLIVIERS OLONZAC ONZE NOVEMBRE 1918 ORANGERS ORB OUTRE MER P CURIE 8 PALAVAS PALAVAS CS PALAVAS CS 1006 PAMPELUNE PARC PASSET PASSEUR CHALLIES PASTEUR PASTEUR 23 PATRICK GEDDES BALMIGERE BRINGUIER DEMARNE DOUMER LOUBET PELISSE RIQUET ROUMAGNAC TEISSERENC VALERY VALERY 57 VIDAL VIDAL BLACHE VIGNE D OCTON PAULHAN PAYSAGERE PECH GALINIERE PERE SOULAS PERE SOULAS RESID BARD SOLEIL PEUPLIERS PEYRE GROSSE PEYROU PEZENAS PHILIPPE LAMOUR PIBOULE PIC PIC LOUP PIC ST LOUP PICPOUL AZEMA BEREGOVOY BEREGOVOY VILLENEUVE BEREGOVOY VILLENEUVE CASTEL CURIE CURIE 5 CURIE 9 D ADHEMAR COUBERTIN DENTAL CURIE FLOURENS GRASSET MOREL LACANS MENDES FRANCE PLANTEE SIRVEN VERDIER PIGNAR PINET PINS PINS PARASOLS PIREE PISCINE PISSE SAUMES PLAGE PLAINE PLAISSAN PLATANE PLATANES PLEIN SOLEIL POILHES POILU 3 POILU 5 POILUS POINT JOUR POMEROLS POMPE POMPIGNANE PONT JUVENAL CS TRINQUAT POPIAN POUGET POUJOLS POUSSAN POUZOLLES POUZOLS PRES PRES D ARENES PRES W PRESIDENT WILSON PROF EMILE JEANBRAU PROF GRASSET PROF LOUIS RAVAS PROFESSEUR LOUIS RAVAS PROGRES PROMENADE PROSPER MERIMEE PRUNUS QUAI QUARTIER BAS QUARTIER HAUT RABELAIS RAIMBAUD D ORANGE RAOUL BAYOU RAOUL BONNECAZE RAOUL CALAS RAYMOND LACOMBE RAYMOND PITET REDOUTE PLAGE REINE HELENE D ITALIE RENE CASSIN RENE GUIRAUD REPUBLIQUE REPUBLIQUE MONTFERRAND RESCLAUZE RHIN DANUBE RICHEMOND RIVET RIVIERE ROBERT FAGES ROC L ESTAN ROCADE ROCHELONGUE ROGER AUDOUX ROGER PONS ROGER SALENGRO ROGER SALINGRO ROMARINS ROMPUDES RONZIER JOLY RONZIER JOLY 13 ROSAIRE ROSERAIE ROUCAS BLANC ROUJAN ROUQUETTE ROYALE BONNECAZE ANDRE NOVIGENS BAULERY BAUZILLE BENOIT D ANIANE CHINIAN CHINIAN COMBEJEAN CHRISTOPHE CLEMENT EXUPERY GELY GUILHEM JACQUES SAINT JUST LAZARE MARTIN MAUR MAURICE MAURICE SAURET MAURICE EL RIO NAZAIRE PARGOIRE PAUL VALMALLE PONS ROCH SEANS VINCENT SAINTE CECILE SAINTE CROIX SAINTE CRX QUINTILLARGUES SALENGRO SAMBUC SAMBUC TERRAL SAMUEL BASSAGET 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LIBRON FARINETTE BARRIERE FRANGOUILLE BARRIERES HAUTES BARROU BARROUBIO BARRYBAS BARTHEZOU HAUT BAS BASE NAUTIQUE REALS BASSIN THAU BASSIN THAU 7 BASSIN PLAISANCE BAT ST BATICOOP BATIMENT MURIERS BATIMENT AA BATIMENT BATIMENT V 2 12 CARRIERA LOU BATIMENT C 6 PERGOLA BATIMENT CE BATIMENT CG BATIMENT DEPARTEMENTAL BATIMENT DFRPA PIOCH BATIMENT EI BATIMENT EUROGARE BATIMENT BORIETTE FONCLARE BATIMENT TM 8 CAUNES BEAULIEU BEAUX ARTS BEGOT HAUT BEL AIR BELBEZET BELBEZET COMBES BELLE FONTAINE BELLE RASE BELLEGARDE BAS BELVEZET BERANGET II 3 PLAN FENOUILLET BERGE THAU BERGERIE SUEILLES BERGERIE HORTUS BERGERIE NEUVE BERGERONNETTES BERNAGUES BESSES BESSES HAUTES BESSIERE BEZIERS BEZIS BAS BEZIS HAUT BIQUIRI BIQUIRY BIRANQUES BAS DOMAINE BOIS CAUMETTE POUILHES ROUVIGNAC BOISSEZON BON ABRI BONNEFONT BONNEVAL BORDEVIELLE BOSC NAUVERT BRASSAC BOUBALS BOUISSIERES BOUISSOUNADES BOULDOUX 11 NOVEMBRE 4 SEPTEMBRE AEROPORT INT MONTPELLIER AEROPORT INTERNATIONAL ALEXANDRE DUMAS ALPHONSE DAUDET AMIRAL COURBET ANATOLE FRANCE ANDRE MOUNIE ANSELME NOUGARET ANTONIN INJALBERT ARCEAUX ARESQUIERS ARISTIDE BRIAND ARMAND DURAND BENJAMIN MILHAUD BERTHELOT BERTRAND DUGUESCLIN BIR HAKEIM BONNE NOUVELLE BOSSUET CAMILLE BLANC CAMILLE BLANC B CAMILLE MONDOU CANTAUSSILS CAPITAINE ESPINADEL CARRIERE POISSONNIERE CASINO CASTELBON BEAUXHOSTES WARNERY CREUX CHEVALIER CLERVILLE CLEMENCEAU COMMANDANT LHERMINIER COMMDT L HERMINIER COMMERCIAL CONDAMINE CORNICHE COSSES SOUS CONDAMINE NATIONAL D ANGLETERRE D ANTIGONE D ESTIENNE D ORVES D HEILBRONN D ORIENT DANIELE CASANOVA BIR HAKEIM GENEVE L AEROPORT INTERNATIONAL L EUROPE L OBSERVATOIRE BOUQUERIE COLLINE DEMOCRATIE LENE LIBERTE LIRONDE NICOLE PERRUQUE REPUBLIQUE LATTRE TASSIGNY LAURIOL MASSANE MONACO STASBOURG STOCKPORT STRASBOURG VERDUN DEMOCRATIE ALOUETTES ARCEAUX CHASSELAS CONSULS MER ECOLES FONTAINES GUILHEMS LUCIOLES MOURES REMPARTS SALINS SOURCES SPORTS DIDEROT DIGUE COMBESCURE DOMENOVES CHAPITRE CREUX COUCHANT MOURRUT FOUR CHAUX FRONT MER GARISSOU GENERAL GENERAL LARMINAT GENERAL DEVOIS TRIANON JEU PAUME LANGUEDOC PLEIN SOLEIL PORT PROFESSEUR LOUIS VIALLETON RIVERAIN SOLEIL DUNES ECOLES EDGAR QUINET EDOUARD DAUMAS EDOUARD HERRIOT EGALITE ELYSEE SAISSET EMILE ZOLA ERNEST MASSOL ERNEST PERREAL ERNEST RENAN ESPLANADE F MISTRAL VILLENEUVE BEZIE FELIX GIRAUD FOCH FRANCISQUE SARCEY FRATERNITE FRATERNITE PROLONGEE FREDERIC FABREGES FREDERIC MISTRAL FRONT MER FRONT MER LOUIS VALIER FRONT MER PORTIRAGNES PLAGE GABRIEL PERI GAMBETTA GAMBETTA VILLENEUVE BEZIER GEN GENEVE CLEMENCEAU POMPIDOU GUILHEM GUILHEMS HELLENES HENRI IV HENRI IV MONTPELLIER J CADENAT JACQUES MONOD BAPTISTE BENEZECH BAPTISTE BENEZETH BERTONY BERTOUY BOUIN DANGA DAUGA JAURES MACE MATHIEU GRANGENT MONNET JEU BALLON JEU MAIL JEU PAUM JOLIOT CURIE JOSEPH GUIRAL JULES CADENAT JULES CADENAT GAYONNE II JULES FERRY JULES GUESDE KENNEDY LAFAYETTE MARINE TOUR GUET LAFAYETTE LAMARTINE LATTRE TASSIGNY LAURIOL D 65 LEDRU ROLLIN LEIBOWITZ LEZBOWIEZ LIBERTE LIRONDE LISSE LOUIS BLANC LOUIS BLAZIN LOUIS UNI DIT APOLLON M GRANGENT MAGENTA MAR LATTRE TASSIGNY MARECHAL FOCH MARECHAL JOFFRE MARECHAL JUIN MARECHAL LATTRE TASSIGNY MARECHAL MARIN MARIUS ROQUEBLAVE MASSANE MATELOTS MENDES F MENDES FRANCE MICHELET MIDI MINERVOIS MOLIERE MONTALANGUE MONTBLANC CATALUNIA MONTPELLIERET MOUCAIROL MOURCAYROL MOURES NORD NOTRE DAME BUIS ONZE NOVEMBRE ORB ORIENT PASTEUR BERT RIQUET VALERY PAULINE BOUISSON PEDRO LUNA PENELOPE GIRAUD MENDES FRANCE PORT PORTAIL VIEIL PROGRES PROSPER GELY PROSPER GERVAIS PUITS ALLIER RABELAIS RECANETTE RENE CASSIN RENE TULET RENOUVIER REPUBLIQUE RIVELIN ROGER AUDOUX RUISSEAU SADI CARNOT CHRIST EXUPERY FRUCTUEUX BOULEVARD LOUIS MAURICE MICHEL SAENS SAINTE CLAIRE SARRAIL SARRAZIN SOURCES STRASBOURG TOUR TOURVENTOUSE VERDUN VICTOIRE VICTOR HUGO VIEUSSENS VIEUX MOULINS VOLTAIRE YVES MANOIR YVES NAT BOULOT BOURGIDOU BOUSCARES BOUSQUET BALME BAS BOUSQUET BALME HAUT BOUSSAGUES BP 1 BP 110 BP 16 BP 25 BP 29 BP 3 BRASSAC BRES BRETTES BRIAN BRIANDES BROUE BUREAUX TOUR POLYGONE C CABANE RAJOLS CABANES CABANES CARNON CABANES L ARNEL CABANES TERRE CABANES LUNEL CABANES PEROLS CABANIS CABASSE CABREROLLES LIQUIERE CABRIALS CABRIERETTE CACAVEL CADE CAILHO CAILHO HAUT CALAMIAC COMBES HAUTES CALVET CAM DEL AUSSELO CAMARIE CAMBAISSY CAMBARON MARTHOMIS CAMBIS CAMBOURAS CAMBOUS CAMI D ANTONEGRE CAMI DAS FANABREGAS CAMI CANTO RANO CAMI GABRIAC CAMI GARELLE CAMI ROQUE CAMI LAS OLIVEIDAS CAMI MALAKOFF CAMI RIBAUTE CAMI DEL ORMEU CAMI ARTISANS CAMI CISTES CAMI CRESSES CAMI OEILLADES CAMI OLIVIERS CAMI CHEVALET CAMI MAS D ARNAUD CAMI PANAT CAMI PEZENAS CAMI TILLEULS CAMIN AYALOU CAMIN BARROUBIO CAMIN CIGALOU CAMIN DAL POTZ PEPIEUX CAMIN DEL AUSSELO CAMIN DEL CACAIRE CAMIN OURMES CAMIN DOU BOSC CAMIN DOU VIRE VIRE CAMIN PUECH REDON CAMIN ENCAVAT CAMIN FONT GLEIZA CAMIN LOU BLAGAIRE CAMISARDE BEAUDESERT CAMP ARIES CAMP D ARIES CAMP DEL TOUR CAMP MERIC CAMP NEGRE CAMPAGNE AURIBELLE CAMPAGNE BONNIOL CAMPAGNE REPUBLIQUE CAMPAGNE CHARMETTES CAMPAGNE MAILHAC CAMPAGNE MOUGNO CAMPAGNE JOSEPH CAMPAGNE LEON CAMPAGNE MARC CAMPAGNE SAVIGNAC HAUT CAMPAGNE ST LOUIS VAUGUIERE HAUT CAMPAGNE VALAUTRES CAMPELS CAMPESCAL CAMPESTRE CAMPING AMAT ORPELIERES CAMPING PT MOTTE CAMPING BEAU CAMPING BEAUSEJOUR MAIRE CAMPING BOSQUET CAMPING GAI SOLEIL FARINETTE CAMPING GRAU VENDRES CAMPING DUNE KABYLIE CAMPING MAIRE MAIRE CAMPING REDOUTE CAMPING YOLE CAMPING BAOUS CAMPING BOSQUET CAMPING REBAU CAMPING CIGALES CONAS CAMPING PEUPLIERS ROUSSILLO CAMPING ROCHELONGUE CAMPING ROUTE CAP CAMPING SABLES D OR ROCHELONGUE CAMPING VAL D HERAULT CAMPLONG CAMPREDON CAMPROGER VINCENT CAMPS MANS CAMPUS BISSY HAUTS BISSY CANAGUE CANAGUE NEUVE CANAGUE VIEILLE CANAL ARESQUIERS CANAL ARESQUIERS CANAL QUILLES CANAL QUILLES CANALETTES CANET CANIMALS HAUT CANTA AOUESSEL CANTAGRIL CANTALAURES CANTEGALS CANTEGRIL CANTMERLES CAP BOURIO CAP SUD MOTTE COUCHANT CAPELLAN CAPOUNE CARABASSE FARINETTE CARIGNAN CARMEDOULE CARNAS CARNON PLAGE CAROUILLO CARRE ROUSSELS 1 CARREAUX BOULDOUX CARREFOUR 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CIAL BEZIERS II MONTIMARAN CIAL CARNON CIAL SUD CIAL PORTES SOLEIL CIAL ST CIAL TROIS MILLE UN FARINETTE COMMERCIAL COMMERCIAL ARAGON COMMERCIAL AUCHAN COMMERCIAL BALARUC COMMERCIAL BALARUC LOISIR COMMERCIAL BEZIERS II COMMERCIAL BOULIDOU COMMERCIAL CAR BALARUC COMMERCIAL CAR GD SU COMMERCIAL CARREFOUR COMMERCIAL CASINO COMMERCIAL COEUR COMMERCIAL COURONNE COMMERCIAL CROISEE COMMERCIAL EPICENTRE COMMERCIAL EST PT PLAISANCE COMMERCIAL FORUM COMMERCIAL FREJORGUES OUEST COMMERCIAL GEANT COMMERCIAL AXE COMMERCIAL SUD COMMERCIAL INTERMARCHE COMMERCIAL J ARAGON C COMMERCIAL J D ARAGON COMMERCIAL MADELEINE COMMERCIAL POLYGONE COMMERCIAL REYMAR COMMERCIAL SALET COMMERCIAL SOLIS COMMERCIAL TERRIEU COMMERCIAL TRIANGLE COMMERCIAL COMMERCIAL 16 COMMERCIAL 33 COMMERCIAL 7 COMMERCIAL MADELEINE COMMERCIAL MONTIMARAN COMMERCIAL MTP COMMERCIAL OUEST PT PLAISANCE COMMERCIAL PAILLADE COMMERCIAL PAILLADE 1 COMMERCIAL PLEIN SUD COMMERCIAL PLEIN SUD AUCHAN COMMERCIAL POLYGONE COMMERCIAL ST COMMERCIAL TERRIEU COMMERCIAL TRIANGLE COMMERCIAL TRIFONTAINE LOISIRS ROUSSIERES VILLAGE CESSO CHAI TERRAL CHAT TERRAL CHALET CHALET 8 CHALET BUIS CHALET LEZARD CABANNES PEROLS CHAMBRE COMMERCE D INDUSTRIE CS CHAMP CROIX CHAMP MATHE CHAMP DEL POUS CHAMP CHAMP FOURNEL CHAMP LONG CHAMP MOULINS CHAMPS VERTS 398 CROUZETTE CHAPPERTIS CHASSEURS CHAT CHAT AGNAC CHAT COUJAN CHAT COUSSERGUES CHAT FESC CHAT ENGARRAN CHAT VISTOULE CHAT LAVAGNAC CHAT MARENNE CHAT MAUSSAC CHAT MONTMARIN CHAT RIBAUTE CHAT TERRAL ADOUZES TRAS CASTEL ARBORAS BANQUIERE VAUGUIERE HAUT BAS COUJAN D AGNAC GREZAN GREZAN LAVERUNE VALCYRE VERARGUES HOSPITALIERS ENGARRAN HT FABREGUES FABREGUES ROQUE VILLAREL MONTESQUIEU PERDIGUIER RIBAUTE ROUMANIERES SERIES SAINTE COLOMBE VALCYRE CHAU VIDOURLE CHD CENT TRENTE UN CHE CHE BAS VALERGUES 1 MAS TROIS CANA CHE COURREN 5 CHE L ESPRIOU 2 CHE TISSON 11 CHE MONTARELS 4 CHE PILOU 39 CHE VENDRES CHEM AIRES CHEM CHEM BALAJADE CHEM BALLATS CHEM BOULIDOU CHEM CANAL CHEM CAZALS CHEM CEINTURON CHEM CHAMP L OR CHEM CHEVRETTE CHEM CHIPRE CHEM CLAUX LOUP CHEM CLAUZES CHEM CLOT CHEM COLOMBIER CHEM COMBES CHEM COMBES GRASSE CHEM COMMUNAL 3 TR BEZIERS CHEM CONDAMINE CHEM COSSE SOUS TOUR CHEM COSTES CHEM COURREN CHEM COURREN 16 CHEM COURREN 9 CHEM CROS CHEM DEPARTEMENTAL 138 E CHEM DEPARTEMENTAL 986 CHEM CHALETS 7 CHEM QUILLES CHEM QUILLES BAT CHEM DEVES LOBRE CHEM DEVES LOBRE 4 CHEM DOUMERGARIE GOURGAS 2 E CHEM DRAILLE CHEM DRAYE MARBRE CHEM PILOU CHEM FONLONGUE CHEM FRERES CHEM GARENNES CHEM BARQUE 5 CHEM CRESSE CHEM GRANGE CHEM GRIVES CHEM GUIRAUDETTE CHEM HAUT 1 CHEM HAUTS CHEM HOPITAL CHEM INFIRMERIE CHEM JANIN CHEM JEU MAIL CHEM LAVAL CHEM LAVANDIN CHEM LEQUE CHEM LOT BORD L HERAULT CHEM LOT BORD HERAULT CHEM LUMINAIRE CHEM MANELLI CHEM MARCADAL CHEM MARQUEROSE CHEM MAS CASTEL CHEM MAS CHAVE CHEM MAS BLANC CHEM MAS CHAVE 1 CHEM MAS CHAVE 2 CHEM MAS ROCHET 5 CHEM MENDROUS CHEM MEUNIERES CHEM MONTARELS CHEM MONTILLES CHEM MONTIMARAN CHEM MORT AUX ANES CHEM MOULARES CHEM CHEM MURIERS CAILET CHEM NOUAU CHEM PECHEURS CHEM PERRIERES CHEM PEZOUILLET CHEM PICHEYROU CHEM PIERREFEU CHEM PINEDE CHEM PLAN TRIOLVEIRE CHEM POMPAGE CHEM PORTEL CHEM PRES CHEM PUECH MAJOU CHEM QUALITE CHEM QUILLES CHEM RADABEL CHEM REPUBLIQUE CHEM RIEU CHEM ROBINE CHEM ROMANY CHEM RONDE CHEM RURAL 1 CARLET CHEM RURAL 106 REFORMES CHEM RURAL 11 ST VINCENT CHEM RURAL 12 CAYROU CHEM RURAL 13 PIEULE CHEM RURAL 137 BREGINES CHEM RURAL 158 DRAGONNE CHEM RURAL 21 CAYROU CHEM RURAL 28 PANSIEROU CHEM RURAL 47 DAUBINELLE CHEM RURAL 55 CHEM RURAL 61 BOUJAN AGDE CHEM RURAL 79 MONTIMARAN CHEM SAINTE EULALIE CHEM SAUVIAC 3 CHEM SORIECH CHEM STE VEZIANE CHEM SUBSTANTION CHEM SUCRE CHEM TANE CHEM TANE 9 CHEM TIRAQUIOULS CHEM TRES CAYRES CHEM TRESSES CHEM TUILERIE CHEM VICCINAL 4 CHEM VIDOURLENQUE CHEM VIEUX CHEM VILLENEUVETTE 8 75 CAM DEL AUSSELO 8 EME STATION ABADES ABAOUSSIER ABATTOIRS ABEILHAN ABEILLES ABYMES ADOUZES AGDE AGONES AIGUES MORTES AIGUILLES AIGUILLIERES AIRE AIRE FREZAL AIRES AIRETTE AIROULE ALICANTE ALIGNAN ALPHONSE LAVALLE AMANDIERS AMAYROLS AMIROU AMOUR AMOUR CAP DALLIECH AMTONEGRE ANCIEN MURVIEL ANCIENNE ANDUZE ANES ANTONEGRE AQUEDUC ARAMON ARBOUNIERES ARBOUSIER ARBOUSIERS ARGENTEILLE ARJALARIE ARMES ARTENAC ARTS ASPHODELES ASQUALS AUBANEL AUBEPINE AUBEPINES AUBERGE AUBERGUETS BADASSAC BADE BADIAUX BAIGNEURS BAILLARGUET BAISSE BALAST BALLATS BALME BALMOUTOU BALOUSSIEYRE BAMBADES BAOU BAOUTES BARAQUE BARAQUES BARAQUETTE BRANCAS BARESSE BARNIER BARQUE BARQUES BARRIERE BASSE BARRYS BARTHELASSE BARTHES BAS BAS D AIGUES MORTES BAS MONTAGNAC BAS BAS LUNEL BASSAC BASTIDE BATIPAUMES BAUDIERE BAUME BAUTIERES BAYONNE BEAU RIVAGE BEAUFORT BEAUMES BEAUVEZET BEDARIEUX BEDISSIERE BEDOS BEGOURES BELBEZE BELBEZETH BELLE BOUCHE BELVEDERE BERANGE BERGERIE BERGERIES BEROS BESSAN BESSES BEZIERS BLANC BLANQUE BLANQUETTES BLAQUIERE FIGAIRASSE NORD GARDIES BONIAN BORIES BOSC BOSC VIEIL BOSQUE BOSQUES BOUCHER BOUDAS BOUFFIES BOUILLES BOUILLES GARRIGUE TRIVIALE BOUIS BOUJAN BOULE RONDE BOULIDOU BOULISTES BOUQUE BOURDIGOU BOURGIDOU BOURGNOUS BOURGUET NAOU BOUYSSIER BRAMA BRANCAS BRANQUEDIEU BRECHE BROUE BROUES BROUGIDOUX BROUTE CABRE BROUTES BUFFE VENT BUFFE VENTS BUFFETTE BUISSON ROBERT CABALOUNAS CABANIS CABANNES CABRIERES CADE CADES CADOULE CAGAROULETTE CAGNES CAIRE CALADE CALAMIAC CALINIERES CALQUIERES CALVAIRE CALVAIRE NOTRE DAME CAMBELLIERS CAMBEZARD CAMI FOUNJUT CAMILLE DESMOULINS CAMIN DEL AUSSELO CAMP AUSSEL CAMP BERTAU CAMP D AUSSEL CAMP FLOURET CAMP FRIZEL CAMP SOULEL CAMPAGNE CAMPAGNES CAMPAS CAMPESCAL CAMPET CAMPIS CAMPREDON CAMPREVO CANAL MIDI CANALET CANET CANTALAURES CANTAUSSEL CANTAUSSILS CAP BOURIO CAPELETTE CAPELLIERES CAPELUDE CAPILLIERES CAPITELLES CAPOULIERE CARABOTTES CARDONILLES CAREIRADE CAREIRASSE CARES CARIGNANS CARLANCAS CARLET CARNETTE CARRAU VIGNOGOUL CARREIRASSE CARREYROU CARRIERE PELERINE CARRIERE POISSONNIERE CARRIERES CARRIRE L ORT CARTAIRADE CARTARIE CASSAGNES CASSAN CASSILLAC CASTELSEC CASTRIES CATHEDRALE CATONNIERE CAURAGNASSES 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CONQUETES COSSE COSSE SOUS TOUR COSTE COSTE CORMIERS COSTES COTE COTEAUX MONTFERRAND COUDAISSAS COUDOUMIERS COUGUELATE COULAYRO COULETS COUREGES COURREGES COURSE COURTIALS COUSSERGUES COUSSOULES COUSTOU CRASSIERES CREISSAN CRES CRES AUX MAZES CRESSE CRETES CREUX CAMPAGNE CREUX REDON CROIX BLANCHE CROIX CAMPAGNE CROIX FER CROIX MISSION CROIX PECH CROIX REDON CROIX VIAS CROIX RENARD CROIX TRIBE CROIX PHILIP CROIX VIEILLE CROS CROSES CROSILLE CROSS CROUZETTE CROUZETTES CROZES CURATIES D AGNAC D AIGUEBONNE D AIGUES MORTES D ALLUT D AMBRUSSUM D AUBAGNAC D AUMET D AYALOU CAMIN AYALOU D AYME D HOMPS DAMES BADONES BALESTRIER BALUFFE BASSAC 1 BEAULIEU BEDILLIERES BELBEZE BELBEZET BELIOL BELLEVUE BENTENAC BERNAUDE BERTRAND BEZERAC BISCAN PASSAGE BORIE BOUBALS BOUDOUISSOU BOUFFOURS BOURGADE BOUSSAGUES BOUZIGUES CABANIS CABRIALS CABRIES CABROLOUS CAMPREDON CANABECH CANTADU CAPEL CARLENCAS CASCABEL CASTRES CASTRIES CAYLUS CHICHERY CHYPRE VILLAGE CLAIRAC CLAPIERS CLAUDEL CLERMONT COSTE ROUSSE 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DEPARTEMENTAL 120 DEPARTEMENTAL 122 DEPARTEMENTAL 127 DEPARTEMENTAL 128 DEPARTEMENTAL 131 DEPARTEMENTAL 132 DEPARTEMENTAL 136 DEPARTEMENTAL 137 DEPARTEMENTAL 138 EME DEPARTEMENTAL 14 DEPARTEMENTAL 141 DEPARTEMENTAL 145 DEPARTEMENTAL 15 DEPARTEMENTAL 158 DEPARTEMENTAL 159 DEPARTEMENTAL 16 DEPARTEMENTAL 168 DEPARTEMENTAL 17 DEPARTEMENTAL 171 DEPARTEMENTAL 172 DEPARTEMENTAL 18 DEPARTEMENTAL 185 DEPARTEMENTAL 189 DEPARTEMENTAL 19 DEPARTEMENTAL 2 DEPARTEMENTAL 2 EME DEPARTEMENTAL 21 DEPARTEMENTAL 23 DEPARTEMENTAL 24 DEPARTEMENTAL 25 DEPARTEMENTAL 25 EME DEPARTEMENTAL 26 DEPARTEMENTAL 27 DEPARTEMENTAL 34 EME DEPARTEMENTAL 35 DEPARTEMENTAL 37 DEPARTEMENTAL 39 DEPARTEMENTAL 5 DEPARTEMENTAL 52 DEPARTEMENTAL 62 DEPARTEMENTAL 66 DEPARTEMENTAL 8 DEPARTEMENTAL 910 DEPARTEMENTAL 986 DEPARTEMENTAL 999 DEPARTEMENTAL MEZE AGDE ABEILLES ABIMES ABREUVOIRS ACACIAS ACATES AGASSES AIGUELLIERES AIRES AIRES PROLONGEE AIROLLES AIROULES ALOUETTES AMANDIERS AMOUREUX AMOURIES ARBOUSIERS ASPES AUBETTES AZEROLLES 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COURS COQUADIX CROZALS CHICANE COURS GENTILSHOMMES PLAISANCIERS GABRIEL PERI GAMBETTA GAMBETTA CS GENTIHOMMES GOELANDS GENTILHOMMES GREGOIRE HAUT GAU JAURES COURS LAFAYETTE LIBERTE LOUIS JOUVET LOUIS PHILIPPE NAPOLEON NATIONAL ORLEANS PERROQUET COURS PLAISANCIERS RAVANIERES REINE REPUBLIQUE SAINTE LUCIE VIGUIER COUSSEMAS COUSSENAS COUSSERGUES COUSSES COUTELET COUVENT DOMINICAIN CPGN BRESCOU CPGN MAILHAC CPGN MOUGNO CPGN PREIGNAN CRESSE MARTIN CROIX JUILLET CROIX MISSION CROIX REILLE CROIX MAS CROIX SUD 35 PLAN SAPRITCH CROIX NEUVE MAS LONDRES CROIX NEUVE LOUPIAN CROIX RASCAS CROS ANEDES CROS VIGNES CROUCUT CROUZELS CRS GAMBETTA CTRE COMMERCIAL AUCHAN MEDITERRANEE CTRE MER SOLEIL ORPELIERES CURE BAS CURE HAUT CYRE D 2 PLAN TRISTAN BERNARD D EMPENE DARE HORTS DAUBINELLE L OLIVIER MER MOULINES DECHETTERIE GENEVRIERS DESCENTE COIFFEUR DESCENTE COSTE DESCENTE FONTAINE DESCENTE HORTS DESCENTE HORTS CONAS DESCENTE OLIVIERS DESCENTE PORTAL DESCENTE EN BARRAT DESCENTE HORTS DESCENTE OLIVIERS DESCENTE RIQUETOU DESCOURNUT DEUX PAYSAGERE DEVES DIGUE CEINTURE DIO DISCOTHEQUE NICAVANA DOM ST NOZET 1013 DOMAINE ABBAYE VALMAGNE DOMAINE AFFANIES DOMAINE ALIBERT DOMAINE AMILHAC DOMAINE ANGLAS DOMAINE ARBRE DOMAINE ARESQUIERS DOMAINE ARGELIERE DOMAINE ARNAUDY DOMAINE ARNET DOMAINE ARNOYE BAS DOMAINE ARTIX DOMAINE ASPIRAN RAVANES DOMAINE ASTIES DOMAINE AUZIERES DOMAINE AZIROU DOMAINE BAILLARGUET DOMAINE BALDY DOMAINE BARETTES DOMAINE BARRALE DOMAINE BARROU DOMAINE BASSANEL DOMAINE BASSOUL DOMAINE BASTIDE VIEILLE DOMAINE BEAU SOLEIL CAPEL DOMAINE BEL AIR DOMAINE BELLE MARE MARCEL DOMAINE BELLE RIVE DOMAINE BELLES EAUX DOMAINE BELLEVUE DOMAINE BELVEZET DOMAINE BESSILLES DOMAINE BLANCARDY DOMAINE BONNE VIGNE DOMAINE BORIE BLANQUE DOMAINE BOSC DOMAINE BOUSQUET DOMAINE BRAMEFAN DOMAINE BRIFAUDE BASSE DOMAINE BRIFAUDE HAUTE DOMAINE BRIGNAC DOMAINE BRUNET DOMAINE CABREROLLES DOMAINE CADIRAC DOMAINE CAILLAN DOMAINE CAIZERGUES DOMAINE CALAMIAC DOMAINE CALMETTE DOMAINE CANAGUE DOMAINE CANET DOMAINE CANTAFAROUNE DOMAINE CANTAGRILS DOMAINE CANTAOUSSELS HAUT DOMAINE CANTAUSSEL DOMAINE CAPION DOMAINE CARABOTTE DOMAINE CARRATIERS DOMAINE CASSAFIERES DOMAINE CASTELET DOMAINE CASTELSEC DOMAINE CAYLUS DOMAINE CELICATE DOMAINE CENTEILLES DOMAINE CHAPITRE DOMAINE CHARMETTES DOMAINE JOUGRAND DOMAINE CIRTA DOMAINE CITERNE DOMAINE CLAIRAC DOMAINE CLAPIES DOMAINE CLAUZALS DOMAINE COLOMBES DOMAINE COMBEFERE VILLAGE DOMAINE CONDAMINE DOMAINE CONSEILLERE DOMAINE COUBILLOU DOMAINE COULETTE DOMAINE COURNELLE DOMAINE CURATIES DOMAINE D AIGUES VIVES DOMAINE D AUROUX DOMAINE D URUS MONTMAL DOMAINE DAURION DOMAINE BAUTUGADE DOMAINE BELLEVUE DOMAINE BLANVILLE RIEUTORT DOMAINE CABREROLLES DOMAINE CADENET DOMAINE CAMPOREL DOMAINE CAPUZARD BARRES DOMAINE CASTELFORT DOMAINE CAYLUS DOMAINE CAZENEUVE DOMAINE CIBADIES DOMAINE COUSSAT DOMAINE DAMMARTIN DOMAINE FARLET DOMAINE FIGARET DOMAINE FONTAINE MARS DOMAINE FOURCADE DOMAINE FOURS FOURS DOMAINE GRANGE NEUVE DOMAINE L ARGENTIERE DOMAINE L AUMONE DOMAINE L ESTANG DOMAINE L ESTELLE DOMAINE L HORTUS DOMAINE BAUME DOMAINE BENOVIE DOMAINE BRUYERE DOMAINE DEVEZE DOMAINE GARRIGUE DOMAINE MIRANDE DOMAINE MOTTE DOMAINE PAILLETRICE DOMAINE REDOUNIERE DOMAINE ROQUE DOMAINE TOUR DOMAINE LACOSTE DOMAINE LAUNAC DOMAINE LAVAL DOMAINE MALMONT DOMAINE MASSANE DOMAINE MAURIN CS DOMAINE MEREVILLE DOMAINE MERMIAN DOMAINE MONT ROSE DOMAINE MONTPEZAT DOMAINE MONTPLAISIR DOMAINE MONTREDON DOMAINE MUJOLAN DOMAINE PARGUET DOMAINE PERDIGUIER DOMAINE PLAISANCE DOMAINE PLANCHENAULT DOMAINE PORTES PORTES DOMAINE ROQUE HAUTE DOMAINE BAUDILE DOMAINE ESPRIT DOMAINE JOSEPH DOMAINE SIMEON DOMAINE SAPORTA DOMAINE SEILHAC DOMAINE SORIECH DOMAINE VALLONGUE DOMAINE VALMAILLARGUES DOMAINE VALMONT DOMAINE VEYRAC DOMAINE VILLENEUVE DOMAINE AIGUETTES 3 PLAN ROUSSEROL DOMAINE ARESQUIERS DOMAINE JASSES DOMAINE LOUBATIERES DOMAINE MAZES DOMAINE ROCHERS DOMAINE SIX TERRES DOMAINE SYLPHES DOMAINE VAUTES DOMAINE VERRIES DOMAINE YEUSES DOMAINE DESLINES DONNADIEU DOMAINE DEVEZE DOMAINE DOURMETTES DOMAINE DOMAINE BOSC DOMAINE BOURDIC DOMAINE LAC DOMAINE MAS NEUF DOMAINE MAS ROUGE DOMAINE MINISTRE DOMAINE PIC D AUREILHAN DOMAINE ROC ROULIO DOMAINE ROUQUET DOMAINE TEMPLE DOMAINE ESPAGNAC ESPAGNAC DOMAINE ESTANG DOMAINE ESTELLE DOMAINE FELINE JOURDAN MARCEL DOMAINE FENOUILLET DOMAINE FESC DOMAINE FLEURIDES VIEL FLEURIDES DOMAINE FLORENCE DOMAINE FONTAINE DOMAINE FONTAINE CAUDE DOMAINE FONTAINE DOUCE DOMAINE FONTARECHE DOMAINE FONTFROIDE DOMAINE FONTMAGNE DOMAINE FONTRAMES DOMAINE FOULERY DOMAINE GAJAN DOMAINE GALABERT DOMAINE GARDIE DOMAINE GASPARI DOMAINE GIMEL DOMAINE GINESTET DOMAINE GINESTOU DOMAINE CLAVELET DOMAINE DUC DOMAINE HOPITAL DOMAINE NAZAIRE DOMAINE ST NAZAIRE ST NAZAIRE DOMAINE PRUNETTE DOMAINE GRANGE DOMAINE GRANGE BASSE DOMAINE GRANGE L AIR DOMAINE GRANGE SALLELES DOMAINE GRANGE PHILIPP CANALS DOMAINE GRANGETTE DOMAINE GRENATIERE DOMAINE GRIONNE DOMAINE GUZAS DOMAINE HAUT FREJORGUES DOMAINE HERMITAGE DOMAINE HONORE AUDRAN DOMAINE HORTE VIEILLE DOMAINE HORTUS DOMAINE JASSE RICOME DOMAINE JUFFET DOMAINE L ARGELIERE DOMAINE L ARGENTIERE DOMAINE L ERMITAGE DOMAINE L ESTANG DOMAINE L HORTE DOMAINE L ILE DOMAINE BORIE VILLAGE DOMAINE CAUMETTE MONTIMAS DOMAINE COMBE BASSE DOMAINE DEVEZE DOMAINE DRAGONNE DOMAINE DROSIE DOMAINE FADAISE DOMAINE GARDIE DOMAINE GARRIGUE DOMAINE GINESTE DOMAINE GUINARDE DOMAINE GUIRAUDIERE DOMAINE JOURDANE DOMAINE MADONE DOMAINE MANDOUNE DOMAINE MARSEILLE HAUTE DOMAINE MARTINE DOMAINE MIQUELLE DOMAINE MONTANE DOMAINE PRADE DOMAINE PREPE DOMAINE RESSE SOULIE DOMAINE SERVELIERE DOMAINE TOUR L ORB DOMAINE TOUR ANDRE DOMAINE TRIBALLE DOMAINE VALMALE DOMAINE VISTOULE VISTOULE DOMAINE LAUMEDE DOMAINE LAUNAC DOMAINE LAVAL DOMAINE BOSC DOMAINE MANAU DOMAINE SESQUIERS DOMAINE AZANDES DOMAINE FRENES DOMAINE HERAILLES DOMAINE MURIERS MONTILLES DOMAINE ONGLOUS ONGLOUS DOMAINE PINS ROQUE PETROU DOMAINE LONGUET DOMAINE LYONNAISE DOMAINE MADIERE DOMAINE MADONE DOMAINE MAGDELAINE DOMAINE MANSE DOMAINE MARENNES DOMAINE ANNE DOMAINE MAS D AUSSEL DOMAINE MAS DOMAINE MAS NEUF DOMAINE MAS OLIVIER DOMAINE MAS VIEIL DOMAINE MATOUR DOMAINE MAUREILHAN DOMAINE MAURIN DOMAINE MAZACY DOMAINE MEDEILHAN DOMAINE MEJEANNE DOMAINE MINISTRE DOMAINE MIRANDE DOMAINE MONT LOUS FROUZET DOMAINE MONTNEGRE DOMAINE MONTPENEDRE DOMAINE MONTPLAISIR DOMAINE MONTREDON DOMAINE MORTIES DOMAINE MOULINES DOMAINE MOULINIER DOMAINE NADAILHAN DOMAINE NOVI DOMAINE OLIVIERS DOMAINE ONGLOUS ONGLOUS DOMAINE PARAGUAL DOMAINE PASSAGE COULET DOMAINE PASSAGE RENARD DOMAINE PASSAGE LOUIS DOMAINE PAUSE DOMAINE PECH DOMAINE PECH D ANDRE DOMAINE PELICAN DOMAINE PERRY DOMAINE BOUET DOMAINE PEYRAT DOMAINE PEYREMALE DOMAINE PIERREFEU DOMAINE PIERREFONT DOMAINE PINS ROQUE PETROU DOMAINE PLAUCHUDE DOMAINE PODE DOMAINE PORTES DOMAINE POUJADE DOMAINE POUJOL DOMAINE PRADE DOMAINE PRADEL DOMAINE PRADINES DOMAINE PREIGNES NEUF DOMAINE RAMEJAN RAMEJAN DOMAINE REINE JULIETTE DOMAINE RESTINCLIERES DOMAINE RIEUSSEC DOMAINE ROQUE DOMAINE ROQUE BASSE 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DOMAINE SORIECH DOMAINE ST SOURCES ENTRE MARS DOMAINE ST MARTIN ST MARTIN VIGNES DOMAINE SUCCURSALE DOMAINE TANES DOMAINE TIRONDEL DOMAINE TOUR DOMAINE TRIVALLE DOMAINE TROIS FONTAINES DOMAINE TUDES DOMAINE VALADASSE DOMAINE VALENSAC DOMAINE VALJOYEUSE DOMAINE VALUZIERE DOMAINE VALUZIERES DOMAINE VAUTES DOMAINE VERGOUNIAC DOMAINE VERNAZOBRES DOMAINE VERRERIES DOMAINE VEYRAC DOMAINE VILLEMARIN DOMAINE VILLENEUVE DOMAINE VISTOULE DOMAINE VIVIERS DOMAINE VOULTE DOMICILE DONNADIEU DOUCH DRAILLE PLO MIDI DRAY GARRIGUE DRAYE GARRIGUE ECLUSE OGNON ECLUSES PORT NOTRE DAME ECLUSES NEUF COSSES BAISSAN AIGUES VIVES JAURES BALARUC VIEUX PLANACAN CROUZET CROUZET SUD TAUSSAC FROUZET PRIMAIRE FRESCATIS COLLINE ECOLES EDEN EL CANTOU ISSANKA EMBOULS EMBUSCALLES ENCLOS AIGRETTES ENCLOS ALAIN GERBAULT ENCLOS BIJUS ENCLOS BLAISE PASCAL ENCLOS BRUYERE ENCLOS CHANOINE JOSEPH G ENCLOS CHANOINE JOSEPH GRAN ENCLOS CHANOINE JOSEPH GRANIER ENCLOS CHARRIN ENCLOS CLEMENT MAROT ENCLOS COLETTE ENCLOS BOUVINE ENCLOS BRUYERE ENCLOS DEBUSSY ENCLOS CAMPANULES ENCLOS CLAIRS ENCLOS COLS VERTS ENCLOS GABIANS ENCLOS MURIERS ENCLOS ORMILLES ENCLOS SANSOUIRES ENCLOS TAMARIS ENCLOS TENNIS ENCLOS TREILLES ENCLOS BELLAY ENCLOS CHANOINE GRANIER ENCLOS FENOUIL ENCLOS LABECH ENCLOS VILLAGE ENCLOS FLAMANDS ROSES ENCLOS GABIANS ENCLOS GALILEE ENCLOS GEORGE SAND ENCLOS H DUNANT ENCLOS HENRI BERGSON ENCLOS HENRI DUNANT ENCLOS HENRI MATI ENCLOS HENRI MATISSE ENCLOS J M REISER ENCLOS J PERRIN ENCLOS NEPER ENCLOS PERRIN ENCLOS JOHANN PACHELBEL ENCLOS KARL MARX ENCLOS LAFFOUX ENCLOS LAVANDES ENCLOS LEON BOURRIER ENCLOS LEURET 18 COURREN ENCLOS LOUISE MICHEL ENCLOS LUIGI PIRANDELLO ENCLOS MARCEL GRAS ENCLOS MAX JACOB ENCLOS MICHEL FARADAY ENCLOS MURIERS ENCLOS OURSINS ENCLOS PACHELBEL ENCLOS PALOURDES ENCLOS LOTI ENCLOS RAVEL ENCLOS RENE CHAR ENCLOS ROBERT SURCOUF ENCLOS ROGER VAILLANT ENCLOS ROMAIN ROLLAND ENCLOS ROMARINS ENCLOS JACQUES ENCLOS SARCELLES ENCLOS SULLY PRUD HOMME ENCLOS TAMARIS ENCLOS TENILLES ENCLOS TINO ROSSI ENCLOS TISSIE SARRUS ENCLOS VICTOR SCHOELCHER ENSERUNE ERMITAGE SIMEON ESCALIER CRAQUE ESCANDALAYRAS ESCOUGOUSSOU ESPACE CAMPANAIRE MALRAUX ESPACE COMMERCIAL FREJORGUES ESPACE COMMERCIAL FREJORGUES OUEST ESPACE BEAUPRE ESPACE FREJORGUES OUEST ESPACE ST ESPACE VIALA ESPAGNAC ESPAROU ESPAZE ESPERANCE ESPLANADE BASSIN PLAISANCE ESPLANADE ESPLANADE COMMUNAL FREJORGUES OUEST ESPLANADE L EUROPE ESPLANADE L ORTET ESPLANADE MUSIQUE ESPLANADE EMILE TURCO ESPLANADE EUROPE ESPLANADE MITTERAND ESPLANADE BEAUMEL ESPLANADE VILLAGE ESPLANADE MUSIQUE ESPLANADE ORTET ESPLANADE TROUBADOURS ESTAGNOL ESTAUSSAN ESTRECHOUX ETABLISSEMENT THERMAL ETANG GREC ETANG L EST EUROGARE EVECHE EXPANSION 6 DEVES EXPANSION 8 DEVES FABREGAT FABREGUES FAGAIROLLES FAJAU FALGOUS FALGOUSE LANGLADE FALGOUZE FALLADE FAMBETOU FARINETTE FARINETTE COSSE FAUBOURG 11 CARRIERA LAS AGASSAS FAUBOURG 8 MAI 1945 FAUBOURG CAMPEMAR FAUBOURG CAMPEMARC FAUBOURG CORDELIERS FAUBOURG CAVE COOPERATIVE FAUBOURG AMIS FAUBOURG ANTIQUAIRES FAUBOURG CORDELIERS FAUBOURG 8 MAI 1945 FAUBOURG COURREAU MONTPELLIER FAUBOURG GUE FAUBOURG PEYRAL FAUBOURG REGAGNAS FAUBOURG MUSCAT FAUBOURG JAUMES CS FAUBOURG MARTIN FAUBOURG ROCH FAUBOURG ROCK FAUBOURG SANGONIS FAVAS FAVAYROLES FAVEYROLLES FELICIEN GRUALGUES FENOUILLEDES FERME CAZILHAC BOUSQUET FERME L ESPINOUSE FERME TRIVALLE FERME MAS GRES FERME EQUESTRE FERME FERRUSSAC FERME JOURDAN VILLAGE FERME LEOUNE ARDOUANE FERME MAS RIOLS FERME PLAINE FERME SERANNE FERME VIALA FESQUET SOUS L FEYNES FINCA LOS MADRONOS FLANC FLEURIE FLORENTINES 2 FONBERGADE FONBINE FONCLARE FOND DEL SAUZE FOND JEANES FONFREGE FONSERANES FONTAINE CARBONNIERE FONTAINE CLAVADE FONTAINE COLOMBE FONTAINE COLUMBA FONTAINE D ENCAUVI FONTAINE D OULETTE FONTAINE FRAISSE LARCHO FONTAINE GRIFFE FONTAINE JANES 1 ISSANKA FONTAINE JANES SYMPHONIE ISSANKA FONTAINE BANQUIERE FONTAINE RIVIERE FONTAINE MAZELLIERS FONTAINE SARAC FONTAINE THUROU FONTAINE DEL REY FONTAINE DEL SAUZE FONTAINE DIABLE FONTAINE FRANCAISE FONTAINE IMBERT FONTAINE IVOIRE FONTAINE VILLAGE FONTAINE MARS FONTAINE MARTIN FONTAINE MARTINIER FONTAINE MASSANE FONTAINE MAUREL FONTAINE MOSSON FONTAINE QUAI LOUIS VAU FONTAINE RAMY FONTAINE ROUGE FONTAINE ROUTE LODEVE FONTAINE SALADE FONTAINE SEQUE FONTAINE VIEILLE FONTALINIERES FONTARECHES FONTARITTE FONTAURIE FONTBERTIERE FONTBONNE FONTCAUDE FONTCLARE FONTENILLES FONTESIE FONTESIS FONTFROIDE BAS FONTFROIDE HAUT FONTMAJOU FONTREGEIRE FORUM MEDICA FOUILHO FOUR FOUR CHAUX FOUR CHAUX 2 BIS FOUR CHAUX PUECH RENARD FOURNEILLADES FOURNELADES FOURNES FOURS FOYER METZ FRANGOUILLE FRAUZIL FREJORGUES OUEST FRESCALY FRESCATIS FRIGOULET FROMENTAL FROUZET GABELAS GABRIAC GAILLERGUES GALERIE GEANT CASINO MONTIMARAN GALERIE INTERMARCHE FRIGOULET GALERIE MARCHANDE CARREFOUR GALERIE MARCHANDE HYPER U GALON BAS GARDIERES GARE CEILHES RIBAUTE CARS ROUTIERE VILLEVEYRAC GARRIC LIXIRIE GARRIGUES LAUNAC GARRIGUES ROUTE RESTINCLIERES GARRISSON GARROUILLETTES GASOTTE GATINIE GAUCHE ORB ORPELLIERES GAUGET MILIEU GAYONNE J CADENAT GAYRAUD SABO GELIBERT GEND COSSE SOUS TOUR GENDARMERIE TOUR PONTS GENDARMERIE ROUTE LACAUNE GENDARMERIE EVEQUE GENDARMERIE J S BACH GENERAL GIBOU GIMIOS GINESTIES GINESTOUS GIRAC GITE COMMUNAL GITE COMMUNAL 556 TRIVALLE GITE CLEDES LESPADAN GITE GENETS GITE RURAL GITE RURAL 9 VALQUIERES GITES RURAUX VILARIS GLORIETTE GOLF MONTPELLIER MASSANE GOUDAL GOULETIER GOUR SEL GOURAUYNE GOURG MAFFRE GOURS GRABAS GRAGNOS CHAMP CHAMP VILLAGE MAIL QUIST ROUTE RUELA PARAGE II 12 PARAGE RUELOT RUERESIDENCE LOTISSEMENT TOUR SALAN TRAVERSE PYRAMIDE GRANDES PYRAMIDES GRANGE 4 SOUS GRANGE BASSE GRANGE PRES GRANGE PRES ARNET GRANGE PIN GRANGE NEUVE GRANGE ROUGE GRANGES BASSES GRASSERIES BASSES GREZAC HAUT GREZAC HAUT GRASSERIES HAUTES GREZAC HAUT GRASSERIES HAUTES GREZAN GRILLE GRIVE GROS CHENES GRANGES GRUALGUES GUA VERGNE GUA BRASSES GUERY GUILHEM GUILHEMS GUILLAUMANT HALLES CENTRALES HALLES CENTRALES HALLES HALLES METZ HALTE GRANGE BASSE GRANGE BASSE HAM CARTELS BOSC HAM STADE 23 CAMI DEL ORMEU ABBES AIGUES VIVES ALBES ALBES OUEST ARDOINE ARDOUANE ARGENTIERES ASPRES AUBERTS AUPIGNO AUZIALE AYMARD BABIO BAISSECURE BAPECH BAPECH PROUILHE BARDEJAN BARDOU BARRES BARTHEZES LOU RAJAL BASSET BEAUQUINIES BELAMAN BELOT BERNAGUES BEROS BERTRAND BESSE BESSES BESSES BASSES BESSES HAUTES BIRANQUES BOISSERON BOISSEZON BONNABOU BORDEVIEILLE BORIE NOBLE BORIO CREMADE BOUBALS BOUBESPRE BOUYSSIER BRAMA BRASSAC BRETTES CABRIALS CAILHOL CALAS CAMPANELLES 18 CAMPELS CAMPLONG CAMPRAFAUD CAMPREDON CAMPROGER CANTIGNERGUES CAPELLE CARTEL BOSC CASSAGNOLE CASTAGNES CASTELBOUZE CATHALO CAUMETTE CAUNAS CAZAL CAZAL VIEIL CAZALVIEL CAZARELS CAZELLES CAZO CHENES VERTS CLASTRE CLAUX COLOMBIER COMBELLES COMTES CONDAX COULET COUMEILHO COURAL COURBOU COURS BAS COUSTORGUES CRES CROS D AIGUES VIVES D AUBAYGUES BELLEVISTE BIRANQUES CAMPESTRE CONAS FRIGOULE GOURGAS JONCELET BOISSIERE SALLE VIEILLE LAMBEYRAN LEVAS MONTMEZE RABEJAC ROQUES AUNES SALELLE SAUVIAC SCIO SOULAGE VIALA VINAS HEMIES SABLETTES SOURCES DROUILLE FOSSE VALAT LEVANT EMBRUSCALES EMBRUSCALLES ESCAGNES ESCOUTET EUZEDES FAGAIROLES FAGAIROLLES FAISSAS FAUZAN FERRIERES FESC FLACHERAUD FLEURS PIOCH FONTJUN FONTMAUMEL BAROCHES FOURNELIERS FOURNOLS FOUSCAIS FRANGOUILLE FRESCALY GABRIAC GACHE GACHES GALINIE GALINIER GARRIGUE GAY GINESTET GINESTOUS GOURDOU GOURGAS GOURGAZAUD GOURS GRANIES GRENOUILLET HONDRAT HORTS ICHIS JONCELETS JULIO L BALME BASSE BOUISSE CALMETTE FABREGUE HAUTE FRAISE JASSE JASSE MAURIN MANIERE PAUTRU POMAREDE ROQUE ROUQUETTE ROUVIERE SALESSE VEGENDE LABADIE LABEIL LACAN LACAUNE LANGLADE LAROQUE LAS PESSES LASCOURS LATUDE LAUDOU LAULO LAUMEDE LAURENQUE LAVAGNES LAVENE CROS LAU PIN PRIOU RIEU THERONDEL LENTHERIC LEREY BORIES BORIES CASTAGNES EMBRUSCALLES MONTADES PINS PLANES SALCES VAILHES LIGNON LIZARNE MADALE MADALET MADIERES MARSO MARTHOMIS MAS ALARY MAS BAS MAS CASTEL MAS GUA MAS NEUF MAS ROLLAND MASCLA MASNAGUINE MATHE MAURERIE MAZARIE MEDAILLE MEJANEL MILHAU MOULIERES D ALGER NAJAC NAVACELLES NAVAROUX NAVAS NOGARET ORCHIDEES ORNAC PABO PADERS PAGUIGNAN PARDAILHO PARLATGES PERCHE PERIES PESTOUS PEYREFICHE PLANACAN PLANS BAS PLANTEYROL POMAREDE POUJOL PRADAS PRADE PRADELS PRADINES VILLAGE PROUILHE PROVERES RABETTES RABIEUX RASPAILHAC RASPAILLAC RIEU RIEUX RIVES RODE BASSE RODOMOULS RONGAS ROQUE ROUSSAS ROUVIOLE SABLETTES SABO AUNES HAMEAU FRECHOUX HIPPOLYTE EST JULIEN JULIEN MEULIERES JULIEN MOLIERES MARTIN COMBES XIST SAINTE BRIGITTE SALABERT SALET SAUGRAS SAUJAS SAUVIAC SEILHOLS SERIEIS SERVIES SIEGES SIEURE SOULAGETS SOULIE SOUTEYROLS ST 22 CHASSEURS TAILLEVENT TARASSAC TAUSSAC TROUBADARIES TUDERY TUILERIE BASSE TUILERIE HAUTE VABRE VABRE C VALAREDES VALMALLE VALRAC VAREILHES VEGENDE VEREILLES VERGNOLLES VEYRAN VIALA VIALANOVE VIEILLE VILLECELLE VINAS VIOLGUES HANGAR 4 HAUT VEREILLES HAUTE HAUTS BOISSERON HAUTS CANTONS HAUTS BISSY HAUTS FONTCAUDE DOMAINE HAUTS VALCYRE HAUTS FONTAINES HERIC HERMITAGE HETRES HLM 8 MAI 1945 HLM TASTAVIN LLOPIS HLM BATI COOP BEAURIVAGE HLM BATICOOP HLM BATIMENT COOP BEAURIVAGE HLM BELLEVUE HLM BRECHE HLM HLM DAMES HLM DAMES HLM CORNICHE HLM CORNICHE II HLM COURRIER PRINTEMPS HLM CROIX D ARGENT HLM HLM F MISTRA HLM F MISTRAL HLM FONTAINE ANCAUVI HLM FONTAINE D ENCAUVI HLM FREDERIC MISTRAL HLM HAUTS PARC HLM HAUTS FRONTIGNAN HLM JARDIN POETES HLM JOLIOT CURIE HLM BRECHE HLM BRECHE N 3 HLM GASCAGNE HLM VERRERIE HLM LAGAMAS HLM PARC HLM ARBOUSIERS HLM LAURIERS ROSES HLM PRES HLM LOGECO HLM LOGECOS HLM LOURCERIES HLM HLM MALRAUX HLM MARBRIERES JAURES HLM MAS HLM MAS RAVIER HLM MEDITERRANEE HLM MONTFERRAND HLM PARC HLM RIQUET HLM PECH MEJA HLM PERGOLA HLM PEYRADE HLM PIC LOUP HLM ARBOUSIERS HLM AUGUSTE GINOUVES HLM HLM RIVAGES L ARNEL HLM ROCHECOMBES HLM ROUTE HLM ROUTE LAGAMAS HLM ROUTE MILLAU HLM 8 MAI 1945 HLM DUNANT HLM HLM VILLA NOTRE DAME HOTEL HOTEL CHEVAL BLANC ICHIS ICHIS BROUES ILE THAU ILE LOISIRS ILE PECHEURS BRIGANTINE ILOT D MAS IMER NEFLIERS CARAVELLE COMBES DEVIQ DEVES BORDELAISE HORTS JAMAICA MILLENIUM STADIUM STADIUM SORTIE 7 BEZIERS SUNSET THALASSA MOTTE COUCHANT NAUTILUS 3 PARADOR RONCERAIE IMP 8 IMP AGASSES IMP AZUR IMP BEAUSEJOUR IMP BERANGE IMP CANAL IMP CAPRIERS IMP CHABANETTES IMP CHANTERELLES IMP DALHIAS IMP FONTAINE 3 IMP FAISAN IMP GRENACHES IMP HENRI BILLE IMP HENRI CLOUZOT IMP CAUCAT IMP JOSEPH DELTEIL IMP JOSEPH ROUMANILLE IMP JULIANE IMP LANGUEDOC IMP LOUISE MICHEL IMP GALANTE IMP MARJOLAINE IMP MILLEPERTUIS IMP MUSICIENS IMP PARC IMP PILETTES 33 IMP PIOCH LABAT IMP RENARD IMP ROCHER IMP ROITELETS IMP SAGUE IMP SAUGE IMP VALENTIN HAUY IMP VESUVE 19 MARS 1962 31 JUILLET 4 VENTS ABATTOIR ABATTOIR 40 ABBE ALLUVAIN ABBE CHAYRRIGUES ABBE FARNARIE ABEILLE ABREUVOIR ACACIAS ACANTHES ADER ADRETS AFFENAGE AGATHE AGAVES AIME GOURC AIRES AIRES BASSES ALAIN COLAS ALBERT SAMAIN ALBERT SCHWEITZER ALBERTINE SARRAZIN ALBIZZIAS ALDEBARAN ALDEBERT ALEXANDRE DUMAS BRUYAS CORNU MUSSET NOBEL SISLEY ALICANTE ALIZES ALLENDE ALOUETTES ALPHONSE DAUDET ALPHONSE LAVALLEE ALPHONSE MENARD ALSACE ALZIEU AMANDIER AMANDIERS AMANS AMANS MARQUES AMBOISE CROIZAT AMBROISE THOMAS ANATOLE FRANCE ANCIENNE GENDARMERIE ANCIENNE ANCIENNES ECOLES ANCIENS ANDRE AMPERE ANDRE CASTANET ANDRE CHASSEFIERE ANDRE DAUCHEZ ANDRE MASSON ANDREA PALLADIO ANDROUNO ANEMONES ANGELE DAVIN ANGELIQUE ARNAUD ANNE FRANCK ANNE FRANK ANTARES ANTOINE BAUME ANTOINE BOURDELLE ANTOINE EXUPERY ANTOINE MAUDUIT ANTOINE MOLINIER AQUEDUC ARAMON ARAMONS ARBOUSIERS ARCHIMEDE ARGELOT ARGENTIERE ARGEOLOT ARMAND BARBES ARRAZAT ARTHUR RIMBAUD ARTHUR TEYSSEYRE AUBES AUGUSTE BAUSSAN AUGUSTE MOURGUES AUGUSTE RENOIR AUGUSTE RODIN AUSSEL AUTAN AVICENNE AVOCETTES BAALBEK BALZAC BARBACANE BARBES BARBEYRAC BARNABE BARRAGE BARRIERE BARRIS BARRYS BARTHASSADE BARTHOU BASSIN BASSIN EAU BASTIDE BATON ROUGE BAUDIN BAUME BEAU SEJOUR BEAU SOLEIL BEAUMARCHAIS BEAUSEJOUR BEETHOVEN BEL AIR BELLE ISLE BELLE VISTE BELLES BELLEVUE BELSOUNARIE BERGER BERGERIE BERGERIES BERLIOZ BERNADAC BERNARD CABANE BERNARD PALISSY BERRY BERRYER BERTASSADE BERTHE ROCHAS BERTRAND PEZARDS BESSIERES BETELGEUSE BETHLEEM BIGOT BIGOU BIROUSTE BISCAN PASSAGE BIZET BLANC BLANQUETTE BLANQUETTES BLATIERE BLAZY BLERIOT BLEUETS BOILEAU BOISSIER BON TEMPS BON VIN BONNEL BONNET BONNETTERIE BONNIER BORIS VIAN BOSQUET BOSQUETS BOSSUET BOULANGER BOUQUET BOURDELLE BOURGUET BOUTONNET BRAGALOU BRECHE BRIGANTINE BROS BROUSSONNET BRUN BRUNO AUDIE BRUNO MOLINARI BRUYERES BUIS BUISSONETS BURGUES CABANIS CABANNES CABESTAN CABRES CACATOIS CADET ROUSSEL CAFE CAGAROLET CAILLES CAIRAT CAIZERGUES CALADE CALADE MARTIN CALECHE CALQUIERES HAUTES CALVAIRE CALYPSO CAMARGUE CAMELIAS CAMILLE BROS CAMP LIEURE CAMPAILLOU CANAL CANDAURADE CANDIDE CANIES CANILHAC CANNAS CANNEAU CANTIE CANTONAT CAPRIERS CARAVELLE CARDELINO CARDINAL CARIGNAN CARIGNANE CARIGNANS CARLES CARNOT CAROUBIER CAROUX CARREE CARRIERE CARRIERETTES CARRIGNAN CASCADE CASERNES CASSE DIABLE CASTEL CASTILLON CASTRIOTES CATALAN CATALANE CATALO CATHARES CATHERINETTE CATINAT CAVAL CAVALIERS CAVE CAYREL CAZALS CECELES CERF CERISIERS CERS CEVENNES CEZANNE CHABLIS CHAIM SOUTINE CHAINE CHAMBERTES CHAMP GARDES CHAMPS CHANTE PERDRIX CHANTEBRISE CHANTECLAIR CHAPEAU ROUGE CHAPELLE CHARCOT CHARDONNAY CHARDONNERETS BAISSE BAUDELAIRE BOURSEUL BRENNUS CROS FOUCAULD DEJEAN FOURRIER GOUNOD JALABERT SAURIA CHARMETTES CHARMILLES CHASSEURS CHATAIGNIER BOSQUE CHATAIGNIERS CHATEAUBRIAND MARSEILLAN CHENAIE CHENES CHENES VERTS CHEVAL VERT CHEVREFEUILLE CHOPIN CHOQUE POUL CIGALAS CIGALE CIGALES CINEMA CINSAULT IMPASSE GELLY GELY CITRONNIER CIVELLES CLAIR MATIN CLAIRETTE CLAIRVILLE CLASTRE CLAUDE LORRAIN CLAUX LOUP CLAVOUX CLE SOL CLERGUE CLOS CLOS BEAUSOLEIL CLOS BOISE CLOS PASCAL CLOS GRILLONS CLOS GOURP SALAT CLOS LOUP CLOS HELIOS CLOS L ETANG CLOS LARNAC CLOS VIGNES COCHER COIN COLLINE COLOMBIERS COLVERTS COMBALES COMBES COMMANDANT MERIC COMMERCE COMORES COMTE ECKART CONDORCET CONSULS CONTINENTAL COPE CAMBES COQ COQUELICOTS COQUILLE CORDILLE CORDONNIER CORMORANS CORNEILLE CORNEILLES CORSAIRE COSTE COSTEBELLE COSTES COSTETTES COTEAUX COUCHANT COUDEE COULONDRE COURBE COURBES COURBEZOU COURREGES COURTE COUSTOU COUVENT CRABE CREISSAC CREMADE CRES CRIN BLANC CRISTOL CROIX MALADIE CROIX REBOUL CROIX REDON CROUZETTE CUGNOT CURE CYCLAMENS CYGNE D ALEMBERT D ALGER D ALLUT D ANDORRE VIEILLE D ARCOLE D ARROMANCHES D AUBES D AVILER D OC D ORADOUR SUR GLANE D ORAN D ORQUES D OSLO DALHIAS DANTON DARIUS MILHAUD DAUBE DAUPHIN DAURADE DAURAT DAVID BLUM BAGATELLE BALUFFE BAYEUX BAZEILLES BONE BRETAGNE CABANIS CANTOUNA CHALDEE CHATEAUDUN COSTESEQUE COULONDRES CREVE COEUR FIGARET FROMONT HOLLANDE HORLOGE JOLY L ABREUVOIR L ABRICOTIER L AGAU L AIGRETTE L ALIZIER L AMANDIER L ANCIEN L ANCIENNE POSTE L APPARENT L ARAMON L ARAUR L ARCHE L ARDENT L ASPIC L ATELIER L AUBE L AUBEPINE L AURORE L AZUR L ECUME L L EMPEREUR L ENCLOS L ENCLOS L HERBETTE L ENSOLEILLADE L EOLE L EOLIENNE L ESPERANCE L ESTANG L ESTRAGON L ETANG D INGRIL L ETANG L OR L ETANG VIC L ETANG PREVOST L ETEULE L EVECHE L HIRONDELLE L HORIZON L INFERNET L OCCITAN L OCCITANIE L OLIVETTE L ORANGERIE L ORATOIRE L OREE L ORIGAN L UNION BADIANE BARAQUETTE BATISSE BATTERIE BAUME BELLE BERGERONNETTE BORDIGUE BOUVINE BRASSERIE BRISE BRUYERE CADETTE CAMARGUE CANABIERE CARDAMINE CARRIERE CASTELLE CAVIDOULE CHENAIE CHENAIS CHEVRETTE CIBOULETTE CIGALE LA D ENDRAUSSE CLAIRETTE CLASTRE CLEDE COCARDE COLLINE COTE D OR COURTINE CROIX CROIX D ARGENT CROIX D AVIGNON DABALADE DAUBINELLE DEJOUQUE DEMOCRATIE FAUVETTE FONDERIE FONTAINE FORGE FOUILLADE FOURMI GARDELLE GARDIOLE GARENNE GAYONNE GENDARMERIE GENETTE GENTIANE GRANGE GRENOUILLERE GRIVE JASSE JULIANE LAGUNE LAVANDE LIBELLULE LIRONDE MACREUSE MARJOLAINE MARNE MAYRE MAZADE MONTAGNE NOIRE MONTAGNETTE MOULINE MOUTTE MUSARAIGNE NOTAIRIE PAIX PANSIERE PASTOURELLE PRUNETTE BLEUE ROUGE PINEDE PISCINE PLANQUE POINTE VIGNE POISSONNERIE POMPE POMPE BLEUE POUDRIERE POULIE PROVIDENCE QUIROU RAMONDIERE RECHE REPUBLIQUE RESERVE RESISTANCE RIVIERE RIVIERETTE ROCAILLE ROQUE ROSELIERE SALADELLE SALAMANDRE SANTOLINE SARRIETTE SAUGE SERANNE SOURCE TERRASSE TORTUE TOUR TOUR BUFFEL TOURAINE TRAMONTANE TREILLE VALFERE VALSIERE VERRERIE VIGNE MADAME LA VILLETTE VISE VOIE ROMAINE LAS SORBES MALPASSET MIUGRANA MONTFERRIER NARVICK NAZARETH NOEL NORMANDIE PERREGAUX PYRGI RATTE ROCHEBRUNE BAUZILLE SAVOIE SEGUR SIDI BRAHIM SUMER TERRADAS TOURAINE TUNIS VALFABEN VIGNY DEBUSSY DEL GABEL DEL TERRET DELORT DENIS PAPIN 4 VENTS ABEILLES ABRICOTIERS ACACIAS ACANTHES AGASSES AGATEAS AGAVES AIGRETTES AIGUES MARINES AIRES AJONCS ALBERES ALBIZIAS ALICANTES ALISIERS ALOES ALOUETTES AMANDIERS AMANDINES ANEMONES ANGROLAS ARAMONS ARENES ARGALAS ARMILLAIRES ARONDES ARTEMISES ARTS ASPHODELES AUBEPINES AUBES AVENTURES AZEROLIERS BAIES BARQUES BARRIS BARTAVELLES BASALTES BASTIDES JOLY BAZILLES BEGONIAS BERGERONNETTES BERGERS BERGES BISCAYEURS BLAQUES BOSQUETS BOUGAINVILLIERS BOUTEILLOUS BOUVREUILS BRECHES BRIQUETIERS BRISANTS BRUS BRUYERES BUISSONS ARDENTS CADES CAILLOUTES CAMELIAS CAPRIERS CAPUCINES CARRIERES CASERNES CAVALIERS CAVES CEDRES CENTAUREES CHABANETTES CHALETS CHANTERELLES CHARDONNERETS CHARDONS CHASSEURS CHATAIGNIERS CHENES CHENES VERTS CHEVALIERS CIGALES CIGALONS CIGALOUS CINSAULTS CLAIRONS CLARETTES COCCINELLES COLIBRIS COLOMBES COLONNADES COLVERTS CONDAMINES CONTREFORTS COQUELICOTS COQUILLES CORBIERES CORDELIERS CORMORANS COTEAUX COURLIS CRANQUETTES CREPUSCULES CRIQUETS CUPIDONS CYTISES DAHLIAS DATTIERS DECURIONS DEUX PINS DINANDIERS ECOLES ECUREUILS EGLANTIERS ELEGIES ELFES EMERAUDES EPICEAS ERABLES FAUVETTES FELIBRES FILETS FLAMANTS ROSES FLEURS FLORALIES FONTANILLES FOUGERES FOULQUES FRENES FUCHSIAS GARANCES GARRIGUES GARRIGUETTES GAUCELMS GENETS GENOIS GERANIUMS GERBIERS GIROFLEES GIROLLES GLADIATEURS GLAIEULS GRANGES GRANGETTES GRENADIERS GRILLONS GUILHEMS GUIRAUDS HELIOTROPES HERONS HESPERIDES HETRES HIRONDELLES HORTENSIAS HORTS HUNIERS HYBRIDES IRIS JACINTHES JARD JARDINIERS JARDINS JARDINS D EDEN JARDINS BEL AIR JONCS JONQUILLES JOUGTIERS JUJUBIERS KAKIS LACTAIRES LAPEREAUX LAURIERS LAURIERS ROSES LAURIERS TINS LAVANDES LAVOIRS LENTISQUES LETAGNES LIBELLULES LILAS LUCIOLES LUCQUES LUQUES MACHOUS MAGNOLIAS MARMOUSETS MARRESCALS MARRONNIERS MARTINETS MASSALIOTES MELIAS MERIDIENNES MERLES MESANGES MICOCOULIERS MILLEPERTUIS MIMOSAS MIRABELLES MONTILLES MORILLES MOUETTES MOULINS MOURGUETTES MOUSSERONS MUSICIENS MYRTES MYRTILLES NARCISSES NEFLIERS NENUPHARS NEUF NOBIS NOCTUELLES NORIAS OEILLETS OISEAUX OISEAUX BLEUS OISELEURS OLIVIERS OLYMPIADES OLYMPIQUES ORCHIDEES ORONGES PALANQUES PAMPRES PAPILLONS PAQUERETTES PARASOLS PARFUMS PASTELS PASTOURELLES PECHERS PECHEURS PENITENTS PERES PELERINS PEUPLIERS PEYRIERES PICHOLINES PICPAILLES PIELLES PINS PINSONS PIONNIERS PIVOINES PLAISANCIERS PLATANES PLUVIERS POMMETTES POMMIERS POUJADES POURPIERS PRES PRIMEVERES PROVINCIALES PRUNUS PUISATIERS RAYROLLES REINES CLAUDES REVES RIVES ROBINIERS ROCAILLES ROCHES ROUGES ROCS ROITELETS ROMARINS ROSEAUX ROSES ROSIERS ROSSIGNOLS ROUGES GORGES ROUQUETTES SABLES D OR SALADELLES SALICORNES SARCELLES SAUGES SAULES SELLIERS SERINGATS SERPOLETS SOPHORAS SORBIERS SOURCES STANCES SUISSES TAILLANDIERS TAMARINS TAMARIS TAMARIS 1 TANNEURS TERRASSES TERRETS THUYAS TILLEULS TONNELIERS TOURNEPIERRES TOURNESOLS TOURTERELLES TRIBUNS TROENES TROUVERES TUILLERES TULIPES TURQUOISES URSULINES VANNIERS VENDANGES VERDIERS VIGNERONS VIOLETTES VIVES VOUTES ZINNIAS DESIRADE DESNOYER DEUX RUISSEAUX DEUX SOEURS DEUX THERESES DEUX VENTS DEVEZES DIDIER DAURAT DISTILLERIE CALMETTE DOU VALADOUN 4 SEPTEMBRE BALESTRIE BANASTOU BASSIN BEAUMONT BEAUPRE BEL AIR BELLAY BELVEDERE BELVEZE BERANGE BIOU COUCHANT BON COIN BOSQUET BOUCLIER IMPASSE BOURRELIER BRAGALOU BRESTALOU BUIS CABRIAN CAGAROL CALVAIRE CANAL CANTONAT CAPCIR CAPITAINE MAURIN CARIGNAN CARMASSOL CAROUX CAYROU CHAMP NOIR CHAPEAU ROUGE CHAPITRE CHARDONNERET CHARRON CHASSELAS CHAT CHATEAU D EAU CHAUME CHEVREFEUILLE CISTE BLANC CISTE ROSE CLAPAS CLAUZET CLOS CLOS L AMOURIE CLOS PINS CLOS CLOS LOUP CLOS VEZY CORDEAU CORDELIER CORIANDRE COUCHANT COUREDOU COURLIS COURREDOU DRAGON FABREGOU FAISAN FENOUIL FORGERON FOUR FRAGON FURET GANDAR GARDIAN GARISSOU GECKO GENERAL GENERAL ROLLET GLACIS GLOBE DUC GRENACHE GRES GREZAC GUE GUI MONTMAUR MESANGES HERISSON HERON HOUX JAPON JARDIN JARDIN PLANTES JASMIN JEU BALLON JOLY MONT JUSANT LAMPARO LANGUEDOC LAVANDIN LEVANT LIERRE LIMOUSIN LITTORAL LOUP LUCIAS MAGNAN MANNANOU MARCHE MARIN MARTIN PECHEUR MAS ARGELLIERS MAS CALME MAS D ARMAND MAS DIABLE MAS VIEIL MAUZAC MAZET MERDAULS MERLE MERLE BLANC MERLOT MISTRAL MONTADOU MONTALET MORASTEL MOULIN VENT MUGUET MURIER MUSCADET MUSCAT NID NOISY NORD PARADOU PARC PAS LOUP PASSAGE SOIXANTE QUINZE PATURAGE PECHER PELERIN PERDIGAL PERE COREAU PEROU PETIT HOUX PIOCH PRE QUIST PEYROU PICADOU PICHEYROU PICPOUL PIN PIOCH PIOCH AGRET POITOU IMPASSE COSTE PORCHE PORTAIL PRADO PRESBYTERE PRESSOIR PROFESSEUR LOUIS THOMAS PUITS PUITS PRE PYREE RECANTOU RECEVEUR REMOULEUR RENARD RESEDA ROC BLANC ROC ROUGE ROITELET ROMARIN ROSSIGNOL ROUERGUE ROUSSILLON SAFRAN SALET SANGLIER SAPHIR SAUVIGNON SCARABEE SERIN SERPOLET SIROCCO SOURAS HAUT SOUVENIR SQUARE STADE TERNEN TERRET THYM TIBESTI TILLEUL TORCAL TOURDRE TRIANON VAL TEYRAN VAL GELY VALENTIBUS VANNIER VENT D AUTAN VER SOIE VERCORS VERGER D EOLE VESUVE VIEUX BASSIN VIEUX CEDRE VIEUX CYPRES ZEPHIR DUKE ELLINGTON DUNETTES DYSSE ECOLES ECOLES ECOLES VIEILLES ECURIES EDGAR DEGAS EDMOND EDMOND CRAMAUSSEL EDMOND ROSTAND EDOUARD LALO EDOUARD MARTEL EDOUARD VUILLARD EDWARD JENNER EGLANTINES ELFES ELLA FITZGERALD ELSA TRIOLET EMBREUX EMILE BOREL EMILE GENTIL EMILE POUYTES EMILE ROUX EMILE ZOLA EMILIA EMMANUEL CHABRIER ENCIERO ENCIERRO ENCLOS ENCONTRE ENRIC COSTE ENSOLEIADE EPI ERASME ERIC TABARLY ERMENGARDE ERMENGAUD ESCURET ESPLANADE ESQUIROU EST ESTEVE ETRILLES EUCLIDE EUGENE CHRISTOPHE EUGENE DELACROIX EUGENE GAVACH FABRE FABRICANTS FAGES FAGETTE FANNY FARIGOULES FAUBOURG ROCH FEDERICO GARCIA LORCA FERDINAND CARRE FERDINAND FABRE FERNAND SOUBEYRAN FERRADE FERRAGES FERRER FERROUL FIGAYRETTE FIGAYROLLE FIGUEROLLES FIGUIER FIGUIERS FIN SIECLE FINO BRICKA FLAMBOYANT FLEURS FLOQUET FLORALIES FLOREAL FLOTS BLEUS FLOUCH FOCH FONTAINE FONTAINE D ENTRECHAT FONTAINE MAUREL BAS FONTENILLE FONTES FONTETE FONTVIEILLE FORGE FORIN ROYAL FOUJITA FOUR FOUR COMMUNAL FOUR FOURNIL FOURRAT FRANCIS JAMMES FRANCIS POULENC BEZIAT COUPERIN MAURIAC POUGET VILLON XAVIER FABRE FRANKLIN ROOSEVELT FREDERIC BAZILLE FREDERIC CHOPIN FREDERIC MISTRAL FREDERIC SAUVAGE FRENES FRESCATI FRIGOULE GABELOUS GABRIEL FAURE GABRIEL PLANTAVIT GAFFINEL GALIBERT GALILEE GALINIER GALLIENI GAMBETTA GARDIAN GARENNE GARIBALDI GARRIGOT GASPARD GASTON BAISSETTE GASTON CUGNENC GASTON LEROUX GAULOISE GAY LUSSAC GENDARMERIE GENERAL BUGEAUD GENETS GEORGE SAND BIZET BRAQUE BRASSENS COSTES DANTON SAND GERANIUMS GERARD NERVAL GERARD PHILIPE GERMAINE RICHIER GERMINAL GIGIRY GILETTE MARCHAND GILLES DELEUZE GINGIBRE GIONO GIRAL MAITRE VERRIER GIRELLES GLOBE GLORIETE GLORIETTES GOELANDS GOLFE GOUNELLE GOYA PAVOIS GRANDS CORMORANS GRANGES GRANIER GRANOUPIAC GRAU GRAU RIEU GREGOIRE GRENACHE GRENACHES GRENADIERS GRENOUILLES GRILLONS GRIMAUD GRISONS GUERIN GUIBAL GUILHAUME GREGORI APOLLINAIRE D ORANGE TELL GUIZARD GUSTAVE CAILLEBOTTE GUSTAVE LOISEAU GUY MAUPASSANT GUYNEMER HACIENDA HALLES HAMEAU VIALA HAUTS HAUTS BAILLARGUES HAUTS GUILLERY HAUTS COMBES HECTOR BERLIOZ HENRI AMIEL HENRI BECQUEREL HENRI BILLE HENRI CLOUZOT HENRI LABROUSTE HENRI LACOMBE HENRI LAURENS HENRI ROUSSEAU HERISSON HERISSONS HERONS HIRONDELLES HOCHE HONORE DAUMIER HORT HORTALESSIO HORTENSIAS HORTES HUNIER HUPPE IFS ILE BRUGUIERES INFIRMERIE IRIS ITIER J B VICTOR BAUDIN J BRULLER DIT VERCORS JACQUARD JACQUELINE AURIOL JACQUES ANGE GABRIEL JACQUES BABINET JACQUES BREL JACQUES CARTIER JACQUES DAGUERRE JACQUES MOLAY JACQUES VAUCANSON JACQUES MAUSSAC JACQUES PREVERT JACQUES ROMANSE JACQUES VILLENEUVE JALLOIS JAMES WATT JARDINS JARDINS D ARNASSERRE JASSES JASSET BAPTISTE LULLI BAPTISTE LULLY BARTHES BOUIN CALAS CAUCAT BRUYERE FONTAINE DOMINIQUE INGRES FALANDRY SEIGNERET MILLET GABIN GRANDEL GUY HONORE FRAGONARD HUS JACQUES ROUSSEAU JAURES LAFFITE LEON LURCAT MALLIER MERMOZ MONNET PHILIPPE RAMEAU VILAR JEANNE GALZY JEANNE HACHETTE JEU BALLON JEU BOULES JEU MAIL JOACHIM MURAT JOHANNES KEPLER JONCASSE JONQUET JONQUILLE JONQUILLES JOSEPH BERTHELE JOSEPH FULCRAND JOSEPH BRIX JOSEPH ROUMANILLE JOSEPH VIDAL JUJUBIER JUJUBIERS JULES FERRY JULES GUESDE JULES MARILL JULES MASSENET JULES SIMON JULES SUPERVIELLE JULES VERNE JULIEN KLEBER L ARGELOT L AUBRAC CANAL CAPELLE CIGALE COMBETTE FONDUE FONTAINE FRIGOULE GARDIOLE LOUVE MARTINE ROCADE ROCHELLE LABADE LABECH LABICARIE LACOMBE LAFAYETTE LAGAREL LAIRANS LAIROU LAITERIE LAMARRE LAMARTINE LANGUEDOC LAOUZAS LAROQUE LAS DEVEZE LAS ROUMPUDAS LAURIER LAURIERS LAURIERS ROSES LAUZES LAVAL LAVALS LAVANDES LAVANDIN LAVANDINS LAVOISIER LAZARE CARNOT CLAIRVAL CLOS CLOS VILLAGE NOTRE LEBOUX IMPASSE LEDERAC LEFEBVRE LEO DELIBES LEO LAGRANGE LEON BARRIERE LEON BLUM LEON FOUCAULT LEONCE GABAUDAN CHARMETTES BOISEES CIGALES HAUTS SERIGNAN HAUTS SABLAS PINS ROCHES THERMES LEVANT LEVERE LIBERTE LIERRES LIGNON LILAS LIONS LIROU LLORENS LO PERDIGAL LONES LOS PERUFAS LOU CIGALOU LOU FELIBRE LOU FIGOUNET LOU MERANCIEN LOU PAILLAS LOU TRAPET LOU TRIOL LOUIS ARAGON LOUIS ARMSTRONG LOUIS BERTRAND LOUIS CALMETTE LOUIS CLAUZES LOUIS DENAT LOUIS FABRE LOUIS FAIDHERBE LOUIS FERDINAND HEROLD LOUIS FOURESTIER LOUIS LANNES LOUIS MARRES LOUISE MICHEL LOUP LUCIE GOUR LUCIOLES LUCQUES M CELESTE MAC GAFFEY MADRILENE MAGENTA MAGNOLIAS MAILLAC MAISON PEUPLE MAISONNETTES MALDORMIR MALITRE MANAVIELLE MANDEL MANON LESCAUT MAQUET MARC SANGNIER MARC SEGUIN MARCEAU MARCEL CAYROL MARCEL DORET MARCEL PAGNOL MARGUERITE YOURCENAR MARIA MONTESSORI CELESTE GALANTE LAURENCIN MARIOTTE MARIVAUX MARJORAM MARNE MARQUIS BAUSSET MARQUISE SEVIGNE MARS MARSEILLAISE MARSOUINS MARTHE AULES MARTIN LUTHER KING MARTINAGE MARTINETS MAS BLANC MAS CALME MAS OLIVIERS MAS TREILLES MAS NEUF MASELIERS MATTHIVETS MAURIAC 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LOTISSEMENT PILETTES LOTISSEMENT PINS LOTISSEMENT PIOCH LOTISSEMENT PIOCH HERBAULT LOTISSEMENT PIOCH HERBAUT LOTISSEMENT PIOCH OURBATIEUX LOTISSEMENT LUMINAIRE LOTISSEMENT PLACES LUMINAIRE LOTISSEMENT PLANS LOTISSEMENT PLATEAU PIQUET LOTISSEMENT PLATEAU PIQUET LOTISSEMENT PLEIN SOLEIL LOTISSEMENT POCURUL LOTISSEMENT POMMIERS LOTISSEMENT PONANT LOTISSEMENT PONS LOTISSEMENT LOTISSEMENT PONTIL LOTISSEMENT PORT CAREME LOTISSEMENT PORTALET LOTISSEMENT PORTES L OR LOTISSEMENT PRADAL LOTISSEMENT PRADAS LOTISSEMENT PRE BOYNE LOTISSEMENT PRES LOTISSEMENT PRES MONTFERRAND LOTISSEMENT PROMENADE LOTISSEMENT PUECH LOTISSEMENT PUECH DOLGUE LOTISSEMENT PUECH FOUR LOTISSEMENT PUECH MEJA LOTISSEMENT PUECH MERLE LOTISSEMENT PUECH MERLE POUSES LOTISSEMENT PUECH ORE LOTISSEMENT PUECHAUMA LOTISSEMENT PYRAMIDE LOTISSEMENT QUATRE AS LOTISSEMENT QUATRE VENTS LOTISSEMENT RACAFOL LOTISSEMENT RACHEL LOTISSEMENT RAIGI LOTISSEMENT RAMPE LOTISSEMENT RAVANIERES LOTISSEMENT RAVEL LOTISSEMENT RAVIN RANS LOTISSEMENT 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TRAS LAFFONT LOTISSEMENT TRAS LAFON LOTISSEMENT TRAUCAT LOTISSEMENT TRAUCATS LOTISSEMENT TREILLES BASSES LOTISSEMENT TREILLES HAUTES LOTISSEMENT TREILLES HAUTES I LOTISSEMENT TREMIE LOTISSEMENT TRIBES LOTISSEMENT TRUC LOTISSEMENT TUILERIES LOTISSEMENT TULIPES LOTISSEMENT TURENNE LOTISSEMENT VAL FLEURY LOTISSEMENT VALLON LOTISSEMENT VALLON RANS LOTISSEMENT VALLON BAUMES LOTISSEMENT VALLON LORIOTS LOTISSEMENT VALS LOTISSEMENT VAUCOULEURS JALAGUIERE LOTISSEMENT VENDEMIAIRE LOTISSEMENT VERDIER LOTISSEMENT VERDIERS LOTISSEMENT VERGERS LOTISSEMENT VERRIERS LOTISSEMENT VERT LOTISSEMENT VERTE PLAINE LOTISSEMENT VERTOUBLANC LOTISSEMENT VESSIERE LOTISSEMENT VIABELLA LOTISSEMENT VIABELLA 2 LOTISSEMENT VIADUC LOTISSEMENT VIERGES LOTISSEMENT VIEUX FRENE LOTISSEMENT VIEUX LOTISSEMENT VIGNE LOTISSEMENT VIGNES LOTISSEMENT VIGNES CAZOULS LOTISSEMENT VIGNES CAZOULS LOTISSEMENT VIGNES L AIRE LOTISSEMENT VIGNES LOTISSEMENT VILLA ROMAINE LOTISSEMENT VILLAE MOLIERI LOTISSEMENT VILLAGE OCCITAN LOTISSEMENT VILLEBRUN LOTISSEMENT VILLEMIN LOTISSEMENT VILLENEUVE COTTAGE LOTISSEMENT VIOLETTES LOTISSEMENT VIOLETTES 2 CAMI TILLEULS LOTISSEMENT YPREAUX LOTLES MURIERS LOTLOT 20 MAS BUISSON 15 LOU LOTMAS L ISLE LOU BOSC LOU BOSC II LOU CANTOU LOU CLAPAS 325 CAMI OLIVIERS LOU CLAVOUX LOU DEVES LOU GARRIGOU LOU GEISSES THEZANEL LOU GRANDS CAMPS LOU GRAVAS LOU GUA LAS PALOS CALAS GUA LOU MATAFAROU LOU MATOU LOU MIRADOU LOU MOULY LOU PAISSEL LOU PAYSSEL LOU PEYRET LOU RAJAL LOU VERDIER LOUS BALADASSES LOUS CADES LOUS CLAITS LOUS CLAUZES LOUS CLAVOUX LOUS GARELS LOUS GEISSES THEZANEL LOUS PLANASSES LOUS PLANS AIRES LR FALGAIROUS CAMI FOUNTJUT LUGNE LUPEC LURENEL LUSSAU MADALET MAGNANERAIE MAGUELONE MAIL ABBES MAIL D 2 84 MAIL PLEGUEBIAUX MAIL ROCHELONGUE MAIL ALBAN MAIL ABBES MAIL HAUTS GELY MAIL JACQUES CARTIER MAIL LOUIS BAISSE MAIL ROCHELONGUE MAIL POESIE MAIL SOLEIL MAIRAN MAISON AGRICULTEURS MAISON BOUTONNIET MAISON CLAUDEL MAISON D EMMA MAISON RETRAITE MAISON RETRAITE FRESCATIS MAISON VILLAGE 1 ROUVIERE PL MAISON AGRICULTE MAISON AGRICULTEURS MAISON PAYS L HERAULT MAISON MAISON EDF TRUSCAS MAISON FORESTIERE TRIVALLE MAISON GENDARMERIE MONTPELL MAISON MAISON MAS ROCH MAISON RETRAITE QUINTESSENCE MAISON ROUANET VIOLGUES HAUT MAISON MAISON MALLET MAISON SALADELLO MAISONS PORT 3 PLAN, CAP D AGDE MALAMBRAC MALAVIELLE MALBOSC MALFRECH MALIBERT MALUZAN MALUZAN II MALVIES MALVIEU MANCES MANSE MARCHE CLOT MARCHE GROS FOUCAULT MARCHE MARCOUINE MARCOULS MARECHAL FRECH AUMET MARENNES MARINES MARINES ALPHA 2 MARQUISES MOTTE COUCHANT MARQUIT MARSEILLAN MARSEILLAN PLAGE MARTHOMIS MAS MAS 3 MOURRE BLANC MAS 3 PONTS MAS AGRES MAS AIGREMONT GARRIGUES MAS AIL GARRIGUES MAS ALEGRE MAS ALHEM MAS AMILHON MAS ARNAUD MAS ARNAUD 14 CAMI ROQUE MAS ARTEMON MAS AUBANEL MAS AUBARET MAS AUBIERS 1 L ESTAGNOLA MAS AUBRAYE CATONNIERE MAS AUDRAN MAS AUJARGUES MAS AUROUX MAS AUSSEL MAS AZAIS MAS BAKARA GARRIGUES MAS BARRAL MAS BARROU MAS BAS MAS BAUDRAN MAS BAZIN MAS BEAUREGARD MAS BEAUVEZET MAS BEDOS MAS BEL AIR MAS BELAURES MAS BELBONNE MAS BELLAURE MAS BELLE MAS BELLET MAS BELLEVUE VILLAGE MAS BIARD MAS BLANC MAS BLANC SALCES MAS BOISSANFEUILLES ROULIO MAS BOMBEQUIOLS MAS BONNEL MAS BONNES OUEST MAS BONNIOL MAS BORIE MAS BOSC LOU BOSC MAS BOUDET MAS BOUET MAS BOUSQUET MAS BOUSSIERES MAS BOUYSSIER MAS BOYER CAUSSE VILLEMAGNE MAS BRESSON MAS BRUNE VAUGUIERE HAUT MAS BRUYERE VILLAGE MAS CABANIS MAS CADOULE THERME MAS CALADE MAS CAMBOUNET MAS CAMBRES COUDOULET MAS CANNEBETH MAS CANNES MAS CANTEGRIL MAS CARBOU MAS CARENEUVE MAS CARMES MAS CAROTTES MAS CARRIERE MAS CASTEL MAS CASTELLAS CASTELLAS MAS CAUNELLE MAS CAUSSE COUPIAC MAS CAUVY MAS CAVALIERS MAS CAYLA VILLAGE MAS CAYROU MAS ROUQUETTE MAS CHRISTOL MAS CITA HAUTS BOISSERON MAS CLERGUES MAS CLOSAL MAS COLOMBIER CAUNAS MAS COLOMBIERS CAUNAS MAS COMBES MAS CONCHILICOLE BEZARDE MAS CONCHYLICOLE MOURRE BLANC MAS CONCHYLICOLE MONTPENEDRE MAS COPAINS MOURRE BLANC MAS COSTE MAS COSTE HAUTE MAS COSTIL VILLAGE MAS COUCOULE MAS COULET MAS COURNON MAS COURREGES MAS CRES MAS CROIX NEUVE LOUPIAN MAS D AGRES MAS D AIL MAS D ALHEM MAS D ALHEN MAS D ARCAY MAS D ARNAUD MAS D ARNET MAS D AUBAGNAC MOUNTADE MAS D AUGE MAS D AVILIER MAS D ENCIVADE MAS D ENGUES MAS D OLIVIER MAS D OLIVIERS MAS DAUMAS GASSAC MAS BAGUAI MAS BALMES MAS BAZIN MAS BELLAURE MAS BETHES MAS BIZARD MAS BOUDET MAS BOUDOUISSOU JOURNEQUES MAS BOURAN MAS BRUYERE MAS CANNE MAS CASSE CASSE MAS CAUSSE MAS CEDAT MAS CHAVE MAS CLEO MAS CONQUETTE FROUZET MAS DETROCAGE MOURR MAS FENAIROUL MAS FONTBONNE MAS FOURQUES MAS GARRIC MAS JANUQ MAS JAUMES MAS L AUBERGE MAS L GARRIGUES MAS L MAS L HERME MAS L OLIVERAIE MAS CARAVETTE MAS CLAUSADE MAS CROIX BEUVIERES MAS DRAILLE VILLAGE MAS GARDIOLE MAS GARRIGUE MAS JASSE JASSE MAS LOUVE BOISSIERE MAS NAVETTE FOURCHES MAS PINEDE CAMI CANTO MAS PINEDE I 41 CAMI MAS POURCARESS VILLAGE MAS POURCARESSE VILLAGE MAS PROUDOUMETTE MAS TOUR MAS VERDIERE MAS VIERGE MAS LAURIOL MAS LAVIT VILLAGE MAS MADIER MAS MALRIV MAS MAROT MAS MARTIN MAS MEJEAN MAS MEZOUL MAS MONNIER MAS MOUNEL MAS PACHE MAS PANSE MAS PERET MAS PEROLS MAS PETRA ROUIRES MAS PIERRETTE MAS POTIS VAUGUIERE HAUT MAS PY CAUNAS MAS ROUX MAS FELIX MAS SIAU MAS SOULAS MAS VAUGUIERES MAS VEDEL MAS VEZIAN MAS DEBRU MAS DELON VILLAGE MAS BONNES OUEST MAS CHEVAUX CABANES MAS COMMANDEURS MAS LAVANDES MOUCHERES MAS LIODRES MAS OLIVIERS MAS PERES MAS PINS MAS PLANES MAS SEPT FONTS MAS DESPLANS MAS DESTRECH MAS DETROQUAGE MOURRE BLANC MAS DEVEZE MAS DISSOL MAS DISSOL FRANGOUILLE MAS DOMAINE OLIVIERS MAS DOSCARES MAS DOUMERGUE MAS DOUMERGUE VAUGUIERE HAUT MAS BEL AIR MAS CABRIE MAS SALAN SALAN MAS GRES MAS HAUT MAS MAROT MAS MAS PT MAROT VAUGUIERE HAUT MAS PUECH HAUT MAS RIEU MAS RUFFAS MAS SALIN MAS TILLEUL GARRIGUES MAS MAS EMBOUGETTE MAS ENCIVADE MAS ENCONTRE MAS ENCOSTE MAS ENDRAUSSE MAS ENRIVIERE MAS ENSAIRE MAS FABRE MAS FANGOUSE MAS FAROUCH MAS FAVET MAS FEUILLADE MAS FIGARET MAS FONTAN LOUP MAS FOURNEL MAS FOURQUE MAS FOURQUES MAS FRAICINEDE MAS FRANCES VILLAGE MAS FRENES MAS FRIGOULET MAS GARRIGOU MAS GERMANE BORIES MAS GINESTE MAS GOURGOUBES MAS BOUET MAS GRANGES VILLAGE MAS GRATET MAS GRES MAS GUILHERMAIN MAS GUINET MAS GUIRALES VILLAGE MAS HAUT MAS HAUT PROUILHE MAS HAUT VILLAGE MAS HELIAULE MAS JANOU MAS JASSE MAS JAULE MAS JOURDANS CAUSSE NISSERGUES MAS JUFFET MAS JUGE MAS L HEBRARD MAS COSTE MAS CROIX VILLAGE MAS FAISSOLLE MAS FAROUCH BATC 2 MAS PARODIENNE GARRIGUES MAS PINEDE 1 CAMI CANTO MAS LABAU MAS LAC SALAGOU MAS LACROIX BEUVIERES MAS LAGAS MAS LAMOURE MAS LAMOUROUX MAS LAPIERRE CROZES MAS LAROQUE MAS LASURBETE MAS D ALBERT LOU PRADAS MAS BOSC VAUGUIERES BAS MAS MARGAUX GARRIGUES MAS MOURRE BLANC MAS LEVANDOU VAUGUIERES BAS MAS LOU SOUREL VILLAGE MAS LOUVADE MAS LUZIERE MAS MARCHANDES MAS MARE MEJE MAS MAREDA LOU GRAVAS MAS MARIOTTE MARIOTTE MAS MARTELLE 1 13 PLAN LURCAT MAS MARTELLE 2 PLAN ANDRE LURCAT MAS MAS PASSERELLE MAS MATELETTES MAS MAURY MAS MAZET MAS MEAUX MAS MEROU BAS MAS MESSIER MAS MICHEL VAUGUIERE HAUT MAS MONNIER GARRIGUES MAS MONPLAISIR MAS MONTELS MAS MONTPLAISIR MAS MOULINES MAS MOURE ROUGE MAS MOURGUES MAS MOUSTACHOU MAS MURENE MAS MUSSEN MAS NAUD MAS NAUSSARGUES MAS NAVAS MAS NEUF MAS NEUFS CLAUSED 1 MAS NOIR MAS NOUGUIER MAS OLIVET MAS OLIVIERES VILLAGE MAS ONGLOUS GIRADOU MAS OSTREICOLE 14 MAS OSTREICOLE MOURRE BLANC MAS PACHE MAS PACHET MAS PADRE MAS PAILLASSE MAS PALOMINES VAUGUIERE HAUT MAS PASQUIER MAS PATHAY GARRIGUES MAS PEROU MAS COGUL MAS PIERREFEU MAS PINEDE 17 CAMI CANTO RANO MAS PINEDE 2 CAMI CANTO RANO MAS PINEDE CAMI CANTO RANO MAS PLAINE MAS PLANS NEUFS MAS PONS MAS POSTE MAS POSTIL VAUGUIERE HAUT MAS PRADINES MAS PRATS MAS PUECH BAUXITES MAS PUJOL MAS PY CAUNAS MAS QUATRE MAS RASPAILLAC MAS RAUZY MAS RECOULY GARRIGUES MAS REDONDEL MAS REDONNEL MAS REGNAGUE MAS RICOME MAS RIEU MAS RIGAL MAS RIGOBERT MAS RIOLS MAS ROLLAND MAS ROQUE MAS ROQUETTE MAS ROUBIAC MAS ROUGE 15 MAS ROUGE 27 MAS ROUVIERE MAS RUFFAS MAS SADOUL VAUGUIERES BAS MAS ANDRE MAS BERTHOMIEU BORIES MAS ESTEVE MAS FELIX MAS GABRIEL MAS JACQUES MAS MAS JULIEN MAS MICHEL GARRIGUES MAS MAS ROCH MAS ROMAN MAS SAUVEUR MAS SAINTON MAS SAM SUFFIT ROUQUETTE MAS SAPORTA MAS SARDA MAS SARRAZY MAS SERRE DEL POUS BORIES MAS SERVEL MAS SOULAS MAS ST GARRIGUES LAUNAC MAS SUEILLES MAS TABLE MAS TAMARIGUIERES MAS THIBAULT MAS TINAL MAURIN MAS TOUR PLAINE MAS TOURNAL DEVANTS CEYRAS MAS TOURNEFORT MAS TRINITE GARRIGUES MAS VAUCOULEURS MAS VEDEL MAS VERDIER MAS VERNEDE MAS VIALA MAS VICTOIRE MAS VILLARET MAS VITROLLES 1015 VILLAGE MASSILIAN MATEPEZOULS MATHE MATTEMALE MAUBOUYS MAURIAN MAURIN II 9 PLAN TERRAL MAUROUL MAYRANNE MAZADE MAZADE BANES MAZET 4 VENTS COUARCH MECLE MEDES MEDES HAUTES MEDIATHEQUE MEGES MERCORENT MERLAC METAIRIE POUZES METAIRIE NEUVE MEUNIERES ROSES MEZEILLES MEZOUILHAC MINI PARC CROIX MIREDANES MOLE LOUIS MOLENTY MONREDON MONS MONS TRIVALLE MONT ROUGE MONTAHUT MONTANA WEST CITY I MONTAURY MONTBARRI MONTCELEBRE MONTEE AIRE MONTEE ARBOUSIER MONTEE BASSINS MONTEE BRISAOU MONTEE CAMBON MONTEE L ARCHE MONTEE L MONTEE FIOLE MONTEE GARENNE MONTEE PICADOU MONTEE RONDE MONTEE POUROLS MONTEE CATINETTES MONTEE CHENES MONTEE ECOLIERS MONTEE NOYERS MONTEE PIERRES BLANCHES MONTEE CLOS MONTEE MICOCOULIER MONTEE MORASTEL MONTEE TERRAL MONTEE ECOLIERS MONTEE FONTENELLES MONTEE L MONTEE POURRE MONTEE LAUZIERE MONTEE MARTIN MONTEE PIERRES BLANCHES MONTEE POUROLS MONTEE POURRE MONTEE REMPARTS MONTEE MARTIN MONTEE TOUR MONTFAUCON MONTIMARAN MONTIMAS MONTMAJOU MONTMAL MONTMAU MONTMEZE MONTPELLIER MONTPENEDRE MONTPLAISIR MONTPLO MONTREDON MOTTE COUCHANT MOTTE COUCHANT MOTTE MARQUISES MOUCHERES ROUVIGNAC MOULES BAUCELS MOULIERES MOULIERES HAUTES VENT BALMES BAS CANTARANE CANTARANE CHABAUD CIFRE ROQUE LAFFOUS MERIC PRADES TREDOS VERGOUGNAC DUC D UZES HAUT L HERAULT LIGNON LIGNON LE VILLAGE CEPS COSTES VIEUX ROUTE MONTPELLIER NEUF VERDIER MOULINE MOULINE EAUX MOULINES MOLENTY MOULLIERES MOULOUNADE MOUN MOURE D AUSSEL MOUSSE MURATEL MURATOU XIST MYKONOS I NABAT BAS NAJAC CREMADE NAUSSARGUES NAVIS NEGUE CATS NEUF ECLUSES NEUVE NISSERGUES NITRO GET NOTRE DAME NOTRE DAME MOUGERES NOTRE DAME GRAU OASIS CONDAMINES SALELLES OLIVET SUD OREE SOULIER ORNAC PABO PAGNERIE PAILLERE PALENQUES PALMIERS PALUSETTE PARC 453 VILLAGE PARC ACTIVA PARC ACTIVITE FONTVIN PARC ACTIVITE PEYRIERE PARC ACTIVITES AFTALION PARC ACTIVITES ECONOMIQUES PARC ACTIVITES MERIDIENNE PARC BELLEVUE PARC CHAT CAMBOUS PARC CHENES PARC CLUB MILLENAIRE PARC D ACTIVITE PARC D ACTIVITES PEYRIERE PARC D ACTIVITES MASSANE PARC D ACTIVITES HORIZON SUD PARC D AFTALION PARC LOISIRS PARC DRYADES PARC EXPO PARC INDUSTRIEL CRESSE MARTIN PARC PEYRIERE PARC MAZES PARC LIXIRIE 2 PARC LOUIS BLERIOT PARC MARCEL DASSAULT PARC MEDITERRANEE PARC OLIVERAIE CAMPI PARC PEYRIERE PARC RESIDENTIEL 6 PARC RESIDENTIEL 7 LIXIRIE PARC PARC BATIMENT 1 ZAC MAS PARC ROCH PARC SCI ENVIRONNEMENTAL PARC SCIENTIFIQUE AGROPOLIS PARC ST HUBERT PARCELLE 209 PARDAILHO PARKING LARGE PARKING PERE MAUREL PARKING COMEDIE PARKING PEPINIERE PARKING POSTE PARKING TEMPS LIBRE PASSAGE ALBERT DUBOUT PASSAGE ALEXANDRE YERSIN PASSAGE ANATOLE THIBAULT PASSAGE ANDREA PASSAGE APPELES PASSAGE ARAMON PASSAGE ARGELE PASSAGE ATELIERS PASSAGE BIQUET PASSAGE BRUYAS PASSAGE CABRI PASSAGE CAGAROTTE PASSAGE CAROUX PASSAGE CAYROU PASSAGE CENTRAL PASSAGE COLBERT PASSAGE DAUPHIN PASSAGE DAVID BELUGOU PASSAGE L PASSAGE L HORLOGE PASSAGE FEDE PASSAGE MARNE PASSAGE VIDAL NORD PASSAGE DELON PASSAGE BEOULOLIS PASSAGE BOUTIQUES PASSAGE LAURIERS PASSAGE OMBRETTES PASSAGE ROSIERS PASSAGE VERTES PELOUSES PASSAGE CRESSON PASSAGE PIGEONNIER PASSAGE ESCANAS PASSAGE FORGE PASSAGE CLAUZEL PASSAGE FREDERIC MISTRAL PASSAGE FRONT MER PASSAGE GAMBETTA PASSAGE GERMAINE BOUSQUET PASSAGE GUITOU PASSAGE IMPRIMERIE PASSAGE IRIS PASSAGE BAPTISTE MALACAN PASSAGE BART PASSAGE COCTEAU PASSAGE CROS PASSAGE MAGROU PASSAGE CROIX PASSAGE LAPIN PASSAGE LAURET PASSAGE DAUPHIN PASSAGE VILLAGE PASSAGE ECUREUILS PASSAGE LISSE PASSAGE LONJON PASSAGE LONJON C S PASSAGE LOUIS MAUREL PASSAGE MEURTRIERE PASSAGE NINA RICCI PASSAGE NIVEAU PASSAGE NIVEAU CHAT RIBAUTE PASSAGE NORIA PASSAGE PONANT PASSAGE ROBERT SCHUMANN PASSAGE ROSSINI PASSAGE LOUIS PASSAGE SAINTE ANNE PASSAGE SALABRE PASSAGE SARAH BERNHARDT PASSAGE SOIXANTE QUINZE PASSAGE SON PASSAGE SQUARE PASSAGE THONNAIRE PASSAGE VERGNES PASSAGE VIOLETTES PASSEREAU PASTEUR PATAOU VALERY PAULHAN PAVILLON SEVIGNE PECH JOIE PECH MOULINS PECH PUJOLAT PECH PEILHAN PELISSOLS PERIES PESTOUS AMBONNE CASSAFIERES LOUPIAN MAS PIGEONNIER FONTFROIDE PIOCH QUIST RIEUMAJOU BRASSAC PETITE VERNE CABANES LUNEL PEYRE PLANTADE PEYREBRUNE PEYREFICHE PEYREMALE PEYRES CANES PEYSSINE PEZ PEZENAS MORTE PIEURE ANDRE PIEUROUQUET PINEDE PIOCH COUMOULET PIOCH COMTE PIOCH MOUSSALOU PIOCH PIRE PIOCH PINS PIOCH MICHEL PIOCHCOUGUIOUL PISCICULTURE PLANS ESPAROU PISTRE PIT POMPIGNANE PIT POMPIGNANE BATIMENT T 4 PIZZERIA PLACE 11 NOVEMBRE 11 NOVEMBRE 1918 14 JUILLET 14 JUILLET 1789 19 MARS 1962 4 SEIGNEURS 56 EME REGIMENT ARTILLERIE 8 MAI 8 MAI 1945 ABBADIE ABBAYE ADOUZES AGDE MARINE AGRIPPA D AUBIGNE AIRE AIRES AIRETTE ALBERT 1 ER ALBERT DELRANC ALBERT LOUVEL ALBERT MARCELLIN ALBERT SCHWEITZER ALBERT THOMAS ALEXANDRE LAISSAC ALLIES ALOYS CAMBON ALPHA ALPHONSE ALLAIS ALPHONSE BEAU ROCHAS ALPHONSE BEAU ALPHONSE DAUDET ALPHONSE LAVERAN ALTAMIRA AMBOISE CROIZAT AMITIE ANCIEN ABATTOIR ANCIEN FOUR ANCIEN HOSPICE ANCIENNE ANCIENNE COOPERATIVE ANCIENNE ANCIENNE GENDARMERIE ANCIENNE ANCIENS COMBATTANTS ANCIENS COMBATTANTS INDOCHINE ANDRE CASTEILLA ANDRE GIDE ANDRE LEROI GOURHAN ANDRE PASSET ANDRE VIDAL ANTONIN DURAND ANTONIO GAUDI ARAGO ARBOUSIERS ARBRE ARBRE LIBERTE ARCHERS ARENES ARISTIDE BRIAND ARTHUR HONEGGER ATHENA AUBEPINE AUGUSTE COTE AUGUSTE FAGES AUGUSTE GIBERT AUGUSTE GINOUVES AUGUSTE RENOIR AUGUSTE RODIN AUTOUR PALOMBES AUX FRUITS AUX HERBES BACCHUS BAPTISTE MILHAU BARBACANE BARONCELLI BASCULE BASSE BASSE BATAILLON L HERAULT BATELERIE BEAU SOLEIL BEAUREGARD BELLE AGATHOISE BELLE VUE BELLEVUE BENEDICTINS BERTRAND GARRIPUY BESSIERES BIBLIOTHEQUE BICENTENAIRE BICENTENAIRE REVOLUTION BLANCHE BOBY LAPOINTE BONNET BORIE BOUIS BOULIECHOU BOUSCHET BERNARD BRIAND BRICE BUIS BYBLOS CAFE CAFES CAIZERGUES CALVAIRE CAMILLE REBOUL CAMILLE SALLAN CANIGOU CAPITELLES CAPUS CARBOUNIERES CARDINAL MARTY CARDINAL VERDIER CARIGNAN CARNOT CARRE DONJON CASSAN CASTELLANE CASTORS CATHARES CEDRES CENT QUARANTE PLACE CESAR CEVENNES CEZANNE CHABANEAU CHABRIER CHALUT CHALUT CHALUT CHANOINE KIR CROS CHARLES FABRY LINBERGH LINDBERG PEGUY RIGAL TARBOURIECH CHARMILLES MONTPELLIER CHASSELAS PLACE D EAU CHEVALIERS MALTE CHEVREFEUILLE CHEVREFEUILLES CHINE CHRIST CHRISTOPHE CISTE CISTES CITADELLE PLACE FONTARITTE CLAIRETTE CLAUDE DEBUSSY CLAUDE MONET CLEMENT BECAT COLETTE COLONEL GUILLAUD COLONEL MIRAMOND COLUCHE COMBATTANTS COMMANDANT MESTRE COMMANDANT DEMARNE COMMANDANT TERRISSE COMMERCE COMPAGNONS COMPORTE COMPOSTEL COMPOSTELE COMPOSTELLE CONSEILLER CONSTANTINE COOPERATIVE COROT COSMONAUTES COUPIAC COUREJOLLES COURNEUVE COUVENT CROIX CROIX MISSION CURNONSKY D ACADIE D AGUESSEAU D ALSACE LORRAINE D ATHENES D AVILER D ISLY D ITALIE DAHLIAS DANIEL BALAVOINE DANTON CABROL BABYLONE CALVI CHINE FONTJUN PLACE L ABBAYE L ABBE D OLQUES L ADRET L AFICION L AGENOUILLADE L AIRE L ANCIENNE L ARCHE L ARMOISE L L EPI L EPICEA L ESPINOUZE L ETANG L OR L HORLOGE L HOTEL L HOTEL L IMBAISSE L OLIVIER BASCULE BOETIE BOUQUERIE CANOURGUE CELETTE CHAMPAGNE CHAPELLE CHAPELLE NEUVE CIME D ENDRAUSSE CLAIRE FONTAINE CLAIRETTE CLAMOUSE COLOMBE COMEDIE CONSTITUTION COOPERATIVE CORVETTE COUPO SANTO CROIX FALAISE FONTAINE FONTAINE NEUVE FRIGOULE GAFFE GLACIERE GARRIGUE GRIFFE LIBERATION LIBERTE MADELEINE MARINE MUSE NACIOUN GARDIANO CAMARGUE FRANCE PINEDE PLAGETTE POSTE RAMADE RAMPE RENARDIERE REPUBLIQUE REVOLUTION RONCERAIE SERPETTE SIGNADE TOUR TOUR RONDE TOURGUILLE TRAMONTANE VENE VICTOIRE VIEILLE POSTE VIERGE VIGNE LASCAUX MONTSERRAT PEDRALBES POBLET ROME SILOE SPARTE STRASBOURG TAGASTE THESSALIE TIBERIADE VERDUN DELACROIX DELILLE DEMOLITION DENFERT ROCHEREAU DENISE GREY 3 FRERES BARTHELEMY 4 VENTS ACACIAS AIRES AIRES PORTEL ALLIES AMANDIERS ANCIENS COMB AFN ARCADES ARCADES MAURIN BARRYS BEAUX ARTS BONS AMIS CAMISARDS CASERNES CHARDONNERETS CHARMILLES CHENES VERTS CHEVREUILS CISTES COMEDIENS COPAINS D ABORD CORALIENNES COSMONAUTES COTEAUX CYSTES ECOLES ECOLES LAIQUES ERABLES ETATS LANGUEDOC FELIBRES FLAMENTS ROSES GALETS GRENADIERS GRES GRILLONS HALLES HEROS ILES MARQUISES LAURIERS LILAS LOISIRS LYS MAZES METAIRIES MEUNIERES MEUNIERS MIMOSAS MONUMENTS AUX MORTS MOUETTES MURIERS MUSCATS OLIVIERS PASTELS PATRIOTES PERVENCHES PLATANES PROVINCES PRUNELIERS ROSSIGNOLS ROUGES GORGES SANTONS SITELLES SONNETS SOPHORAS TAMARIS TOURTERELLES TRITONS TROIS PORCHES TROPHEES VIOLETTES DJANGO REINHARDT CLEMENT DOMAINE DOUMERGUE DOYEN GASTON GIRAUD 1 ER OCTOBRE 11 NOVEMBRE 11 NOVEMBRE 1918 14 JUILLET 18 JUIN 1940 2 DECEMBRE 1852 4 SEPTEMBRE 8 MAI 1945 BALLON BICENTENAIRE BOUAOU BOUQUET BRAGALOU CALVAIRE CAPITAINE VINCENT CARTEL CASTEL CEDRE CHARDONNAY PLACE VIEUX CHRIST CLOS FLEURI COMMANDANT DEMARNE COMMANDANT VIBERT COMMERCE COMTE TOULOUSE COUVENT DARDAILHON PLACE FORUM FOUR FOUR CHAUX FOY FOYER FRONTON GARDIAN GAZIAN GENERAL GEVAUDAN DUCHE GRENADIER GRIFFE GRILLON JARDIN L INFANTE JEU BALLON JEU BOULES JEU PAUME JOUG JUMELAGE LEVANT LOUVRE LUMINAIRE LYCABETHE MARCHE MARCHE AUX FLEURS MAS D AZIL MIDI MILLENAIRE MINERVOIS MOLE MONT AIGOUAL MONT LOZERE MONT VENTOUX MONUMENT AUX MORTS MONUMENT VILLAGE MOURRAS NOMBRE D OR PARNASSE RHONE SCEL PEYROU PIC LOUP PLO LEVIS TRINQUAT PORCHE PORT PORTAIL PORTALET PRADET PUITS PUITS JACOB QUAI QUEBEC ROUERGUE TERRAL THYM TRIDENT VACCARES VERGER VIEUX VIEUX PUITS VIGAN E ZOLA PLACE ECOLES EDGARD BOUSQUET EDOUARD EDOUARD ADAM EDOUARD BARTHE EDOUARD HERRIOT PLACE VILLAGE ELSA TRIOLET EMBRUNS EMILE AIN EMILE BARTHE EMILE COMBES EMILE TURCO EMILE ZOLA EMMANUEL CHABRIER EMMANUEL KANT EOLE ERABLES ERNEST GRANIER ERNEST HEMINGWAY ESPINOUSE ESQUIFS ESTEREL GRANIER JODELLE PASCAL SANIER ETOILE EUGENE BATAILLON EUGENE DELACROIX EULER EUROPE EUROPE FLANERIE EXUPERY FABRE D OLIVET FABRERIE FAUBOURG AMIS FAULQUIER FEDERICO GARCIA LORCA FELIX MOURET FERDINAND FABRE FERNAND GOUGES FERNAND JANIN FERRER FLORIAN FOIRAIL FOIRALET FOLCO BARONCELLI FONFONNE GUILLERME FONTAINE FONTAINE OR FONTAINE VEDEL FONTAINE VIEILLE FONTAINES FONTANELLES FORGE PLACE FOUR FOURIER FOYER FRANCE FRANCIS CARCO FRANCIS MORAN FRANCIS PONGE ASTIER CONESA HIRAILLES JAUMES MANSART MAYNARD MITTERRAND PHEBUS RASPAIL FREDERIC FREDERIC MISTRAL FRENES FRUITERIE G PELLICIER GABRIEL PERI GABY MORLAY GAFARO GALILEE GAMBETTA GANGUI GARIBALDI GASTON BAISSETTE GENERAL CLAPAREDE GENERAL GENERAL GUILLAUT GENERAL REYNES GENETS GEOGES BRASSENS BEAUME BIZET BRASSENS CLEMENCEAU GERARD CALMEL GILBERT HERMET GILBERT SENES GIRONDINS GLOBE GOURGUET LARGE GRIFFE GRIFFOUL GUILLAUMET GUINGUETTE HALLES HAUT MARSEILLAN HAUTE HENRI AUGE HENRI BECQUEREL HENRI CABANEL HENRI BORNIER HENRI IV HENRI MATISSE HENRI MOISSAN HENRY MONTMORENCY HENRY MOISSAN HERMET HERSILIE HERVE BAZIN HERVE HARANT HIPPOLYTE NEGROU HOCHE HONORE DAUMIER HONORE ROQUES HORLOGE HORTUS HOTEL HUGUES BEAUVIGNAC INNOCENTS IRIS J CHAPTAL JACOBINS JACQUES ANQUETIL JACQUES MIROUZE JANIN JASSE ANTOINE CHAPTAL ANTOINE BAIF ANTOINE HOUDON BAPTISTE COROT BAPTISTE POQUELIN BAUMEL BENE BONHOMME DORAT GAILLAC GAU GIONO GLEIZE JAURES MOREAS PLACE SARTRE ROUVIER JEANNE D ARC JEU BALLON JEU BOULES JEU BALLON JEU BALLONS JEU BOULES JONCS JOSEPH BOUDOURESQUE JOSEPH BOUDOURESQUES JOSEPH BOUSSINESQ JOSEPH DELTEIL JOSEPH DURAND JOSEPH VIDAL JULES BALESTIER JULES FERRY JULES MILHAU JULES ROMAIN JULES VALLES JULES VERNE JUNOT JURANDES KELLERMANN KID ORY L L HERMET CROUZETTE PRINCE LAMARTINE LAMPARO LANGUEDOC LANNES LAVANDE VILLAGE LEDRU ROLLIN COTEAUX LEVANT LIBERATION LIBERTE LINDBERGH LINO VENTURA LOTISSEMENT TAMARIS LOUIS ARAGON LOUIS BLANQUI LOUIS BREGUET LOUIS CHRISTOL LOUIS D ANJOU LOUIS TREGARO LOUIS GRIFFE LOUIS LOUIS SOULIE LOUIS XIV LUMINAIRE LUMINAIRES MAGELLAN MAIRIE ANNEXE MAITRE MALLET CAISSO MANET MANGEOT MARCEL BARRERE MARCEL COMPAN MARCEL GODECHOT MARCEL LEGAUT MARCEL PAGNOL MARCEL PAGNOL 21 MARCEL PROUST MARCELIN ALBERT MARCELLIN ALBERT MARCHE MARCHE AUX HERBES MARCHE COUVERT MARCHE TROIS SIX MARCO POLO MARECHAL D LATTRE TASSIGNY MARECHAL LATTRE TASSIGNY MARECHAL FOCH MARECHAL JUIN MARGUEROUSSES MARIANNE MARIUS MAROCAIN MAROCAINS MARQUIS BARONCELLI MARRONNIERS MARTYRS MARTYRS RESISTANCE MAS GARENC MAURICE RAVEL MAX LEENHARDT MEDITERRANEE MEJARIE MELEZES MENDES FRANCE MER SOLEIL MERIL POUJADE MICHEL COLLUCCI MICOCOULIERS MIMI AZAIS MINUTE MIOLANE MIREILLE MISSION MOLIERE MONSEIGNEUR HIRAL MONTESQUIEU MONUMENT MONUMENT AUX MORTS MONUMENTS AUX MORTS PLACE MOURCAIROL MURIERS MUSCAT MYOSOTIS NEUVE NOMBRE D OR NOMBRE OR D ANTIGONE NOTRE DAME OCCITANIE OLIVETTES OLIVIER OLIVIER SERRES OMEGA ONZE NOVEMBRE ORMEAU ORMEAUX PALADHILLE PALADILHE PAMPARIO PASCAOU PASTEUR PATRIOTES BEC CHASSARY GAUFFRE GAUGUIN LANGEVIN PEPIN VALERY VIGNE D OCTON PECHEURS PENITENTS PROVENCE PETRARQUE PEUPLIERS PEYROU DAVID D ANGERS DUHEM LAVENE MASSE MENDES FRANCE RENAUDEL SEMARD THIEULE VIALA PINS PLAN PLANEL PLANET PLANOL PLATANES PLO PLO D EN HAUT POINT JOUR POLKA POMPE PONANT PONCET PLACE PORCHE PORTAIL PORTAIL BAS PORTAIL HAUT PORTAIL VIEIL PORTAL PORTALET POSTE PRADETTE PRE PRESBYTERE PRESSOIR PRINCIPALE PRUNUS PUITS JOSEPH QUAI QUATORZE JUILLET QUEBEC RAMPE RENARDIERE RENE CASSIN RENE NELLI REPUBLIQUE RESERVOIR RESSEGUIER REVOLUTION REVOLUTION FRANCAISE RG INF COLONIALE MAROC RHIN DANUBE RHIN DANUBE RIEUCOULON RIVELIN ROBERT GRANIER ROBERT SCHUMAN ROC BLANC RODE ROGER ABBAL ROGER SALENGRO ROMAIN ROLLAND ROMARINS RONDELET ROOSEVELT ROSEAUX ROSETTE ROUGET L ISLE ROUGET LISLE SACRE COEUR AIGNAN APHRODISE ARIGE CERONNE CLAIR COME CYR DENIS ESPRIT GENIES GERAUD COURTET JACQUES PLACE JOHN PERSE LAURENT FONTCAUDE MARC MARTIAL PLACE RAVY ROCH SIMON VINCENT VINCENT SAINTE BARBE SARCELLES SARDINAL SARRAZINS SAULES SEAN MAC BRIDE SEBASTOPOL SEINCHOLE SENECHAUSSEE BEAUCAIRE SERANNE SIRENES SOL SOLEIL D OC SOULT SQUARE TEYSSERENC STALINGRAD SUBERBET SUZANNE VALADON SYDNEY BECHET TABOURIECH TAMARINS TAMARIS TARBOURIECH TERRE DEL REY TERRISSE THEATRE THERMIDOR THERON THUYAS TOULOUSE LAUTREC TOUR TOUR TOURGUILLE TRAINE TREILLE TRIBUNS TUILERIE TUYAS URBAIN COSTE UTRILLO VALENTIN HAUY VASCO GAMA VAUBAN VENTOUX VERADIER VERDUN VERLAINE VICTOIRE VICTOR HUGO VIEILLE VIERGE VIEUX VIEUX PORT VILLAGE VIVALDI VOLTAIRE YVONNE LEROUX PLACE5 GD PLACES LUMINAIRES PLACETTE PLACETTE 78 EME PLACETTE AMANDIERS PLACETTE ARAMONS PLACETTE CLEMENT CUGNENC PLACETTE ELFES PLACETTE FARFADETS PLACETTE GENEVRIERS PLACETTE GOELANDS PLACETTE PIOCH CABRIES PLACETTE ROCAILLE PLAGE ARESQUIERS PLAGE CORNICHE PLAGE ROQUILLE PLAGE VENDRES PLAGE EST PLAGE MAIRE PLAGE ORPELIERES PLAGE NATUR ORPELIERES PLAGE OUEST PLAGE OUEST GRAU VENDRES PLAGE OUEST YOLE PLAGE RICHELIEU PLAGE ROQUILLE PLAGE ROUTE ARESQUIERS PLAGE GRAU PLAGES DOREES PLAINE 111 PLAINE BASTIDE PLAINE BELOT PLAINE BOISSIERE PLAINE BELOT PLAINE CAMPETS PLAINE MAS GENTIL PLAINE LACAN PLAINE MAS GENTIL PLAINE NISSERGUES PLAINE PLAINE TOURRIERE PLAN 4 SEIGNEURS PLAN AFFENAGE PLAN ALBAT PLAN MUSSET PLAN ALICE SAPRITCH PLAN ANATOLE FRANCE PLAN ANCIENNE DOUANE PLAN ANCIENNE POSTE PLAN ANDRE LURCAT PLAN ARTIGUES PLAN BARTHE PLAN BEFFROI PLAN BELLEVUE PLAN BELVEDERE PLAN CADE PLAN CAFFAROT PLAN CAGNETTES PLAN CAMBROL PLAN CANETONS PLAN CARIGNANS PLAN CASTANIE PLAN CASTELLAS PLAN CATELLAS PLAN CATHARES PLAN CAZAL PLAN CERISIERS PLAN CHANTERELLES PLAN CHAPELLE PLAN PLAN EAU PLAN CHEVREFEUILLE PLAN CHOPIN PLAN CITADELLE PLAN CLAUSTRE PLAN CLOITRE PLAN CONSULS PLAN CORNEILLE PLAN COUPERIN PLAN CROIX PLAN D ARTIGUES PLAN DARIUS MILHAUD PLAN CLAUSTRE PLAN GINESTIERE PLAN L ANCIENNE PLAN L ANSE MOUJAN PLAN L ARAMON PLAN L ARNEL PLAN L ARTIMON PLAN L PLAN L HORLOGE PLAN L OLIVIER PLAN L OLY PLAN L OM PLAN L UNIVERSITE PLAN BOUVINE PLAN BRIGANTINE PLAN CHAPELLE PLAN PLAN COURSE LIBRE PLAN CROIX PLAN CURE PLAN FONTAINE PLAN GASCOGNE PLAN GROTTE PLAN POSTE PLAN TOUR PLAN TRAMONTANE PLAN TRINQUETTE PLAN PALLAS PLAN SEPTIMANIE PLAN AGAVES PLAN AIRES PLAN ARAMONS PLAN ARBOUSIERS PLAN ARGONAUTES PLAN ASPHODELES PLAN BARRYS PLAN BOUCANIERS PLAN BOUSCARLES PLAN CACAUSSELS PLAN CAFES PLAN CARDOUNILLES PLAN CASTORS PLAN CHEVREFEUILLES PLAN CYPRES PLAN ECOLES PLAN FOULQUES PLAN FOURS PLAN HAUTS L ARNEL PLAN HIRONDELLES PLAN HUNIERS PLAN MERLES PLAN MIMOSAS PLAN MURIERS PLAN MUSCATS PLAN OLIVIERS PLAN PASTOURELLES PLAN PINSONS PLAN PITTOSPORES PLAN ROITELETS PLAN ROMARINS PLAN ROUBINES PLAN SALADELLES PLAN SAUGES PLAN TAMARIS PLAN TAUREAUX PLAN TREILLES PLAN YEUSES PLAN DIONYSOS PLAN BASSIN PLAN BEAUPRE PLAN BOSC PLAN CANNAU PLAN CARIGNAN PLAN CASTELLAS PLAN CASTELLOU PLAN PLAN D O PLAN VIEUX PLAN CLAPAS PLAN COLOMBIER PLAN COUCHANT PLAN FAUBOURG PLAN FENOUILLET PLAN FOUR PLAN FOUR BANAL PLAN FRAMBOISIER PLAN LAC PLAN MAS PLAN MAS BOISSON PLAN MAS COCON PLAN MAS SARDAN PLAN MEJEAN PLAN MENAN PLAN PALLAS PLAN TINAL PLAN PIC LOUP PLAN PORT PLAN POURTAL PLAN PUITS PLAN PUITS COMMUNAL PLAN RIVELIN PLAN ROUCH PLAN TERRAL PLAN TRIOLVEIRE PLAN DUCHE PLAN ECOLES PLAN PLAN ESCARPE PLAN ESTRAMBORD PLAN FOUR PLAN FOYER PLAN FREDERIC CHOPIN PLAN GARRIGUES PLAN GASCAGNE PLAN GIROLLES PLAN GOUTIER PLAN GRILLE PLAN GRISETTES PLAN GROTTE PLAN GUIRAND SCEVOLA PLAN HIRONDELLES PLAN HOPITAL PLAN HOSPITALET PLAN ICARE PLAN PLAN JEANJEAN PLAN JULES FERRY PLAN L AUTOUR PALOMBES PLAN L ORMEAU PLAN COSTE PLAN SAUTE PLAN LAMARTINE PLAN LAMENNAIS PLAN LAS PLAN LAVANDES PLAN CORBUSIER PLAN DELPHINES PLAN LOUIS JOUVET PLAN LULLI PLAN MADAME MERLE PLAN MALDORMIR PLAN MANDARIN PLAN MARCEAU PLAN MARGUERITE YOURCENAR PLAN MAS ANGLES PLAN MAS GAUSSEL PLAN MICOCOULIER PLAN MINOTAURE PLAN HUILE PLAN MOUSSERON PLAN MOZART PLAN MURIERS PLAN NEUF PLAN NICEPHORE NIEPCE PLAN NYROCA PLAN OLIVIER PLAN ORMEAU PLAN OUM PLAN PALLAS PLAN PASTOUREL PLAN VALERY PLAN PERDIGAL PLAN PILETS PLAN PINS 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FLUVIAL PORT JONATHAN MOTTE COUCHANT PORT LOUPIAN PORT MOURRE BLANC PORT NATURE 1 PORT PLAISANCE PORT CAPITAINERIE PORTAIL FER PORTES POUJOLS POUSSADOU POUSSINES POUTENTIOS POUX SEC POUZAC CHEV 14 PRADAL PRADELLE PRADES PRADINELS PRADINES HAUT PRAT LONGUET PRATALARIC PRATS DEL AIRE PRE CABROL PRE FONTAINE PRE SAGNE PREIGNES VIEUX PRES PRESQU ILE THAU PRESQU ILE THAU PRIEURE GRANDMONT PRIEURE ANDRE PRISE D EAU FRAISSE PROLONGEE PASTEUR PROM BAPTISTE MARTY PROM MAR PROMENADE ANCIEN STADE PROMENADE BELLE SCRIBOTE PROMENADE PROMENADE VERNETS PROMENADE J B MARTY PROMENADE BAPTISTE MARTY PROMENADE LOUIS NAVARRO PROMENADE LOUIS VAILLE PROMENADE MARECHAL PROMENADE NAFFRIE PROMENADE VERNET PROUILHE PROUILHES PUECH PUECH ARNAUD PUECH JOIE PUECH LABADE PUECH LAYRE PUECH PUECH DELON PUECH FOUR PUECH ESPEROU PUECH ESTEVE PUECH LONG PUECH MERLE PUECH MONTCAL PUECH ORE PUECH PIQUET PUECH RADIER PUECH ROUCH PUECH LAURENT PUECH TROUPAN PUECHAUMA PUITS SECHON QUAI ADOLPHE MERLE QUAI ANTONIN GROS QUAI 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BARBO QUARTIER BARRY QUARTIER BAS QUARTIER BRANCAS QUARTIER CALQUIERES QUARTIER CANDINIERE QUARTIER CASTAN QUARTIER CASTEL QUARTIER CAYENNE QUARTIER CAZILHAC HAUT QUARTIER NEUF QUARTIER COMBE QUARTIER QUARTIER COUPIAC QUARTIER HORTS QUARTIER RIALS QUARTIER FONZAL QUARTIER BARRE QUARTIER QUARTIER EGLISETTE QUARTIER ENCIVAL QUARTIER EUZIERES QUARTIER FENOUILLET QUARTIER QUARTIER GISSE QUARTIER GRANDES VIGNES QUARTIER HORTES QUARTIER ILE CAZOT QUARTIER ILE LOISIRS QUARTIER JALAGUIERE LAVAGNE QUARTIER BARQUE QUARTIER BRECHE QUARTIER PEYROU QUARTIER LISSERT QUARTIER MONTBRUN CASTELLAS SUD QUARTIER NAVIS QUARTIER NORIA QUARTIER NOTRE DAME QUARTIER PEROU QUARTIER PEYROU QUARTIER PIOCH QUARTIER PLAGNOLE QUARTIER PLEIN SOLEIL QUARTIER POINTE QUARTIER QUARTIER PORTAIL HAUT QUARTIER POUROLS QUARTIER PUECH LAZERT QUARTIER RAFFEGAN QUARTIER RIEUTOR QUARTIER ROUBIADA QUARTIER ROUCARASSE QUARTIER JACQUES QUARTIER MARTIN QUARTIER QUARTIER SAURINE PEYRES GROSSES QUARTIER SERCOGNES QUARTIER SOUS QUARTIER TERRE QUARTIER VAUCOULEURS QUARTIER VERSAILLES QUARTIER VILLENEUVETTE QUATER LODEVE QUATER SERGENTS QUATER GUETTE QUATER COLONEL MARCHAND QUATER REPUBLIQUE QUATER TOUR CONSTANCE QUATRE QUATRE QUATRE VENT VALSIERE QUERCI JASSE RABETTES RAMASSOL RAMEJAN RAMPE 96 EME RAMPE CAMILLE SAENS RAMPE PASTRE RAMPE MOULINS RAMPE MAISON PEUPLE RAMPE MARCHE RAMPE PALISSADE RAT MERLE RD PT APPEL 18 JUIN REALS REBAUT BAS REDONIERE REGIMONT HAUT RELAIS RADIO DDE REMIECH RENTEILLE REPUBLIQUE RES GUILHEM RESCOL RESID ALLEES BATIMENT 65 25 RESID ALLEES BATIMENT 65 45 RESID ALLEES BATIMENT 66 81 RESID BELLES PLAGES BATIMENT 1D3 RESID BLAISE PASCAL 2 BATIMENT 11 41 RESID CARREYROU 2 BATIMENT Y25 4 RESID CLOS FONTAINE BATIMENT B 65 RESID CLOS FONTAINE BATIMENT C 92 RESID JEU MAIL BATIMENT 5 118 RESID JEU MAIL BATIMENT B2 115 RESID LOTISSEMENT MUSICIENS II BATIMENT 7 8 RESID PARC ST ROCH BATIMENT 3 105 RESID PARC ST ROCH BATIMENT 4 121 RESIDENCE 1 GRANGE VILLAGE 3 OLIVIERS 55 CAMI MAS ARNA 4 SAISONS 4 VENTS CAMPREDON 59 HORIZON BLEU 8 BASTIDES B FLEURIE D ESCALE ABC DIXIE LAND ACROPOLE AGORA AIRE BROUSSON 1 ALBATROS ANCIENNE ANEMONE MER MOTTE COUCHANT AQUALIOS ARBRE ARC EN CIEL ARENA ARIANE 14 PLAN DIONYSOS ATRIUM AURIOLES AVANT PORT RESIDENCE CAMILLE D ASSAS JOCKEYS M PASTEUR BAIE D AZUR BALCON PREMERLET BASTIDES MER BATIMENT F BEAU SITE BEAUX ARTS BELLEPIERRE BELVEDERE 8 CAMI CHEVALET BERGERIE BISSY BISSY 2 BISSY 2 HAUTS BISSY BISSY I AURE D ALCO LON PRIEUR II 7 PLAN JEANJEAN BRESIL CALANQUES VISE CALANQUES CLA CALANQUES CLAIR CALANQUES CLA CALIFORNIA CALIFORNIE CALYPSO CAMI 2 14 CAMIN GARELLE CAMPING CAMPING 15 DONADIVE CAMPING BOREPOS CAMPING FLOTS BLEUS FARINETTE CAMPUS BISSY CAMPUS BISSY 1 CAMPUS BISSY HAUTS BISSY CANAL QUILLES CAP ARIANA CAP CLAIR CAP LOUIS I CAP LOUIS III CAP SOLEIL CAPRICORNE CAREYROU 2 3 CARRIERA LOU PERD CAREYRROU 2 CARIGNAN CARREYROU CARREYROU 2 17 COSSES SOUS CARREYROU 2 5 CARRIERA LOU PER CARREYROU 2 8 CARRIERA LOU PER CARREYROU II CARREYROU II 10 CARRIERA LOU P CASERNE POMPIERS CATALANES CAUSSE CENTRAL CERISIERS CH BLAQUIERE GARDIES CHAI MONESTIE CHAMP ROBERT CROZES CHANTEBRISE CHANTECLAIR TAMARISSIERE RESIDENCE 21 VILLAGE D EAU LOUIS VERT CHATEAUBRIAND CHE ALLAGE 46 CABANES CARNON CHE POUJOL GRASSERIES BASSES CHE VERGNE CABANES TERRE BELVEDERE CALADE CLAUZADES COMMUNAL I VILLAGE BICONQUE MOUILLA LAVAGNE PRES PHARE QUILLES CHENES VERTS 3 PEYRES CANES CHEVALIERS MALTE DAMES MURIERS EDF SIEGE PASTEUR CIVADIERE CLAIR AZUR CLOS CLOS BEATRIX 33 CLOS COL CASSE PECHEURS CLOS L CLOS OLIVIERS CLOS DOMITIA CLOS MELGUEIL CLOS OLIVIERS COFINIERE 11 PLAN COLLINES D ESTANOVE COLONNIE VILLAGE COMBE ABEILLES COMBES ROUJAL COMMERCIAL CORALINES CORALLINES CORMORANS CORNICHE NEUBURG CORSAIRE COTEAU LAC CHICANE 601 CPG MEDITERRANEE KABYLIE CROISETTE CROUZETTE D AMERIQUE DAUPHINE BISSY MONACO TOURNEZY BATIMENT D 2 PLAN TRIST DENIS DIDEROT OLIVIERS 42 CAMI CANTO OLIVIERS II 42 CAMI CAN DEVOIS ROUSSEL DOMAINE DOMAINE BERGERIE MAZES DOMAINE BESSES BESSES DOMAINE VENNE DOMAINE MONTBARRI MONTBARRI DOMAINE GOLFE DOMAINE TIRONDEL DOMAINE FLORENCE DOMAINE MONESTIE DOMAINE ROCAHLAS DOMAINE GABRIEL DRAILLE GORNIER DRAILLE PLO MIDI SOLEIL DUNES DUNES MOTTE COUCHANT CALMETTE 147 PLAN QUATRE INFANTERIE ROMAN ECUREUILS EDOUARD BRANLY RESIDENCE ENARBO ENCLOS MONTCEAU ENCLOS TRIPESSAILLE ENCLOS DEBUSSY ENTRE ESCOLETTES ESPACE ESPACE 21 ESPADON ESPERANCE ESPLANADE COMMERCIAL FREJORGUES F 1 BARD FARINETTE COSSE FARINETTE POSTE FAUVETTES FAYOLLE FLAMANTS ROSES FLEURS I FLORALIES FLORE BATIMENT 2 PLAN MADAME MERL FLORENCE FLORENTIN FLOTS BLEUS FOND DESJEANE PIOCH ST MICHEL FORSYTHIAS FORUM FORUM MAURIN II FOURS CHAUX MONTFAUCON FOYER FOYER RAOUL BOUBAL FREDERIC FREDERIC MISTRAL FRESCATIS FRONT MER GABRIAC VILLAGE GARRIGUES 13 LOU PERDIGAL GARRIGUES GARRIGUES GAUPEYROU GENDARMERIE GERNDARMERIE GINESTOUS GIROLLES IV 1 PLAN MOUSSERON GITE GITE CHABBERT 8 CALADE GITE MILLEPERTUIS GITE NUMERO 2 MAUROUL GOLF GOLF THOMAS BLEU OLIVETTE MATHE PINEDE GRANGETTE GREEN HAMEAU 4 SEIGNEURS 61 PIOCH BELLE VISTE BESSES BASSES COSTE HAMEAU JULIO HAUTS BISSY HAUTS BISSY HAUTS BISSY HAUTS VEREILLES VEREILLES HELENA HLM HLM TERRIEU HORIZON BLEU HORIZONS BLEUS HTS BISSY II HTS BISSY RESIDENCE L AZUR IRIS 8 PLACETTE FARFADETS JALAGUIERE AUBANEL JARDIN POETES JARDINS JARDINS 2 LORT BRUNE JARDINS D ULYSSE JARDINS THAU JARDINS LEVANT 6 PLAN LI JARDINS MER JARDINS VIDOURLE EDOUARD MASSAL JAURES JUIN JULES CESAR L AGORA L ENSOLEILLE LAMALOU VIEUX L ESCALE ONGLOUS L OASIS CIGALES VALSIERE L OMBRADE KABYLIE L OREE CHENES BERGERIE BORNE CAILHO HAUT CAHUTE POSTE CARRIERETTE CIGALIERE CIGALIERE PENDANCES COLI 2 PLACETTE COLLINE CONQUE FROUZET CROISETTE CROIX SUD BATIMENT F 35 PLAN FARLETTE FERRIERE FOULQUIERE ETANG GALIOTE LAS CADENEDES GOELETTE GOUTINE COSSE REDOUTE GRANGE GRANGE GINIEIS MARTHOMIS HUNE MAISON CIGALE VILLAGE MAISON ROSE ROUSSAS MER NORIA PERGOLA PLAGE PLAINE PLANE POTERIE PARC VILLAGE POUSSANAISE PRADE VILLAGE REMISE REMISE BEAUDESERT TERRASSE LAC LAGON BLEU LAS BARTASSIERES VILLAGE LAURIERS LD BENTENAC CABANES SALAISON BELVEDERE 10 CAMI RIBEAUTE BIRDIE TRUQUES CADUCEE CALYPSO 1 CAP DEL MAS CASTANET BAS CAPRICONE CAPRICORNE CARDENAL SESQUIERE CARREYROU CARREYROU 2 5 CARRIERA LOU CARREYROU 2 B 23 3 CARRIERA CARREYROU 7 CARRIERA LOU PE CARREYROU BAT CARREYROU BATIMENT 9 6 CARRIERA CARREYROU V 4 7 CARRIERA LO CHALET CLAIR PLAGE CLOS OLIVIERS CLUB COLOMBIER COLUMBIA CORSAIRE COTEAU MOLIERE DANIELI DEYMOU DEYMOU TRIVALLE FLORE BATIMENT 2 PLAN MADAME M FLORE BATIMENT B 4 PLAN MADAME M FLORE BATIMENT C 6 PLAN MADAME M GITE 1 VILLAGE GLOBE BLEU BRANLE PAVOIS LAGON BLEU MALPAS MAS SAMBUC VILLAGE MAS FABRE HORTS HAUTS MAURIN MAZET MERCURE MERIDIEN LE D ISSANKA MOZART MUSCAT NEPTUNE PAILLERAS 1 PLAN HONORINE PAILLERAS 3 PLAN HONORINE PAILLERAS 5 PLAN HONORINE PARC PARC CHAT CAMBOUS PARC L ORB VILLAGE PASSAGER COUSSE PATRIMOINE PEROU PHOEBIA PIC PONANT VILLAGE PRADET PUECH LOUBATIERES RAMASSOL 3 PLAN NYROCA RAMASSOL 4 PLAN NYROCA RELAIS RIVAGE SIEGE RONCERAY SAGITTAIRE SCARLETT SEPION SEVIGNE SOLEIL COUCHANT SOULIE HAUT SULLY TEMPLE SOLEIL VERDALE VIALA VIALARET VILLAGE VINCI D GRENADINES AMANDIERS ARBOUSIERS ARBRES COMMEYRAS ARCHES VILLAGE ARGONAUTES ARGUSSELS AVANTS 3 PLAN LAVANDES BATISSES BEAUX ARTS BELLES PLAGES BERGES MIDI BOSQUETS VILLAGE CADES CAMPETS CASTAGNES VILARIS CEDRES CERISIERS CERISIERS 16 LAGASSE CHANLORDS FOURNIEUX CLACHES LOUS CLAITS CLAVELLIERES CLOSADES FAVAS COMBES 40 CONDOMINE CORALINES CORALLINES CORMORANS CORRALINES CYPRIANNES ECUREUILS ECUREUILS 4 ECUREUILS IV FERRIERES AUTHEZE FORSYTHIAS GARRIGUES 32 LOU PERDIGAL HAUTS BISSY IFS INCAS JARDINS MER JARDINS PORT JASSES MARQUISES OLIVIERS OLIVIERS 2 ORANGERS 1 CAMI FOUNTJUT PLAGES CLAIR PORTES COUCHANT PORTES SOLEIL PRES ROUQUANS REALS SABLES D OR ROCHELONGUE SEPT FONTS SESTERIADES 1 VILLAGE SOLEILLADES THERMES BAINS TREILLES VERGERS VIGNALS VOILES BLANCHES RESIDENCE FAISSES BOUBALS GAUSME VILLAGE NALBIO CANALS SALCES LITTORINE LIXIRIE II LIXIRIE LOGEMENT LOTISSEMENT 31 LOTISSEMENT LAC LOTISSEMENT LOU LOTISSEMENT MON LOTISSEMENT N 1 HORTS LOU CLAPAS 10 CAMIN LOU BLAGAI LOU GAREL LOU PERDIGAL LOU PERDIGAL 1 PLAN PERDIGAL LOUGAREL CINQ CLOCHERS LOUIS ARAGON LT LOU GARELS MAGELLAN MAGNANERIE MAIL MAISONS MAISONS PORT 3 PLAN BOUCANI (RESIDENC MAJESTIC III MARCEL CACHIN MARCHE LAND MARCHE LAND 2 COLVERT MARINES ANTARES I MARINES ANTARES II MARINES HAUTE PLAGE MARINES CLAI MARQUISES MARTHOMIS SALLE MAS MAS AMADOU MAS BELLEVUE VILLAGE MAS CAYLA VILLAGE MAS D EN VALAT MAS D OLIVIER MAS BRUYERE MAS COSTES MAS L ARCHE MAS MER MAS PINEDE 16 CAMI CA MAS PINEDE CAMI CANTO MAS PLAGE MAS TOURIERE MAS FLEURS MAS LAUZES MAS THERMES MAS DREVON MAS TAUREAU TOMBETTES MAS DUCASSE VILLAGE MAS FABRE MAS FONTANILLES MAS OLIVERAIE MAS ONDINE MONTPENEDRE MAS PINEDE CAMI CANTO RANO MAS PINEDE CAMI CANTO RANO MAS PINEDE II CAMI CANTO RA MAS REBOUL JEUNE MAS ROUGE MAS ROUGE 85 MAS PEYRE MAS SARDA MASSOLEO GARISSON MAZETS MAIL MENDES FRANCE MER SOLEIL MERIDIEN MILLE FLEURS PUECH BOURDEL MONACE MONPLAISIR MONTIFORT MONTPLAISIR MOTT MOTT LAND RESIDENCE VENT MUSCAT NAOUS HORTS NAUTIC LAMPARO NAUTICA NAUTILUS III NAUTIQUE CLAIR NAUTIQUE ST CLAIR NAUTIQUES CLAIR NEPTUNE NOE OLIVETTE OLIVIERS OLIVIERS 2 42 CAMI CANTO RA ONDINES OREE P M FRANCE PALAIS DOGES PALAZZO DEL RIALTO PALOMBIERE 3 PARADOR MOTTE COUCHANT PARC PARC PROBY PARCELLE 1077 PARCELLE B 869 PLAN TERRASSE PAS LOUP PASSAGE NIVEAU RIQUET PECAN REALS PECHERIE I PERGOLA BARD PEYRIERES PUECH CAMP PHIL DO 6 CHATEAUDUN PIC LOUP PIC ST LOUP 115 CAMI ORMEU PIERRES BLANCHES PIGEONNIER PINS PIOCH 3 EME AGE RESIDENCE PROUILHE VIEILLE PLAGE CLAIR II PLAGE LIBRON POSTE PLAGE CLAIR PLAGES CLAIR PLAGES DOREES PLAINE PONANT MAIRE ONGLOUS PORT ATHENA PORT CARNON PORT CROS PORT JUVENAL PORT NATURE PORT RAPHAEL ETRUSQUES PORT CLAIR PORT SUD PORTES MILAN POSEIDON POUSSANAISE PRESQU ILE THAU PROVENCE PUECH PUECH ORE PUECH ORE PUITS ROSAY QUEBEC QUERCI JASSE RESIDENCE HORIZON BLEU RESIDENCE LAGON BLEU RESIDENCE MAZETS MAIL RIVES RIVES CORAIL ROCHES MARINES ROMANY ROND POINT BAILLARGUET ROUTE ALIGNAN VENT MANSE ROUTE BALARUC ROUTE BIERANQUES ROUTE PEZENAS GASPARY ROUTE MAS RTE COURNONTERRAL MOULIERES RTE RESERVOIR LAS CADENEDES L FONTETE CAMPREDON LIEGE QUAI VILLAGE EGALITE MICHEL CLERC BERTHOMIEU BORIES CLAIR MARC MARTIN BOSC ROCH SAN MARCO SAPHIR SCARLETT SECT B 288 SEPT FONDS SEVIGNE SEVIGNE THERMAL SEVIGNE VI SOLEIADES SOLEIL MER SOLEILHAN SOLEILLADES SOLEILLAN MOTTE LEVANT SOUTAYROL SPEED MARINE 16 ST CHINIAN FARGOUSSIERES ST CLAIR RIVE SUN SURVILLE SUS TARANTELLE TARENTELLE TEMPLE SO TENNIS TERRASSES GREZAC TERRASSES L ESPLANADE TERRASSES VENE 26 CAMI D TERRES MARINES TERRIEU THALASSA THERMALINES THERMES THERMIDOR TORTUE TOURNEVILLE II TOURNEZY II TRIANON TRIDENT TRIREME TRIRENE TROISIEME TSSE AVENE 13 CAMI MARTELET ULYSSE PLAGE VAL FLEURIE VALLON RADABEL VILLAGE VELINI 6 PLAN DIONYSOS VENDEMIAIRE VERGERS PEROU 23 PLAN CO VERGERS PEYROU 20 PLAN C L AVENIR VIGNES BERTRAND VILLA VILLA 11 11 VILLA CHRISTINE MATTEMALE VILLA COJABRI CONTOURNE VILLA SOURCES VILLA THERMES VILLA CHARDONS POUGET VILLA MEDICIS 2 VILLA JACQUES 601 VILLA VIEUX VIGNES COMMEYRAS VILLA ZANELLA VILLAS SOURCES VILLEMAGNE VIOLETTES WEST CITY WEST END RESIDENCEESC 80 ILE THAU GLOBE RESIDENCERESID LOU THONNAIRE BATIMENT 19 PASS RESIDENCES L RIEUCOULON RIEUSSEC RIEUTORT RIOLS FALGOUZE RIVAGES MAGUELONE RIVE CABANES SALAISON RIVETTES RN 113 RN 113 160 RN 113 3 RN 113 ZONE ARTISANALE TROIS RN 9 ROC L ESTAN ROC DEL DUC ROCHELONGUE ROCHELONGUE QUIST ROCOZELS RODE BASSE RODOMOULS ROMARINE ROND 17 ROND 908 ROND POINT AGROPOLIS ROND POINT BESSAN ROND POINT CAP ALPHA ROND POINT CARIGNAN ROND POINT CARIGNANS ROND POINT CARRIGNAN ROND POINT GIRAC ROND POINT L EUROPE ROND POINT L OASIS ROND POINT VINCENT ROND POINT BAGNAS ROND POINT BON ACCUEIL ROND POINT RADOUB ROND POINT SOUVENIR FRANCAIS ROND POINT TENNIS ROND POINT EUROPE ROND POINT GENERAL CHAFFARD ROND POINT J COEUR ROND POINT FONTVIN ROND POINT RIEUCOULON ROND POINT MONTAGNETTE ROND POINT RIEUCOULON ROND POINT ALBERT CAMUS ROND POINT LAURENT ROND POINT TENNIS 62 LIXIRIE ROND POINT VIERGE RONGAS ROQUAIROLS ROQUE HAUTE ROQUE PISTOLE ROQUECAVE ROQUEMENGARDE ROQUEREDONDE ROQUES BASSES ROQUES GIRONDE ROQUES HAUTES ROSIS ROTONDE CHEM PLANTADE ROUCAILLOU ROUCAN ROUEIRE ROULIO ROUMANIFIERE ROUND BOUISSOUSE ROUQUAIROL ROUQUETTE HAUTE ROUSSAS ROUTE ABEILHAN ROUTE ADGE ROUTE ADISSAN ROUTE ADISSAN ESTANG ROUTE AEROPORT ROUTE AGDE ROUTE AGEL ROUTE AIGNE ROUTE AIGUES VIVES ROUTE ALIGNAN ROUTE AMBRUSCALLES ROUTE ANGLES ROUTE ANIANE ROUTE APPARITION ROUTE ARBORAS ROUTE ARGELIERS ROUTE ARGELLIERS ROUTE ARTIX ROUTE ASPIRAN ROUTE ASSAS ROUTE ASSIGNAN ROUTE AUMES ROUTE AUTIGNAC ROUTE AVENE ROUTE AZILLANET ROUTE B 5 ZIERS ROUTE BAILLARGUES ROUTE BALARUC ROUTE BALARUC BAINS ROUTE BANNIERES ROUTE BARRY ROUTE BEAUFORT ROUTE BEAULIEU ROUTE BEDARIEUX ROUTE BEL AIR ROUTE BELARGA ROUTE BENOVIE ROUTE BERLOU ROUTE BERLOU BORIETTES ROUTE BESSAN ROUTE BEZIERS ROUTE BIZE ROUTE BLANC ROUTE BOIRARGUES ROUTE BOISSIERE ROUTE BORDEVIEILLE ROUTE BOUIS ROUTE BOULIDOU ROUTE BOUSQUET D ORB ROUTE BOUSQUETS ROUTE BOUSSAGUES ROUTE BRAMA ROUTE BRESTALOU ROUTE BRIGNAC ROUTE BRISSAC ROUTE BUFFE BENS ROUTE CABANES ROUTE CABRIALS ROUTE CABRIERES ROUTE CAILHOL ROUTE CAMARES ROUTE CAMBELLIES ROUTE CAMBOUS ROUTE CAMPAGNAN ROUTE CAMPAGNE ROUTE CAMPELS ROUTE CAMPLONG ROUTE CANET ROUTE CAPESTANG ROUTE CAPION ROUTE CARCASSONE ROUTE CARCASSONNE ROUTE CARNAS ROUTE CARNON ROUTE CARNON 13 ROUTE CARRIERES ROUTE CASAIS ROUTE CASERNE ROUTE CASTANET BAS ROUTE CASTELNAU ROUTE CASTRES ROUTE CASTRIES ROUTE CAUSSE ROUTE CAUSSES ROUTE CAUSSES VEYRAN ROUTE CAUX ROUTE CAVES ROUTE CAYLAR ROUTE CAZEDARNES ROUTE CAZENEUVE ROUTE CAZOULS ROUTE CAZOULS BEZIERS ROUTE CECELES ROUTE CESSENON ROUTE CESSERAS ROUTE CEVENNES ROUTE CEZAS ROUTE CHATAIGNERAIES ROUTE ROUTE D EAU ROUTE EAU ROUTE ROUTE CHENE ROUTE ROUTE CISSO ROUTE CLAIRAC ROUTE CLAIRETTE ROUTE CLAPIERS ROUTE CLARET ROUTE CLEDELLE ROUTE CLERMONT ROUTE CLERMONT HERAULT ROUTE CLERMONT L HERAULT ROUTE CLOS ROUTE CLOS VIGNES ROUTE COLOMBIER ROUTE COLOMBIERS ROUTE COMBAILLAUX ROUTE COMBEJEAN ROUTE COOPERATIVE ROUTE CORNEILHAN ROUTE CORNICHE ROUTE CORNICHE CAP CLAIR ROUTE COS ROUTE COUJAN ROUTE COURNONSEC ROUTE COURNONTERRAL ROUTE ROUTE CREISSAN ROUTE CUXAC ROUTE D ABEILHAN ROUTE D AGDE ROUTE D ALIGNAN VENT ROUTE D ASPERE ROUTE D ASSAS ROUTE D AUBAYGUES ROUTE D AUMES ROUTE D ESPAGNE ROUTE D ESPONDEILHAN ROUTE D HEREPIAN ROUTE D OCTON ROUTE D OLMET ROUTE D OLNET ROUTE DARDAILLON ROUTE BAILLARGUES ROUTE BALARUC ROUTE BEAULIEU ROUTE BEDARIEUX ROUTE BEL AIR ROUTE BESSAN ROUTE BESSAN 45 ROUTE BEZIERS ROUTE BOISSERON ROUTE BOUJAN ROUTE BOYNE ROUTE BRAVEILLE ROUTE BRIGNAC ROUTE BRISSAC ROUTE BUZIGNARGUES ROUTE CABREROLLES ROUTE CABRIERES ROUTE CANDILLARGUES ROUTE CANET ROUTE CANTEMERLE ROUTE CAPESTANG ROUTE CARNON ROUTE CASTRIES ROUTE CAUNETTE ROUTE CAUX ROUTE CESSENON ROUTE CLAPIERS ROUTE CLERMONT ROUTE CLERMONT PUECH BOURDEL ROUTE CLERMONT VILLAGE ROUTE CORNEILHAN ROUTE FABREGUES ROUTE FLORENSAC ROUTE FONTES ROUTE FOZIERES ROUTE FRANGOUILLE ROUTE GALARGUES ROUTE GANGES ROUTE GANGES MERCADEL ROUTE GARRIGUES ROUTE GATIGNIE ROUTE GIGEAN ROUTE GOURGAS ROUTE GRABELS ROUTE GREZAC ROUTE GUZARGUES ROUTE JACOU ROUTE L EGALITE ROUTE L ROUTE L ESPINOUSE ROUTE L ESTANG ROUTE COMBAILLERE ROUTE FOIRE ROUTE FONTAINE ROUTE ROUTE GLORIETTE ROUTE GRANGE ROUTE ROUTE MORT ANES ROUTE PLANTEE ROUTE PLAGE ROUTE POMPIGNANE ROUTE RECHE ROUTE SABLIERE ROUTE SICA ROUTE TAMARISSIERE ROUTE VERNIERE ROUTE LACOSTE ROUTE LAMBEYRAN ROUTE LANSARGUES ROUTE LATTES ROUTE LAUROUX ROUTE LAVERUNE ROUTE LESPIGNAN ROUTE LEZIGNAN ROUTE LIAUSSON ROUTE LISSAC ROUTE LODEVE ROUTE LOUPIAN ROUTE LUNEL ROUTE LUNEL VIEIL ROUTE MARAUSSAN ROUTE MARGAL ROUTE MARSEILLAN ROUTE MARSILLARGUES ROUTE MAUGUIO ROUTE MAUREILHAN ROUTE MAURIN ROUTE MAYRES ROUTE MENDE ROUTE MEZE ROUTE MILLAU ROUTE MIREVAL ROUTE MONTADY ROUTE MONTAGNAC ROUTE MONTBAZIN ROUTE MONTBLANC ROUTE MONTFERRIER ROUTE MONTLAUR ROUTE MONTPELLIER ROUTE MONTPELLIER ROUVIERE ROUTE MOUREZE ROUTE MUDAISON ROUTE MURVIEL ROUTE MURVIEL JONCASSES ROUTE NAGES CROUZET NORD ROUTE NARBONNE ROUTE NEBIAN ROUTE NIMES ROUTE NIZAS ROUTE NIZAS CAUSSE ROUTE PALAVAS ROUTE PAULHAN ROUTE PEGAIROLLES ROUTE PEROLS ROUTE PEZENAS ROUTE PEZENES ROUTE PIGNAN ROUTE POMEROLS ROUTE POUJOLS ROUTE POUSSAN ROUTE PRADES ROUTE RABIEUX ROUTE RESTINCLIERES ROUTE ROCHELONGUE ROUTE ROUJAN ROUTE ANDRE ROUTE AUNES ROUTE BAUZILLE ROUTE BRES ROUTE CHRISTOL ROUTE CLEMENT ROUTE DREZERY ROUTE ROUTE D ISSENSSAC ROUTE FELIX ROUTE GELY ROUTE D ORQUES ROUTE HILAIRE ROUTE ROUTE MARTIN ROUTE NAZAIRE ROUTE VIGNIER ROUTE PONS ROUTE PONS MICHEL ROUTE PRIVAT ROUTE ROCH ROUTE VINCENT ROUTE SAINTE CROIX ROUTE SALASC ROUTE SATURARGUES ROUTE SAUSSAN ROUTE SAUSSINES ROUTE SERIGNAN ROUTE SERVIAN ROUTE SETE ROUTE SETE LABRUNIER ROUTE SIMIARGUES ROUTE SOMMIERE MAZETS ROUTE SOMMIERES ROUTE SOULAGE ROUTE SOUMONT ROUTE TEYRAN ROUTE TOURBES ROUTE USCLAS ROUTE VALERGUES ROUTE VALMASCLE ROUTE VALRAS ROUTE VAUGUIERES ROUTE VENDARGUES ROUTE VENDRES ROUTE VERARGUES ROUTE VILLEMAGNE ROUTE VILLENEUVE ROUTE VILLETELLE ROUTE VILLEVAYRAC ROUTE VILLEVEYRAC ROUTE VINAS ROUTE VIOLES ROUTE DEPARTEMENTAL 127 ROUTE DEPARTEMENTAL 138 ROUTE DEPARTEMENTAL 145 ROUTE DEPARTEMENTAL 2 ROUTE DEPARTEMENTAL 37 ROUTE DEPARTEMENTAL 5 ROUTE DEPARTEMENTALE 116 ROUTE DEPARTEMENTALE 127 ROUTE DEPARTEMENTALE 185 ROUTE DEPARTEMENTALE 2 ROUTE DEPARTEMENTALE 986 ROUTE ARESQUIERS ROUTE CABANES ROUTE CABANNES ROUTE CARRIERES ROUTE CEVENNES ROUTE COMBES ROUTE CROZES ROUTE PLANS ROUTE SAJOLLES ROUTE VERRIERS ROUTE OMBRAS ROUTE BEAL ROUTE ROUTE BOSC PUECH ROUTE BOUIS ROUTE CAMP D AVIATION ROUTE CAP ROUTE CAUSSE ROUTE CAYLAR ROUTE COLOMBIER ROUTE GRAU D AGDE ROUTE GRAU ROI ROUTE LAC ROUTE LITTORAL ROUTE MAS AUDRAN ROUTE MAS BONNEL ROUTE MAS DESPORTS ROUTE PERAS ROUTE PERTHUS ROUTE PUECH ROUTE SALAGOU ROUTE VIALA ROUTE EMBRUSCALLES ROUTE EMBUSCALES ROUTE EMBUSCALLES ROUTE ESCALADOU ROUTE ESPONDEILHAN ROUTE ESTANG ROUTE FABLIERE ROUTE FABREGUES ROUTE FAJOLE ROUTE FARINETTE ROUTE FAUZAN ROUTE FERRIERES ROUTE FLEURY ROUTE FLORENSAC ROUTE FONTANCHE ROUTE FONTANES ROUTE FONTES ROUTE FONTMARIE ROUTE FOUR CHAUX ROUTE FOURCHES ROUTE FOURNOLS ROUTE FOUZILHON ROUTE FOZIERES ROUTE FRAISSE ROUTE FRANGOUILLE ROUTE FRONTIGNAN ROUTE FROUZET ROUTE GABIAN ROUTE GABIAN SAFRANIERE ROUTE GALARGUES ROUTE GANGES ROUTE GANGES 31 CLEMENTIDE ROUTE ROUTE GIGEAN ROUTE GIGNAC ROUTE GORGE D HERIC ROUTE GRAISSESSAC ROUTE GRANGE ROUTE GRAU ROUTE GRAU VENDRES ROUTE GRAVES ROUTE GREZAC ROUTE GRIGEAN SAINTE CATHERINE ROUTE GROTTE ROUTE GUZARGUES ROUTE HEREPIAN ROUTE HOMPS ROUTE IMPERIALE ROUTE JACOU ROUTE JALAGUIERES ROUTE JARDINS ROUTE CORNIES ROUTE JONCELS ROUTE JONCQUIERES ROUTE JONQUIERES ROUTE PLANTEE L HORTE ROUTE LAC ROUTE LACAUNE ROUTE LACAUNE LANDES CASSEN ROUTE LACOSTE ROUTE LAGAMAS ROUTE LAMALOU ROUTE LAMALOU VIEUX ROUTE LATTES ROUTE LAURENQUE ROUTE LAURENS ROUTE LAURIERS ROUTE LAVENE ROUTE LAVERUNE ROUTE CAYLAR ROUTE LESPIGNAN ROUTE LIEURAN ROUTE LIEURAN CABRIERES ROUTE LITTORAL ROUTE LIXIRIE ROUTE LIZARNE ROUTE LODEVE ROUTE LODEVE SALCES ROUTE LODEVE LOIRAS ROUTE LOIRAS ROUTE LOTISSEMENT MONTEILLE ROUTE LOTISSEMENT BARTH ROUTE LOUPIAN ROUTE LUNAS ROUTE LUNEL ROUTE MAGALAS ROUTE MAGUELONE ROUTE MAILHAC ROUTE MAIRE ROUTE MALIBERT ROUTE MALVIES ROUTE MANIERE ROUTE MARAUSSAN ROUTE MARCHANDES ROUTE MARGON ROUTE MARSEILLAN ROUTE MARSEILLAN MOUGERES ROUTE MARSEILLAN PLAGE ROUTE MARTHOMIS ROUTE MAS ROUTE MAS BLANC ROUTE MAS BONNEL ROUTE MAS MAZEL ROUTE MAS GARD ROUTE MATELLES ROUTE MAUGUIO ROUTE MAUGUIO GARRIGUES ROUTE MAUREILHAN ROUTE MAYRES ROUTE MENDE ROUTE MER ROUTE MEULIERES ROUTE MEZE ROUTE MILLAU ROUTE MINE ROUTE MINE VIGNECAVE ROUTE MINERVE ROUTE MIREVAL ROUTE MONS ROUTE MONTAGNE NOIRE ROUTE MONTARNAUD ROUTE MONTAUD ROUTE MONTBAZIN B ROUTE MONTBLANC ROUTE MONTESQUIEU ROUTE MONTOULIEU ROUTE MONTPELLIER ROUTE MONTPELLIER VILLAGE ROUTE MONTPEYROUX ROUTE MONTPLO ROUTE ROUTE VENT ROUTE MOURGUES ROUTE MURAT ROUTE MURLES ROUTE MURVIEL ROUTE MURVIEL MONTPELLIER ROUTE MURVIELS ROUTE MUSCADELLES ROUTE NALE 113 ROUTE NARBONNE ROUTE NATIONAL 113 ROUTE NATIONALE ROUTE NATIONALE 108 ROUTE NATIONALE 109 ROUTE NATIONALE 1112 ROUTE NATIONALE 112 ROUTE NATIONALE 112 BEZIERS AGDE ROUTE NATIONALE 113 ROUTE NATIONALE 13 ROUTE NATIONALE 608 ROUTE NATIONALE 9 ROUTE NATIONALE 908 ROUTE NATIONALE 909 ROUTE NATIONNALE ROUTE NEFFIES ROUTE NEFFIES LANDE BASSE ROUTE NEUVE ROUTE NIMES ROUTE NISSAN ROUTE NIZAS ROUTE NIZE ROUTE NOTRE DAME ROUTE OLARGUES ROUTE OLARGUES TRIVALLE ROUTE OLMET ROUTE OLONZAC ROUTE OPPIDUM ROUTE OUPIA ROUTE PAILHES ROUTE PALAVAS ROUTE PARC HUITRES ROUTE PARCS ROUTE PAULHAN ROUTE PAULHAN MOULIERES ROUTE PERET ROUTE PERTHUS ROUTE PEYRE ROUTE PEYRES CANES ROUTE PEYROU VIEUX ROUTE PEZENAS ROUTE PEZENES MINES ROUTE PIC LOUP ROUTE PIGNAN ROUTE PINET ROUTE PINS ROUTE PLAGES ROUTE PLAISSAN ROUTE PLAISSAN CINQ CLOCHERS ROUTE PLANS ROUTE PLANTADE ROUTE POMEROLS ROUTE POMPIGNAN ROUTE POMPIGNANE ROUTE POPIAN ROUTE POUJOL ROUTE POUJOLS ROUTE POUSSAN ROUTE POUZOLLES ROUTE PRADAL ROUTE PRADES LEZ ROUTE PROVERES ROUTE PUECHABON ROUTE PUECHABON 5 ROUTE PUIMISSON ST MARTIN CHAMPS ROUTE PUISSERGUIER ROUTE QUARANTE ROUTE QUISSAC ROUTE RAMEJAN ROUTE RAMEJEAN ROUTE REALS ROUTE REDOUTE ROUTE RIBAUTE ROUTE RIEUSSEC ROUTE ROC BLANC ROUTE ROQUE ROUTE ROQUEREDONDE ROUTE ROUJAN ROUTE SAHUC ROUTE ALBAN ROUTE ANDRE ROUTE ANDRE SANGONIS ROUTE AUNES ROUTE BAUZILLE ROUTE BAUZILLE MONTMEL ROUTE BAUZILLE MATTES ROUTE CHINIAN ROUTE DREZERY ROUTE BAINS ROUTE FELIX ROUTE GENIES ROUTE ROUTE D ORQUES ROUTE ORGUES ROUTE GERVAIS ROUTE GUILHEM ROUTE GUILHEM DESERT ROUTE GUIRAUD ROUTE HILAIRE ROUTE HILAIRE BEAUVOIR ROUTE ROUTE BLAQUIERE ROUTE BUEGES ROUTE FOS ROUTE JUST ROUTE LAURENT D AIGOUZE ROUTE MARCEL ROUTE MARTIN ROUTE MARTIN LONDRES ROUTE MARTIN LONDRE ROUTE MATHIEU ROUTE MATHIEU TREVIERS ROUTE MATHIEU GARDIOL ROUTE MAURICE ROUTE NAZAIRE ROUTE ROUTE VALMALLE ROUTE ROUTE FAGE ROUTE PONS ROUTE PONS MAUCHIENS ROUTE PONS ECO ROUTE PONS MAUCHIENS ROUTE PRIVAT ROUTE SATURNIN ROUTE SIXT RUFFAS ROUTE THIBERY ROUTE VINCENT ROUTE XIST ROUTE SAINTE BRIGITTE ROUTE SALELLES ROUTE SALELLES LOIRAS ROUTE SALLES ROUTE SALVERGUES ROUTE SALVETAT ROUTE SAULZE ROUTE SAUSSAN ROUTE SAUTEYRARGUES ROUTE SAUVIAN ROUTE SERIGNAN ROUTE SERVIES ROUTE SETE ROUTE SOMMIERE ROUTE SOMMIERES ROUTE SORTEILHO ROUTE SOULAGET ROUTE SOULIE ROUTE SOURCE LEZ ROUTE SOURCES LEZ ROUTE ST 9 ROUTE STADE ROUTE STADE LOIRAS ROUTE SUSSARGUES ROUTE TAILLADE ROUTE TEYRAN ROUTE THAU ROUTE THEZAN ROUTE TOULOUSE ROUTE TOUR SUR ORB ROUTE USCLAS ROUTE USCLAS D HERAULT ROUTE USCLAS LOIRAS ROUTE USINE PLANQUE ROUTE VACQUIERES ROUTE VAILHAN ROUTE VAL ROUTE VALAUTRES ROUTE VALRAS ROUTE VALRAS PLAGE ROUTE VALROS ROUTE VAUGUIERE BACCHUS ROUTE VENDARGUES ROUTE VENDEMIAN ROUTE VENDRES ROUTE VERDIER ROUTE VERDISSE ROUTE VERRIERS ROUTE VIEILLE ROUTE VIGAN ROUTE VIGNOLE ROUTE VIGNOLLE ROUTE VILLECUN ROUTE VILLENOUVETTE ROUTE VILLESPASSANS ROUTE VILLESPASSANTS ROUTE VILLEVEYRAC ROUTE VIOLS ROUTE VIOLS ROUVIALS ROUVIERE PATUS ROUVIGNAC RTE ARESQUIERS RTE BALARUC RTE BEAULIEU RTE BEL AIR RTE BEZIERS RTE BOUCHER 1 PLAN MURIERS RTE BRIGNAC RTE CAMBELLIES RTE CAMP AVIATION RTE CANET RTE CESSENON RTE CLERMONT RTE COLOMBIERS 2 RTE CORNEILHAN RTE COS RTE GANGES 22 RTE NARBONNE 13 RTE ST PONS 8 PERRIERE RTE DEP 2 RTE DEP 37 RTE DEP 5 RTE DEPARTEMENTALE 132 RTE SALINS 24 RTE SALINS 32 RTE CAP RTE FOIRE RTE GANGES RTE JONQUIERES 11 RTE LAC RTE LAGAMAS RTE LAGAMAS 14 RTE LATTES RTE LAVERUNE RTE LESPIGNAN RTE LODEVE RTE MONTARNAUD RTE MONTPELLIER RTE MOURE BLANC 36 PONANT RTE NISSAN RTE NIZAS RTE PALAVAS RTE PALAVAS 6 RTE PAULHAN 4 RTE PRADES PUECH RTE QUISSAC RTE R MATTES RTE SAJOLLES RTE SAUSSAN RTE SAUVIAN RTE SETE RTE ST D ORQUES RTE ST GERVAIS RTE ST GUILHEM DESERT RTE ST MARTIN LONDRES RTE ST RTE ST 9 RTE ST ROCH RTE STADE RTE VALERGUES RTE VENDEMIAN RTE VILLENEUVE RTE VILLEVEYRAC 1 11 NOVEMBRE 11 NOVEMBRE 1918 12 AVRIL 13 JUILLET 1920 14 JUILLET 17 LIGNE 1907 18 FEVRIER 1931 18 JUIN 1940 19 AOUT 1944 19 MARS 1962 20 AOUT 20 AOUT 1944 22 AOUT 22 AOUT 1944 22 SEPTEMBRE 25 AOUT 1944 25 OCTOBRE 1591 32 EME 4 PONTS 4 SEPTEMBRE 56 EME REGIMENT ARTILLERIE 56 EME REGIMENT D ARTILLERIE 6 JUIN 1944 8 MAI 8 MAI 1945 8 MAI 45 81 EME REGIMENT D INFANTERIE 81 REGIMENT INFANTERIE AUGUSTIN PARMENTIER DAUDET DUMAS FLEMMING NEUF LAURENT LAVOISIER MAILLO MAILLOL NOBEL S DUMONT BOITE POSTALE SANTOS DUMONT ABAOUSSIER ABATTOIR ABATTOIRS ABBAL ABBAYE ABBE BREUIL ABBE BROCARDI ABBE CHAYRIGUES ABBE L EPEE ABBE EUSTACHE ABBE NOUARI ABBE ROUQUETTE ABBES ABEILLES ABEL GANCE ABERT ABOUSIERS ABRAHAM MAZEL ABREUVOIR ABRIVADO ABRIVADO BOITE POSTALE ACACIAS ACANTHES ACCACIAS ACCOULES ACHILE BAUTES ACHILLE BAILLE ACHILLE BEGE ACHILLE MUNIER ACHILLE THERON ACHILLE VACASSY ACHILLE ZAVATTA ACROPOLE ADAM CRAPONNE ADJUDANT ANTONIN RICOME ADOLPHE ADAM ADOLPHE MION ADOLPHE NOURRIT ADOLPHE SAISSET ADRIEN BERNARD ADRIEN PROBY ADRIENNE BOLAND AFFENAGE AGANTIC AGAPANTHES AGASSE AGENCE POSTALE AGNES D ARAGON AGRICOL VIALA AGRICULTURE AGUILLON AGUSSOL AIGOUAL AIGRETTES AIGUES MARINES AIGUES VIVES AIGUILLERIE AIGUILLERIE BASSE AIGUILLERIE HAUTE AIGUILLON AIGUILLONS AIME TREBOULON AIMEE PREBOULON AIRAL AIRE AIRE LANCYRE AIRE VIEILLE AIRES AIRES BASSES AIRES HAUTES AIRETTE AIRETTES ALAIN COLAS ALAIN GERBAULT ALAIN RENE LESAGE ALAUZET ALBAN BARBIER ALBAREDE ALBERT ARNAUD ALBERT ARNAVIELLE ALBERT AUBES ALBERT CAMUS ALBERT DEPORT ALBERT DUBOUT ALBERT EINSTEIN ALBERT FABRE ALBERT GLEIZES ALBERT JOSEPH ALBERT JUSTIN ALBERT LAURENS ALBERT LEENHARDT ALBERT LUTHULI ALBERT MARC ALBERT MARCELLIN ALBERT MARQUET ALBERT MOUTON ALBERT ALLIES ALBERT ROBERT ALBERT ROUSSEL ALBERT SAMAIN ALBERT SCHWEITZER ALBERT THOMAS ALBERT VIGER ALBERTI ALBERTINE SARRAZIN ALBERTO SANTOS DUMONT ALBIERES ALBIGEOIS ALBISIAS ALBIZIAS ALBIZZIAS ALCIDE TRINQUAT ALCIDE VIALA ALCO ALCYONE ALESIA ALEXANDER FLEMING ALEXANDRE CABANEL ALEXANDRE CABANNEL ALEXANDRE DUMAS ALEXANDRE LAGRIFFOUL ALEXANDRE LAISSAC ALEXANDRE LANGLADE ALEXANDRE LEDRU ROLLIN ALEXANDRE MAURY ALEXIS ALQUIE BOUAT BRUYAS CORTOT MUSSET VIGNY JARRY MANESSIER NOBEL NOBEL MILLENAIRE PONS SABATIER SAUVY ALGUES BLEUES ALGUES MARINES ALI BEN CHEKHAL ALICANTE ALICANTES ALIZE ALIZEE ALIZES ALLENGRY ALOUETTE ALOUETTES ALPHONSE ALLAIS ALPHONSE ARNAUD ALPHONSE BAUDIN ALPHONSE BEAU ROCHAS ALPHONSE DAUDET ALPHONSE LAMARTINE ALPHONSE ROCHAS ALPHONSE LAVALLEE ALPHONSE MENARD ALPHONSE TAVAN ALSACE ALSACE LORRAINE AMADOU AMANDIER AMANDIERS AMANS AMANTS AMARABTES AMARANTES AMAT AMAZONES AMBROISE THOMAS AMEDE BORREL AMEDEE BORREL AMELIE AMERGAL AMETLIE AMILCARE CALVETTI AMIRAL COURBET AMIRAL LAURIA AMIRAL LORIA AMNDIERS AMOREUX AMOUR AMOURIERS AMPERE AMY MOLLISSON ANATOLE FRANCE ANCIEN ABATTOIR ANCIEN BASSIN ANCIEN PEZENAS ANCIEN ANCIEN COURRIER ANCIEN ECOLES ANCIEN FOUR ANCIEN HOPITAL ANCIEN JEU BALLON ANCIEN LAVOIR ANCIEN MARCHE L HUILE ANCIEN VENT ANCIEN POSTE ANCIEN PRESBYTERE ANCIEN PUITS ANCIEN STADE ANCIEN TEMPLE ANCIENNE ANCIENNE AUBERGE ANCIENNE CASERNE ANCIENNE DISTILLERIE ANCIENNE ANCIENNE FORGE ANCIENNE ANCIENNE GENDARMERIE ANCIENNE ANCIENNE NOTRE DAME ANCIENNE POSTE ANCIENNE POSTE COMBEJEAN ANCIENNE TUILERIE ANCIENNE ANCIENNES ECOLES ANCIENNES PRISONS ANCIENS ANCIENS COMBATTANTS ANCIENS LAVOIRS ANDOQUE ANDRANS ANDRE AMPERE ANDRE AUGUSTE ANDRE BLONDEL ANDRE CADELARD ANDRE CHAMSON ANDRE CHAUSSOUY ANDRE CHENIER ANDRE COMBET ANDRE DERAIN ANDRE FRANQUIN ANDRE GABRIEL ANDRE GEORGE ANDRE GIDE ANDRE JOLIVET ANDRE NOTRE ANDRE NOTRE LOTISSEMENT FLAHAU ANDRE LENOTRE ANDRE LHOTE ANDRE MALRAUX ANDRE AMPERE ANDRE MARQUES ANDRE MAUROIS ANDRE MESSAGER ANDRE MICHEL ANDRE MOCH ANDRE NAVARRO ANDRE NOUGARET ANDRE PORTES ANDRE REBUFFAT ANDRE RECOULES ANDRE ROBERT ANDRE TANNIERES ANDRE VEYGALIER ANDRE VEYRAC ANDRE VEYRAC MICOCOULIERS ANDREA PALLADIO ANEMONES ANGELIQUE ARNAUD ANGUIALES ANIEL ANNE FRANCK ANNIBAL ANSEELE ANSELLE ANSELME MATHIEU ANTEQUERA ANTERRIEU ANTHELME BRILLAT SAVARIN ANTHERIEU ANTHONIN BOULIECH ANTOINE ARQUENIA ANTOINE BAUME ANTOINE BONNEFOUS ANTOINE CHATINIERE ANTOINE CONDORCET ANTOINE BAIF ANTOINE CAMBOUS ANTOINE JUSSIEU ANTOINE EXUPERY ANTOINE JUST ANTOINE DI LUCA ANTOINE FABRE ANTOINE GUYNEMER ANTOINE JEROME BALARD ANTOINE LAURENT JUSSIEU ANTOINE LOUIS BARYE ANTOINE MIQUEL ANTOINE MOLINIER ANTOINE ANTOINE PALAZY ANTOINE PICARD ANTOINE ROUX ANTONIN CHAULIAC ANTONIN GROS ANTONIN LAVERGNE ANTONIN MAFFRE ANTONIN SOUCAILLE AQUARELLES AQUEDUC AQUEDUC ROMAIN AQUEDUC LOUIS AQUITAINE ARAGO ARAGON ARAMON ARAMONS ARAMONT ARAVIS ARBORAS ARBOUNIERES ARBOURY ARBOUSIER ARBOUSIERS ARC ARC EN CIEL ARCADES ARCHEOLOGIE ARCHIMEDE ARCS BOUTANS ARENES ARGELOT ARGENCE ARGENCE FOYER ARGENTERIE ARGENTIE ARGENTIER ARGEOLOT ARISTEE ARISTIDE BRIAND ARISTIDE CHAPUSOT ARISTIDE MAILLOL ARISTIDE OLLIVIER ARISTIDE ROUZIERE ARISTIDES SOUSA MENDES ARJALAS ARJOLLE ARLMAND FALLIERES ARMAND BARBES ARMAND DANEY ARMAND LUNEL ARMAND SABATIER ARMANDS ARNASSERRE ARNAUD ARNAUD VILLENEUVE ARNAUD LART ARNAUDS ARNAULT PEYRE ARNAULT PEYRE LAC ARSENE D ARSONVAL ARTHEMIS ARTHUR HONEGGER ARTHUR RAMADE ARTHUR RIMBAUD ARTILLERIE ARTISANAT ARTISANS ARTISTES ARTOIS ARTS ARTS METIERS ASPHODEDES ASPHODELES ASPHODELLES ASPIC ASPIRANT LEBARON ASSAS ASTE ASTROLABE ASTRUC ATGIER HAZARD AUBEPINE AUBEPINES AUBER AUBERGE AUBERT AUBERTIN AUBES AUBRE SANT AUCELOUS AUDIBERT AUGEREAU AUGUSTE BAUSSAN AUGUSTE BLANQUI AUGUSTE BROUSSONNET AUGUSTE COMTE AUGUSTE DORCHAIN AUGUSTE FABREGAT AUGUSTE GIBERT AUGUSTE LUMIERE AUGUSTE LUMIERE 10 AUGUSTE MALINAS AUGUSTE MEYNIER AUGUSTE RENOIR AUGUSTE RODIN AUGUSTIN FRESNEL AUGUSTIN GAU AUGUSTIN GIBAUDAN AUGUSTIN VEZIAN AUGUSTINS AUMONERIE AUNIAC SALELLES AUTAN AUTOUR AUVERGNAT AUX HERBES AUZIALE AVANTS AVANTS MONTS AVENIR AVENQ AVENTS AVENTURIN AVERO AVOCETTE AYET AYRALS AYROL AYROLE AYROLE HAUTE AYROLLE AZAIS BARTHES AZALAIS D ALTIER AZALAIS PORTIRAGNES AZALEE CHAMBERTE AZEMA BABET BABEUF BABEUF VILLENEUVE BEZIERS BABYLONE BAC BACHAGA BENHAMIDA BACHAGA BOUALEM BADASSAC BADE BADIANE BADIAUX BADOC BAGATELLE BAGES BAILLI SUFFREN BAINS BAIRADEL BAISSE BAL BALAD BALAINVILLIERS BALARD BALCON BALLAURIS BALLESTRAS BALLON BALTHAZAR BALTHUS BALTHAZAR FLORET BALTHAZAR JORDAN BALZAC BAMBOUS BANDIDO BANYS BAOU BAOUSSES BAPTISTIN SOULIE BAPTISTINE RABAT BAPTISTOU BAQUE BAQUET BARA BARBACANE BARBARIE BARBAROUX BARBES BARBEYRAC BAREYRES BARI BARILLE BARILS BARNABO BARRA BARRAQUE BARRAQUES BARRATERIE BARREME BARRI CAMPEMAR BARRI BARRILS BARRIS BARRIS HAUT BARROU BARRY BARRY BAS BARRY D ORB BARRY HAUT BARRYS BARRYS HAUTS BARTHELEMY GUIBAL BARTHELEMY LUCHAIRE BARTHELEMY RIGAL BARTHES BARTHEZ BARTHOLDI BARTHOU BARYLS BARYMBAL BASALTE BASCULE BASSE BASSIN BASSIN THAU BASSIN THAU BASSINS BASTARD SENAUX BASTIDE BASTIDE L OULIEU BASTIDES BASTILLE BASTIT BATELIER BATISSES BAUDASSE BAUDELAIRE BAUDIN BAUMELLE BAUMES BAUTE BAYARD BAZILLE BALARD BEAL BEATRIX MARGUERITE BEAU RIVAGE BEAU SEJOUR BEAU SITE BEAU SOLEIL BEAULAC BEAULIEU BEAUMES BEAUREGARD BECHAMP BEDOS CELLES BEETHOVEN BEFFROI BEGUDE BEL AIR BEL HORIZON BELAMAN BELBEZETH BELBEZETH 26 BELFORD BELFORT BELGES BELIGOU BELLAY BELLE AUDE BELLE MAGUELONE BELLE OMBRE BELLES BELLEVILLE BELLEVUE BELLONE BELMONT BELOUMBET BELUMBET BELUMET BELVEDERE BELVEDERES BENJAMIN CONSTANT BENJAMIN FABRE BENJAMIN FRANKLIN BENJAMIN GUIRAUDOU BENJAMIN SAURI BENJAMIN SAURY BENOIT DELMAS BENOIT MALON BENOVIE BENOVIE 18 BERANGER BERBERIS BERENGER FREDOL BERENGER FREDOL BERGAMINI BERGERE BERGERES BERGERIE BERGERIE CAUNELLES BERGERIE CAUNELLE BERGERONNETTE BERGERONNETTES BERGERS BERGES BERGES THAU BERLIOZ BERNARD BLIER BERNARD BUFFET BERNARD D ANDUZE BERNARD D AURIAC BERNARD TREVIERS BERNARD DELICIEUX BERNARD LYDT BERNARD PALISSY BERNARD POURQUIE BERNARD TREVIEZ BERNIS BERTHAS BERTHE FLEURENCE BERTHE MORISOT BERTHELOT BERTHEZENE BERTHIE ALBRECHT BERTHUS BERTIN BERNADOU BERTIN BOUSQUET BERTRAND BERTRAND BORN BESCAUME BEZIERS BEZOMBES BICENTENAIRE REPUBLIQUE BICHETTES BIGOT BILIERE BILLIE HOLIDAY BIR HAKEIM BIRADOUS BIRON BIZE BIZET BLAISE PASCAL BLAISE PASCAL MALHAUTE BLANCHARD BLANCHE CASTILLE BLANQUETTE BLANQUI BLEUETS BOBBY LAPOINTE BOBY LAPOINTE BOCAUD BOIELDIEU BOILEAU BOILEAU CASTELNAU CHENES BOISSY ANGLAS BOISSY D ANGLAS BOLETS BOMBE CUL BON MARCHE BON VIN BONA DONA BONNARD BONNARIC BONNIE BONNIER D ALCO BONNIER MOSSON BONNIER MOSSON BONS ENFANTS BORIE BORIES BORIS VIAN BORNE BORNIER BOSC BOSQUE COMBEJEAN BOSQUET BOSQUETS BOSSES BOSSUET BOTTE BOUCARIE BOUCARIE BASSE BOUCARLOU BOUCHERIE BOUDARD BOUGAINVILLEES BOUILLON LANDAIS BOUISSES BOUISSETTES BOUISSON BERTRAND BOUISSOUSSES BOULEAU BOULEAUX BOUQUERIE BOURGOGNE BOURGUET BOURRELY BOURVIL BOUSCAREN BOUSCHET BERNARD BOUSQUET BOUSSAGUES BOUSSAIROLLES BOUSSINESQ BOUT MONDE BOUTEILLOUS BOUTONNET BOYER BOZENE BRAHMS BRAMA BRAY BREAU BRECHE BRESCOU BRETON BRIGANTIN BRILLAT SAVARIN BRINDEJONC MOULINAIS BRIOU GARENNE BROSSOLETTE BROUSSONNELLE BRUEYS BRUMAIRE BRUNELS BRUNO BRUNEL BRUTUS BRUYERES BUFFADOU BUFFETIERE BUFFON BUGADIERES BUGAREL BUGEAUD BULHER BURGUET C ADER C LINDBERGH C M TALLEYRAND PERIGORD C NUNGESSER C MAURICE RAVEL CABANE CRIC CABANNES CABANON CABASSERIE CABERNET CABERNETS CABINET GEANT CABROL CADE CADELLE CADES CADET FERRAND CADOULE CADRAN CADRAN SOLAIRE CADUCEE CAFE CAFFAROT CAGAREL CAGAROLET CAGNES CAIRE CAIZERGUES PRADINES CALADE CALADE MARTHOMIS CALADE MARTIN CALADOU CALCALDIS CALENDAL CALIXTE CASSAN CALIXTE SANSON CALMETTE CALQUIERES CALQUIERES HAUTES CALVAIRE CALVIN CALVIN 34 F 5 CALYPSO CAMBACERES CAMBEFORT CAMBON CAMBOUS CAMBOUX CAMBRIDGE CAMELIAS CAMI FOUNT CAMI PANAT CAMIDEL ORMO CAMIE PANAT CAMILLE CLAUDEL CAMILLE CLAUDEL 34 CAMILLE DESCOSSY CAMILLE DESMOULINS CAMILLE PELLETAN CAMILLE SAENS CAMINADE CAMINOL CAMP CAMP JOYEUSE CAMP PALAIS CAMP PALAIS CAMP LAURIER CAMP NEUF CAMP ROUGE CAMPAGNAN CAMPAGNE CAMPAGNE DAUDET CAMPAN CAMPANULES CAMPERNAT CAMPING CAMPOUL CAMPS NEUFS CANABASSERIE CANAL CANAL L ABBE CANAL NAU CANALS CANDAURADE CANDIDE CANEPETIERES CANET CANIGOU CANNIERS CANORGUES CANOTTE CANOTTES CANTAGRIL CANTALOUPS CANTEMERLE CANTERELLE CANTERELLES CANTON CANTONADE CANTOUNADE CAP BAGES CAP GOS CAP NICOLE FARRET CAPDALLIECH CAPELAN MORT CAPELANIE CAPELETTE CAPELIERE CAPELLAN MORT CAPESTANG CAPIMONT CAPISTOLAT CAPISTOUL CAPITAINE BONNET CAPITAINE DAVID CAPITAINE GUYNEMER CAPITAINE MAURICE DELON CAPITAINE NICOLE FARRET CAPITAINE PONTAL CAPITAINE VAISSEAU ESCANDE CAPITAINERIE CAPITANE DREYFUS CAPITELLES CAPITOUL CAPORAL AVIGNON CAPOULIERE GRACE CAPRIERS CAPUCINES CARABOTTES CARAMBELLE CARAUSSANNE CARBOUNIERES CARDINAL CARDINAL CABRIERES CARDINAL FLEURY CAREIRETA CARIGNAN CARIGNANS CARL LINNE CARL VON LINNE CARLENCAS CARNOT CARNUSSE CARON CAROUX CARRE ROI CARREAU CARRETAL CARRIERA DEL PORTANEL CARRIERA TEULIERA CARRIERAS LAS AYRAS CARRIERASSE CARRIERASSES CARRIERE CARRIERE OBSCURE CARRIERES CARRIERETTE CARRIERRASSE CARRIERRETTE CARRIGNAN CARRIGNANS CARROBE CARROTTES CARROUX CARTOUCHERIE CASANOVA CASCADE CASERNES CASIMIR PERET CASINO CASSAN CASSEROLLES CASSILLAC CASSIN RENE CASTAN CASTANS CASTEL CASTEL MOTON CASTEL MOUSTOU CASTEL RONCERAY CASTEL VIEIL CASTELBON BEAUXHOSTES CASTELLAS CASTILHON CASTILLON CASTORS CASTORS LANGUEDOCIENS CASTRES CATALAN CATALANE CATALANES CATALPAS CATARINA CATET CATHARES CATHERINE WILLIAM BOOTH CAUBEL CAUCADIS CAUDEJO CAUQUILLAS CAUSSE CAUSSES CAUSSIGNES CAUZIT CAVAILLE COLLEGE CAVALIERS CAVE CAVE COOPERATIVE CAVE CONSULS CAVELIER SALLE CAVES CAVES BASSES CAVES HAUTES CAVES NEUVES CAVILLE CAZAL CAZALS CAZILHAC CECILE BOURREL CEDRES CELLENEUVE HILAIRE CELTES CENTAURES CENTENAIRE CENTRALE CENTRE CAILHOL CERCLE CERESSOU CERFEUIL CERISAIE CERISIER CERISIERS CERS CESAR FRANCK CESSE CESSERO CEVELOUS CEVENNES CEZAS CHAINES CHAINES SIRENES CHALET CHALETS CHALUT CHAMARAULLE CHAMAYOU CHAMBERT CHAMP BATAILLE CHAMP BATAILLE CHAMP FOURNEL CHAMP L HORTE CHAMP MATHE CHAMP FOUR CHAMP NOYER CHAMP ROY CHAMP LONG CHAMP ROND CHAMPAGNE CHAMPIONNET CHAMPS L AIRE CHAMPS NEGRES CHANDELLES CHANOINE BESSEDE CHANOINES CHANTEBRISE CHANTECLERC CHANTEPLEURE CHANTERELLE CHANTERELLES CHANTIERS CHANTILLY CHANZY CHAPELLE CHAPELLE PENITENTS CHAPELLE ROCH CHAPTAL CHARAMAULE CHARANCY CHARCOT CHARDONNAY CHARDONNERET CHARDONNERETS CHARDONS AMANS BADERON BARTHE BARTHES BAUDELAIRE BOMBAL BONAPARTE BONNET BORDES BORROMEE BOURSEUL BRENNUS CABROLIER CAMICHEL CAUQUIL CROS COULOMB CHARLES MONTESQUIEU TOURTOULON DELESCLUZE DESPIAU DIDION FLAHAUT FLOQUET GIDE GOUNOD GUY LABOR LECOCQ LINDBERGH LOUBENS MESSIER NARDONNE NEL NICOLLE NUNGESSER PEGUY PERRAULT REBOUL RICHET SAUVAGE TEXIER THOMAS THOMAS THIBAULT TRENET VANEL VERGENNES CHARLEVAL CHARMETTES CHARROIS CHARRON CHARTREUSE CHASSEFIERE CHASSELAS CHASSES CHASSEURS CHAT BARBU CHAT D EAU CHAT EAU CHATEAU BARBU BON BON 2 D EAU EAU VIEUX CHATEAUBRIAND CHATEAUDUN CHAUDIERES CHAUDRONNIERS CHAUMIERES CHAVASSE DAMES GIGNAC L EVEQUE RONDE DAMES NEUF CHEMINOTS CHENE BLANC CHENES CHENES VERTS CHENEVIERE CHENG CHERCHE MIDI CHEVAL MARIN CHEVALET CHEVALIER BERNARD CHEVALIER BARRE CHEVRE CHEVREFEUILLE CHEVREFEUILLES CHEVREUIL CHEVRIER CHICANE CHICANETTE CHICO MENDES CHICOURAS CHIO CHOPIN CHOUPILA CHRESTIEN CHRISTIAN BENEZECH CHRISTIAN DIOR CHRISTIAN TEIL CHRISTOPHE COLOMB CHRISTOPHE RICARD CIBADE CIGALES CIGALONS CIMENTEE CINEMA CINEY CINSAULT CINSAULTS CIRCE CIRCEE CIRQUE CISTES CITADELLE CITE BENOIT BOUSQUET LAS CAZES MAS TESSE MICHEL NOUVELLE ROCH SIMON VALETTE CITERNASSE CITERNE CITRONNIERS CIVELLE CLAIR D OC CLAIR MATIN CLAIR SOLEIL CLAIRDOUY CLAIRETTE CLAIRETTES CLAIRIERE CLAPAREDES CLAPAS CLAPIERS CLAPIES CLAR LUNO CLARET CLASTRE VIEILLE CLAUD DAME CLAUDE BERNARD CLAUDE BERNOUD CLAUDE BERTHOLLET CLAUDE BOURDET CLAUDE BROUSSON CLAUDE CHAPPE CLAUDE DEBUSSY CLAUDE FARRERE CLAUDE CLAUDE GOUDET CLAUDE LAURES CLAUDE MAIMONE CLAUDE MAZET CLAUDE MONET CLAUDE PAPI CLAUDE SAVARY CLAUDE SERRES CLAUSEL CLAUSES CLAUZELS CLAUZES CLEDAT CLEDE CLEMENCE ISAURE CLEMENCEAU CLEMENT ADER CLEMENT MAROT CLEMENT MIRABEL CLEMENT NEGRE CLEMENTVILLE CLOCHER CLOITRE CLONISSE CLOS CLOS CHIVAUD CLOS L ECLUSE CLOS L ISABELLE CLOS CEVENNES CLOS VIGNES CLOS CINSAULT CLOS FESQUET CLOS GUE CLOS L ETANG CLOS MONTLOUBAT CLOSADES CLOSERIE GUILHEMS COCCINELLES COINS COLBERT COLET COLETTE COLI COLIBRIS COLIGNY COLIN COLLEGE COLLINE COLLINE COUCHANT COLLOT COLOMBIE COLOMBIER COLONEL DELMAS COLONEL DENFERT COLONEL MARCHA COLONEL PAILHES COLONEL PAILLES COLONEL ROZANOF COLONEL ROZANOFF COLONEL SERVENT COLONEL TARNIQUET COLS VERTS COLVERT COLVERTS COMBATTANTS COMBE RENARD COMBE LIBRO COMBELLES COMBES COMFRERIE COMMANDANT ALENGRY COMMANDANT BASSAS COMMANDANT DUMAS COMMANDANT ESPINADEL COMMANDANT FARRET COMMANDANT FULCRAND COMMANDANT HEROUARD COMMANDANT HERROUARD COMMANDANT KIEFFER COMMANDANT RIVIERE COMMANDANT VILAREM COMMERCANTE COMMERCANTS COMMERCE COMMUNAUTE COMMUNE COMMUNE PARIS COMMUNES COMORES COMPAGNONS COMPORTE COMTE BERNARD COMTE MELGUEIL COMTES MELGUEIL CONCORDE CONDAMINE CONDAMINE MAJOUR CONDE CONDORCET CONNELLE CONQUE CONQUES CONQUETTES CONSEIL GENERAL CONSULS CONTI CONTOUR CONVENTION CONVENTIONNEL BARRAS COOPERATIVE COOPERATIVES COPAS CAMBAS COPE CAMBE COPE CAMBES COPE CAMPES COPECAMBA COPENHAGUE COPO CAMBOS COPPE CAMBE COQ COQUELICOTS COQUILLE CORAIL CORAIL ROUGE CORBEAU CORDA CORDAT CORDELIERS CORDERIE CORDIERS CORDILLE CORMIER OISEAUX CORMORANS CORNEILLE CORNELIS VAN DONGEN CORNICHE NOSTRA DONA CORNIERS CORNUT COROLLES CORONILLES COROT CORPS GARDE CORSAIRES CORSIER COSTE COSTE FREGE COSTEBELLE COSTES COSTES BELLONTE COSTES FRESCH COSTETE COSTETTE COTE COTE PAVEE COTEAUX COUCHANT COUCOU COUDAIR COUDOURET COUGUELATTE COULET COULMIER COULMIERS COULONDRE COUPE CAMBES COUPO SANTO COURBE COURBET COURBEZOU COURETTE COURNONTERRAL COURNUT COURPOURON COURPOUYRAN COURREAU COURREGES COURTE COURTE OREILLE COURTELINE COUSSAT COUSTEL COUSTIERRASAS COUSTOU COUTELLERIE COUVENT CRABE CRANTON CREISSES CREMADE CREMADES CRES CRETE CREUX CRIANEL CRIQUE L ANGLE CROCUS CROISADES CROIX CROIX D ARLES CROIX FIGOU CROIX FIGUEROLLES CROIX MISSION CROIX REILLE CROIX LAS CAZES CROIX LAVIT CROIX POUMEYRAC CROIX REBOUL CROIX BARRIERES CROIX SUD CROIX HAUTE CROIX MALAUTIES CROIX MISSION CROIX RAMS CROIX ROUSSE CROIX ANTOINE CROIX VANTAIL CROIX VERTE CROUTOU CROUZELS CROUZET CROUZETTE CROVA CROYES CROZES CRUCIFIX CURAYE CURE CURIE CUVIER CYCLAMENS CYGNES CYPRES CYPRIEN TOUREL CYRANO BERGERAC D AGATHA D AIGREFEUILLE D AJACCIO D ALBAN D ALBE D ALBISSON D ALCO D ALEMBERT D ALGER D ALLEMAGNE D ALMERAS D ALSACE D AMSTERDAM D ANIANE D ANJOU D ANTIPOLIS D AOUT D AQUITAINE D ARCOLE D ARGENCOURT D ARROMANCHES D ARTOIS D ASSAS D ASSISE D ASTIER VIGERIE D ATHENES D AUBETERRE D AUSTERLITZ D AUVERGNE D EMBONNE D EMPORION D ENCROS D ENVEDEL D EOLE D EPIDAURE D ESPAGNE D ETE D HOLBACH D IBIZA D IENA D INGRIL D ISSANKA D ITALIE D OBILION D OBSEN D OCCITANIE D OLBIA D ORAN D OSTIA D OVALIE D OXFORD D UPPSALA DAHLIAS DAHOMEY DALHIAS DAME D ORCAS DANIEL DANIEL MAYER DANIEL RICHAUD DANIEL SAGE DANIEL SORANO DANIELLE CASANOVA DANSE DANTE DANTON DANTON MARCHE DAPHNE DAPHNEES DAPHNES DAPHNIES DARE HORTS DARIUS MILHAUD DARU DASLAVALS DAUBINELLE DAUDET DAUMESNIL DAUMIER DAUNOU DAUPHIN DAUPHINE DAUPHINS DAURADE DAVALADAS DAVID D ANGERS ATELIER BAILLARGUET BALE BARCELONE BARI BARRY VILLAGE BASTIT BASTIT MAIL BEAU SOLEIL BEL AIR BELFORT BELGIQUE BELLA VISTA BELLE VISTE BELLEVUE BERCY BERLIN BIONNE BIR HAKEIM BISSARGUES BOLOGNE BONE BONSI BOURGOGNE BRAINE BRAY BRESCOU BRETAGNE BRUXELLES BUGAREL BURANO CADIX CAMARGUE CAMBRIDGE CAMERONE CANDOLLE CANTABEN CANTAUSSEL CANTE GRIL CANTEGRIL CARIGNAN CARTAGO CASSAN CASSEYROLS CASTELNAU CATALOGNE CAUBEL CAYLUS CENTRAYRARGUES CEUX D ARGELIERS CHAMBERT CHARENTE BON CHATEAUDUN CHERCHELL CHIMINIE CHOLET CIRY SALSOGNE CLAIR SOLEIL CLAIRAC CLAIRIE CLARET CLEMENVILLE COIMBRE COLOMBIERS CONSTANTINE COPENHAGUE COQUELICOTS CORNEILHAN CORSE COSTEBELLE COSTESEQUE COULONDRES BOUYRAN COURNONTERRAL CRES CRONSTADT DELOS DOSCARES DUBLIN FERRAN FERRARE FLOURA FONTAINE CARRADE FONTAINE CAUDE FONTAINE SEGUGNE FONTCARRADE FONTCOUVERTE FONTFROIDE FONTGRANDE FONTMAGNE FONTVIEILLE FORCRAND FRANCE GARASTE GARGAILHAN GASCOGNE GENES GIGNAC GIRARD GIRONE GRANOUILLE GRECE JALABERT JAUSSERAND JUVENEL L ABATTOIR L ABBAYE L ABBE CAMOZY L ABBE L EPEE L ABEILLE L ABELANIE L ABONDANCE L ABREUVOIR L ABRIVADO L ACROPOLE L ADRET L AFFICION L AFICION L AGATHOIS L AGENAIS L AIGLE DORE L AIGLON L AIGOUAL L AIGUE VIVE L AIGUELONGUE L AIGUILLERIE L AIGUILLERIE BASSE L AIRE L AIRETTE L ALBIGEOIS L ALMA L ALOUETTE L AMANDIER L AMETHYSTE L AMITIE L AMOUR L ANCIEN L ANCIEN COLLEGE L ANCIEN COURRIER L ANCIEN COUVENT L ANCIEN HOPITAL L ANCIEN L ANCIEN HUILE L ANCIEN THEATRE L ANCIENNE AUBERGE L ANCIENNE DISTILLERIE L ANCIENNE POSTE L ANGEL L ANJOU L ANSE GIFRAN L ARAMON L ARBOUSIER L ARBRE BLANC L ARC L ARC MOURGUES L ARC EN CIEL L ARDOISE L ARGEALAS L ARGENTERIE L ARGILE L ARLESIENNE L ARMEE D AFRIQUE L ARMISTICE L ARNEL L ARSENAL L ARTISANAT L ARTOIS L ASPIC L ASPIRANT PIDOUX L ASTROLABE L ATRIUM L AUBEPINE L AUBIER L AUBUN L AUMORNE L AURORE L AUVERGNE L AVENIR L AVENTIN L L AVOCETTE L AZUR L DROIT L MEDECINE L PARC L MAGE L NORMALE L PUBLIQUE L REPUBLICAINE L ECRIN L EGALITE L L EMERAUDE L ENCIERRO L ENCLOS L ENCLOS PINS L ENCLOS VIGNES L ENCLOS VILLAGE L ENCLOS JOUVENEL L ENFER L ENGOULEVENT L EOLIENNE L EPARGNE L EPERON L EPERVIER L EPIRE L EQUERRE L ERMITAGE L ERMITE L ESCADRE L ESCALETTE L ESCOLETTE L ESCOUDE L ESCOUTADOU L ESPENDIR L ESPERANCE L ESPERE L ESPEROU L ESPINOUSE L ESPLANADE L ESPOIR L ESPRIOU L ESTELLE L ESTRAGON L ESTRAMBORD L ETANG L ETANG L OR L ETOILE L ETOILE BLEUE L EVANGILE L EVECHE L HARMATTAN L HERBE D AMOUR L HERBE FIGUIERE L HERBERIE L HIRONDELLE L HOPITAL L HOPITAL VIEUX L HORLOGE L HORT MONSEIGNEUR L HORTET L HORTUS L HOSPICE L HOSPITALET L HOTEL L HOTEL MONNAIES L ILE FRANCE L IMPRIMERIE L INDEPENDANCE L INDUSTRIE L INFANTERIE MARINE L INTENDANT BASVILLE L INTENDANT D AGUESSEAU L OASIS L OBSERVANCE L OCCIDENT L OCCITANIE L OEILLADE L OEILLET L OLIVERAIE L OLIVETTE L OLIVIER L ONYX L OPALE L OPPIDUM L ORB L OREE L ORIENT L ORIGAN L ORMEAU L ORPHEON L OUSTALET L UNION L UNIVERSITE L YEUSE 1 ERE ARMEE 1 ERE ECLUSE 2 EME ECLUSE 32 EME BADIANE BALAURIE BANDIDO BARALLERIE BARBE BARONNIE BARQUE BARRALERIE BARRIERE BASCULE BAUSSAS BEAUMETTE BEGUDE BELLE BELLE GRAPPE BENOVIE BERGERIE BERGERONNETTE BISLOU BONTE BORIE BOURGINE BOUVINE BRASSERIE BRECHE BRECHE BASSE BRECHE HAUTE BRECHETTE BRIGANTINE BRIQUETERIE BRUYERE BUFFETTE BUTTE BUTTE RONDE CADORQUE CADOULE CALADE CALECHE CAMARGUE CAMARGUE D OR CAMBALADE CANNELLE CANTAPERDRIX CANTARELLE CANTONADE CANTONNADE CAPELANE CAPELETTE CAPELIERE CAPELLE CARAUSSANE CARBONNERIE CARDONILLE CARRIERASSE CARRIERE CASCADE CASEMATE CASTELLE CAVALADE CAVALERIE CAVE CAVE COOPERATIVE CHAPELLE CHARITE CHARRUE CHATRE CHAUSSEE CHENAIE CHEVALERIE CHICANE CHICANETTE CIBLE CIBOULETTE CIGALE CIME CITADELLE LA BLANCHE D ENDRAUSSE MONTMARTRE ROUANET VERDIER VIGNAL CITERNE CLAIRETTE CLAIRIERE CLARISSE CLASTRE CLAU CLAVIERE CLOSERAIE CLOVISSE COLLINE COLLINE VIVARAIS COMBE COMBE CAUDE COMBE BONESTA COMEDIE COMETE COMMUNALE COMMUNE PARIS COMPORTE CONCORDE CONDAMINE CONFRERIE CONSTITUANTE CONVENTION COOPERATIVE COQUILLE CORDERIE CORDONNERIE CORNALINE CORSE COSTA BRAVA COSTA DORADA COSTE RECTEUR COURAL COURBE COURONDELLE CRETE D OC CROISETTE CROIX CROIX BONHOMME CROIX D OR CROIX BELLEAU CROIX FELIX CROIX GUILLERY CROIX JALLEE CROIX LAVIT CROIX TRICOUX CROIX ROSIERS CROIX SUD CROIX ROUGE CROIX VERTE CROIX VERTE MINIPARC CROUZETTE CURE CUVE DANSE CORDELLES DANSE TREILLES DANSE CHEVALET DAUBINELLE DAVALADE DENTELLIERE DESCOLE DEVINERESSE DIGUE DISTILLERIE DOUANE DOUGUE DRAILLE DRAILLE BOEUFS DRAYE DUCQUE FABRIQUE FAIENCE FALAISE FARANDOLE FARENQUE FARIGOULE FARIGOULETTE FAUDRENQUE FAUVETTE FEDERATION FELOUQUE FENAISON FERRADE FIGAIRASSE FIGUIERE FLORE JUVENALE FOIRE FON ST BERTHOMIEU FOND FROIDE FONDERIE FONTAINE FONTAINE BANQUIERE FONTAINE CELLENEUVE FONTAINE BANQUIERE FONTAINE NEUVE FONTAINE ROSSIGNOLE FONTAINE ST BERTHOMIEU FONTAINE VIEILLE FONTAINETTE FONTANELLE FONTFROIDE FORET NOIRE FORGE FORTUNE FOUILLADE FOURMI FRAGE FRATERNITE FREGATE FREGERE FRIGOULE FRIPERIE GAFFE GAIETE GALERA GALINETTE GALUSTE GARANCE GARDIOLE GARENNE GARNISON GAROUILLE GARRIGUE GASCOGNE GAYONNE GAZE GENDARMERIE GEODE GERBE GINIEISSE GLACIERE GLORIETTE GOELETTE GOULE LAVAL GRANGE GRANGETTE GRAPPE GRATTE GRAVE GRENADE ENTROUVERTE GRENOUILLERE GUETTE GUISSAUME GUYENNE HALLE HAUTE FUTAIE HAYE JALADE JASSE JASSE MAURIN JASSETTE JEANNOTTE JEUNE PARQUE JEUNESSE JONCASSE JOURDANE JULIANE LAUZE LAVANDE LERGUE LIBERATION LIBERTE LINCE LIRONDE LITIERE LOGE LUCQUE LYRE MACREUSE MADELEINE MADELEINE SCUDERY MADONE MADRAGUE MAGETTE MAGNANERIE MAITRISE MAMECHE MANADE MARBRERIE MARECHAUSSEE MARGALIERE MARGELLE MARGERIDE MARGUERITE MARJOLAINE MARNE MARQUEROSE MARTEILLIERE MAYRE MAZADE MEDITERRANEE MER MERCI MERIDIENNE MESANGE METAIRIE SAYSSET MINE MOGERE MONNAIE MOSSON MOTTE MOULENAGUE MOUNEDA MURAILLETTE MUTUALITE NATION PAILLE PAIX PALISSADE PALMERAIE PALOMBE PALOURDE PALUS PAQUIERE PAREE PART ANTIQUE PASTIERE PASTOURELLE PATRIE PELLETERIE PENICHE PENSEE PEPINIERE PERDRIX PERLE NOIRE PEROUSE PERRUCHE PETANQUE CAMARGUE LOGE PEYRADE PEYRIERE PIALE PICHOLINE BLEUE MARTIN ROCH PILE LOUP PINEDA PINEDE PISCINE PISTOULE PLACETTE PLAGE PLAINE PLAUCHUDE PLIE POESIE POISSONNERIE POLKA POMAREDE POMME POMPE POMPE VIEILLE PORTE LOMBARDE PORTE LAURENT PORTETTE POSTE POTERIE POUDRIERE POULAILLERE PRADE PREFECTURE PREMONADE PROCESSION PROMENADE PROVENCE PROVIDENCE PRUDHOMIE PRUNETTE RAFFINERIE RAMOGNERE RASE RASTAGAGNE RAUZE BASSE RAVE RAZE RENAISSANCE RENARDIERE REPUBLIQUE RESISTANCE RESTANQUE REVOLUTION RIVIERE ROCHELLE ROMPUDE ROQUE ROQUETTE ROQUETURIERE ROSE ROSE LOTISSEMENT VIGNE ROSELIERE ROSERAIE ROTISSERIE ROTONDE ROUBINE ROUBINE LATTES ROUQUETTE ROVERAIE SALADELLE SALAMANDRE SALLE SALLE L EVEQUE SARDANE SARRIETTE SAURINE SAUZEDE SAVOIE SAVONNERIE SCIENCE SEICHE SERANNE SERP SERPE SERRE SOLIDARITE SOMME SOURCE SOUS PREFECTURE SYRAH TAILLADE TARTANE TEINTURERIE TELEGREPPIE THERIAQUE TIBLE TOUR TOUR BUFFEL TOUR D AUVERGNE TOUR CANDELON TOUR SAINTE EULALIE TOURMALINE TRAMONTANE TRAVERSIERE TREILLE TREILLE MUSCATE TRIDE TUILERIE VACCADE VALENE VALFERE VALLONE VALSIERE VAUGELY VERDALE VERITE VERRERIE VERRERIE BASSE VIALLE VICTOIRE VIEILLE VIEILLE PORTE VIEILLE POSTE VIERGE VIGNE VIGNE VIERGE LA VILLETTE VISE LACOSTE LAMBECQUE LARENAS LAS CARRETAS LAS DAVALADAS LAS SORBES LASSUSSE LATTRE TASSIGNY LAUNIAC LAUSANNE LAVOISIER LERGUE LERIDA LEYDE LIEGE LINTENDANT BOSVILLE LONDRES LONGUYON LORRAINE LOUN LOUVAIN LOUVOIS MADAME MADRID MAJORQUE MALBOSC MALRIVES MARCOUI MARSEILLE MASSACAN MASSILLAN MAUMARIN MENDE MERIC MESSINE METZ MOISSAC MONTASINOS MONTFERRIER MONTJUICH MONTLAUR MONTMEDY MONTREAL MONTSEGUR MOUREZE NAFOURNES NARBO NARVICK NAUCELLE NAVACELLES NAZARETH NEAPOLIS NIKAIA NISSAN NORMANDIE PARIS PATCHWAY PECOULE PEYNE PEYREFIOC PICA TALEM PICARDIE PINVILLE PIQUEPOULT PORT VENDRES PORTO PRAGUE PREDIMAU PROVENCE RABASSOUS RATTE REGAGNAS RETHEL RETHONDES 1918 RHODA RHODES RIPOLL ROCAGEL ROCHELONGUE ROME RONDE ROQUEBLANQUE ROTONDE 1918 BAUZILLE BITOU EXUPERY GENIES HILAIRE SAINTE COLOMBE SAINTE CROIX SALAISON SALAMANQUE SALELLE SALERNE SALLELLE SARAGOSSE SARDAIGNE SAURET SAUVIELLE SAUZE SAVOIE SERVIAN SETIF SICILE SOMMIERES SOULONDRE SOUVILLE STRASBOURG SUBSTANTION SUEZ SUFFREN SULLY TARRAGONE THOMASSY TIPASA TOLBIAC TOURBAT TUNIS VALENCIA VALFALIS VALLAT VALMONT VALMY VEGA VENDARGUES VENUS VERDUN VERGAVILLE VIGNE LONGUE VIGNEBELLE VILLEFRANCHE VIVIERS DEBES DEBUSSY DECHARGE DEDALE DEL BON PAIS DEL BON SOULEOU DEL CATET DEL LANGADOC DEL LANGUADOC DEL MIEJOUR DEL TROBADOR DELMAS DELPECH DELPHINE SEYRIG DEMOLITION DENFERT ROCHEREAU DENIS BLANC DENIS DIDEROT DENIS PAPIN DENISE DEPORTES DEPORTES 1852 DEPORTES 2 DEC 1851 DEPOT DEPUTE DERIDIERE DERRIERE DERRIERE AIRES DERRIERE MURS 13 VENTS 3 JOURNEES 3 PINS 3 POINTES 4 SERGENTS 4 VENTS ABEILLES ABIMES ABREUVOIRS ABRICOTIERS ABRIVADOS ACACIAS ACCONIERS ACONITS ADIEUX AFFERAGES AGAPANTES AGASSES AGATES AIGRETTES AIGUERELLES AIGUES MARINES AIRES AIRES BASSES AIROLLES ALBATROS ALBIGEOIS ALBIZIAS ALBIZZIAS ALISIERS ALIZES ALOES ALOUETTES ALPES AMANDIERS AMARANTES AMARYLLIS AMETHYSTES AMOUREUX AMOURIERS AMOURS D ANTAN AMPHORES ANCIENNES ARENES ANCIENNES ECOLES ANCIENS ABATTOIRS ANCIENS COMBATTANTS ANCIENS D ALGERIE ANCIENS D INDOCHINE ANCIENS METIERS ANDES ANEMONES ANGUILLES ANOUBLES ANTHEMIS APHYLLANTES APHYLLANTHES APOTHICAIRES ARAMONS ARAMONS 6 ARAUCARIAS ARBOUSIERS ARCADES ARCEAUX ARDENNES ARENES ARESQUIERS ARGALAS ARNAUD ARQUEBUSIERS ARTISANS ARTS ASILES ASPHODELES ASPRES AUBEPINES AUGUSTINS AVANTS MONTS AVELANIERS AVELINES AVENTURES AVOCETTES AZALEES AZEROLIERS BAGUENAUDIERS BAINS BALANCES BALCONS L ETANG BALESTRIERS BARQUES BARRIS BARRYS BASSINS BASTIDES BATELIERS BAUX BEAUMES BEGONIAS BEGUDES BENGALIS BERGERIES BERGERONNETTES BIRADOUS BLAIREAUX BLANQUIERS BLEUETS BONNES GENS BOSCS BOUGAINVILLEES BOUGAINVILLIERS BOUISSES BOUISSETTES BOULEAUX BOULES BOUQUETINS BOURBOUISSOUS BOURNEAUX BOUTONS D OR BOUVREUILS BOUZIGUES BRAGALOUS BRUSSES BRUYERES BUGADIERES BUIS CABERNETS CABIROULES CACTUS CADES CAILLES CALADONS CALQUIERES CALQUIERES BASSES CAMARGUES CAMBIADOURS CAMELIAS CAMINIERES CAMPANULES CAMPS NEGRES CANABASSIERS CANARIS CANAUX CANDELIERS CANDINIERES CANEPETIERES CANTAGRILS CAPECHADES CAPESTANS CAPITELLES CAPRIERS CAPUCINES CAPUCINS CARAVETTES CARDEURS CARIGNANS CARMELITES CARMES PALAIS CAROUBIERS CARRIERES CARRIERETTES CASERNES CASIERS CASSIS CASTORS CATALPAS CATHARES CAVES CAVES ANTIQUES CEDRES CEMAILLETS CENTURIONS CEPES CERISIERS CETOINES CEVENNES CHAMOIS CHAMPS CHAMPS L AIRE CHANTERELLES CHANTIERS CHARBONNIERS CHARDONNERETS CHASSELAS CHASSEURS CHATAIGNIERS CHAUMIERES CHEMINOTS CHENES CHENES KERMES CHENES VERTS CHEVALIERS MALTE CHEVREFEUILLES CHRISTOLS CIGALES CIGALOUS CIGOGNES CINSAULTS CISTES CITES CITRONNIERS CLAIRETTES CLAOUZES CLAPAS CLAPASSES CLAUSADES CLAUZES CLEMATITES CLOSADES CLOSES COCCINELLES COLCHIQUES COLIBRIS COLLINES COLOMBES COLS VERTS COLVERTS COMBATTANTS COMBELLES COMBES COMMANDEURS COMMERCANTS COMMUNAUTES COMPAGNONS CONDAMINES CONGREGATIONS CONIFERES CONSCRITS CONSTELLATIONS CONSULS CONTES PROVENCAUX CONTREFORTS COQUELICOTS COQUIERES COQUILLES CORBIERES CORDELIERS CORMORANS CORONILLES CORREGIERS COTEAUX COTEAUX LAURIOL COURBES COURLIS COURREJOLS COUSSES CRESSES CROCUS CROISADES CROSASSES CROZES CYCLADES CYCLAMENS CYPRES D ANDRE DAHLIAS DAUPHINS DECURIONS DEPUTES DEVEZES DOCKERS DOCKS DOCTEURS BOURGUET DOMITIENNES DUNES ECLUSES ECLUSIERS ECOLES ECOLES CENTRALES ECOLES LAIQUES ECOLES PIES ECRIVAINS ECUREUILS ECUYERS EGLANTIERS EIDERS ELEPHANTS D ANNIBAL ELFES EMBRUNS ENDEMESSES ENTREPOTS EPICEAS EPIS EPOUX PIGNOL ERABLES ESCAIS ESCALIERS ESCANAS ESCARCELIERS ESCHOLIERS ESPERASSEDES ESTAMPES ETANGS ETATS LANGUEDOC ETATS GENERAUX ETENDOIRS ETOFFES ETOILES ETOILES MER ETOURNEAUX ETUVES EUCALYPTUS FABRES FAIENCIERS FAISSES FAISSETTES FANGADES FAUTERNES FAUVETTES FELIBRES FENOUILS FERRONNIERS FIGUIERS FLABEGUES FLAMANTS ROSES FLAMBOYANTS FLANDRES FLEURS FLEURS BLANCHES FLORALIES FONTAINES FORGERONS FOSSES FOULQUES FOURBISSEURS FOURNELS FOURS FRAISIERS FRAMBOISIERS FREGATES FRENES FRERES ARGAND FRERES CALAGE FRERES FAGET FRERES LUMIERE FRERES LUMIERES FRERES PLATTER FUCHSIAS FUSAINS FUSCHIAS FUSTES FUSTIERS GABARES GABIANS GAGNE GALAXIES GALFIERES GALINES GARDENIAS GARDIANS GARRIGUES GARRIGUETTES GAUCELMS GELINOTTES GEMEAUX GENETS GENEVRIERS GERANIUMS GERFAUTS GINESTES GLACIERES GLADIATEURS GLAIEULS GLYCINES GOELANDS GORGES D HERIC GOURS GRAMENOUS GRANDES TERRES GRANDS MANTEAUX GRANDS PINS GRANGES GRAVES GRENACHES GRENADIERS GRESES GREZES GRILLES GRILLONS GRISETTES GRIVES GROSEILLIERS GROTTES GYNERIUMS HAMEAUX HAUTS BOISSERON HAUTS FONTAINE HAUTS HELIOTROPES HERMINES HERONS HIBISCUS HIPPOCAMPES HIRONDELLES HORACES HORTENSIAS HORTS HORTS PROLONGES HORTS VIELS HOSPICES IBIS IMPRESSIONNISTES INSTITUTEURS IRIS JACINTHES JACOBINS JARDINIERS JARDINS JARDINS D ISIDORE JARDINS D ISIS JARDINS L ABBAYE JARDINS GARDIOLE JARDINS MER JARDINS SOLEIL JARDINS STADE JARRES JASMINS JASSES JASSETTES JONCASSES JONQUILLES JUJUBIERS JUSTES KERMES LABOURS LAURIERS LAURIERS AMANDES LAURIERS ROSES LAURIERS TINS LAUZIERES LAVANDES LAVANDIERES LAVANDINS LAVOIRS LENTISQUES LEVADES LIBELLULES LIERLES LIGNIERES LILAS LISERONS LITANIES LOIS LORIOTS LOUTRES LUCIOLES LUSTREUSES LUTINS MACREUSES MAGNOLIAS MALAUTIERES MANADIERS MANDARINIERS MANGUIERS MAQUISARDS MARELS MARGUERITES MARINS MARJORIES MARRONNIERS MARTINES MARTINETS MARTINS PECHEURS MARTRES MAS BAILLARGUES MASADES MAZES MAZETS L AUBE MEGISSIERS MELEZES MERES MESANGES MICOCOULIERS MILANS MIMOSAS MINIMES MOINEAUX MOISSONS MONTILLES MOUETTES MOUFLONS MOULIERES MOULINS MOULINS HUILE MOURGUES MOURGUETTES MULTIPLIANTS MURIERS MUSARDISES MUSCADELES MUSCARIS MUSES MYOSOTIS MYRTES MYRTILLES NAIADES NARCISSES NASSES NAVIGATEURS NEBULEUSES NEFLIERS NEFS NEGADOUIRES NENUPHARS NEREIDES NIVEOLES NOISETIERS NORALES NORIAS NOUVELLES NOUVELLES ARENES NOYERS OEILLETS OISEAUX OLIVETTES OLIVIERS OLIVIERS BOHEME OLYMPIADES OMBRINES ORANGERS ORCHIDEES ORCHIDEES SAUVAGES ORDILLONS ORFEVRES ORMEAUX ORMILLES ORTOLANS ORTS OULETTES OURSINS PALMIERS PALOMBES PALUDES PALUDS PAPILLONS PAPYRUS PAQUERETTES PARADES PARADISIERS PARENNETTES PARETS PASSEREAUX PASSERINES PASSIFLORES PATINEURS PATRIOTES PAYROLIERS PEBRIERS PECHERS PECHEURS PELICANS PENICHES PENITENTS PENITENTS NOIRS PENSEES PERCE NEIGE PERDIGALS PERDREAUX PERDRIX PERTUISANES PERVENCHES PETETES PETITS CHAMPS PETUNIAS PEUPLIERS PEYRALLES PEYRIERS PHALENES PHENICIENS PICHOLINES PICS VERTS PIELLES PIERRES PLATES PILLES PINEDES PINS PINSONS PIOCHS PIPISTRELLES PITTOSPORES PIVERTS PIVOINES PLANETES PLAQUEMINIERS PLATANES PLOS POETES POILUS POINTES POIRIERS POIRIERS SAUVAGES POLYANTHAS POMMETIERS POMMETTES POMMIERS PORCHES POSANDIERS POSTES POTIERS POUNTILS POURPRIERS PRADELS PRADES PRADIERS PRES PRES MARTIN PRESSOIRS PREVOTS PRIMEVERES PRUNELIERS PRUNIERS PRUNUS PUITS PYRENEES QUATRE VENTS RAISINS RAMIERS RASCASSES RASETEURS RAZETEURS RECANTOUS REFLETS RELIGIEUSES REMPARTS RENONCULES REVES RIEGES RIVES RIVETTES ROBINIERS ROCAGNAS ROCAILLES ROCHES ROCHES ROUGES ROCS ROITELETS ROMARINS ROQUILLES ROSACEES ROSEAUX ROSES ROSIERS ROSSIGNOLS ROUDERES ROUGES GORGES ROUQUETTES RUCHES SABINES SABLES D OR SAINTS SIMONIENS SALADELLES SALANGANES SALANQUIERS SALICORNES SALINS SANSONNETS SANSOUIRES SANTOLINES SARCELLES SARDELLES SARMENTS SARRALIERS SAUGES SAULES SAUNIERS SAUNIERS 14 SCABIEUSES SCARABEES SEIGNEURS SEMAILLES SERINGATS SERINS SERPOLETS SERVENTS SEYCHELLES SIGALIES SIGALIES EN HAUT DROITE SIRENES SOEURS SOEURS GRISES SOEURS NOIRES SOLDANELLES SOLDATS SOPHORAS SORBIERS SOURCES SOURCES RIEUMASSEL SOUS SPAHIS SPORTS STERNES SUREAUX SYCOMORES TADORNES TAMARINS TAMARIS TAMBOURINS TANNEURS TAULIS TEISSIERS TEMPLIERS TENNIS TEREBINTHES TERRASSES TERRES BLANCHES TERRES MEGERES TERRETS BOURRETS TERRIES TETES THERMES THUYAS TIERCELETS TILLEULS TINES TIOULETS TIRADOUS TIRE GARES TISSERANDS TONELLES TONNELIERS TONNELLES TOURMALINES TOURNESOLS TOURTERELLES TRAVAILLEURS TREILLES TREMBLES TRIBUNS TRITONS TROENES TROIS PORTES TROLLES TROPIQUES TROUBADOURS TUILERIES TULIPES UGNIS BLANCHES VAISSEAUX VANNAUX VANNEAUX VENDANGES VERBIERES VERDIERS VERMELLES VERRIERS VERRIES VERVEINES VESTALES VIALETTES VIEILLES PRISONS VIEUX REMPARTS VIGNERONS VIGNES VIGNES BLANCHES VIGNES D ANDRE VIGNES ROUGES VIGUIERS VIOLETTES VIVIERS VOILES VOILIERS VOLONTAIRES VOSGES YEUSES YUCCAS ZOUAVES DESAIX DESCARTES DESCENDANTE DESCENTE PORTAL DESCENTE MUSES DESIRE BALAMAN DESIRE KORANYI DESMAZES DESPETIS DESSALLE POSSEL DESSALLES DEUX FOURS DEUX FRERES DEUX MANSES DEUX MERS DEUX PONTS DEUX PORCHES DEUX PUITS DEVEZE DEVEZES DEVOIS DIDEROT DIDIER DAURAT DIEUDONNE VIDAL DIGUE DIMITRI AMILAKVARI DIONYSOS DISTILLERIE DIX HUIT JUIN 1940 DOCK BATIGNE BLANCHON COLLON CORBIN COTE DUCROS EMBRAS EMILE MARIGNAN ERNEST BLANCHON FABRE JOSEPH SENTY MAGNE MALABOUCHE MARES MAS MAUZAC OMBRAS PAUZIER PERALDI PHILEMON RASTOUL PRIVAT ROUX TARBOURIECH VERNHES DOMAINE MER DOMAINE PERIGNON DOMAINE SAINTE CLAIRE DOMAINE VAISSETTE DOMERGUE DOMINIQUE GIORDANO DOMINIQUE LARREY DON BOSCO DONJON DONNAT DORADE DORADES DORCHAIN DOREE DORIA DORIAN DORIS MAJESTIC DOUAIS DOUANE DOUANIER ROUSSEAU DOUCHES DOUMET DOYEN JOURDA DOYEN RENE GOSSE DRAGONNEAU DRAILLES DRAP DRAPERIE ROUGE DRAPERIE FIRMIN DRAPS DRAYE DRESSIERE DROITE DROITS L HOMME DRYADES 11 NOVEMBRE 1918 14 JUILLET 19 MARS 1962 22 SEPTEMBRE 24 FEVRIER 4 SEPTEMBRE 4 SEPTEMBRE 1870 6 JUIN 1944 8 MAI 8 MAI 1945 BAC BALCON ROND BANASTOU BAPTISTOU BARNIER BARREAU BARRI BARRY BAS GREZAC BASILIC BASSIN BASTET BASTION VENTADOUR BAYLE BEARN BEAUMONT BEL AIR BEL HORIZON BELVEDERE BERANGE BERGER BERRY BIGORRE BIOU D OR BLE D OR VIERGE ROUE VIGIER BON PASTEUR BOSC BOSC PLAINE BOSQUET BOULIDOU BOULODROME BOURG BOURGET BOUVIER BOUZANQUET BRAS FER BROUAS BRUCQ BUFADOU CABARETOU CABERNET CADE CADRAN SOLAIRE CADUCEE CAIRE CALITOR CALVAIRE CAMP FABRE CAMP FOUR CAMPS SOLEIL CANAL CANALET CANARI CANET CANNAU CANNEAU CANTONAT CAPITAINE MENARD CAPITAINE MOREAU CAPITAINE ROUCHER CAPITOLE CAPNAU CAPUS CARDINAL CARIGNAN CAROUX CARRE ROI CARROSSE CASINO CASTANOU CASTELLOU CAUSSE CAYRE CAYROU CAZALS CEFI DU CEP VIGNE CERCLE CERS CHAMP D AZUR CHAMP L AIRE CHAMP BLANCHE CHAMP CROIX CHAMP REDON CHANOINE BESSEDE CHANVRE CHAPEAU ROUGE CHAPITRE CHARDONNERET CHARRON CHASSELAS CHATEAU D EAU ROUQUET RIQUET CHELIA POUPOU DAMES MOULINES SALINIER VIEUX CHENE KERMES CHENE LIEGE CHERCHE MIDI CHEVAL BLANC CHEVAL VERT CHEVREFEUILLE CHEVREUIL CHEVRIER CIGALOU CINSAULT CIRQUE CIRQUE MOUREZE CLAIR MATIN CLAIR SOLEIL CLAIRVAL CLAU CLAUD CLOS CLOS L AQUEDUC CLOS CERISAIE CLOS DAME CLOS AIRES CLOS CHENES CLOS HIRONDELLES CLOS PUITS CLOS MARTIN CLOS RENE CLOS CLOS COLLEGE COLLEGE DUVERGIER COLOMBIER COLONEL FABIEN COLONEL MARCHAND COLONEL PAGES COMBALS COMMANDANT MALET COMMANDANT RAYNAL COMMERCE COMPAGNON BLANC COMTAT VENAISSIN COMTE FOIX COMTE MELGUEIL CONCILE CONTROLE COQ COQ ROCHE CORAIL COROSSOL COTEAU COUCHANT COULAZOU COUNIL COURLIS COURNELET COURREAU COURRIER COMPLEMENTAIRE COUSTOULIES COUVENT CROSS CROUZET CURAT CYGNE DABALADOU DAHLIA DAHOMEY DANEMARK DARDAILLON DARDALHON DAUPHINE DELTA RHONE DEPUTE MOLLE DEPUTE SALIS DEVES DEVOIS BARRAL BASTARD CHARCOT LACHAPELLE LOUIS PERRIER MAGNE MARTIN PONS ROUX SERVEL DROIT DUC RENE CASTRIES FANABREGOU FAUBERT FAUBOURG FAUBOURG BONAPARTE FAUBOURG SAUNERIE FAUBOURG NIMES FAUBOURG COURREAU FAUBOURG NORD FAUBOURG FIGUEROLLES FAUBOURG JAUMES FAUBOURG TROUSSEAU FAUCON PELERIN FELIBRE FELIBRIGE LATIN FENOUIL FER CHEVAL FERRAGIL FESC FESQUET FIGARO FIGUIER FOOTBALL FORT CRANS FORUM FOUR FOUR CHAUX FOUR PAIN FOUR NATION FOUR ELOI FOURNIL FOYER FRANC FRERE OLIVE FRONTIGNAN FRONTON GALOUBET GARAJOUS GARIGLIANO GARISSOU GARRIGOU GAZIAN GENERAL BERTHEZENE GENERAL CAMPREDON GENERAL CHANZY GENERAL CLAPAREDE GENERAL GENERAL LINARES GENERAL MONSABERT GENERAL LAMARQUE GENERAL MARGUERITTE GENERAL MATHIEU DUMAS GENERAL MAUREILHAN GENERAL MAURIN GENERAL RENE GENERAL RIU GENERAL VINCENT GENEVRIER GENIE GENIEVRE GEVAUDAN GILET ROUGE GINGEMBRE GIROFLIER GOLFE GOURPE D AGNES CALADOU CAMP CHAMP JARDIN LARGE MESTRE LEDERAC PLANTIER PRADET TETRAS VERGER GRAU GREEN GRENACHE GRES GREZAC GROS OLIVIER GUE GUESCLIN GYMNASE HANGARD JARDIN COLAS JARDIN REINE JARDIN HESPERIDES JARDIN VIOLETTES JARDIN DURAND JARDIN MARTEL JARDIN PUBLIC JARDINS FLEURS JEU JEU BALLON JEU BOULES JEU L ARC JEU MAIL JEU MAIL ABBES JEU PAUME JOKARI JUGE LABECH LABOURAGE LANGUEDOC LANTISSARGUES LASSEDERON LATIUM LAURIER TIN LAVANDIN LAVOIR LEVANT LIERRE LIEUTENANT AUGUSTE RAMES LIEUTENANT MARIUS BAZILLE LIEUTENANT CALS LIMOUSIN LISTAN LITTORAL LLOBREGAT LORIOT LOTISSEMENT PARC LOUP LUMINAIRE LUXEMBOURG LYCIET MAIL MAIRE AUSSENAC MALVOISIE MAQUIS MAQUIS BIR HAKEIM MARATHON MARBELLA MARCHE MARCHE AUX BESTIAUX MARCHE AUX RAISINS 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PLANTE GASTON THOURET GAU GAUCELIN GARDE GAUGER GAUJAL LUDOVIC GAUTING GAVEAU GAY LUSSAC GAYON GELIBERT GENERAL BROSSET GENERAL CABRIE GENERAL CAUSSE GENERAL CROUZAT GENERAL GENERAL GOYS GENERAL DOMERGUE GENERAL DURAND GENERAL DUVAL GENERAL FAIDHERBE GENERAL FONDE GENERAL FRAISSE GENERAL GROLLIER GENERAL HENRIC GENERAL KOENIG GENERAL LAFON GENERAL GENERAL LOUIS FAIDHERBE GENERAL MARES GENERAL MARGUERITTE GENERAL MIQUEL GENERAL MONTBRUN GENERAL PAILHES GENERAL POUGET GENERAL POUGOT GENERAL REYNES GENERAL SIMONNEAU GENERAL THOMIERES GENETS GENETS PLANE GENETS SAUVAGES GENEVRIER GENEVRIERS GENTILLES BASSES GENTILLES HAUTES GEOFFRE GEORGE SAND GEORGES AURIC BARNOYER BAYROU BESSE BIZET BRAQUE BRASSENS BRIQUET CLAUDE CLEMENCEAU COURTELINE CUVIER DANTON DENIZOT DURAND FABRE GUYNEMER GIRVES MANDEL MATHIEU MELIES PICOT POMPIDOU PUJOL RICARD ROUAULT SAND SEURAT THARY VALETTE VAN PARYS GERANIUMS GERARD PHILIPE GERHARDT GERMAIN GERMAIN ENCONTRE GERMAIN FOURNIER GERMAIN GALINIE GERMAIN SARDA 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HOURTALESSIO HOUX HUGENOTS HUGUENOTS HUIT MAI HUIT MAI 1945 HUNE I F JOLIOT CURIE IBERES ICARD ICARE IENA IFS IGNACE BRUNEL IGNACE MALET IGNACE MALLET IGNACE PLEYEL IGOR STRAVINSKI IMPERATRICE IMPERIALE IMPRESSIONNISTES INDUSTRIE INDUSTRIE BOITE POSTALE INDUSTRIE CS INJALBERT INSTITUT INTENDANT BEZON IRENE FREDERIC JOLIOT CURIE IRENEE BAPTISTE IRIS ISAAC AZIMOV ISIDORE GIRARD IZARD J BAKER J DELTEIL J F BRETON J JAURES J P CHABROL JACINTHES JACINTO MORENO JACOBINS JACQUARD JACQUELINE AURIOL JACQUELINE MAILLAN JACQUES ANQUETIL JACQUES ANTOINE MOURGUE JACQUES BALMAT JACQUES BECKER JACQUES BERTRAND JACQUES BEZIAT JACQUES BOE JACQUES BOUNIN JACQUES BREL JACQUES BRIVES JACQUES CARTIER JACQUES COEUR JACQUES COPEAU JACQUES COSTE FLORET JACQUES CROUZET JACQUES D ARAGON JACQUES DRAPARNAUD JACQUES DUCLOS JACQUES DURAND JACQUES ESPRIT JACQUES GABRIEL AZAIS JACQUES FERREOL MAZAS JACQUES GARRIGUES JACQUES IBERT JACQUES LAZERNE JACQUES LOUIS DAVID JACQUES MONOD JACQUES OFFENBACH JACQUES PREVERT 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JUSTICE JUSTIN ALBERT KARL MARX KERMES KLEBER KOCH L AIRE L AMELLO L ANTOINE BOUGAINVILLE L ARNEDE L AYET L AYROLLE L CARNOT L INTENDANT AGUESSEAU PATIO L MICHEL BRUYERE CALADE CHAPELLE CHASSE AUX PAPILLONS CROIX FIGUEROLLES FONTAINE FOURNAQUE LAFAYETTE MEJARIE METAIRIE L OISEAU MONTADE PEROUSE PLANTADE PLEIADE PORTALIERE MASQUES ROQUE SEGNE TOUR VICTOIRE MARNE VIGNERAIE LABADIE LABADOU LABBE LABECH LABECHT LABOURAGE LABOUREURS LABOURNAS LACAN LACAZE DUTHIERS LACOMBE LADOU LAENNEC LAETITIA LAFAYETTE LAFEUILLADE LAFFITE LAFONT PORTAL LAFONTAINE LAGARDE ROLLAND LAGAREL LAGRIFFOUL LAITIER LAKANAL LALLEMAND LALOU LAMAJARIE LAMARCK LAMARRE LAMARTINE LAMASSE LAMBEQUE LAMBERT MAUREL LAMBERT MAUREL PRE MARIN LAMENNAIS LAMER LAMMENAIS LAMPARO LAMPAROS LAMPY LANGLADE LANGUEDOC LANTISSARGUE LANTISSARGUES LAPAROT LAPEROUSE LAPEYRONIE LAPEYRONNIE LAROQUE LAS DAVALADAS LAS GENESTAS LASCOURS LASSEDERON LASSON 1 ER LAURAGUAIS LAURELLE LAURENS LAURENS RAVANEL LAURENT CHABRY LAURENT MOURGUET LAURIER 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LIONS LIRON LIROU LISERON LISERONS LITTRE LO PERDIGAL LO TARRAL LODEVE LOGIS LOIS LOISIRS LOMBARD LONG MURAILLE LONGUE LONGUELANNES LONGUYON LORIOT LORRAINE LOTISSEMENT AMANDIERS LOTISSEMENT ESCANDALAYRAS LOTISSEMENT HERON BLANC LOTISSEMENT PATIOS PEROLS LOTISSEMENT TRAS CASTEL LOU CASTEL LOU FELIBRE LOU GREGAOU LOU PLANAS LOU PRADAS LOU ROUSSEGOU LOU TARIDO LOUIS ABBAL LOUIS ANCELIN LOUIS ARAGON LOUIS ARAGON 12 LOUIS ARCELIN LOUIS ARMAND LOUIS BAGES LOUIS BAISSE LOUIS BARTHOU LOUIS BAUSSET LOUIS BERNARD LOUIS BERTILLON LOUIS BERTRAND LOUIS BLANC LOUIS BLERIOT LOUIS BLERIOT BOITE POSTALE LOUIS BOUCHET LOUIS BOUSCARAIN LOUIS BRAILLE LOUIS BREGUET LOUIS BUFFON LOUIS CAHUZAC LOUIS CAPITAN LOUIS CARAYON LOUIS CAUQUIL LOUIS CAUX LOUIS CHEVALIER LOUIS CLAVEL LOUIS DAGUERRE LOUIS DARDE LOUIS DAUBENTON LOUIS DAVID LOUIS DEJEAN LOUIS DELAGE LOUIS DELLUC LOUIS EMBERGER LOUIS FEUILLADE LOUIS FIGUIER LOUIS FONOLL LOUIS FOURMAUD LOUIS FREDERIC ROUQUETTE LOUIS GASTON SEGUR LOUIS GUY LOUIS HUC LOUIS 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MALAPAGUE MALBEC MALBOURGUET MALESCA MALIRAT MALLET MALPAS MANADE MANADES MANEI MANSOURAH MAQUIS BIRHAKEIM MAQUISARDS MARAT MARBRERIE MARBRIERE MARC ANTOINE MENARD MARC AUGE MARC CHAGALL MARC MOUNIER MARC RIGAL MARCADAL MARCEAU MARCEL ACHARD MARCEL ARNOYE MARCEL AYME MARCEL CACHIN MARCEL CARNE MARCEL CERDAN MARCEL COCHIN MARCEL SERRES MARCEL DELOUPY MARCEL DORET MARCEL ICARD MARCEL JALADE MARCEL MALAFOSSE MARCEL MAUMEJEAN MARCEL MAURY MARCEL PAGNOL MARCEL PAGNOL MALHAUTE MARCEL PAGNOL PERIES MARCEL PALAT MARCEL PALLAT MARCEL MARCEL PREVOST MARCEL PROUST MARCEL RAJMAN MARCEL SEMBAT MARCELIN ALBERT MARCELLE HUC RESISTANT MARCELLE HUC RESISTANTS MARCELLIN ALBERT MARCELLIN BERTHELOT MARCENAC MARCHE MARCHE AUX ANES MARCHE COUVERT MARCHE MURAT MARCO POLO MARE MARECHAL MARECHAL LATTRE TASSIGNY MARECHAL PLANTAVIT MARECHAL FOCH MARECHAL GALLIENI MARECHAL JOFFRE MARECHAL MARECHAL PASSAGE MARECHAL THOMAS BUGEAUD MARESCHAL MARGUERITE MARGUERITE BEATRIX MARGUERITE NAVARRE MARGUERITE LONG MARGUERITE YOURCENAR MARGUERITES MARIA CASARES MARIANNE MARIANNE ROS BOUILLET CAIZERGUES CHRISTINE KEKENBOSCH CURIE MONTPELLIER DURAND MAJOREL MARTIN MARVINGT MULLER MARIN MARIN BLANC MARIO ROUSTAN MARIO ROUSTAN BOITE POSTALE MARIOGE MARIOTTE MARISTE MARIUS ALES MARIUS CARRIEU MARIUS LAUREZ MARIUS MICHEL MARIUS PASCAL ROUSSEL MARIUS PETIPA MARIUS RAZIMBAUD MARMITE MARNE MARQUERIE MARQUEROSE MARQUIS TOURETTE MARQUIS MAURICE MARRIERES MARRONIERS MARS MARSEILLE MARTEL MARTELLE MARTHE AULES MARTIAL MARTIAL CALAS MARTIAL PERRET MARTIN MARTIN CHOISY MARTIN LAGARDE MARTIN LUTHER KING MARTINET MARTINETS MARTINS PECHEURS MARTYRS MARX DORMOY MARYSE BASTIE MAS BASTIAN MAS BRINGAUD MAS L HOSTE MAS L OPPIDUM MAS TOUR MAS PORTALY BOITE POSTALE MAS GENERAL MAS GERVAIS MAS NEUF MAS NEUF BELVEDERE MAS PERRETTE MAS RICHEMONT MAS RICHEMONT VILLA MAS ROUGE MAS MAS BOITE POSTALE MASACY MASCON MASELIERS MASSACAN MASSALOUP MASSANE MASSARGUES MASSE MASSENET MASSILIAN MASSILLON MASSOL MATHIEU MATHIEU LAURENS MATINE MAUBON MAURES MAURICE BELLONTE MAURICE BIRAUD MAURICE BOUSQUET MAURICE CHAUVET MAURICE CLAVEL MAURICE COMBES MAURICE VLAMINCK MAURICE KATIA KRAFFT MAURICE FOL MAURICE JOURDAN MAURICE LAURENS MAURICE LAUTIER MAURICE BOUCHER MAURICE NOGUES MAURICE RAVEL MAURICE RONET MAURICE ROSSI MAURICE SARRAULT MAURICE SARRAUT MAURICE SOL MAURICE THORES MAURICE THOREZ MAURICE UTRILLO MAURY MAUSSE MAX COURRIEU MAX COURRIEU 12 MAX DORMOY MAX JACOB MAX MOUSSERON MAXIMILIEN ROBESPIERRE MAXIMILIEN ROBESPIERRE MAXIMILIEN SULLY MAXIMIN LICCIARDI MAZAGRAN MAZEL MAZELIERE MAZES MAZET MEDECIN COMMANDANT DUMAS MEDECIN GRAUWIN MEDIEVALE MEDITERRANEE MEDUSE MEGEANE MEGERE MEGERIES BASSES MEGERIES HAUTES MEGISSIERS MEGUILLOU MEILLADE MEIRIO DORIAN MEJANE MEJEANE MELETTES MELGUEIL MELIAS MENDES FRANCE MENDES FRANCE 7 MENESTREL MER MERCIER MERCIERE MERCORAN MERCURE MERIGEAUX MERIL POUJADE MERLES MERLOT MERLOT MUSCAT MEROU MERSENNE MESANGE MESANGES MESSIDOR MESSINE METAIRIE LOISEAU METAIRIES METALLURGIE METIERS METZ MEULE MEULIERE MEUNIER MEYERBER MEYRUEIS MICHAEL FARADAY MICHEL ANGE MICHEL BATAILLOU MICHEL CHASLES MICHEL CLERC MICHEL CREPEAU MICHEL LAMBERT MICHEL MONTAIGNE MICHEL MAS MICHEL TEULE MICHEL TROCME MICHEL TROCME ALIZEES MICHEL VERNIERE MICHELET MICOCOULIER MICOCOULIERS MICOUCOULIER MIDI MIEJOUR MILHAU VIDAL MILLENAIRE MIMOSAS MINOS MION MICHEL MIRABEAU MIRABEL MIRAMAR MIREILLE MIREPOIX MISERICORDE MISSION MISTRAL MITON MITTON MOLIERE MOLIERE PORTIRAGNES MOLINIER MOLLE MONDIAL 1998 MONDIAL 98 MONFLEURY MONGE MONOIKOS MONSEIGNEUR LAS CASAS MONSEIGNEUR JARLIN MONT TROIS TERRES MONT PEROUX MONTAGNE MONTAIGNE MONTANTE MONTARNAUD MONTASINOS MONTBEL MONTBRUN MONTCALM MONTE CINTO MONTEE MONTEE MONTEE FEUILHE MONTEE JOLY MONTEE MONTEE BASSINS MONTEE ECOLIERS MONTEE MONTEIL MONTELS MONTELS MONTELS L MONTESQUIEU MONTFERRAND MONTFO MONTGOLFIER MONTIBEL MONTILLE MONTILLES MONTMORENCY MONTPELLIER MONTPELLIERET MONTPEYROUX MONTPLAISIR MONTSEGUR MONUMENT MOQUIN TANDON MORGANE MORILLES MOSSON MOTT LAND MOUETTES MOULIN HAUTE HUILE VENT CIFFRE RIVES SEPTS CANS HUILE SEMALEN VERT MOULINAS MOULINE MOULINS MOULOUNADE MOULOUNADEE MOURADE MOURGUETTES MOZART MUETTE MUGUET MUGUETS MUNUERA MURAILLE MURAILLETTE MURATEL MURELLE MURIER MURIER COMBEJEAN MURIERS MURIES MURS MUSCADET MUSCAT MUSCATS MUSES MUSIQUE MYOSOTIS NARCISSES NATIONALE NAUBINE NAUCELLES NAVARIN NAVERGES NAVIGATEURS NAZARETH NEFLIER NEFLIERS NEGADIS NEGAFOLS NEGROU NEGUE CAT NELSON MANDELA NEPTUNE NEREE NEREIDE NEUVE NEUVE D HORTES NEUVE HORTS NEUVE MARCHES NEUVE GAMBETTA NEY NICEPHORE NIEPCE NICOLAS APPERT BOILEAU CONTE COPERNIC POUSSIN TESLA NICOLE FARRET NIEPCE NINOU NISSOLE NIVOSE NOEL BLASCHER NOEL ANDRE AVERSENQ NOEL FORESTIER NOEL SYLVESTRE NOGAIRET NOGUIER NOILLY NOISETIERS NORBERT CASTERET NORD NORIA NORIAS NORMANDIE NORMANDIE NIEMEN NOTAIRES SANTY NOTRE NOTRE DAME NOTRE DAME D ESPERANCE NOTRE DAME PAIX NOTRE DAME CHAMPS NOUVELLE NOUZERAN CHEVAS NOYERS NOYON NOZERAN NUIT 4 AOUT 1789 OASIS OBSCURE OCCIDENT OCCITANE OCCITANIE OCTAVE MIRBEAU OCTAVIEN BRINGUIER OCTROI OEILLETS OFFENBACH OFFICIERS OISEAUX OLIVERAIE OLIVERAIE BARNIER OLIVERAIE BASTIDES OLIVERAIE OLIVET OLIVETTE OLIVETTE BARDEJEAN OLIVETTES OLIVIER OLIVIER MONTLUCON OLIVIER SERRE OLIVIER MESSIAEN OLIVIERS OLOF PALME OPERA OPPIDUM ORANE DEMAZIS ORB ORCHIDEE ORCHIDEES OREE LAC ORIENT ORION ORJOLS ORMEAU ORMEU ORPHEE ORQUES ORTOLANS OSEILLE OSTALON OULETTES OURGOUILLOUS OURSINS P CARON BEAUMARCHAIS P BERT P COUBERTIN P DENFERT ROCHEREAU P PICASSO P RIMBAUD PABLO CASALS PABLO CAZALS PABLO NERUDA PABLO PICASSO PACIFIQUE PAGES PAGEZY PAILLE BASTIDE PAILLERS PAIX PALADILHE PALAIS ANGLE PALMIERS PALUS PAMPARIO PANICAUTS PAPIN PAQUERETTES PARADIS PARAGE BASSE PARAGE HAUTE PARAMELLE PARC PARC CHENES PARC GARRIGUES PARCS PARDONS PARLIER PARMENTIER PARNASSE PARROS PASCAL PASCAL MARTI PASCAL PIAZZA PASCAL ROUYER PASIPHAE PASSAGE NIVEAU PASSAGE D ESTRECH PASSE VITE PASSEREAUX PASSERELLE PASSERELLES PASSERINES PASSIFLORES PASTENAGUES PASTEUR PASTEUR NIEMOLLER PASTEUR PROLONGEE PASTURALS PATELLES PATRICE LUMUMBA PATTES PATUS ALARY BALMIGERE BARON BERT BONNEFOI BOURGET BOUSQUET BROUSSE CARRA CAUVY CAYROL CERE CEZANNE CHALON CHASSARY CHAVEZ CLAUDEL CODOS CUNY DARDE ROUVILLE DEBIA DELBES DOUMER DUKAS DURAND ELUARD EMILE ENTERIC PAUL FOUGASSIER GAUGUIN GIERA GOGUIN GUERY HENRI VERGNES HEROULT ISOIR JULIEN LACROIX LANGEVIN MARRES MARTIN MAURIAC MOULINIER NOGUIER PAGET PAINLEVE PARAY PELISSON RIMBAUD RIQUET ROCA SABATIER SEJOURNE SERS VAILLANT COUTURIER VALERY VERLAINE VIDAL PAULE TIFFY PAULIN ARNAUD PAULIN PORTAL PAULINE CARTON PAULINE RAMART PAUME PAUMELIERE PAVEE PAVILLON PAVOT PAVOTS PAYS NEUF PEBRIERS PECH PECHEURS PELICAN PELLETAN PENDU PENICHES PENITENS PENITENTS PENSEES PENSEES HORIZON BLEU PENTECOTE PEPEZUT PEPIN PEPINE PEPINIERE PERA FRERES PERANASSE PERAS PERBEN PERCEPTEUR CAUQUIL PERCHE PERDIGAL PERDIGUIER PERDUE PERE J B SALLES PERE BAPTISTE SALLES PERE JOURDAN PERE SOULAS PERET PERET BATTUT PERET BATUT PERIDIER PEROUSE PERRET BATTUT PESSEPLANE PETANQUE PETANQUEURS PETIT ENCONQUE MADRID SOLEIL CALADE COLLINE CENTURIONS FONTVIEILLE JERUSALEM PETITS ESCALIERS PETITS FOURS PETUNIA PEUPLIERS PEYRALS PEYRE PLANTADE PEYRES HUBERT PEYRIERE PEYROU PHARMACIE PHILIPPE CHAPPERT PHILIPPE DAUBY PHILIPPE LAMOUR PHILIPPE LEBON PHILIPPE PINEL PHILIPPY PHOCEENS PIC PIC LOUP PICA TALEN PICADE PICARDAN PICASSO PICHOLINE PICPOUL PICPOUL PINS PIE GRIECHE PIEDMOUTON ASTRIE AUGE BEREGOVOY BERTIN BETORZ BOISSIER BON BONNARD BROSSOLETTE BRUNIQUEL CABANE CABANES CARDENAL CAUSSE CLARIS COCHEREAU CORNEILLE COUDER CROS CURIE D AUVERGNE COUBERTIN MARIVAUX PROVENCE RICULPHE RONSARD VERNOLS DUCHARTRE DURAND AUGUSTE MASSALOUP CURIE FAVIER FAVIER DUMOULIN FERMAUD FLOURENS FRESNAY GIRAUD JALABERT BEDARD BERANGER OLIVE VAILLARD LACANS LAPORTE LAROUSSE LOTI MAGNOL CURIE MARTY MECHAIN MENDES FRANCE MILAN OLIVE PANSIER RIQUET PROUDHON PROVENCE PUGET RENAUDEL ROUANET SEGUIER SEGUY SEMARD SOLOMIAC VALADIER VIALA VIDAL VILLON PIGALE PIGALLE PIGEONNIER PIGNAREL PILES ANTIQUES PILETTES PILLES ANTIQUES PILLETTES PILORY PIN PIN PARASSOL PINEDE PINS PIOCH PIOCH 20 PIOCH BOUCHETE PIOCH BOUTONNET PIOCH LIROU PIOCH OURBATIEU PIOCH OURBATIEU PIOCH OURBATIE PIOCH REDON PIOLET PIOTR TCHAIKOVSKI PISCINE PITCHOUNETS PITOT PITTORESQUE PLACE COMBEJEAN VIEILLE PLACENTIN PLACETTE PLACETTES PLAGE PLAGES PLAGNE PLAINE PLAN PLAN ARBRE PLAN CHAPELLE PLAN L ARBRE PLAN L EUZE PLAN L OUM PLAN CHAPELLE PLAN SAUGES PLAN FOUR PLAN GUIRARD PLAN OUM PLANCHON PLANCOU PLANEL PLANELS PLANOL PLANS PLANTADE PLANTADES PLAT PLATANE PLATANES PLATEAU PLATEFORME PLAUCHE PLEIN SUD PLO PLOMBIERES POESIE POETES POINCARE POIRIERS POITEVINE POMAREDE POMIER LAYRARGUES POMMERAIE POMMIERS POMPE POMPE VIEILLE POMPEI POMPEI HAUTS MARTIN POMPES POMPES PROLONGEES PONANT PONS L HERAULT PONT D EMMA GIGNAC GAZE LAS PEYRES DOURDOU GROSLARD LAVERUNE NOTRE DAME VIEUX PONTEIL PONTIL PONTIL VILLAGE PONTS PONTUS THIARD PORCHE PORCHE COMBEJEAN PORCHE L HOSPICE PORCHE PAGUIGNAN PORCHE PORCHERON PORCHES PORT NOTRE DAME PORTAIL PORTAIL BAS PORTAIL D AMONT PORTAIL SIERRE PORTAIL LAROQUE PORTAIL NAOU PORTAIL NEUF PORTAIL VIEUX PORTAL BLANC PORTALET PORTANEL PORTANELLE PORTE BEZIERS PORTE D ENSERUNE PORTE BEZIERS PORTE L HOPITAL PORTE MONTPELLIER PORTE PEZENAS PORTE CEVENNES PORTE MONTELS PORTE PRADELLES PORTE DOUILLE PORTE ENSERUNE PORTE GAUBERT PORTE MONTPELLIER PORTE NEUVE PORTE OLIVIER PORTE PEROT PORTE GUILHEM PORTE POSEIDON POSTE POSTES POTAROUCH POTERIE POTERNE POTET POTIER POTTIER POUDEROUZES POUDRIERE POUGET POUILHES POUJADE POUJOULA POULO GRASSO POUMPIDOU POUNCHOU POURCAYROL POURTALET POUSSAN POUSTERLE POUSTERNE POUZADOUS PR BALMES BOITE POSTALE PRADE PRADELS PRADET PRADIERS PRAIRIAL PRAIRIES PRALON PRE PRE AUX CLERCS PRE BAN PRE CEZES PRE CHAPITRE PRE LONG PRECHE PRELES PRELLES PRES PRES PRES JACQUES PRESBYTERE PRESIDENT KENNEDY PRESSOIR PREVOST PREVOT D AUGIER PRIMEVERES PRINCE PRINCIPALE PRINCIPALE BOULDOUX PRISON PRIVAT PROF HENRI ROSEAU PROF JOSEPH 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QUENTIN TOUR QUILLE QUINTEFEUILLE R CLAIR R SCHNEIDER RABASSOUS RABELAIS RACINE RAFFANEL RAGEOLLE RAGUSE RAIMON TRENCAVEL RAIMU RAJOL RAMASSIERE RAMBAUD RAMEJAN RAMEL RAMON GRAN RAMON GRAU RAMON LULL RAMPE RAMPE PALISSADE RANCHIN RANQUET REY RAOUL RAOUL BAYOU RAOUL CALAS RAOUL DAUTRY RAOUL DUFY RAOUL FOLLEREAU RAOUX RAPHAEL BESSOU RAPHAEL JALBY RAPIDE RASCASSE RASPAIL RAUZIERE RAVANIERES RAVEL RAVIN RAYMOND ALBALADEJO RAYMOND ARIS RAYMOND BERNADOU RAYMOND CAU RAYMOND CROUZAT RAYMOND TRENCAVEL RAYMOND GAUSSELIN RAYMOND LAMBERT RAYMOND LEFEBVRE RAYMOND NAVES RAYMOND PITET RAYMOND TRENCAVEL RAYMOND VIALA RAYRETE RAZE RAZIMBAUD REALS REBAYRAL REBUFFY RECANTOU RECATOUS RECEVEUR RECEVEUR RIALS RECEVEUR RIEU RECEVEUR REYNARDE RECOLLETS RECOULY REDONDEL REGUS REINE AMELIE RELIN REMISE REMPART REMPARTS REMY BELLEAU RENARD RENARDIERE RENAUDOT RENE ANGLADE RENE BAZIN RENE CAILLE RENE CASSIN RENE CHAR RENE CLAIR RENE DESCARTES RENE DEVIC RENE FILLANG RENE FONCK RENE FOURNIER RENE FRANQUES RENE 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SALASC ROGER SALENGRO ROGER VIEUX ROHAN ROITELETS ROLAND DORGELES ROLAND GARROS ROMAIN ROLLAND ROMAINE ROMAINS ROMANI ROMARIN ROMARINS ROME ROMIEU ROMPUDE ROMPUDES ROMY SCHNEIDER RONDE RONDELET RONSARD RONZIER RONZIER JOLY ROQUE ROQUE SEGUI ROQUE SEGUI STADIUM ROQUEGROSSE ROQUELIEU ROQUETTE ROQUETURIERE ROSA LUXEMBURG ROSE ROSE ROC ROSELIERE ROSELIERES ROSES ROSIERS ROSS ROSSEL ROSSET ROSSIGNOLS ROSSINI ROUANELS ROUBIADE ROUBINE ROUCAN ROUCAS BLANC ROUCAYROL ROUCHER ROUDIL ROUET ROUGAS ROUGE GORGE ROUGET L ISLE ROUGET LISLE ROULADE ROUMANILLE ROUMANISSIERE ROUMYRE ROUQUAIROL ROUQUETTE ROUQUETTE HAUTE ROUSSEL ROUSSELLE ROUSSILLON ROUVIER ROZIER RTE MONTPELLIER RUDYARD KIPLING ROBESPIERRE RUISSEAU RUISSEAU POURRE RULLADOU S DALI SABES SABLAS SABLEDE SABLES SABOTIERS SABOUREL SACHA GUITRY SADI CARNOT SADI CARNOT EDEN ROC SAIGNES ALBAN ALEXANDRE ALEXIS AMANS ANDIU GALINIERE ANDRE ANTOINE ANTONI BARTHELEMY BAUDILE BAUDILLE BAULERY BAUZELY BAUZILLE BENOIT ANIANE BERTHOMIEU BLAISE CERNIN CHRISTOPHE CLAIR CLAUDE CLEOPHAS COME CYR DENIS DIDIER ESPRIT ESTEVE EXUPERY FIRMIN FLORENTIN GENIES GEORGES D ORQUES GEYRENS GREGOIRE GUILHEM GUIRAUD HONORE HUBERT HUBERY JACQUES SAINT CASERNE PUITS JOSEPH JUST LAURENT LEON LOUIS MARC MARTIAL MARTIN MARTIN VIGNES MICHEL MODESTE NAZAIRE SAINT RHEDES TREVISY PRIEST RAVY ROCH SACREMENT SAENS SALVY SATURNIN SEBASTIEN SEVER SIMON VENUSTE VICTOR VICTOR STADIUM VIEL VINCENT VINCENT SAINTE AGATHE SAINTE ANNE SAINTE BARBE SAINTE BRIGITTE SAINTE CATHERINE SAINTE CECILE SAINTE CLAIRE SAINTE CROIX SAINTE FLORENCE SAINTE FOI SAINTE GENEVIEVE SAINTE HELENE SAINTE LUCIE SAINTE MARGUERITE SAINTE MARTHE SAINTE MAXENCE SAINTE MONIQUE SAINTE URSULE SAINTE VERONIQUE SAINTES MARIES SAIX SALABERT SALADELLE SALAMANE SALANGANES SALINIERE SALLELE SALLELES SALOMON COUDERC SALUBERT SALVADOR ALLENDE SALVADOR ALLIENDE SALVADOR DALI SALVADOR MONTAGNE SAMUEL BASSAGET SAMUEL CHAMPLAIN SAMUEL MORSE SANS DEBASSES SANS SOUCI SANSOUIRE SANTO ESTELLO SANTOLINES SANTONS SAPEURS SAPHORAS SARAH BERNHARDT SARCELLES SARDIERE SARRET COUSSERGUES SATURN SATURNE SAUDADIER SAUGE SAUGES SAULES SAUMAILLES SAUNERIE SAUTADOU SAUTE SAUVANES SAUVE SAUVIGNON SAVETIERS SAVIGNAC SAVOIE SAVONNERIE SAVORGNAN BRAZZA SAVOYARD SAVOYE SCHUBERT SCOURTINS SEBASTIEN BOURDON SEBASTIEN VAUBAN SEBASTIEN LENORMAND SEBASTOPOL SEGUIN SENEGONDE SENTIER SEPIOUS SEPT CHENES SEPT CONTOURS SEQUE SERANE SERGE GOUSSEAULT SERGE LIFAR SERGENT BOBILLOT SERGENT FERNAND TOURRIERE SERINGAS SERPENT SERPENTIN SERPENTINE SERPENTINS SERPOLET SERRE SETEREE SEUIL NAUROUZE SEVIGNE SICILE SIDONIE COLETTE SIEYES SIFFLEURS SILENE SIMON SIMON REYNAUD SIMONE BEAUVOIR SIMONE SIGNORET SIMONE WEIL SINSAULT VIGNES GRANDES SIPLET SIRE JOINVILLE SIRENES SIROCCO SISAL SOEURS SOLE SOLEIL COUCHANT SOLEIL LEVANT SOLEILLADES SOLEILLEE SOLEILLERS SOLFERINO SOLITAIRE SOMAIL SOMMEIL SONJA HENIE MONTFERRAND SONNERIE SONYA HENIE SOPHIE GERMAIN SOPHORAS SORBIER SORBIERS SOUBIELLE SOUFRE 2 SOULANE SOURCE SOURCES SOUS AIRES SOUS FENETRES SOUS SOUVENIR FRANCAIS SOUVIELLE SPELEOLOGUES SPORTS SQUARE SQUARE COMBEJEAN ST CLAIR ST EXUPERY ST GENIES ST PRIEST ST VICTOR VILLENEUVE BEZIE STADE STANISLAS DIGEON STE GENEVIEVE STERNES STRASBOURG SUBLEYRAS SUBSTANTION SUFFREN SULLY PRUDHOMME SUPERNOVA SUR PUITS SURCOUF SUREAU SUREAUX SUTRA GERMA SUZANNE BOUISSON SUZANNE LENGLEN SUZANNE VALADON SYCOMORES SYLVA SICARD SYLVAIN MAGNAVAL SYRAH TADORNE TAILLANDERIE TAILLEBOURG TAILLEFER TAMARIN TAMARINS TAMARIS TAMBOUR TAMBOURIN TANDINE TANNERIE TANNEURS TAP TARIO TARTANES TARTARE TASTAVIN LLOPIS TASTEVIN TASTEVINS TATIUS TATTI TAUR TEISSIER TELINES TEMPLE TEMPLE VENUS TENDES BASSES TENDES HAUTES TENNIS TEREBINTHES TERME ROUGE TEROIGNE MERICOURT TERON TERRAL TERRASSE TERRASSES TERRE ROY TERRE ROY TERRE MEGERE TERRE ROUGE TERRE ROUSSE TERRES ROUGES TERRIER TERRISSE TERRISSE PITORRE TETES THALASSA THALES THEODORE AUBANEL THEODORE BOTREL THEODULE RIBOT THEOPHILE GAUTIER THEOPHRASTE RENAUDOT THERESE THERMIDOR THERON THESEE THETIS THIERS THIRONDEL THOMAS THOMAS EDISON THOMAS MANN THOMAS MILLEROT THOMASO ALBINONI THONGUE THUYAS THYDE MONNIER THYM TIBERETTE TIFFIS TILLEULS TINE TIPLE TIQUETONNE TISSIER PONS TISSON TITAN TITUS TIVOLI TOIRAS TOISON D OR TONNELIER TONNELIERS TONY PALAZY TOPAZE TOPINERIE TORIDE TORRENT TOULOUSE TORRENT TOULOUZE TORRENT TOULOUZE TORTE TORTUE TOUAT TOULOUSE LAUTREC TOUR TOUR CONSTANCE TOUR D ENCEINTE TOUR L HORLOGE TOUR MATHIEU TOUR RONDE TOUR TOUR CAVES TOUR HORTES TOUR ROULIERS TOUR BOUTON TOUR TOUR MOUTON TOUR GAYRAUD TOUR HORTES TOUR LAGAREL TOUR NEUVE TOURAINE TOURBELLE TOURELLES TOURETTES TOURNEFORT TOURNEJOLE TOURNEJOLLE TOURNESOL TOURS SALLES TOURTERELLES TOURTOUREL TOURVENTOUSE TOURVILLE TOUSSAINT LOUVERTURE TOUSSAINT MAZEL TOUSSAINT ROUSSY TRAINE TRAISORS TRAITE ROME TRAJAN TRAMONIERE TRAMONTANE TRANCHEE TRANSVERSALE TRAS CASTEL TRAS GLEIZE TRAS MURAILLE TRAS MURS TRAS MAYOUS TRAVERSE TRAVERSETTE TRAVERSIERE TREILLE TREILLES TREIZE TRENCAVEL TRENTE DEUXIEME TRENTE QUATRE TRESOR ROYAL TRESORERIE TRESORIER FRANCE TRESORIERS BOURSE TRIANE TRIANGLE TRIGNAN TRINQUAILLERE TRINQUAT TRIOLET TRIPERIE VIEILLE TRISTAN MONTLAUR TRITONS TRIVALLE TROENES TROIS TROIS GRACES TROIS GRAPPES TROIS HORIZONS TROIS MULETS TROIS PIGEONS TROIS PINS TROIS POINTES TROIS PONTS TROUBADOURS TROUILHES TRUC D ANIS TRUEL TUILERIE TUILERIES TUILIERE TULIPES TURBOT TURENNE TURGOT TYR UGNIS BLANCS ULYSSE ULYSSE VERGNES UPPSALA URANIES URBAIN GROLLIER URBAIN V URGANTE VACQUERIE VAILLANT COUTURIER VAISSIERE VAL LAS SERS VAL OLIVIERS VAL FLEURI VAL VALAT VALAT FOSSE VALAUTRE VALAUTRES VALDAU VALEDEAU VALENTIBUS VALENTIN DUC VALENTIN HAUY VALERY LARBAUD VALESSIE VALETTE VALFERE VALLON VALMY VAN GOGH VANIERES VANNEAU VANNEAUX VANNIERE VARIETES VASCO GAMA VATEL VAUBAN VEDEL VEGA VELASQUEZ VENDANGES VENDEMIAIRE VENDEMIAIRE CS VENDEMIAS VENE VENEL VENELLE FOURNIL VENT DAMES VENTABREN VENTS VENUS VERBOIS VERCINGETORIX VERCORS VERDALE VERDALE 18 VERDALES VERDI VERDIERS VERDINE VERDUN VERGER VERGERS VERGNIAUD VERLAINE VERMOUTHERIES VERNHES VERNHES VILLENEUVE BEZIERS VERNIAUD VERNIERE VERSAILLES VERT VERTE VEYRAC VEZIAN VIALA VIALBRUNE VIALETTES VICTOIRE VICTOR ANTHERIEU VICTOR BALTARD VICTOR BONNARIC VICTOR MAULEON VICTOR GELLY VICTOR GUIRAUD VICTOR HUGO VICTOR LACHAUD VICTOR ROGER VICTOR SCHOELCHER VIEILLE VIEILLE AIGUILLERIE VIEILLE CITADELLE VIEILLE COMMUNE VIEILLE REMPARTS VIEILLE VIEILLE INTENDANCE VIEILLE MAISON VIEILLE POMPE VIEILLE PORTE VIEILLE POSTE VIEILLE TOUR VIEILLE VIEILLES COMMUNES VIENNET VIERGE VIEUX BASSINS VIEUX BOUSQUET VIEUX FOUDRES VIEUX FRENE VIEUX VIEUX VIEUX PRESSOIR VIEUX PUITS VIEUX TEMPLE VIEUX VILLAGE VIGIE VIGNAL VIGNE VIGNERONS VIGNERONS LIBRES VIGNES VIGNES L AIRE VIGNIER VILETTE VILLAR VILLARET JOYEUSE VILLARET JOYEUSE VILLEBOIS MAREUIL VILLEFRANCHE VILLENEUVE VILLENEUVE MAGUELONE VILLENEUVETTE VILLENOUVETTE VILLETTE VINCENT BADIE VINCENT BRIGNOLLES VINCENT D INDY VINCENT SCOTTO VINCENT VAN GOGH VIOLETTES VIOLLET DUC VIORNES VIRAC VIRGINIE FLOURENS VIRGINIE MONTAGNOL VIS VISE VISTOULE VITAL MICHALON VITICULTURE VIVALDI VIVE VIVES VOIE FERREE VOIE ROMAINE VOILES BLANCHES VOILIERS VOLQUES VOLTAIRE VOLVIRE BRAS VOLVIRE BRASSAC VOUTE W CATHERINE BOOTH WALDECK ROUSSEAU WASHINGTON WATTEAU WILLY KLEIN WISIGOTHS WISIGOTS XAVIER DEZEUZE YEHUDI MENUHIN YEUSES YVAN YVAN BECK YVES CALVET YVES MONTAND YVES NAT ZENOBE GRAMME ZEPHIRS ZINNIAS ZOUAVES RUELLE NACELLES RUELLE L ORB RUELLE RUELLE SAINTE RUFFAS RUISSEAU BAYLE SABO SABO ROCADEL SADDE SAILLES ALBAN AMANS MOUNIS AMANT MOUNIS ANDRE ANDRIEU AUBIN HAUT BARTHELEMY BLAISE CLAUDE CRISTOL ETIENNE ISSENSAC FELIX L HERAS SAINT JULES FERRY D AUREILHAN FONTAURY JOSEPH JULIEN JUST MARCEL MARTIAL MARTIN MARTIN D ORB MARTIN CHAMPS MARTIN VIGNES MARTIN SUD MENS MICHEL NAZAIRE NAZAIRE BAS PAUL HAUT SAINT ROCH SERNIN SIMEON VINCENT XIST SAINTE ANNE SAINTE AUBINE SAINTE CATHERINE SAINTE COLOMBE SAINTE CROIX SAINTE GERMAINE SALABERT SALAGOU SALAMOU SALLE J LADOUMEGUE SALLELES SALS SALVAGNAC SALVERGUETTES SAN PARADOU SANSAC SARRAIL SARRAZO SAUJAS SAURINES PEYRE GROSSE SAUSSENAC SAUT L EGUE SAUTS CHAMPS SAUVAJOT SAUVIAC SAVIGNAC BAS SAVIGNAC HAUT SECT AE 120 ROQUE SECT AH 195 BOUBALS SECT BC 276 CLAIRAC SECT D 876 ROUTELLY SECT P 333 MEGES SECTION AB SECTION AP 252 CAUSSE HAUT SECTION B 801 VALQUIERES SECTION C 67 AUTHEZE SEGUINERIE SENAS SENEGRA SENTIER FAIENCE SENTIER GLACIERE SEPT FAUX SERIEGE SERIGNAN SERRE LINOTTES SERROTTES SERVIERES SERVIES SEVIGNAC BAS SEVIRAC SIEGE SOCIAL PLANS BAS SOLEIL MER SORTEILHO SOULAGE SOULAGES SOUMARTRE SOURAS BAS SOURLAN BAS SOUS TRUQUES SOUS L SOUS TIOULLIERES SOUS STATION SNCF SOUS VEILLE SOUSTRE SQ CEZANNE SQ CORTE SQ JACOU SQ MURILLO SQUARE NOBEL SQUARE ANDRE CHENIER SQUARE CEZANNE SQUARE FINALE SQUARE COS SQUARE COUTHON SQUARE CROIX POUMEYRAC SQUARE BONNIER SQUARE CORTE SQUARE COS SQUARE L ENSOULEILLADE SQUARE BABOTE SQUARE BRASUCADE SQUARE LIBERTE SQUARE POSTE SQUARE SURVILLE SQUARE ARBOUSIERS SQUARE COCCINELLES SQUARE LIBELLULES SQUARE PAPILLONS SQUARE BORDES SQUARE 25 JUIN 1944 SQUARE BORDES SQUARE MONUMENT SQUARE MUSCAT SQUARE ROUAT SQUARE EMILE CARRERE SQUARE FONTAINE SQUARE FRAGONARD SQUARE VAILHE SQUARE GASCOGNE SQUARE GEO ANDRE SQUARE AURIC SQUARE FOUREST SQUARE VALLEREY SQUARE GIRONIS SQUARE APOLLINAIRE SQUARE HENRI REGNIER SQUARE FELIX SQUARE LOGNOS SQUARE MATHEU SQUARE SQUARE OTHATS SQUARE JUPITER SQUARE LAURENS RAVANEL SQUARE TRIDENT SQUARE LIONEL TERRAY SQUARE LUCIEN GAUDIN SQUARE LUCIEN BRETON SQUARE SQUARE MARCEL BISCAYE SQUARE MARCEL CERDAN SQUARE MARCEL PAGNOL SQUARE MARCELLIN ALBERT SQUARE MARECHAL FOCH SQUARE MIREPOIX SQUARE MURILLO SQUARE NEPTUNE SQUARE OCCITANIE SQUARE BROSSELETTE SQUARE BROSSOLETTE SQUARE PLAGE SQUARE RENE CRABOS SQUARE RENE DEZ SQUARE ROBERT SCHUMAN SQUARE AUBIN SQUARE FELIX SQUARE SATURNE SQUARE SELMO SQUARE THEODORE AUBANEL SQUARE URANUS SQUARE VINCENT SCOTTO STADE STADE COUQUEIROLLES STATION D EPURATION STATION HYDROMETRIQUE STATION HYDROMETRIQUE CHAU STATION HYDROMETRIQUE STATION HYDROMETRIQUE VILLAGE STATION LEZ STATION POMPAGE SUN SUN CITY TABARKA TAILHOS TAILLEVENT TAMARISSIERE NEUVE TARASSAC TARBOURIECH TARTANES TAUSSAC TENEMENT CONSISTOIRE TERRASSE TERRASSE ALLEES TERRASSE ALLEES D TERRASSE ALLEES TERRASSE TERRASSE D OC TERRASSE GREZAC TERRASSE ALLEES TERRASSES TERRE BLANCHE TERRE L AIRE TERRE ROUGE THERON THEZANEL THOLOMIERS THOS TINTEYNE TIRAQUIOULS TOMBEILLES TORTEILLAN TOUR CATALOGNE TOUR CAVES TOUR HORTES TOUR HOTES TOUR OLIVIERS TOUR PONTS TOUR ROUILIERS TOUR FARGES TOUR HORTES TOUR L ORB TOUR VILLAGE TOUR RONDE TOUR VALERNAU TOUR PONTS TOUR ROULIERS TOURNEZYMONTPELLIER OUEST TR BEZIERS TR CAVES TR BEZIERS TRAMONTANE TRAS TRAS CASTEL TRAVERSE ANTOINE TRAVERSE ARTS TRAVERSE BASSAIRES TRAVERSE BEZIERS TRAVERSE CASERNE TRAVERSE CERS TRAVERSE CHASSEURS TRAVERSE COLOMBIERS TRAVERSE CASERNE TRAVERSE COLOMBIERS TRAVERSE L ARTIMON TRAVERSE BRIGANTINE TRAVERSE COLLINE TRAVERSE GLACIERE TRAVERSE TRINQUETTE TRAVERSE LATTARA TRAVERSE MENS TRAVERSE BARREURS TRAVERSE HIRONDELLES TRAVERSE HUNIERS TRAVERSE JOUTEURS TRAVERSE MUSICIENS TRAVERSE ROBINIERS TRAVERSE ROSSIGNOLS TRAVERSE SAVONNIERS TRAVERSE CARDINAL TRAVERSE CERS TRAVERSE JARDINS TRAVERSE LIROU TRAVERSE TRAVERSE FOSSE TRAVERSE HUNIERS TRAVERSE JARDINS TRAVERSE LAS COSTAS TRAVERSE LISSE PLAN TRAVERSE TRAVERSE D HONORINE TRAVERSE PINSONS TRAVERSE RIGAL TRAVERSE ROC CARLET TRAVERSE NAZAIRE TRAVERSE SAINTE EULALIE TRAVERSE TOUR TRAVERSE TREILLES TREADOU TREILLES BASSES TREIZE VENTS TRIANON TRUSCAS TUILERIE BASSE TUILIERE TURES GITES RURAUX VILARIS ULYSSE USCLATS MILIEU USCLATS BAS USCLATS HAUT USINE HYDROELECTRIQUE TRUSCAS V 4 7 CARRIERA LOU PERDIGAL VABRE HORTS VACARIALS VACQUIERES LAS CAUX VAISSE PLEGADE VAISSIERE VAISSIERES VAL AUX VIGNES VAL D ORB VAL VAL OMBREUSE VALLEE MALIBERT VALQUIERES VALQUIERES OLLIER VALSIERE VARAILLAC VASPLONGUES HAUT VAUGERY VAUGUIERE HAUT VAUGUIERE HAUT MAS COMBET VENDANGEURS VERARGUES VERDIER VERDIER BAS VERDIER HAUT VERDUN VEREILHES VEREILLES VERENOUX VERGNOLES VERGNOLLES VERGOUNIAC VERNAZOUBRES VERTS VESSAS VEZOLES VGE ARTS METIERS RICAZOULS VIALANOVE VIARGUES VICKING VICTOIRE VIEILLE VIEUSSAN HAUT VIEUX VIEUX PORT VIEUX QUARTIERS VIGNES GRANDES VIGNES ROUGES VILARIS VILLA 1 LILAS VILLA 122 BALEARES VILLA 13 CAMI CANTO RANO VILLA 1324 GRES VILLA 2 GRILLONS VILLA 20 TINAL VILLA 23 L ENSOLEIADO VILLA 31 PONANT VILLA ACACIAS GARRIGUES VILLA PINS VILLA AMANDIERS VILLA ANNE CROUZETTE VILLA ATANEE MARTINAIGUES VILLA BEAUSEJOUR VILLA BEL AIR BEL AIR VILLA BLEUE TREILLES HAUTES VILLA BOUISSE MER TAMARISSIERE VILLA BOUYSSE MER TAMARISSIERE VILLA CANOTTES 4 ETOURNEAUX VILLA CAROUBE GIRADOU VILLA CAUX VILLA COOPERATIVE VILLA COSTES VILLA PEYRIERES VILLA CHEVREFEUILLE CLAPS VILLA COCCINELLE VILLA D AGNAC CHAT AGNAC VILLA DESIREE REALS VILLA ESPERANCE VILLA FONTAINE CLAIRE BORIES VILLA GALINETTA 15 CABANES L ARNEL VILLA GALLO ROMAINE VILLA PROCONSUL VILLA L AVENIR VILLA L ESTIVE VILLA L OIKIA VILLA CAMINADE VILLA FRIGOULE VILLA LAUZIERE BASSE VILLA PLANQUE VILLA ROTULE VILLA LAZIOLS VILLA NID VILLA PRINTEMPS VILLA RUISSELET VILLA TRIDENT VILLA LEONTINE TAMARISSIERE VILLA 4 PINS VILLA 4 VENTS BREGINES VILLA ACACIAS GARRIGUES VILLA CIGALES MIREPES VILLA COSTES VILLA GARRIGUES VILLA GENETS PINEDE LAUZE VILLA MURIERS VILLA OLIVIERS CLAPAS VILLA TILLEULS VILLA LOU VILLA LOUIS TAMARISSIERE VILLA LOUISETTE TAMARISSIERE VILLA MARCELLOU VILLA MATOMA GIRADOU VILLA MEDICIS 2 VILLA MY VILLA NUMERO 89 VILLA OASIS CHEV BIZE VILLA PASTOR VILLA REGNAGUE VILLA ROMAINE VILLA ROUTE NEUVE VILLA ROUTE VIOLS VILLA FLANDRES DUNKERQUE VILLA VILLA VILLA RHEDES VILLA SAUVAGERE GARRIGUES VILLA SAUVAGINE PREMIERES CABANES VILLA SERPOLETTE VILLA SYLPHES PINS SAURINE VILLA TAHITI VILLA TOURBILLON ISSANKA VILLA UL PITCH TREILLE VILLA VEDETTES PREMIERES CABANES VILLA VINCENETTE VILLA YUCCAS FARINETTE VILLAGE VILLAGE ARTS METIERS RICAZOULS VILLAGE VIEUX VILLAGE MER VILLAGE DOMAINE COUDOUGNES VILLAGE FONTCAUDE VILLAGE VILLAGE PUECH VILLAGE MARTINET VILLAGE LOTISSEMENT AMELYS VILLAGE NATURISTE VILLAGE QUAI VIEUX VILLAGE ROUTE VACQUIERES VILLAGE VILLECELLE VILLENEUVE VILLENOUVETTE VILLEVIEILLE VINAS VIOLGUES VIOLGUES MILIEU VIOLGUES BAS VIOLGUES HAUT VIRANEL VIVIES VOIE AMPERE VOIE COMMUNALE 11 VOIE COMMUNALE 12 VOIE COMMUNALE 69 VOIE COMMUNALE 9 VOIE DOMITIENNE VOIE EXPRESS EST VOIE OCCITANE VOIE PASTORALE VOIE ROMAINEAdresse ABBAYE ABBAYE BOURG ABEAUZIT ABEILLE ABRENAC ADOUX AGADIS AGE AGE CANTAUD AGE AGE MEILLAUD AGE PEYRAMONT AGES AGNOT AGRIERS AIGUE MORTE AIGUEPERSE AIGUILLONS AIRAIN AIXETTE AJAUX LOGE ALEUX ABBE DESGRANGES ADRIENNE BOLLAND AGE ALBERT RIBIERE LEROUX AN 2000 ANATOLE FRANCE ANDERSEN ANDRE BRETON ANDRE MALRAUX ANDRE MAUROIS ANNIE FRATELLINI ANTONIN MAGNE ARBRE SANS BRANCHE ARBRE SANS BRANCHES ARISTIDE BERGES ARTHUR ZORNIN ARTIMON ARTOIS ATHALIE AUBEPINES BARBARA B BORN BEAUPRE BELVEDERE BERNARD COURTOIS BERTHE MORISOT BERTRAND BORN BICHE BLERIOT BLEUETS BOILEAU ROBIN BORIS VIAN BOS COTE BOSTS NOUSSAT BOULEAUX BREUIL BUGEARAS BUIS CAMILLE DESMOULINS CAPUCINES CARDERIES CELLE CERISIERS CEZANNE CHABOURDE CHAISEMARTIN CHALET L AGE CHANTEGRELE CHANTEGREU CHANTEGRIVE CHANTEMERLE CHANTEPIE BOURSEUL DULLIN MOREAU RICHET CHARRIERS CHATAIGNERAIES CHATAIGNERS TEYTEJEAUD CHATAIGNIERS CHATEAUBRIAND D EAU CHATENET CHATRE CHATRE BOUCHERANNE CHENES CHENES CHABROULIE CHENES CHABROULIE CHENES PEURY CHENES SENON CHENEVIERE THEILLOL CHEYROUX CICERON CIREIGEAUX CLAREUIL CLAUDE DAUPHIN CLEDIER COCTEAU COLBERT COLETTE COLLEGE COLOMBIER COMBE CONDILLAC COQUELICOTS CORNUE COROT CROIX CHARRIERS CUBES CYPRES D AQUITAINE D ARDENNES D AUVERGNE BABAUDUS BEAUMONT BEAUPRE BIENNAC BIGORRE BOURDOULET BOURGOGNE BRETAGNE CHAMPAGNE CHARLOTTE CHATEAUBRUN CHATIVAUX CHEZ FRUGIER CHEZ MONDIE CHEZ THARAUD COURBIAT COYOL FONTBESSE GRINJOLLES BASSE PLAGNE BERGERE BERGERIE BREGERE BRIANCE CAMARGUE CHABASSERIE CHABAUDIE CHAPELLE CORNUE CROISETTE CROIX FER FAYE FONTAINE GARDE MARNE MORELIE L AN 2000 JAURIE PORCELAINE PORTE RIBIERE ROSELLE BASSE HAUTE LASDOUMINGEAS TERRASSE L ESTEREL L HIPPODROME MAISON ROUGE MASMONEDE MAZERMAUD MAZEROLLAS MONTAZEAU MONTPLAISIR MORPIENAS MURIOL NAVARRE NORMANDIE PALALAUD POMMET POMPADOUR QUERCY ROQUEBRUNE ROUMAGNAC ACACIAS SAINTONGE TROPEZ ALBATROS ALOUETTES ALPES AUBEPINES BERGERONNETTES BLEUETS BOULEAUX BOUVREUILS BRUYERES BUCOLIQUES CALANQUES CAPUCINES CARRIERES CEDRES CERISIERS CHAMPS CHARDONNERETS CHATAIGNIERS CHENES CHEVAUX CHEVREUILS COQUELICOTS CORMORANS CYPRES CYTISES EGLANTIERS ERABLES ETANGS FAUVETTES FAYES FLEURS FRENES GENETS GENEVRIERS GEORGIQUES GERANIUMS GRANGES HETRES HETRES MALEFONT HIRONDELLES IFS IRIS JARDINS LORIOTS MARRONNIERS MARTIGUES MAURES MAZEAUX MERLES MESANGES MYCENES MYRTILLES NARCISSES NOISETIERS PAQUERETTES PENSEES PETITES CHAUMES PEUPLIERS PINSONS PLATANES POMMIERS PRIMEVERES RIVAILLES NEUVILLAS ROITELETS ROSEAUX ROSSIGNOLS ROUGES GORGES SAULES SUREAUX TAUBAYES TOURTERELLES VERGNES VIOLETTES VILLEFELIX WATTIGNIES DIGITALES D IMBOURDEIX D IRLANDE D ITHAQUE DROUILLE BLANCHE DROUILLE NOIRE 19 MARS 1962 BAS FARGEAS BEARN BELLAY BELVEDERE BUISSON CARMEL CHANOINE FAYAUD CHAZAUD CHEYRAUD CLOS FLEURI COLIBRI COLISEE COLONEL LEDOT COTEAU COYOL DAUPHINE FER CHEVAL GAMAGNAC GINKGO MALLEVIALLE GOLF LANGUEDOC LIMOUSIN MAINE MARECHAL FAYOLLE MARECHAL FOCH MARECHAL LIAUTEY MARECHAL LYAUTEY MAUZELET GAILLARDIE PINARD MUGUET DUNOYER SEGONZAC PACAGE MILIEU PARC PRE GAYAUD PUITS PUY PUY ANDRAUD PUY ROI PUY LAS HAUTAS PUY MAROT PUY MAURY PUY REYJAUD ROUSSILLON SEQUOIA STADE TULIPIER VAL BRIANCE VERDON VINCOU BLED ECUREUILS EDME MARIOTTE EDOUARD HERRIOT EDWIGE FEUILLERE EGLANTIERS EMILE CLAPEYRON EMILE KAHN ERABLES ERASME ESSARTS ETANG BALUZE EUGENE SENAMAUD FABRE D EGLANTINE FAUGERAS FAUVETTES FEDERICO GARCIA LORCA FELIBRES FILATURE FLEURS FONTAINE FONTAINE NEUVES FONTS NEUVES FORET FORGERONS FRAGONARD FRANCINES FAUCHER MAURIAC RICHARD VILLON FRANCS FROMENTIN GABRIEL LIPPMANN GALVANI GENERAL BROSSET GENETS GEORGE SAND BERNANOS DANTON VERYNAUD GERARD PHILIPE GERMAIN PILON GIRAUDOUX GLAIEULE REPAIRE ROYERE GRANDS CHENES GRANDS JARDINS GRANGE BOEIL GUERINIERE GUYNEMER HAUTS FAUGERAS HELENE BOUCHER HENRI ANDRE LAFAYE HENRIETTE HENRI LAGRANGE HENRI MOISSAN HERNANI HERTZ HONORE BALZAC ITALIE JACQUELINE COCHRAN BELLEGARDE JACQUES CALLOT JACQUES MONTAGNE JAUFRE RUDEL BAPTISTE GREUZE BAPTISTE RAMPIGNON BLANZAT BOUIN CLAUDE SAVARY DEMARS FOUQUET POULAIN PERRIN REBIER ROBIC SEGUREL JEMMAPES JEROME THARAUD J M AMEDEE PAROUTAUD JOCELYN JOCONDE JONQUILLES JOSE MARIA HEREDIA JOSEPH GUILLEMOT JOSEPHINE BAKER JOULE JULES ROMAIN BRUYERE FAYETTE FRANCIADE HENRIADE LAMARTINE LANDE QUINTINIE LARGILLIERE LAS GERSAS LATTERIE LAVAUD LAVOIR GRECO LEO LAGRANGE LEONARD DAURIAT LEONARD REGNIER LEON BLUM LEON DELHOUME MEDITATIONS TITIEN LILAS LOISIRS LOUIS ARAGON LOUIS CHAREYRE LOUIS FUNES LOUIS JOUVET LOUIS LEPRINCE RINGUET LOUISON BOBET LOUIS PERRIER LOUIS RENAULT MAISONNEUVE MANET MANSART MARCABUR MARCEL ACHARD MARCEL CERDAN MARCEL JOUHANDEAU MARCEL PAGNOL MARCEL PROUST MARECHAL FRANCHET D ESPEREY MARECHAL LIAUTEY MARGUERITE GENES MARGUERITES MARRONNIERS PEURY MARTIN PECHEUR MARYSE BASTIE MASBATIN MATHIEU MOREL MAURICE BARRES MAURICE CABET MAURICE FALCONNET MEDITATIONS MESANGES MILLET MIREILLE MISTINGUETT MOLIERE MONTMERY ROYERE MOULINS MOULOUDJI MUGUET MURET MURILLO VAUQUELIN NIOLLET NOISETIERS NOISETIERS MORTEROLLES NOYER OCTAVE BARIANT ORMES PABLO CASALS PARC CEZANNE CHARBONNIER ELUARD PORTIER ROIFFE SABATIER VATINE PEIRE CARDENAL PENELOPE PENSEES PERE NEGRIN PERGAUD BERTHUSSIE FREGEFOND MAINEAUTOUR PETIT ROYERE VERGER P LOUIS PLANTELIGNE PEUPLIERS PHILIPPE CHAMPAIGNE PICART DOUX BLANCHAR MIGNARD OVIGNY PAPILLAUD PINS PLAISANCE BAS RIBIERE PLAISANCE HAUT RIBIERE PLANTAGENET PLATANES PLIERE POITOU POMMIERS POMMIERS BOURG SOUPIRS POUSSIN PRADES PRE L AGE PRES FLEURIS PRES VERTS PRIAM PRIMEVERE PRIMEVERES PROVISEUR STORCK PRUNIERS SENON PUY PUY CHATENET PUY MONTEIL PUY GAUTHIER PUYMAURY PYTHAGORE RACINE RAOUL FOLLEREAU RAOUL PONCHON RENE CASSIN THIAS RENTIER RIVAILLE ROBERT CHAPATTE ROBERT FRANCOLIN ROBERT MARGERIT ROBERT SCHMIDT ROCHE ROITELETS ROLAND GARROS ROMAIN ROLLAND RONSARD ROSA BONHEUR ROSEAUX ROSSIGNOLS ROSSINI RUISSEAU EXUPERY JOHN PERSE MAIXENT SALAMMBO SAPINS SAPINS PEURY SAULES SAUTY CHALON ALLEES L ILIADE ALLEES L ODYSSEE ALLEES MONTBRUN SEURAT SOLEIL LEVANT SORBIERS SUZANNE VALADON TAMAROU MONTEIL TELEMAQUE THEIL THEODORE GERICAULT THUYAS TILLEUL TILLEULS TOURTERELLES TRAMONT TRICART LEONARD TROUBADOURS TULIPES TUQUET TUYAS UTRILLO VAL BRIANCE PLANCHE VAN DYCK VAN LOO VAUZELLE VEDRENNE VERANDAS SENON VERONESE VICTOR GRIGNARD VIGIE VIGNE VIGNERIE VIGNES BLANCHES VILLAGORY VILLARDS VOLTAIRE WEBER XAVIER BICHAT ALLIER ALLOIS ALLOIS 14 FORET ALLOIS FORET ALOUETTES AMBET AMBOIRAS ANCIENNE BRUMAS ANCIEN PRESBYTERE BOURG ANGELARD ANGLARD ANTEBES ANVAUD ANVEAU ANVERS APPEAUX ARBRE FAUX ARBUSSEAU ARCHAYAUX ARCHES ARCOULANT ARDAILLIER ARDANT ARDENNES ARFEUIL ARFEUILLE ARGIROLAS ARLIQUET ARMAMTIOUX ARMANTIOUX ARMENTIOUX ARNAC ARS ARSAC ARTHOUT ARTHUGERAS ARTIGEAS ARTIGE VIEILLE PUY ARTIN ARTINSEC ARTRAT ARVAUX ASAC AUBEPEYRE AU BERNARD AUBEYPIE AU BRAGART AUBROCHE AUGERAS AUGERAUD AUGERE AUGERES AUGES AUGUSTINS AULIERES AULIERES BOS AULIETTE AULLIAT AU LUNADE AUMONERIE AU MOUSSEAUX AUPHELLE AU POUX AU PUEIX AURIERAS AURIN AU TEMPLE AUX BREGERES AUX GRANDES LIGNES AUX LOGES AUX PRADELLES AUX PRADILLES AUZIAT AUZILLAC AVENT 10 JUIN 10 JUIN 1944 11 NOVEMBRE 11 NOVEMBRE 1918 14 JUILLET 19 MARS 1962 8 MAI 8 MAI 1945 8 MAI 45 ABATTOIR ADRIEN PRESSEMANE ADRIEN TARRADE ALBERT THOMAS ALSACE ALSACE LORRAINE AMEDEE DESERCES AMEDEE TARRADE ANATOLE FRANCE ANDRE BESSE ANDRE DESMOULINS ARISTIDE BRIAND ARMAND MARCEAU ARMAND MARSAUD ARSONVAL BAGATELLE BAUDIN BEAUSEJOUR BEL AIR BELLAC BELLEVUE BELVEDERE BERGERE BERTHELOT GILLES BONNEVAL PACHA BRUGEAUD CARNOT CASERNES CHABANNES CHAPELLES BAUDELAIRE CHARLES FERRAND FERRANT CHATAIGNIERS CHATEAUGAILLARD CHENE VERT CHEZ MAILLARD MORTEROLLES COLLEVILLE CONFOLENS COROT CROIX FINOR D ANJOU D ARMAGNAC D ARSONVAL BEAUBREUIL BEAUNE BIENNAC BRACHAUD CLERMONT COUSSAC DRUSENHEIM FONTGEAUDRANT GASCOGNE GLANDON JURIOL L ABATTOIR CROIX LIEUE L AEROPORT LIBERATION LIBERTE MADONE MAZELLE LANDOUGE REPUBLIQUE REVOLUTION TELEVISION VIENNE WANTZENEAU LIMOGES LOCARNO LOUYAT MALASSERT MERIGNAC NAUGEAT DENIS PAPIN NORMANDIE PERIGUEUX PLAISANCE PRECOIN PROVENCE ACACIAS SAVOIE BASSES VIGNES BAYLES BENEDICTINS CARS CASSEAUX CHATAIGNIERS COURRIERES COUTURES ECOLES GREVES LILAS PAQUERETTES PERRIERES RIVAILLES RUCHOUX SINGUELLES D ESTER D ESTIENNE D ORVES VANNIERS VILLAS VITAILLES DESCUBES D ORADOUR SUR GLANE 19 MARS 1962 BUISSON CHAMP MARS CHATELARD CHEYRAUD DAUPHINE GENERAL GENERAL DEVERINE GENERAL GENERAL MARTIAL VALIN GENERAL RENE CHAMBE MARECHAL FOCH MARECHAL JUIN MIDI PALAIS SUR VIENNE PARC PONTEIX PRESIDENT CARNOT PRESIDENT JOHN KENNEDY PRESIDENT LEON BLUM PRESIDENT RAMADIER PRESIDENT RENE COTY PRESIDENT VINCENT AURIOL PRESIDENT WILSON PROFESSEUR MARCEL FAURE ROUSSILLON SABLARD STADE ELISEE RECLUS EMILE LABUSSIERE EMILE ZOLA EMILE ZOLA CANNE EMILE ZOLA CASSEPIERRE ERABLES ERNEST RUBEN FERDINAND MENU FOCH FOUCAUD JOSNIERE MITTERRAND ROBERT FREDERIC PLAY GABRIEL PERI GAMBETTA GARIBALDI GARTEMPE GENERAL GENERAL DESVERINES GENERAL DESVERRINES GENERAL DEVERRINE GEORGE SAND BRASSENS CLEMENCEAU DUMAS POMPIDOU SAND G VALENTIN LEMOINE GIUSEPPE GARIBALDI GRODNO GUGLIELMO MARCONI GUTENBERG GUY MAUPASSANT HENRI BARBUSSE HENRI DUNANT HENRI IV HONORE ARNOUL HORTENSIAS HOTEL IN MAY 8 TH 1945 IOURI GAGARINE JACQUES BREL JACQUES PREVERT BAPTISTE BETOUT BAPTISTE COROT BAPTISTE POUTARAUD COCTEAU CRUVEILHER GAGNANT GIRAUDOUX JAURES LOUIS PAGUENAUD JEANNE PICHENAUD REBIER TIMBAUD ZAY JOLIOT CURIE JOSNIERE JULES COURIVAUD JULES FERRY JULES FRAISSEIX LAC FONTAINE LAMARTINE LANDOUGE PERELLE LEO LAGRANGE LEONARD LIMOSIN LEON BETOULLE LEON BLUM LEON DUNAUD LEONTINE VIGNERIE LIBERATION LIGURES LIMOGES LIMOGES BOURG LIMOGES POULOUZAT LORRAINE LOUIS PASTEUR LOUIS RICOU LOUIS RICOUX LUCIEN FAURE LUCIEN LIORET MAISON ROUGE MAL LATTRE TASSIGNY MARCEAU MARCEL PAGNOL MARECHAL LATTRE TASSIGNY MARECHAL FOCH MARECHAL LATTRE TASSIGNY MARTIAL VALIN MARTIN LUTHER KING MARYSE BASTIE MAXIME LETOURNEUR MAZELLE MICHEL SINIBALDI ROUTE STADE MIDI MONISME MONTJOVIS OBERNAI PAIX PARC PASTEUR LANGEVIN VAILLANT COUTURIER PEYRADE PICASSO BROSSOLETTE GUILLOT TRAVERSAT VERGNIAUD PINGAULT PINS PLAN D EAU POINT JOUR PONT PEYRAT PRAIRIE NEUF PONTY PRESIDENT POMPIDOU PRESIDENT POMPIDOU PRESIDENT VINCENT AURIOL PROGRES PROMENADE RAYMOND POINCARE RENE COTY RENE GOURINCHAS REPUBLIQUE RHIN DANUBE RICHARD COEUR LION ROLAND DORGELES ROMAIN ROLLAND ROSA LUXEMBOURG ROSA LUXEMBURG ROSES ROUGET L ISLE SADI CARNOT SAGNAT AMAND ANNE SAINTE ANNE SAINTE CLAIRE DEVILLE ELOI SURIN THEAU SELTZ SINIBALDI SOLEIL LEVANT SOUFFLENHEIM SOUFFLENHIN STADE TARDOIRE TELEVISION TILLEULS TOULOUSE TOUR TUILERIE TURGOT VAUDIEUX VELASQUEZ VENTENAS VENTENAT VERDUN VERGNE VERSAILLES VIADUC VICTOR HUGO VICTOR ROCHE VIENNE VIGNES VOLTAIRE WINSTON CHURCHILL AYAUX AYRES BABAUD BABAUDUS BABINERIE BACHEIRAT BACHELLERIE BACHELLERIE MIDI BACONIE BADIE BAGATELLE BAGENGE BAGENGETTE BAGNAC BAGNARD BAGNOL BAGOULAS BAGROS BAILLANT BAILLOT BAISSES BALABEAU BALANDEIX BALANDIE BALENDEIX BALEZY BALEZY CHABROULIE BALLANGEAS BALLANGES BALLERAND BALLOT BALOT BAMBOURNET BANAUD BANCAUD BANCHARAUD BANECHE BANEIX BANNAUD BANNE BANNEIX BANNES BANTARD BAR BARABANT BARASSAT BARAUDERIE BARAUGOUT BARBAROUX BARBE BARBE SECHE BARBOEUF BARDAUD BAREILLE BARGEAS BARIGNAUD BARIS LUSSAC BARLETTES BARNAGAUD BARNIQUE BARNOUILLE BARONNEAU BARRAGE L ARTIGE BARRE BARRE JUNIAT BARRE MASVIGNAN BARRE DEFFANT BARRES BARRES D AMBOIRAS BARRIERES BARRYS BART BAS BASSAGNE BAS BEAUMONT BASBONNAUD BAS BOSFRAND BAS BREUIL BAS CAILLAUD BAS BASCHAUDS BAS COURTIOUX BAS DIEULIDOU BAS FELIX BAS FUT BAS GENETS BAS JAILLOU BAS LEYCURAS BAS MERIGNAC BAS MONTEIL BAS MONT GENIE BAS MOULARD BASMOULIN BAS PANLA BAS POUYAUD BAS PUY MONT BAS RAYNAUD BASSE BARRIERE BAS SEBIOUX BASSE BROUSSE BASSE COTE BASSE FORET BASSE GARDE BASSE PLAGNE BASSE POMELIE BASSE ROCHE BASSES COMBES COMBES BASSES MAISONS BASSE TENAILLE BAS SIVERGNAS BAS SIVERGNAT BASSOLEIL BAS TAUVERAT BAS TEXON BAS THOVEYRAT BAS TOUR BAS VAUZELAS BAS VERTHAMONT BATAILLE BATAILLES BATAILLOU BATD 2 JOLIBOIS BATIDE BATIDES BATILLES BATIMENT BATIMENT BOURG BATIMENT BRUTINES BAS 53 BRUTINES BATIMENT VIEUX HUREAU CLOUX BATIMENT MEZIERES BATIMENT NEUF BATIMENT POUL CHATAIGNERAIE BATIMENTS NORD BATIS BATISSOU BAUBIAT BAUDROUZE BAUDY BAUMAR BAURIJAUX BAUTARIAS BAUX BAY BAYERIE BAYLES BAZENAGRE BAZENANT BAZINE BEAUBATOU BEAUBEYROT BEAUBIAT BEAUBLANC BEAUBONNAT BEAUBOURG BEAU BOURNA BEAUBREUIL BEAUCHABROL BEAUCHAMP BEAUFET BEAUGACHE BEAUGUET BEAUJOUR BEAULIAU BEAULIEU BEAULOUX BEAUMANOIR BEAUMONT BEAUNE MINES BEAUNE MINES GOUPILLOUX BEAUNE MINES HAUTES BEAUNE MINES LAURIERE BEAUNE MINES PLAISANCE BEAU PLAT BEAUREGARD BEAUREGARD 2 CHEVAILLES BEAUREGARD BEAUREIL BEAUREPAS BEAUSEJOUR BEAUSEJOUR BESSE BEAUSOLEIL BEAUTALET BEAUVAIS BEAUVALET BEAUVALLET BEAUVERT BEAUX GENETS BECHADERGUE BECHADIE BECHEGUT BECHEMOURE BEDOIREIX BEGOGNE BEIGE BEILHAC BEIREIX BEISSAT BEL AIR BELAIR BELLABRE BELLAUDIE BELLAY BELLE CRETE BELLEGARDE BELLEIX BELLEMENIE BELLEPRADE BELLETERIE BELLEVUE BELLEVUE CINTRAT BELLEVUE VALADAR BELLEVUE VIGNES BELNESSE BAS BELZANNES BENECHIE BENEIX BENIZOU BERBERIDE BERCEAU BERGERIE BERGINERIE BERGNES BERGUERIE BERLANDE BERLOTS BERNARD BERNARDIE BERNEZE BERSAC BERTHE BERTHIES BERTHUSSIE BERTRANDES BESSADE BESSAGERIE BESSAGUET BESSAILLES BESSE BESSE BAS BESSE HAUT BESSEREIX BESSES BESSIERES BESSILLAC BESSOUILLES BESSOULADE BESUNIERAS BETHE BETINAIS BETOULET BETOULLE BETOULLES BETOULLES CROIX BETOUR BEYLIE BEYNAT BEYRAND BEYTOUR BEZASSADE BEZAUD BEZEAU BEZEAUD BEZENAS BIARD BIARDS BIARNEIX BIART BICHEUIL BIENNAC BIEZ BIGOURIES BIOSSAC BISCHE BLADS BLANCHE BLANCHE EPINE BLANCHER BLANCHETS BLANZAC BLANZAT BLEMONT BLODEAU BOCAGE BOHEME BOIGES BOIGES NORD BOIGILLES BOIJOUX BOINES BOIS AU BOEUF BADARAUD BARRAT BERTRAND BOURRU ALLOIS CHABROL CHAREIX CHARREIX CHENU CHEVILLAT D ANGLARD D ARDENNES BELLY DIANE DEDUIT FAREBOUT FORIE L AGE LAGE GENTE ROCHE LAVAUD VIGNE LESSINES L ETANG LIGNAC MAUD MAURY MAUVAIS D EN HAUT GARONNE NIVET ROSES CLOS LAC LORIOT MONT RECLOUX BOISEJOU GARNAUD GOURDON BOISJEUNE BOISJOUT LABROUSSE LACOMBE LAGRESSE L ANAUD ALLOIS RIBIERE LAROUSSIE LAVAUD MALDENT MALLET BOISMANDE MARTIN BOURG MEUNIER MORT NOUVEAU PRIEUR RAYNAUD RIGAUD BOISSAC BOISSARDE BOISSE BOISSIERE BOISSONNIE BOISSOUR BOISSOURNET VICOMTE VICOMTE NORD BOISVIEUX BOIVERT BOLANGE BOLEIX BOLEZAT BOLZAT BOMATHE BON ABRI BONABRY BONHOMME L OMBRE BONNEFOND BONNEFONT BONNESSEIX BONNESSET BONNEUIL BONNEVAL BORD BORDAS BORDAS NORD BORD LAC BORDE BORDELEIX BORDERIE BORDERIE GAUDINOTTES BORDERIES BORDES BORDES OUEST BORDESOULLE BORDESSOULE BORDESSOULLE BORDIER BORIE BOSCARTUS BOSFRANC BOSGILLER BOSGILLIER BOSMARECHE BOSMATHE BOSMIE BOSPERTUS BOSQUET BOSREDON BOST BOST LOUP BOST LAS MANGEAS BOST LAS MONGEAS BOSTRICHARD BOSVIEUX BOTEAUX BOTTIERE BOUBAS BOUBEAU BOUBON BOUBRAUD BOUCARET BOUCHAILLOU BOUCHAUD BOUCHEFAROL BOUCHERIE BOUCHERON BOUCHET BOUCHETTES BOUDONIE BOUEGES BOUET BOUIGE BLANCHE BOUIGES BOUJONNIERE BOULANDIE BOULE D OR BOULESSIE 8 MAI 8 MAI 1945 ADRIEN PRESSEMANE AIGUES VIVES ALBERT CALMETTE ALBERT CAMUS AYGUES VIVES BEL AIR CARNOT BEAUBLANC FLEURUS BORIE LA CORDERIE CORNICHE GLANE REPUBLIQUE L EUROPE L HOTEL ARCADES LILAS PETITS CARMES VANTEAUX MAS BOUYOL MAS NEUF VIGENAL EMILE ZOLA GAMBETTA GAY LUSSAC CLEMENCEAU PERIN GROSILLE HENRI BARBUSSE I F JOLIOT CURIE JULES GUESDE KARL MARX GROSILLE LENINE LIBERATION LOUIS BLANC MARCEL CACHIN MARCEL ROUX NEIGE PASTEUR PYRENEES RHIN DANUBE ROBERT SCHUMAN ROCK LOUIS MAURICE TANNERIES TOUR D AYEN VAUBAN VICTOR HUGO BOUMIER BOUQUET BOURASSAT BOURCEVET BOURDAILLE BOURDELAS BOURDELAS L AMBROISIE BOURDELLE BOURDELLIERES BOURDIEU BOURDOULET BOURG BOURG 4 BEL AIR BOURGADE BOURG CHAMP FOIRE BOURG D AUREIL BOURG CLOS BOURG MAS BARETTE BOURG MAS BARRETTE BOURG PINS BOURG LOTISSEMENT BOURG BOURG LOTISSEMENT BOURGONNIE BOURG WANTZENEAU BOURG ROUTE LADIGNAC BOURG ROUTE TREMOUILLE BOURG ROUTE MOISSAC BOURG ROUTE ALLOIS BOURG ROUTE SEGUR BOURG ROUTE D EYJEAUX BOURISSOUS BOURNAT BOURNAZAUD BOURNAZEAU BOURNET BOURRISSOU BOURSE BLANCHE BOUSSAC BOUSSELAS BOUSSELAT BOUSSEROUX BOUSSIGNAC BOUTICARI BOUTINOTIERE BOUTONNES BOUYERIE BRACONNERIE BRACOURT BRAGARD BRAMAUD BRAMEFAN BRAMEFANT BRAMEFORT BRANGERIE BRANLEMAR BREGEAT BREGERAS BREGERAS FONTAINES BREGERAS FONTAINES BREGERAS BREGERE BREGERES BREGEROUX BREIX BREJOUX BRELAUDIE BRENAC BRET BRETAGNE BRETEIX BRETET BRETHET LATOUR BRETONNIERE BREUIL BREUIL GORRE BREUIL FERRAND BREUILH BREUIL LAVERGNE BREUILLE BREUIL PELICIER BRIANCES BRIANSOLLE BRIDIER BRIE BRIGADE BRIGNAC BRINDE BRISSETTE BRONZEAU BROSSAS BROSSES BROUILHET BROUILLET BROULIE BROUSSAS BROUSSE BROUSSELY BROUSSES BROUX BRUDIEUX BRUGEASSOU BRUGEAUX BRUGERAS BRUGERES BRUGERIE BRUGERIE NORD BRUGES BRULADES BRULES BRULHATOUR BRUMAS BRUNERIE BRUNETERIE BRUNIE BRUTINES BUAS BUCHEUIL BUCHILLE BUFFANGEAS BUFFENGEAS BUIS BUISSON BUISSON NEUVILLAS BUJALENA BUJALENAS BUREAU BURG BURGNASSOU BURGT BURJADE BUSSAC BUSSEROLES BUSSEROLLE BUSSEROLLES BUSSIERE BUSSIERE AUPIGNY BUSSIERE ETABLE BUSSIERE HAUTE BUSSIERE MONTBRUN BUSSIERE RAPY BUSSIERES BUSSIGNE BUSSIGNET BUSSIN BUSSY BUSSY VARACHE BUTTE AUX CAILLES BUTTE CHAMP BUTTES CHAMPS BUXEROLE BUXEROLLE BUXEROLLES BUXEROLLES BUXEROLLES GOUTAILLES BUZATIERE CABANES CADILLAT CADOPHIES CAILLAUD CAILLAUDOU CAILLE CAILLOU BLANC CALINE CAMPALAUD CAMP CESAR CANARD CANNE CANTIES CANTINES CARCAGNOLLE CARCANIOLLE CARDERIES CARDINAL CARPARIE CARREFOUR BEAUPEYRAT CARREFOUR HAUT GANDELOUP CARREFOUR TANTAULIE CARRIERE ROUTE GARDE CARRIERES CASINE CASSEPIERRE CASSEPIERRE MONTIGNAC CASSEPIERRE SAVOIE CATABEAUNY CATABONY CATALA CATHERAUD CAURE BOST CAUX CAVOU CAZAT COMMERCIAL CAR LANGEAS CEREIJOU CERISERAIE CERISIER GAURIE CERISIERS CERVALOZE CERVEIX CESSAGUET CESSAT CHABAN CHABANAS CHABANAUD CHABANIE CHABANNE CHABANNES CHABANT CHABASSIERE CHABAUD CHABAUDIE CHABEAUDIE CHABIRAN CHABLARD CHABRELOUTRE CHABRENAS CHABREROU CHABREUIL CHABREYROUX CHABROULIE CHADALAIS CHADALEIX CHADEFAINE CHADIERAS CHAILLAS CHAISE CHAISE MARTIN CHAIZE CHAIZE MIDI CHAIZE NORD CHALARD CHALARDS CHALET CHALET ST VINCENT PUY GAILLARD CHALIVAT CHALUSSET BRIANCE CHAMBAUDIE CHAMBENEGRE CHAMBERTIE CHAMBERY CHAMBINAUD CHAMBON CHAMBONNAUD CHAMBORD CHAMBORET CHAMBOURAUD CHAMBOURSAT CHAMESSOUZE CHAMEYRAT CHAMONT CHAMONT TOUS VENTS CHAMOUROUX NEYCHAT CHAMOUSSEAU CHAMP CHAMPA CHAMPAGNAC CHAMPAGNAT CHAMPARNAUD CHAMPAUBERT CHAMPAUX CHAMP COMMUNAL CHAMPCONTEAU CHAMP BEAUVAIS CHAMP GRAULE CHAMP LUCHAT CHAMP VIGNE CHAMPEAUX CHAMPEIX CHAMPERON CHAMPETY CHAMPFLEURY CHAMP JOLI CHAMP VIGNE CHAMPMAGNAC CHAMPOD CHAMPOREES CHAMPOREIX CHAMPOUR CHAMP PERDU CHAMP RIBIERE CHAMPS CHAMPS PEYROU CHAMPSEAU CHAMPSIAUX CHAMPVERT CHANAUD CHANDAT CHANDERAUD CHANDIAT CHANGAS CHANLIAT CHANONCLE CHANSIGAUD CHANTALOUETTE CHANTARDIE CHANTEBOEUF CHANTECOUDERT CHANTEGRELLE CHANTEGREUX CHANTEGRIS CHANTEGROS CHANTEGROS NORD CHANTELAUVE CHANTELAUVE MALLEDENT CHANTELOUBE CHANTEMERLE CHANTE MIAULE CHANTEPERDRIX CHANTE PERDRIX CHANTERANNE CHANTOT CHANTOUANT CHAPELAS CHAPELIERES CHAPELLE CHAPELLE BLANCHE CHAPELLE CHAUVEIX CHAPELLE QUEROIX CHAPELLES CHAPELLE SECHAUD CHAPOULAUDIE CHAPTERIE CORRE CHARAILLAUX CHARAILLES CHARANNAT CHARAPOUX CHARAUDS CHARBONNIAUD CHARBONNIER CHARBONNIERES CHARDAILLAC CHARDENT CHARDIS CHARDONNERIE CHARDRAU CHAREILLE CHAREILLES CHARENSANES CHARENSANNE CHARENSANNES CHARGEAS CHARIERAS CHARLANNE BEAUVALET CHARMEAUX CHARNAILLAS CHARNAILLAT CHARRAIN CHARRAT CHARRAUD CHARRAUDS CHARRAUDS L HOSNE CHARRIERE CHARRIERS CHARROUX CHARZAT CHASAUD CHASSAGNAS CHASSAGNAT CHASSAGNE CHASSAGNES CHASSAIN CHASSAT CHASSEIN CHASSENEUIL CHATAIGNAUD CHATAIGNERAIE CHATAIGNIER CHATAIGNIERE CHATAIGNIER HAUT CHATAIGNOL CHATAIGNONS CHATAIN CHATAIN BARGAUD CHATAIN BUEIX CHATANDEAU CHAT BEAUNE CHAT CINTRAT CHATEAU MAUZELET BATARD CHATEAUBLANC BOURG CHATEAUBRUN CHATEAUCOURT D AIGUEPERSE BONNEVAL BONNEVAL BOURG CURSAC CURSAC GORRE VIGNE L ETOILE NEUVILARD VILLEJOUBERT ELYAS ESCURAS FRAISSE GAILLARD CHATEAULAMANCE BOURG MERLE CHATEAUNEUF PENITENTS PLANTADIS CHATEAURENON REPENTI REPENTIT RIVIERE ROCHER SAUTERIE TROMPETE TROMPETTE VAL D ENRAUD VALMATE CHATEAUVERT VILLENEUVE CHATEIGNEAU CHATELARD CHATELINE CHATENET CHATENET COLON CHATENET MARTY CHATENET MAUSSAN CHATENET MAUSSANT CHATEVEAU CHAT GAILLARD CHATILLOU CHATIN L AMOUR CHATONETTE CHATONNETTE CHATRE CHATREIX CHATRE PLANE CHATRES CHATRUSSE CHAUCHER CHAUCHIQUE CHAUD CHAUDERIER CHAUDOT CHAUFFARIE CHAULET CHAULIVET CHAUME CHAUMEIX CHAUMES MEYNIEUX CHAUMET CHAUMONT CHAUSSADE CHAUSSEILLES CHAUSSERIE CHAUTRANDIE CHAUVAN CHAUVENCON CHAUVIE CHAUVIERE CHAUVOUR CHAUZADIE CHAUZAT CHAVADE CHAVAGNAC CHAVAGNAC FAURE CHAVAGNAC REZAURIER CHAVAIGNAC CHAVANAS CHAVANAT CHAZAUD CHAZELAS CHAZETAS CHEBRANE CHEBROLLE CHEDEVILLE CHEGURAT CHEIVAUDOU CHEIX AMOUREUX ANCIEN ARRIVAUD ATELIER ROUSSET AUBEPINES BARBESECHE BARDONECHES BARRETTES BARRYS BEAUVAIS BELLEVUE BELLE VUE BELVEDERE MITARD FARGES BOISSEUIL BONNEFONT BONSHOMMES BORDERIE BOS BEYS BOSCHAUDERIER BOS COTE BOULERAIE BOULOU FARGES BOURDON BREGERES BREUIL BREUILHAUFA GARDE BUISSON BUREAU CAILLOU BLANC BOURDELAS CARRIERES CAVALIERS CHABANT CHAISE CHANCELADE CHANTEGROS CHAPELOT CHATAIGNIERS CHATENET CHAUMEIX CHEROUX CHEYRAUD CHEYRAUDS CHEZ GUILHEM CHEZ JANOT CHEZ LEVRAUD CHEZ RAYMOND CIREIGEAUX CLAPIER CLOS ROY COLOMBIER COMBEAU COMBE CLAIRE CONGERIES CONTAMINES COTE COTES DENT COTES PUYBOURREAU COULAUDS CROIX BILLARD CROIX BONNAUD CROIX BEAUPUY CROIX CHARRIERS CROIX NIOLLET CROS BLANC CUBES CURE D ANTONY D AUVIGNAC BEAUFORT BELLEVUE BOURDOULET BREGERE CHANSELADE CHEZ FRUGIER CHEZ LAMBE CRAMAUD GEROUX GIGONDAS JOCONDIAC BASSE BEAUSSERIE BICHE BORDERIE BREGERE BROUSSE L ACADEMIE CHASSAGNE CHATAIGNERAIE CHATTE CROIX MOTHES FILATURE FONTAINE PUITS FONTAINE NEUVILLAS FORESTERIE FORESTIERE FORGE FER GAILLARDIE GARENNE GAUDINE L AGE GORRE ECURE JOURDANIE MANORIE PASSERELLE JAURIE POUGE RIBIERE ROYERE FONTBESSE SABLIERE LASCAUX SCIERIE NEUVILLAS LASDOUMINGEAS SOURCE LAUGERE VIELLERIE NEUVILLAS MAINTENON MALEPLANE MASMONEDE MAZEROLLAS BLANCHE POMMET PROFAS AMAND SAINTE MADELEINE ANDALOUX ARCHES CHANTELAUVE BERTRANDES BORDES CHAUMES CHAUSSEILLES CLOS TORDS DILIGENCES ECUREUILS FONDS SALADAS FORGES GERAUDS GOUTTES HAUTS BRIANCE HOUMEAUX LEMOVICES LONGES D ORADOUR MAUMONTS MAZEAUX MONTS NOISETIERS PETITES MEZIERES PLATS PRADELLES PRADES PRES RENARDS RIVES ROITELETS ROSSIGNOLS SEILLES SOURCES TERMES TOURS VERSANNES TROUPIN DEUX ROCHERS GRASSEVAUD VERINAS VIVIENAS D ORADOUR DOURNAJOU DROUILLE BLANCHE BAS LEYCURAS BREUIL GORRE BREUIL VAYRES BUCHOU TOULONDIT AUTHIER D EAU COLOMBIER GALLIER GANET GORCEIX FONTBESSE GOTH GUE GIRAUD MAINE MARIANAUD RIBIERE FOLLET PAVE CROS BLANC PRESSOIR NEUVILLAS PUY LAS HAUTAS PUY MERY PUY REGNIER PYLONE FONTBESSE RE PICARD SAPIN STADE TARD TERTRE TERTRE NEUVILLAS TILLEUL VILLAGEAS ECHANIE ECOLIERS ENCLOS GARDE ETANG ETANGS ROUSSET FANTAISIE FERASSE FERRE FEUX FOLLETS FONTAINE QUINSAC FONTAINE RUTHIER FONTS SALADAS BELLEGARDE FOSSES FOURS FREGEFOND FREGEFOND THEILLOL FRESSENGEAS FRESSINGEAS GANE GARNAUDIE GAUT GENETS GLANE GLANET BARRE GOT GRAMMONT CEDRE CHALIER ROYERE GRANDS PRES GRANGE GRANGE BOEIL GRANGES GRAULES GUE LAVAUD GUE VIENNE HAUT FELIX HECTOR BERLIOZ HORS JOUHE TAURINERIE JULIANE LANDES LAPLAUD LASSAT LATTERIE LAVADOUR LAVANDIERES VIGNE LAVOIR LONGUEVERGNE LOUBIER MAISONNEUVE MANDIERAS MARGUERITES MARRONNIERS MESSE MONCHATY MONTS CHABROU L EAU BELHOMME MAZELLE MOURAUD MOUTES NIEUL NOALHAS NOGEAS NOTRE DAME GOTH NOYER OPPIDUM ORMES PACAGES PAGNAC PAMPISSES PEPINIERES BOURDELAS PERETTES PERMENIERS PUYBOURREAU PERRIERES PERVENCHES CROS BLANC LOUBIER PEYRADE BELLE BLANCHE CARIAUX CARRIAUX PINSONS PLANCHE BLED PLANCHES POMPERIES VIEUX PRADES VEDRENNE PREJERAUD PRES POUGE PRIX PROCESSIONS PUITS RIBIERE PUITS TAURINERIE PUY CHATENET PUY BREUIL PUY PORTAIL PUYPALAIS PUYBOUREAU QUATRE VIAS REGIMENT INFANTERIE MAURY REGLE REILLE RIBIERES RICHARD COEUR LION RIVEAUX BARRE ROCHER ELOI ROUDET ROUSILLE RUISSEAU RURAL NANCY CLOUD SCIERIE SERVES SOEURS SOUPEZE SOURCES STADE TILLEUL TREIZE VENTS TUILERIE TUILIERE VALADE VALETTE VEDRENNES VEILLERIE VERGNE BURJADE VERGNOLE VERGNOLLES VERSAILLES VERTIGUEUIL VEYRAC VIE CREUSE VIERGE VIEUX VIGNE VIGNERIE VIGNES VILLARD CHENAUD CHENE BLANC CHENE PIGNIER CHENE VERT CHENEVIERE CHENEVIERES CHENIANT CHENOUR CHERANDIE CHERCORAT CHEROUX CHERS CHERVAIS CHERVEIX HAUT CHERVIX CHEUGER CHEVAILLES CHEVAILLES RIEUX CHEVILLAUD CHEVILLOU CHEVRERIE CHEVRETTE CHEVRIER CHEYRAUD CHEYROU CHEYROUX CHEZ CHEZE CHEZEAU CHIGOT CHINORAILLE CHINORAILLES CHINQUIOUX CHIRON CHOHUT CHOISY ROI CHOISY ROI CINQ CHOLET CHOLUS CHOME CHOMES VERTES CHOUETTES CHOUVIAT CHURLERIE CILAS CILAS QUINSAC CIME PUY CINQ CINTRAT CINTURAT CIPIERE CIRAT ACACIAS AMEDEE TARRADE ANDRE BRUN ANTOINE PREVOST APSAH 150 POMPADOUR AUGERES BAS MERIGNAC BEL AIR BELLEVUE BOURG BELLEVUE GLANE GILLES BOTTEAUX BREUILH BRUTINES 100 BRUTINES CASTORS CHANTEGROS CLOS D AUGERES DOUILLAC GLANE L AMPHITHEATRE ACACIAS ARCADES IRIS PAQUERETTES BARRIERE BREUIL MAS NEUF PARC PRINTEMPS EDF MAZET ESCHOLIER FAYOLAS FAYOLLAS FLEURAT FONTAINE FONTAINE CREUSE FONTBOUILLANT FONTCREUSE FONTCREUSE 62 BOURG FONTCREUSE 92 BOURG FONTCREUSE BOURG HENRI LAFARGE HLM HLM 11 BOURG HLM 12 BOURG HLM 31 BOURG HLM 33 BOURG HLM 34 PUY CORNICHE HLM 37 BOURG HLM BELLEVUE GLANE HLM BOURG HLM PUY ROYERE JACQUES BOUTARD 14 BOURG 6 BOURG JOLIVET JOLIVET BOURG JULES FERRY JUMEAUX LABICHE CROIX RAMEAUX VANELLE L ECHOLIER CLOS DONJON 6 BOURG LEON BETOULLE LEONTINE VIGNERIE LOUIS CASIMIR RANSON MONTPLAISIR MONTROZIER MOTTES NIEDERBRONN BAINS PARC PENICAUD PLACES PUY PUY VIGNES RAOUL DAUTRY RENE BARRIERE ROCHE ROCHEBRUNE RODOLPHE MAON SAINTE SOLEIL LEVANT STADE TARRADE TOURETTES VANELLE VICTOR THUILLAT CLAIFAYE CLAIRBOIS CLAIREFAYE CLAIRIERE CLAPIERS CLAREUIL CLAUD CLAUDS CLAUTRE CLAUVEIX CLAVEROLAS CLAVEYROLAS CLAVEYROLLAS CLAVIERE CLAVIERES CLEDAT CLEMENSANNE CLIDIERE CLOMART CLOPS CLOS ANITA CHATENET CLOS BARIANT CLOS BARRIANT CLOS BEAULIEU CLOS BRIANCE BOURG CLOS CORGNAC CLOS BUXEROLLES CLOS COULAUD BRUMAS CLOS CUBES CLOS GORRY CLOS GUINOT CLOS VIEILLE CLOS LEIN CHANTEGROS NORD CLOS NICOT CLOS QUINOT CLOS BESSES CLOS SENAS CLOS SUSSAC CLOS BAC CLOS GOT CLOS FLEURI CLOS GIGONDAS CLOS LAFARGE CLOS LONGE CLOS MASBATIN CLOS MINOT CLOS MOMBESSIER CLOS NEUF CLOS NICOT CLOS PATUREAU CLOS GERALD CLOTURES CLOUD CLOUP CLOUX CLOVEIX CLUSEAUX CLUZEAU CLUZEREAU CODILLE COIN PARC COL ROCHE COL ROCHE COLLEGE STADE COLLEGE REBIER COLLET COLLINE COLOMBIER COMAILHAC COMBAS COMBE COMBEAU COMBEAUX COMBE AUX MAURES COMBE BOYER COMBECHOU COMBECHOUX COMBE CLAIRE COMBE DERRIERE COMBE COMBE GENECHAUX COMBES COMBES FOUGERAS COMBES BASSES COMBES MARTY COMBORIAS COMBRET COMBROUZE COMBRUN COMERGNAC COMMANDERIE COMMUNAL COMMUNAUX COMTEREIX CONCES CONCOURRE CONDADILLE CONDADILLE POULOUZAT CONDAMINES CONJAT CONORE CONSIDAT CONTAMINE CONTAMINES CONTENTIGNE CONTEREIX CORDELLE CORIVE CORNEDIE CORNUDE CORNUES CORRIGE COTAS COTE AU CHAPT COTE COTE CROCHAT COTE CROYER COTE MINOTERIE COTE ALLOIS COTE D OR COTE COTE THEIL COTE COTE SUD COTE PRES COTES MAINE COUDERT COUDERT BORDESOULLE COUDERT LATTES COUDERT LANDOUGE COUDIER COUEGNIAS COUERRASSAS MERIGNAC COUFAUDIERE COUFEIX COUFOUEIX COULEREDE COULEROLLES COUPELIVENCE COUPES GORSAS COURANTS COURAUD PUY GARSAULT COURBES COURBOULET CALECHES COURCELLE BOURG COURDEIN COURDELLES VERGER COUREDE COURET GRANGE BOEIL COURIERE COURIEUX COURJAC BOURG VERGER COURNEIX COURRIERE COURRIERES COURROUX BUGEAUD COURSELLAS GAY LUSSAC PENICAUD JOURDAN CURIE VERGNIAUD COURTIAUD BARRES COURTIAUX CHAIZE COURTIAUX BARRES COURTIAUX ROMANET COURTIEUX COURTILLAS COUSSAC COUSSADES COUSSADES BEAUSEJOUR COUSSAS COUTANTIGNE COUTAUDELS COUTEILLAS COUTURE COUTURE CHARBON COUTURE FREDAIGUE COUTURE LANDE COUTURE ASTIERS COUTURES COUTURES LAJOUX COUTURES L HERMITERIE COUTY COUVENT COUX COYOL CRAMARIGEAS CRAMAUD CRAQUETTES CRASSAT CRAULOUP CRAVAYAC CRECHE CRESSAC CREUZENET CREZEUNET CROCHAT CROCHEPOT CROIX BATISSOU CROIX LOUP CROIX BREUIL CROIX COMBATAUD CROIX COMBATEAUD CROIX D ANGLARD CROIX BORDE CROIX COYOL CROIX DAUMART CROIX FER CROIX GONDAT CROIX LABRAN CROIX BROUSSE CROIX CHARDIERE CROIX L ANGE CROIX L ARBRE CROIX L ARRET CROIX VALADE CROIX VALLADE CROIX LAVERGNE CROIX VIGNE CROIX L ESSART CROIX LEYCURAS CROIX L HOZANE BOURG CROIX MASMONT CROIX MASMONT PIECE CROIX MAUBLANC CROIX MAULOUP CROIX MILAN CROIX MURET CROIX D EPIED CROIX PUY MONT CROIX BORDES CROIX CHARRIERS CROIX VILLETTES CROIX VIRADES CROIX VALETTE CROIX D HERVY CROIX BATAILLON CROIX LOUP CROIX BOS CROIX BREUIL CROIX COMTE CROIX COQ CROIX MECHANT CROIX MONT CROIX CROIX PARC CROIX PATRE CROIX FEUILLE CROIX THAY CROIX TITAN CROIX VOYER BOURG CROIX FERREE CROIX FORGE CROIX JANIQUET CROIX FAYE CROIX GANNE CROIX MOUCHE CROIX LATTEE CROIX MARGOT CROIX MARTIN CROIX PAROT CROIX SAINTE VALERIE CROIX SAINTE VALERY CROIX MARTIAL CROIX SENAMAUD CROMIERE CROMIERES CRORIEUX CROS CROS CHASSAGNAT CROS BALLET CROSSAS CROSSAT CROUSILLAT CROUX CROUZEIX CROUZETTES CROUZILLAT CROUZILLAUDS CROUZY CROYER CRUET CRUMAUD CUBERTAFOND CUL PRADEAU CUREBOUTEILLE CURMONT CURSAC CURZAC CURZON CUSSOU DAME DAMONT DANDALAIS DAR EL BAIDA DARVIZAT DAUGE DAUGERE DAUGERIE DAUMARD DAUMART DEBAILLADE DEBAS DEBAT CAUX CHAILLAC PERISSAT DEFANT DEMOUDILLOU DENT DEPOT DEROT SAZY DESCENTE GRANGE BOEIL DESERT ETANCHES DESHAIES DESHAIS DEVANT ROCHES DIEULIDOU DIGARDECHE DOGNEIX DOGNON DOGNONNET DOGNONS DOMAINE COURRIERES DOMAINE CHAMEYRAT DOMAINE BRIANCE PLANCHE DOMAINE CATABEAUNY DOMAINE GARDE DOMAINE LAVAUD DOMAINE MERIGNAC DOMAINE ROCHES CHAPELLE DOMAINE FONTAINE AU LOUP DOMAINE LIGOURE DOMAINE MONTAURAN DOMAINE MOTHE DOMAINE PAVILLON BOURG DOMAINE PLANCHE DOMARIAS DONADIEU VERNON DONNARIEIX DONNARIEX DONZENAT DOUEINEIX DOUENEIX DOUGIER DOUGNEIX DOUGNIER DOULAYE DOUMAILHAC DOUMAIX DOUMEIX DOUMEZY DOURNADILLE DOURNAN DOURNAND DOURY DROLET DRONNE DROUILLE DROUILLES DROULY BOS MAS DUNE DURIS VERT ECHALARDERIE ECHALIERS ECHAUDIERAS ECHIZADOUR ECHIZADOUX ECLUSE L GRANDMONT ECOLE MORT BOURG PRIMAIRE BOURG DOUCHES ECOLES BOURG ECORNEBOEUF ECUBILLOUT ECURE NEUVE ECURES EGAUX EGLEZEAUD ELACIERS EMBESSAC EMBROSSE ENTRAYGAS ENTREYGAS ENVAUD EPANOURS EPAROU EPIED ERIN ERMITAGE ESPERANCE ESPINASSOU ESSARTS ESSELET ESSENAC ESSINDIAS ESTIVEAUX ESTRADE ETABOULIE ETANCHE ETANG AUTHIER ETANG BERINA BERINA ETANG CONORE ETANG BORT ETANG GENCAY ETANG GOUILLET ETANG JONAS ETANG FORGE HAUTE ETANG LANDE ETANG PUYMOREAU ETANG SAGNES ETANG THIAS ETANG TRICHERIE ETANG THEIL ETANG JAUNAS ETANG JUGE ETANG L BAUDY ETANG L CONORE ETANG L VALETTE VALETTE ETANG CHATAIGNOL ETANG MARIONNETTES PARADE ETANG L ROMPU ETANG L ROMPU ARTIN ETANG L VALADE ETANG L VILLERAJOUSE ETANG THIAS ETANG VALADE ETANG VALETTE ETIVAUD ETRADET EVNANCAS EXCIDEUIL EXCIDIOUX EXIDEUIL EXIDIOUX EXPARDELIERE EXPINASSOU EYBORD EYCOUVEAU EYCOUVEAUX EYCURAS EYCURAUDOUX EYGOUTIERAS EYJAUVET EYNANCAS EYVEAUX EYZAC FABRIQUE FABRIQUE TROIS BORNES FAFREIX FAITES FANAY FANIAS FANTAISIE FANTAISIES FANTOUILLET FARDISSOU FARDISSOUX FAREBOUT FARGE FARGEAS FARGES FARGETAS FARGETTAS FARGETTES FARSAC FAUBOURG ANCIEN HOPITAL FAUBOURG AUGUSTE BLANQUI FAUBOURG BEAULIEU FAUBOURG CHAPTERIE FAUBOURG CHAPTERIE AMBRETTE FAUBOURG CHAPTERIE CORRE FAUBOURG FAUBOURG GARANNE FAUBOURG FAUBOURG GAILLARD FAUBOURG CHATENET FAUBOURG LIEBKNECHT FAUBOURG MACAUD FAUBOURG MACAUX FAUBOURG MANIGNE FAUBOURG MOUSTIER FAUBOURG MOUSTIERS FAUBOURG MOUTIER FAUBOURG FAUBOURG GUE FAUBOURG PORTE DINSAC FAUBOURG PUY FAUBOURG PUY FAUBOURG GILLES FAUBOURG TANNERIES FAUCHERIE FAURAS FAURIE FAURIES FAUX FAVASSAUDS FAVINIE FAYANNAUX FAYAT FAYE FAYEMENDY FAYERES FAYES FAYES PERRET FAYES FAYOLAS FAYOLLAS FAYOLLE FAYOLLES FENCHIE FENEROLE FENIEUX FERANCHIE FERME FERME COYOL FERME TOUR FERME JOLIBOIS FERME LOUISE BOISSE FERME RAZES FERMIGER FETCHOLET FETILLIERE FEUILLADE FEUILLEE FEUX FEUYAS FEYTAUGOT FEYTCHOLET FEYTOGOT FEYX FIANAS FIEF FIEUX FIGEAS FLAQUECORNE FLAQUES FLAVANDERIE FLAYAT FLEIX FLEUR FLEURAT FLORENSAC FLOTTE FONBANOUX FONBELHOMME FONCHAUVAUX FONCOMBEAU FONDANECHE FOND BERTRAND FOND LANDES FONDELLES FOND FROIDE FONDREAUD FONDS PEUX FONGOURGOUSE FONSOUMAGNE FONTAGULY FONTAINE AU BRU FONTAINE BOUDOIRE FONTAINE BUFFAUD FONTAINE CAILLAUD FONTAINE CLAIR FONTAINE CREUSE BOURG FONTAINE CHIEN FONTAINE GENEST FONTAINE DUVERT FONTAINE VERT FONTAINE FROIDE FONTAINE GRENIER FONTAINE MACAIRE FONTAINE MARTIN FONTAINE MARTIN HAUT FONTAINE PARADIS FONTAINE PERILLAS FONTAINE QUE BUREAU FONTAINES FONTAINE MARTIAL FONTAINE MARTIAL PLAGNE FONTAINE MARTIN FONTAINE MARTIN BOURNAT FONTAINES BREGERAS FONTAINE VERGNOLLE FONTALGUY FONTANDRAU FONTANELLE FONTANES FONTANILLES FONTAUBE FONTBELLE FONTBESSE FONTBLANCHE FONTBONNE FONTCHABRIER FONTCOMBEAU FONTCREUSE FONTENELLE FONTENILLE FONTFERIAS FONTGEAUDRANT FONTGRENIER FONTPUY FONTRAYNAUD L ARTIGE FONTREAU FONTS FONTS SALADAS FONTVIEILLE FONVIEILLE FORESTIERE FORESTILLES FORET FORET ALLOIS FORET BASSE FORET BRUNE FORET CHABROUTY FORETERIE FORET HAUTE FORET LANDOUGE FORETS FORETS CARS FORGE FORGEAS FORGE BESSOUS FORGE CHABRENAS FORGE MONDON FORGEIX FORGE JACQUES FORGENEUVE FORGES FOSSE AUX BOEUFS FOUCAUDIE FOUGEOLLE FOUGERAS FOUGERES FOUGEROLLES FOUILLOUX FOULENOUR FOULENOUX FOULVENTOUR FOUR FOURCELAS FOURNE FOURNET FOURNIAL FOUR VIEUX FOUSSAT FOUSSETTE FRACHET FRAIGEFOND FRAIS FRAISSE FRAISSEIX FRAUD FREDAIGUE FREDIERE FREGEAIGUE FREGE FOND FREGEFOND FREGUILIEN FRESSANGES FRESSIGNAS FRESSONEIX FREYSSINET FRIAUDOUR FRIOUX FROMENTAL FROMENTAUX FRONTIGNAC FRONTOIN FRONTOUIN FROUGEIX FRUGERIE FRUMINI FUMOUSE FURSANNES FURSANNES FOLLES FUTAIES GABIE GABIE POULE GABIE MESURAT GABIE COLOMBIER GABIES GABILLOU GABISSES GABOUREAU GACHERIE GACHES GADOUX GAFFARY GAGNADOUR AUREIL GAGNEPOT GAGNERIE GAIN GALACHE GALAMACHE GALATAUD GALERNE GALIFOR GALLANDS GALLERIES GALLIE GALLIER GANDALONIE GANDELOUP GANE GANE D ARRIERE GANE MAS GANES GANES PENAUD GANET GANETTES GANNE GANNE AU BARRY GANNES GARAUDIES GARDE GARDELLES GARDENET GARDE NORD GARDES GARE MESANGES BEYNAC CHATEAUNEUF BUJALEUF BEYNAC LEYCURAS GAREILLE ROCHES LOTISSEMENT BONHEUR MAGNAC VICQ GARENNE GARENNES GARNIMENT GARRAUD GASNE GAT GATEBOURG GATE SALESSE GATE MARSAC GATE QUEROY GATE SOLEIL GATE SOLEIL LANDOUGE GATINES GATTE GATTEBOURG GAUCHARAUD GAUDEIX GAUDINOTTES GAULIER GAUTHIER GAUTHIERS GELIF GENCAY GENEBRIAS GENETE GENETEIX GENETS GENETTE GENEVIEILLE GENSAIS GENSENAS GENTAUD GENTE GERMANAUD GERVAIS GERVAUDIE GIGONDAS GILARDEIX GIPOULOUX GIRAUDONIES GITE RURAL 2031 GITE RURAL QUATRE VENTS GLANDOULAS GLANDOULAT GLANE GLANGETAS GLAYOLADES GLIANDRETTE GOBELETS GODONNEIX GOLAS GONDAUD GORCE GORCEIX GORCILLE GORGE LOUP GORGERE GORRETIE GORSAS GOT GOT 12 BOSCARTUS GOT SOUX GOT MAZET GOUAINEIX GOUDOU GOUDOUT GOUHAUD GOUHAUT GOULET GOUMEIX GOUMINDROU GOUPILIERE GOUPILLAT GOUPILLOUX GOUPILLOUX PASSADOURS GOURDONNAUD GOURINCHAS GOURLY GOURSALINE GOUTAILLES GOUTAILLES BUXEROLLES GOUTEIL GOUTENEGRE GOUTILLE GOUTTAS GOUTTE GOUTTE NOIRE GOUTTEPAGNON GOUTTES GRAFEUIL GRAFFEUILLE GRAFOUILLADE GRAFOUILLADES GRAISSES GRAMAGNAC GRAMMOND LAVAUD GRAMMONT LAVAUD GRANCHAUD ARBRE BAGNOL BEAUBIAT BEAUNE BECHASSEAU BILLAT BOIS THIAS GIRAUD BONNEFONT BOS BOUCHET BOUILLON BRUGEAU BUEIX CEDRE CHAILLAUD CHALIER CHAMP CHARRAT CHATAING CHAUD CHAVANNAT CLOS COLOMBIER COUDERT DOMAINE BOULESSIE CARRIERE CHAISE ECHERAT COMBE COMBORNIE COUTURE CHABANT ESCURE FORET GARDE LANDE LOCHERIE MAISON METAIRIE MONNERIE PARLIERE RENAUDIE ROCHE GRANDES FORGES GRANDES LIGNES GRANDES MAGNELLES GRANDES TERRES GRANDES VIGNES VEYSSIERE VILOTTE FOSSE GRAMMONT LANTERNAT LUZAT MACHETARD MASVAYRAUD MESURAT MEYNIEUX GRANDMONT MONTEIL GRANDMONT LAVAUD MONTMERAUD MURET PAGNAC PEYROUTEAU PIC PLAUD PRE PRE VERSANAS PUYCONIEUX PUYCONNIEUX QUARTIER QUEROIX QUEROY SALE SAUVOT GRANDS GRANDS BOS GRANDS CAIRES GRANDS CHAMPS GRANDS CLOS GRANDS FATS GRANDS MARMIERS GRANDS METAYERS GRANDS MONTS GRANDS RIEUX GRANDS TAILLIS THEIL VALEIX GRANDVAUD VAUX VEAUX VEDEIX VILLAGE GRANGE GRANGE 2 TOURNIOL GRANGE CHATAIN GRANGE BOEIL GRANGE FOIRE GRANGE SELLES GRANGE NOIR GRANGE FLEURANT GRANGENAUD GRANGE NEUVE GRANGE PERIN GRANGERE GRANGES GRANGES CHEZ GILET GRANGE VIEILLE GRANGE VILLEDON GRANVAUD GRASMAGNAC GRATADE GRATELOUBE GRATERESSE GRATEYROLLE GRATEYROLLES GRATOULET GRATTELOUBE GRAVAIS GRAVATAUD GRAVATEAUD GRAVE GRAVELAT GRAVIER GRAVILLAS GREIGNAC GRELET GRELETTE GRENAUD GRENERIE GRENIER GRENOUILLER GRENOUILLERE GRIGEAS GRILLERE GRIMAUDIE GRIMAUDY GRIMIERE GRIMINIERE GRIS GRISONNAUD GROS GROS GROS BOS GROS CHENE GROS FAYAUD GROS FONTAINE GROSILLE GROSLAND GROSLIERE GROS POMMIER GROSSE BORNE GROSSE GROSSEREIX GRUDET GRUE GUE ROCHE GUE VERTHAMONT GUERAUDIER CROUZEIX GUERILLERIE GUERINDOUX GUEUNIERE GUILLAT GUILLAUDRETTE GUILLAUMEIX GUILLOT GUILLOUX GUIMONT GUINAY GUINEMARD GUIONIE GUTARIAS GUTTARIAS GUYONNERIE HACHES HALLE DUPUYTREN HALLES CENTRALES HALTE MASSEAUX BROSSES GARDES PUYCONNIEUX FARGEAS FORTUNAT HAUT HAUT COURTIOUX HAUT DIEULIDOU HAUTE BARRIERE HAUTE CROUZILLE HAUTE FAYE HAUTE PACAILLE HAUT EPAGNE HAUTE RENAUDIE HAUTE RIPPE HAUTES PILATERIES HAUT FELIX HAUT JARDIN HAUT MALLEDENT HAUT MARIN HAUT MONT GENIE HAUT MONTMAGNER 3 POSTE HAUT MONTMAGNER POSTE HAUT NEUVIC HAUT PANLAT HAUT POUZINIERE HAUT PUY CHETIF HAUT QUARTERON HAUT ROSEAU ROSEAU HAUTS COURTIOUX HAUTS BEAUCHAMP HAUTS CHASSAGNAT HAUTS PUY HAUT SEBIOUX HAUT SURZOL HAUT TAUVERAT HAUT THOVEYRAT HAUT TOUR HAUT VAUZELAS HENRIAT HERMITAGE HERMITERIE HERSES HIPPODROME HLM 124 MAS HLM 1 BOURG HLM BOURG HLM CHAPELLE 13 CHAPELLE HLM MAS 80 MAS HLM PARC HLM COUEGNAS 3 BOURG HLM COUEGNAS 4 BOURG HLM COUEGNAS BOURG HLM HLM CHAPELLE 5 BOURG HLM PRADE HLM HLM PARC HOLY HAT BRIANCE COMBRET HOME HOMME BOST HOPITAL HOUILLERES HOUMEAUX 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CHASSAGNE COUTURE CROIX VERTE FABRIQUE FONTAINE FORGE GLANE GRELE JAURIE LUNE MAUVENDIERE L ANCIEN ABATTOIR NOGERIE PERDRIX RAMPE RIBIERE RIVIERE ROCHE ROCHE BLANCHE ROYERE FONTBESSE SOURCE L OLIVIER MARCILLAC MAURIENNE MONTPLAISIR NANCY NAUGEAT NAVARRE NEXON DENIS PAPIN POMMET PRECOIN ROC CHENE SAINTONGE ARGENTIERS AUDOUINES BLEUETS BOUGUILLONS BOULEAUX BOUVREUILS DESCARTES CHAINERIES CHARENTES CHARMILLES CHAUSSEILLES CHENES VERTS COUTURES NEUVILLAS CROS GENETS GRANDS BAUX HAUTS VIENNE HETRES JARDINS LAQUES LEZES LILAS LYS MAZEAUX MOINEAUX MURIERS SOLOGNE PELOUSES PETITES ECURIES PLACES PLATS ROCHES RUCHOUX TAMARIS TEINTURIERS D ESTIENNE D ORVES TILLEULS D ETIQUET TOULOUSE VANTEAUX VERDUN DIDEROT D ISLY DONY D ORADOUR BARRY BAS FARGEAS BELVEDERE BOULOU BOURBONNAIS BUISSON CLOS BECHADE CLOS BELETTE CLOS CHOME CLOS L ECHALIER CLOS MARGUIL CLOS JARGOT CLOS PLANCHE CLOS BRUNES COURGNAT GUE VERTHAMONT GUESCLIN HAUT PUY LAS HAUTAS MAS BOURIANNE MAS NEUF MUGUET NIVERNAIS DUPLEIX PORTEFAIX PRE GUILLOT PUY LAS HAUTAS PUY POUYADE NEUVILLAS DUPUYTREN ROUERGUE ROUSSILLON TROUMIGRAND VAL L AURENCE VIEUX CREZIN ECOLES EDGAR DEGAS EDITH PIAF EDOUARD CHOLET EGLANTIERS EGLANTIERS BREUILH EGLANTIERS PEURY ELIE BERTHET EMETTEUR EMILE ZOLA ENCOMBE VINEUSE ERABLES SILHOUETTE EVECAUD FERME FONTAMAUD RIPPE FONTANAS FONTBESSE FORGE FORGE SISSAC FOURNEAUX SARRE VILLON FREDERIC CHOPIN FREDERIC MISTRAL FREDON FRENES FUSILLES GALIEN GAUGUIN GAY LUSSAC GENTIANES BARRE PERIN GLANE GLUCK GORCEIX GOYA COUDERT BOURG GRANGE BOEIL GRANGES BLEUES GRAVILLAS GREUZE GUSTAVE COURBET HAUTE RIPPE HAUT TRAMONT HELENE BOUCHER HENRI BECQUEREL HENRI DUNAN HENRI DUNANT HENRI MATISSE HOCHE HONORE BALZAC ISSOIRE JACQUARD JACQUES BREL JACQUES CARTIER JAMES COOK JARDINS ANTOINE CHAPTAL BAPTISTE GREUZE CHARCOT COQUILLAUD FONTAINE DORAT EFFEL EIFFEL FAYAUD MILLET GABIN GAGNANT GIRAUDOUX HONORE FRAGONARD JACQUES ROUSSEAU JAURES LOUIS PAGUENAUD REBIER ROUGERIE SEBASTIEN BACH SEGUREL JOLIOT CURIE JOSEPH MAZABRAUD JOSEPH ROUX JOURDAN JULES CLARETIE JULES MASSENET JULES MICHELET JULES NORIAC JULES RENARD JUMILHAC KLEBER BARRE LABRUNIE LAC LALO LAMARTINE LANDE LAUGERE LAURIERS LAVOIR LAVOIR CHEZ CHABROL LELONG LEONARD DUCLOU LESAGE VERRIER LILAS LOUIS ARAGON RIPPE LOUIS BOURDERY LOUIS CASIMIR RANSON LOUIS CODET LOUIS PERGAUD LUIS MARIANO MACE MAILLOL MAISON ROUGE MALHERBE MALUCHE MARC ANTOINE MURET MARCEL AIME MARCEL AYME MARTIN NADAUD MARYSE BASTIE MAS MASSOU MAURICE CHEVALIER MAURICE RAVEL MERMOZ MINUTE MOLIERE MONTESQUIEU MUGUET MURILLO MYLORD NANTILDE NEFLIERS BOILEAU POUSSIN NIEPCE NOBEL NOILEAU NOISETIERS OISEAUX ORFEVRES ORMES ORPAILLEURS PAPETIERS PAQUERETTES PASCAL EMILE VICTOR MAZABRAUD THOMAS PECHERIE PECHERIE RIPPE PELGROS PETIT POULOUZAT BERTHUSSIE PEUGOUT RONSARD LOTI PIERRES RIPPE PINGAULT PRE BOSSU PRE GUILLOT PRE MADAME PRES PRE VERT PRUNIERS VIEUX BOISSEUIL PUITS PYRAMIDE RACINE RACINE POULOUZAT REMPARTS RENOIR RIBES RIBIERE RIEU RIEUX PEROU ROCHE ROCHER ROCHES RONSARD RONSARD POULOUZAT ROUSSEAU RUISSEAU RUISSEAU PUYBOURREAU SABOTIERS CHRISTOPHE SAINTE CLAIRE EXUPERY SAENS SURIN SARMENTS SAULES STADE STALINGRAD TARDOIRE TEILLET TERRASSES THIERS TILLEULS TUQUET TURGOT VAL VIENNE VALLIS VAUZELLE VAN DYCK VEDRENNE VENASSIER VENTENAT VERGNE VICTOR ROCHE VIEILLE ROUTE VIEUX VIGNES VOLTAIRE INTRA INTRAS ISLE JABERLAN JAGON JALADE JALADY JALBOSSE JALIGNE JALINIER JALINIEUX JALLADAS MIDI JALLADAS NORD JALLADEAS MIDI JALLARDS JALOUNEIX JALOUSIE JAMETIE JAMMOTTES JANAILHAC JANOUZEIX JARAFY JARCELLERIE JARGEAILLES JAROSSES JAROUSSE JAROUSSES JARRETAUD JARRIGES JAUBERTERIE JAUNIE JAUNIERES JAURIE JAUSSONIE JAVAUDOUX JAYE JEAMMEYRAT JEUNES CHATAINS JEUNES HURAUX JIOUX JOFFRENY JOLIBERT JOLIBOIS JONAS JONCHAPT JONCHERES JONCHEROLLES JONQUES FARGEAS JOUANLEIX JOUBERT JOUBERTIE JOUHE JOURDANIE JOURDIERES JOUVEAU JUDIES JUGIE JUILLAC JUILLARDIES JUILLERY JULES MICHELET JULIANDE JULIKA JULLIARDIES JUMEAU JUMEAU JUNIAC JUNIAT JUNICAUD JUSSAC JUVET KIOSQUES KOLIBUFO BACHELLERIE BADIE BAISSE BANNE BARAQUE BARBARIE BARBE BAREILLE BARNOUILLE BARRE BARRE LOTISSEMENT BARRE BARRIERE BASSE BORDE BASSE FORET BASSE GORCE BASSE PLAGNE BASSE ROCHE BASTIDE LABASTIDE BATISSE L ABATTOIR BAUBERIE L ABBAYE REPAIRE BEAUSSERIE BECHADIE BECHONIE BEIGE BEIGE MAUBERT L ABEILLE BELAUDIE BELLE BELLEMEMIE BELLEMENIE BENECHIE BERANGE BERGERE BERGERIE BERGINERIE BERINA BERTHE BERTHUSSIE BESSADE BESSE BETOULLE BEYLIE BEZASSADE BICHE BLANCHARDIE BLANCHER BOCADIE BOGUERIE BOIGE BOILERIE BOINE LABOINERIE BOISSARDE BOISSERIE BOISSIERE BOISSONNIE BONNAIGUE BONNE MERE BONNETIERE BORDE BORDE HAUT FELIX BORDERIE LABORDERIE BORDERIE GAUDINOTTES BORDES BORIE LABORIE BOTTIERE BOUCHARDIE BOUCHERIE BOUCHIE BOUCOLE BOUDONIE BOUILLE BOULADE BOULE D OR LABOURADOUR BOURGADE BOURGONIE BOURLHADE BOURREBLANCHE BOURSETTE BOUTEILLE BRAMAUDIERE BRANCHIE BRANDOUILLE BRANGERIE LABRAUD BRAVINE BREGERE BRETONNIERE BRIDERIE BROCANDIERE VILLARDS LABROUSSE BROUSSE BRUGERIE LABRUGNAS BRUNIE BRUYERE BUCHERIE BUCHILLE BUREAU BUSSIERE LABUSSIERE LABUSSIERE ETABLE BUSSIERE RAPY BUTTE BUZATIERE LAC CABANE CAILLE CALIFORNIE CANE CANTINE CARPARIE CASINE LACAUD LACAUX FARGE CHABANIE CHABASSIERE CHABEAUDIE CHABROULIE CHAISE LACHAISE CHAIZE CHALUSSIE CHAMBERTIE CHAPELLE CHAPELLE CLAIR CHAPOULAUDIE CHARDIERE CHAROUFFERIE CHARRIERE CHASSAGNE CHATAIGNERAIE CHATAIGNERAIE ANGLARD CHATRE CHATRUSSE CHAUCHERIE LACHAUD CHAUME CHAUME VERTE CHAUSSADE CHAUSSERIE CHAUVIE CHAUVIERE CHAUZADIE CHAVADE CHAVADE BUXEROLLES CHEBLOTIERE CHEBROLLE LACHENAUD CHENAUMORTE CHENIN LACHESE CHETARDIE CHEVRERIE 2 BIARDS CHEZE LACHEZE CHIRADE CIPIERE CLAUTRE CLEDIANE CLIDIERE LACLOTRE COLLERIE COMBE LACOMBE COMBE AUX BOEUFS COMBE MOISSAC COMMANDERIE CONDAMINE CONTAMINE CONTIE CORBINERIE CORDELLE CORNUE COSSE COSTE COTE LACOTE COTTE COUADE COULINIERE LACOUR COURANDIERE COURCELLE COUREDE LACOURIERE COURRIERE LACOURRIERE COURTETTE COUSSEDIERE COUTURE LACOUTURE COUTURE CHARBON COUTURE LANDE LACOUX COUX LAC PLAISANCE CRECHE CROISIERE CROIX CROIX BACHAUD CROIX BERTRAND CROIX BORDES CROIX CHATAIN CROIX COMBATAUD CROIX CONSTANT CROIX COYOL CROIX D ANGLARD CROIX D ARFEUILLE CROIX FER CROIX LABAN CROIX LABRAN CROIX BROUSSE CROIX L ARBRE CROIX LEYCURAS CROIX MALIBAS CROIX MASMONT CROIX D ENVAUD CROIX BORDES CROIX BOUCHATS CROIX COMBES 15 CROIX COMBES 16 CROIX COMBES 18 CROIX COMBES 20 CROIX COMBES 21 CROIX COMBES 22 CROIX COMBES 24 CROIX COMBES 26 CROIX COMBES 27 CROIX COMBES 31 CROIX COMBES 32 CROIX COMBES 33 CROIX COMBES 34 CROIX COMBES 35 CROIX COMBES 36 CROIX COMBES 37 CROIX COMBES 38 CROIX COMBES 40 CROIX COMBES 42 CROIX COMBES 44 CROIX COMBES 48 CROIX COMBES 49 CROIX COMBES 50 CROIX COMBES 52 CROIX COMBES 53 CROIX COMBES 54 CROIX COMBES 6 CROIX COMBES 8 CROIX FORGES CROIX VIRADES CROIX VALETTE CROIX D HERVY CROIX BAC CROIX LOUP CROIX MONT CROIX CROIX LASTOURS CROIX PARC CROIX THAY CROIX THE COMBAS CROIX VERDIER CROIX FERREE CROIX GUILLEMAIN CROIX JANIQUET CROIX LANDE CROIX LATTEE CROIX LAVAUD CROIX MARAND CROIX MARTIN CROIX PANCHARAUD CROIX PARAUD CROIX PAROT CROIX CROIX SAINTE VALERY CROIX VERDIER CROUZETTE CROUZILLE CROZE DAPEIRE DAUDINERIE DAUGE DAUGERIE DAUMARIE LADEGAILLERIE LADEVE DIGUE DORADIE LADRAT DROUILLE LAFA FABRIQUE LAFARGE FARGE FAURIE FAUVETTE FAUX FAVERIE FAVINIE FAYE LAFAYE FAYOLLE FEGENIE FERANCHIE FERME GARDE GARDE FERME MURES FERME FREDIERE FERMETTE FERRIERE FEUILLEE FEUILLEE ARTHUGERAS FEYRE LAFFAIT FLAVANDRIE LAFLEUR FLOTTE FOLIE FOND LAFONT FONTAINE FONTANILLE FORESTERIE FORESTIERE FORESTIERE VERGNES FORET FORET BASSE FORET ALLOIS FORGE LAFORGE FORGE MONDON FORGE MORTEROLLES LAFOSSE FOUILLERE FOUILLIERE FOUSSETTE FRUGERIE GABIE GABIE COLOMBIER GABIE TEULET GACHERIE LAGACHERIE GAGNERIE GANCE GANE GANE CHASSOUNAUD GANNE GANNE AU BARRY GANNE JUMEAUX GARDE GARE BEYNAC GAREILLE GARENNE GARENNE CONDADILLE GARONNE GASNERIE GAUBERTIE GAUDINE GAUDONIE GAUTAUD L AGE LAGE LAGEAS LAGEAUD LAGEBEAUDEUF LAGEBOUILLERAND L AGE LAC LAGE LAUCHOUX LAGEMICHENE GENETIE GENETTE GENTE GERBAUDIE GERVAUDIE LAGEYRAT GLANE MAILLORAT GLAYOLLE GLORIETTE LAGORCE GORCE GORCE HAUT GORCILLE GORGERE GOTAUD GOTTERIE SAVEZE LAGOUTE GOUTILLE GOUTTE GRAINERIE GRANDE AUBINERIE COTE ECURE FORET GAGNE GRANGE INTRADE LANDE ROCHE VALADE VEYSSIERE GRANGE LAGRANGE GRANGE CHATAIN LAGRANGE CHATAIN LAGRANGE GRANGE GRANGE FLEURANT GRANGE NEUVE BOURDELAS GRANGERE GRANGE VIGNAUD GRANGE VILLEDON GRATADE GRAUGERIE GRAULE GRAULIERE GREGE GRELE GRELLE GRENADE VERGNES GRENARDERIE GRENERIE GRENOUILLERE GRILIERE GRILLERE GRIMAUDIE GRIMIERE GRIMINIERE GROSSE BORNE OUEST GROUSSIE LAGUDET GUERILLERIE GUERITE GUIDE GUINANDIE GUIONIE GUIONNIE GUYONNERIE HALTE HAUTE BARRIERE HAUTE FAYE HAUTE GORCE HAUTE RENAUDIE HAUTE RIPPE LAILLOUX JALIGNE JALOUSIE JANTINE JAROUSSE JARRIGE LAJAUMONT JAUNIE JAURIE JAYE JONCHAPT JOUBERTIE LAJOUE LAJOUMARD JOURDANIE JOUSSELINIE JOUVIE JOUVINERIE LAJOUX JUDIE JUGIE JULIANE LAKE SAILING KITIMA LANDE LANDE BASSE LANDE CASSEPIERRE LANDE CHANDEAUX LANDE DURIS LANDE GREIGNAC LANDE SAUVEUR LANDE ALLOIS LANDE DEST LAURENT LANDE CHAZAUD LANDE DEGEAI CHAUVOUR LANDE DOLMEN LANDE MAS GAUTHIER LANDE POIRIER LANDE GREIGNAC LANDE SAUVEUR LAPIDERIE LAPINERE COMBES LARDIE LEGERIE LALEUF LIBERTY LALLET L ALLEUE LALLIOUX LOGE LONGE LOUDONNIERE LALUE LALUE CHATAIN MADELAINE MADELEINE MADONE MAGNIGNE LAMAGRAS MAILLERIE MAISON BLANCHE MAISON BRULEE MAISON MAISONNETTE MAISON NEUVE MAISON ROUGE MALADRERIE MALAISE MALATIE MALETIA MALETTERIE MALIGNE MALLETIE MANDIE MANIGNE MARAUDE MARCHADERIE MARCHE MARCHERE MARCOUX LAMARDELLE MARONNIE MAROTTE MARSSE MARTINE MARTINERIE MARTINIE MASSE MAURIE MAZAURIE MAZERE MAZIERE LAMBAUDIE LAMBEAUDIE LAMBERTERIE LAMBERTIE LAMBERTIERE MENARDIER MENEIREIX MERCERIE MERDIE MERGOULETTE LAMERINE MERLIE METAIRIE METAIRIE 32 BARDYS MINARDIERE MOLE MONERIE MONGIE MONNERIE LAMONNERIE LAMONT MONTAGNE MONTEILLERIE MORNIERE MOTHE MOTTE MOULADE MOULINE LAMPON NADALIE LANAUD LANCLAUZE LANDAIS LANDE LANDE CASSEPIERRE LANDE BARBESECHE LANDE BEAUVAIS LANDE BOS CAUX LANDE CONTAMINE LANDE DURIS LANDE GROLIER LANDE JOUY LANDE BRAVINE LANDE LACHAUX LANDE FAVERIE LANDE LANDOUGE LANDE MOMPAL LANDE PLAUD LANDE LAURENT LANDE SAUVEUR LANDE ALLOIS LANDE POUYADES LANDE VANAUX LANDE MAS GAUTHIER LANDE PEYROU LANDEIX LANDES LANDES BOISSERIE LANDES LACHAUX LANDES MEUZAC LANDES MOREAU LANDES NEGRELOUBE LANDES PAULHIAT LANDES PUYMIE LANDES QUINSAC LANDES ROUVEIX LANDES VIOLEZEIX LANDES D OZIER LANDES PUY JUDEAU LANDES ROUSSIS LANDES VERT LANDES JUNIAT CHATEIGNEAU LANDES LANDOUGE LANDOU LANDOUGE LANDOUGE BAS COUDERT LANDOUGE BEAUSEJOUR LANDOUGE BELLEGARDE LANDOUGE CAVOU LANDOUGE GATE SOLEIL LANDRODERIE NEPLE NEPLE TABAILLOU L ANGE GARDIEN LANGLADE LANGLARD LANGLE NIGONNERIE NOISETTE NOUAILLE NOUZILLE LANTAIGNIE LANTERNAT PACAILLE PAGENIE PAGNADE PALISSE PALLADE PAPETERIE PAUSE PAUSERIE PAUZADIE PELLEGERIE PENDUE PEPINIERE PERCHE PERELLE PERIERRE PERRIERE AUBINERIE BERTHE BOUEINE BOUENNE BOULESSIE CHATRE FAYE FAYOLLE FORET GARDELLE GASNE GRUE LANDE PARLIERE RENAUDIE REUX RIBIERE ROCHE SEYNIE VALADE VARY MONT VEYSSIERE PEURUSSE PEYRIE LAPEYRIERE PEYROUSSE PEYTAVIGNE PIDERIE BLANCHE NEUVIC PINETTE PISSAROTTE PIVAUDERIE PLAGNE LAPLAGNE PLAINE LAPLAINE PLAINE BEAUSOLEIL PLAINE FAYES FAYES PLANCHE PLANCHE LAVAUD PLANTADE LAPLAU LAPLAUD POCHE VERGNE POINTE POITEVINE POMELIE POMMERAIE PORTE POSTE POUETTE POUGE POUMEROULIE POUYADE POUZAUDIE POUZE LAPRADE PRADE PRAIRIE PRONCHE PRUNATIERE PRUNIE LAPS QUAIRE QUINTAINE QUINTANE RAIX LARAT L ARBRISSEAU L ARBUSSEAU LARCY LARDIDIE LARDIE LARDIMALIE LARDY REBALIERE REBEYROLLE REGAUDIE REGONDIE RENARDIERE RENAUDIE RESERVE RESERVE BAS PANLA RESERVE L ERMITAGE RESERVE FRAUD LAREUX REUX REYNIE LARGERIEUX LARGES LARIAUD RIBANDIERE RIBIERE RIBIERE BOST RIBIERE MIDI RIBOUILLE RICHARDIE RIPPE HAUTE RIPPE RISSANDERIE RIVAILLE RIVIERE LARMAT LARMONT ROBINERIE LAROCHE ROCHE ROCHE CORBIERE ROCHE NANTIAT ROCHE PICOU LEYRISSAUDE ROCHETTE RODE RODIERE RONDE RONGERE ROSERAIE ROUDARIE ROUDIE ROUEILLE LAROUSSE ROUSSEILLE ROUSSELLERIE LAROUSSERIE ROUSSIE ROUSSILLE ROUVELADE ROUVELIERE L ARRET LARREUX LARRON L ARTIGE LARTIGE EST LARTIGE VIEILLE LARTIMACHE LARTISSIE LARUE LAS ABBAYAS SAGNE SALESSE SAUTERIE SAVOIE CASSEPIERRE LAS BARRIERAS LAS BATAUDAS LASBAYAS LASBEINEIX LAS BENEIX LASCAUD LASCAUX LAS CHABASSIERAS LASCHAMP LASCHAMPS LAS COMBAS LAS COSAS LAS COSSAS LAS LASCOUX LAS CROUX SEILLE SELIVE SENEGIE SENTINELLE SEPIERE SERMONIERE SERRE SERVE SEYBANE SEYNIE LAS FIX LAS FOND LASFONT LAS FUTAS LASGEAS HAUTE LAS GORCEIX LAS GOUTTAS SIAUVE LAS LEBREIX LAS MICAUDAS SOLITUDE SOUCHERE SOUMAGNE AUGERE SOURCE RAYERES SOUS STATION SOUS STATION BAS MERIGNAC LAS PEIRAUBADE LAS PEYRADAS LAS PUAS LAS RETONDAS LAS RIBIERAS LAS ROCHAS LAS RONDEIX LAS ROUSSARIAS LAS RUAS LASSALLE LAS SOLAS STATION LAS TAUVEYRAS LAS TAUVEYRAS PUY MONT LASTIPEYRAS LASTOURS SUBERTANGE SUDRIE LAS VERGNAS TABLE TACHE TAMANIE TAURINERIE L ATELIER TERMENIERE TERRADE TERRASSE TEXONNIERE TEYSSIERE LATHIERE LATHIERES THIVERIE LATHONIE THUILIERE LATIERE TOUCHE TOUR LATOUR TOURELLE TOURNERIE TRACHAUSSADE TRAINIE TRAPPE TRAVERSE TREILLE TREMIE TREMOUILLE TRIBARDIERE TRIGALLE TRIQUERIE TRONCHAISE TRONCHAIZE TRONCHE TRONCHERE TRONCHEZE TROUBADIE TROUVAILLE BOISVIEUX LATTERIE LATTIER TUILERIE TUILERIE ARFEUILLE TUILERIE BROUSSE TUILERIE PICQ TUILERIE ROCHE TUILERIE CHATENET TUILIERE TUILIERE BORDES TUILIERE SECHERES TUILLIERE LAUBANERIE LAUBANIE LAUBARD LAUBART LAUBAUDIE LAUBIGNAC LAUCOURNET LAUDONIE L AUGE LAUGEAS LAUGEATS LAUGERE LAUGERIE L AUMONERIE LAUNAY LAURENCIE LAURERIE LAURIE LAURIERAS LAURIERE LAURIERE BRUNE LAURIGET LAUTERIE LAUTRETTE LAUZALET LAUZAT L AUZE LAUZELLE LAUZERAUD LAUZET LAVACHERESSE VALADE LAVALADE VALADE SUR AIXETTE VALETTE LAVALETTE VALLADE VAREILLE VARLIETTE LAVARS LAVAUBLOIS LAVAUD LAVAUD BOURGOIN LAVAUD BOUSQUET LAVAUD BUISSON LAVAUD CHEVALIER LAVAUD COUTEILLAS LAVAUD COUTHEILLAS LAVAUD COSSAS VAUDELLE LAVAUD FLEURET LAVAUD FOURNAUD LAVAUD JALOUNAUD LAVAUD LASCAUX LAVAUD PACAUD LAVAUGRASSE LAVAUGUYON VAULOUBE LAVAUPOT LAVAUX VAUZELLE LAVAUZELLE VEDRENNE LAVEDRENNE LAVENAUD VERGNADE LAVERGNADE LAVERGNE VERGNE VERGNE JOURDE LAVERGNE MINET LAVERGNOLE VERGNOLE MAZAUBERT VERGNOLLE LAVERGNOLLE LAVERINE VERINE VERNINE LAVEYRAT VEYRIE VEYRINE VEYTISOU VEYTIZOU LAVIALE LAVIALLE VIALLE LAVIALLE RABAZEIX VIE VIGNE VIGNE BOURG VIGNERE VIGNE ROBERT VIGNOTTE VILETTE VILLA VILLAINE LAVILLATE VILLATTE VILLAUBRUN LAVILLAUREIX LAVILLEDIEU BOST LAVILLEMICHEL VILLENEUVE VILLENUE SOUS GRANGE VILLETTE VILLOTTE VILOTTE VIOLETTE VIRADE LAVOUST LAVOUX VOZELLE L AZALEE ZAPHIX L FAYOLAS L EAUPERIDE PIC BANCAUD BANCHARAUD BARNAGAUD BARRY BAS BOSFRANC BAS BOURG ALLOIS BAS CAILLAUD BAS CHALARD BAS FAURE BAS FELIX BAS FUT BAS MERIGNAC BAS TEXON BASTIER BAS TOUR BAS VAUD BAS VAUX BAS VIGNAUD BATIMENT BATY BEAUVERT BEAUVERT VILLEMEDEIX BELHOMME BESSAUD BETOULET BLANCHET LE AU BOEUF CARTON L AGE LANAUD LAVAUD LESSINES NIVET BREUIL MAS RIS JALLOUX LAVAUD BOISMALDENT MALLEDENT MALLET VICOMTE BON ABRI BORDELEIX BOS BOS GRENIER BOS RABAUD BOST BOST LOUP BOST MARTIN TERRESSUS BOUCARET BOUCHAGE BOUCHAUD BOUCHERON BOUCHET BOUDEAU BOUIJOUX BOUQUET BOUQUET BUXEROLLES BOURASSAT BOURG BOURG BLANCHETS BOURG BOISMARTIN BOURG MOISSANNES L EBOURLIAT BOURNAT BOURNAZAUD BOURNAZEAU BOURNAZEAUD MAISONNETTE BOURNEIX BOURNEUIL BRAGARD BRAGUARD BRANLE BRET BREUIL BREUILH BREUILHATOUR BREULHATOUR BROUILLET BUIS BUISSON BURG BURGT BUSSIN CAILLAUD CAILLAUDOU CAILLOU CAILLOU BLANC CALOMET CAMP CAMPALAUD CAMP CESAR LECANIE CARTERON CATHALOT CERVEIX CHABLARD CHALARD L ECHALARDERIE CHALAT CHALET BELLERIES LECHALIER CHALUMEAU CHAMBON CHAMP CHAMPBLANC CHAMP LUCHAPT CHAMPLONG CHANDOS CHAPELOT CHARBONNIER CHASSAIN CHASSIN CHATAIGNIER CHATAIGNOL CHATAIN CHATAIN D AMOUR CHATAIN NEUF MINES LE FRAISSEIX REPENTI CHATELAT CHATENET CHATENET EN DOGNON CHAZEAU CHAZEAUD CHEDAIL CHENE PIGNIER CHENE VERT CHERBOIS CHEVAL BLANC CHEYREAU CHEYROL CHEYROUX CHEZEAU CHIRON L ECHIRPAUD L ECHO CLOPS CLOS CLOS JARGOT CLOUP L ECLUSE CLUZEAU CLUZEAUD COIN PARC L BILLAT COLLET COLOMBIER COMBAUD COMBEAU COMMUNAL COMPTANT COTEAU COUDERCHOU COUDERT COUDERT LATTES COUDERT MOISSAC COUDIER COUFFEIX COURET COURTIEUX COURTIOUX COUSSAC COUSSAT COUTEIL LECOUTERIE COUVENT COUX CROCQ CROIZET CROS CROUP CROUZY LECURA LECURAS LECURBIE LECURE BEAUDIFE DAUMAIL DEBAT DEGE DEGEAI DEJAI DEMANT DEPOT DESERT DOGNON DOLMEN DOUET DOUGNEIX DOUNEIX DOYONNET LEDUVEX FANIA FARGEAS FAURE FAUX FOND CLOS FORMANEIX FOT FOUILLOUX FOUR CHAUX FOURNET FOURVIEUX FRAISSE FRAISSEIX FRANCOUR FRESSINAUD FROMENTIN FUTIER GAI SALMON GALLIER GANET GARABOEUF GAROUT GARRAUD GAT GATINAUD LEGAUD GAUILLARD GAULIER GELINIER GENDRE GENET GENETEIX GENEVRIER LEGERIE GERMANAUD GERMANET L EGLEIZEAUD GONDEAU LEGONIE GOT GOT SOUS GOULET GOURCEROL GOUTHEIL ARBRE BEAUNE BEAUNE BILLAT LE BOISSE BONNEFONT BOS BOSVIGER BOUCHET BUEIX CHAILLAUD CHALEIX CHAMP CHATAIN ARTHUGERAS CHAVANAT CLOS COLOMBIER GRAMMONT MACHINAL MANCHINAL MASVEYRAND MASVEYRAUD METAYER MEYNIEUX MONTEIL MOULARD LE PAGNAC PEISSE PIC PIN POIRIER PRE PRE MONTAUD RIEUX SALE VEDEIX VERNET GRAULIER GRAVIER GRENOUILLER GRILLON GROS BOST GROS CHENE GUE ROCHE GUERAUDIER HAUT FELIX HAUT MALLEDENT HAUT MELIAUD HAUT SURZOL HAUT TOUR HAUT VAUZELAS LEITOR JAGON JANOUZAIS JONOUX JOUVEAU LAC LAC NORD LANDAIS LANDEIX LATIER LATTIER LERY LIAT LIBOUREIX LOUBARET LOUBIER LYS MADERAS MAGISTER MAGNAUD MAILLORAT MAINE MAINE TEXIER MAISONNIAU MALABRE MALABRE NORD MALABRE SUD MALAGNAC MALIBAS MANECHAUD MARACHE LEMARIERE LEMARRIERE MARTINET MARTOULET MARTOULET BLANCHERES MAS MASBARAUD MAS BARBU MAS BARTEIX MAS BOUCHER MASBOURDIER MAS BOYER MAS BRUNET MAS BUISSON MAS CERISE MAS CHATAIN MAS COUDERT MASCROISIER MAS GLANE MAS DEHORS MAS L AURENCE MAS VERGNE MAS VIE MAS L ETANG MAS L OR MAS LANDES MAS THOURON MASDOUMIER MAS BOST MAS LOUP MAS PUY MAS FAUCHER MASFERAT MAS FETY MAS GAUTHIER MAS GENEST MAS GENET MAS GIRANDEIX MAS GOUDIER MASHYVERNET MAS JOLY MAS JUDE MAS COLLERIE MAS ROCHE MAS SEUVE MAS LEVRAULT MAS 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LAC PUY PORTAIL PUY TOUR PUY VISAGE GUERINDOUX PUY FAUCHER PUYFRAUD PUY GARSAULT PUYGRENET PUY JOLI PUY LAGEYRAT PUY LAROUSSE PUY MAGNOT PUY MAILHAC PUY MALLET GREIGNAC PUY MARTIN PUY MASURE MAZURE PUYMAUD PUYMENIER PUY MERY PUY MESNIL PUY MIALLET PUY MORY PUYNEGRIER PUY PARLIER PUY PERVIER LEPUY PUY PEZARD PUY REDON PUY RICHARD PUY ROBI PUY ROGER PUY ROUDIER PUY PUY TABARLET PUY TEIGNEUX PUYTIGNON PUYTISON PUYTREN PYLONE FONTBESSE QUARTERON L EQUERRE QUEYRAUD QUEYRAUX QUEYROI QUEYROIX QUEYROUX LERABAZEIX RACA RACCA RATEAU RATELAN RATELAND LERAUT RECLOS REGLAUDIS REISSEIX REJATAS RELAIS PECHER GOT REPAIRE REPAIRE TARDIVET LERETERIE REVEIL REVIDAUD REYNOU RIS CHAUVERON L ERMITAGE ROCHER ROCHER CARPARIE ROCHILLON LERODIE RONLARD ROSEAU ROUCHILLOU ROUCHILLOUX ROULE ROUVEIX ROUVEIX BAS ROUVEIX HAUT ROUVERAT ROUVEREAU ROUVERET ROUZEIX RUFFASSON LERY 4 VENTS 7 CHATAINS SABLARD ACACIAS SAGE AGES AJAUX ALLEES ALLOIS ANTEBES APPEAUX ARBOUILLERES ARCIS ARRETS AULIERES SAUZET SAVARY BACHAUDS BAISSES BALOFFES BARNAUDS BAROUTIES BARRAS BARRES LESBARRES BARRIERES BASCHAUDS BASSES FAYES BATAILLES BATAUDAS BATIES BATITS BATTES BAUX BEAUX MAS BEIGES BELIAS BELLERIES BERGNES BERTIES BESSADES BESSAILLES BESSIERES BETOULLES BIARDS BIEZ BILLANGES BIZARDIES BLADS BLANCHERES BOIGES BOIGES MIDI PALAIS BORDERIES BORDES BORDES OUEST BOS BOSNAGES BOUCHATS BOUEGES BOUIGES BOUIJOUX BOULEAUX MONTIMBERT BOULINIERES BOURNATS BRANDES BRAS BREGERES BROSSES BROSSES CHANTEAUX BROSSES PERROT BROUSSES BRUGEAUDS BELLEVUE BRUGEAUX BRUGES BRUTINES BRUYERES BUFFANS BUSSIERES CADOPHIES CAILLOUX CAIRES CAIRES SYLVESTRE CAMBUSES NORD CANTINES CARDERIES CARRIERES CENDRILLES BOS CERISIERS CHABANNES CHALARDS CHAMBOUX CHAMPS CHAMPS CHANCEAUX CHAPELLES CHARBONNIERES LES CHARRAUDS CHATAIGNAUX CHATAIGNOLES CHATRES CHAUMES CHAUMETES CHAUMETTES CHAUVEIX CHAVES CHEINAUDS CHENES ROUGES CHEROUX CHEVAILLES CHEVALIERES CHEYROUX CHIERS CHINAUX CHOMES CHOUETTES CIMES CINQ CIREIJAUX CLAUDS CLEDIERES CLIDES CLOSES CLOTURES COMBES COMBES AUVENT CONCES CONCES VEYRINAS CONDAMINES CONGERIES COREIX CORNUES COSJANOTS COSSES COTEAUX COTES COUPES GORSAS COURBES COURRIERES COUTILLES COUTURES COUTURES L HERMITERIE COUX CREUSES CROS CROUZETTES CROUZILLAUDS LESCURAS LESCURE BAUDUFFE DAMES DAUGES FORGEIX DAUMIERS DEBATS DEFAIX DOGNONS SECHAUX ECOLES ECURES EFFES EGAUX EGUZONS ESSARDS ESSART ESSARTS SEUX FAITES FARGES FARGETTES FAYES FAYES PERRET FENIEUX FLAMANCHIES FLAMENCHIES FONTAINES BREGERAS FONTAINES BREGERAS FONTAINES VERT FONTANILLES FONTENAILLES CLAVEYROLAS FONTENILLES FONTS FORETS FORGES FOSSES FOUGERES FRAUDS FROMENTAUX GABARRES GABIES GABIES POULOUZAT GABILLOUX GABISSES GACHERES GACHES GALLERIES GANES GANETTES GANNES GARAUDIES GARDELLES GARDES GARENNES GATINES GAUDINOTTES GAUILLARDS GENETS GENETS VERGNE GENTES GERMANES GIRONNEAUX GORSES GOTERIES GOUGES GOUTAILLES BUXEROLLES GOUTILLES GOUTTELETTES TUILERIE GOUTTES GRAFOUILLADES GRANDCHAMPS GRANDES BORDES GRANDES CHAUMES GRANDES FORGES GRANDES HERBES GRANDES LIGNES GRANDES MAGNELLES GRANDES PIECES GRANDES TERRES GRANDES VIGNES GRANDS BOS GRANDS CAIRES GRANDS CHAMPS GRANDS FATS GRANDS MAGNEUX GRANDS MAGNIEUX GRANDS MONTS GRANGES GRANGES FOIRE GRANGETTES FARGEAS GRATTES GRAULES GRAVAIS GRAVILLES GRECHOUX GRENOUILLES GRILLERES GRILLONS GUILLOUX HAUTS D ENVAUD 10 ENVAUD HAUTS D ENVAUD ENVAUD HAUTS PUY HERBETS HERSES LESIGNAS LESIGNAT SIRIEIX JALINIEUX JAMMOTTES JAROUSSES JARTRES JAULIES JEUNES CHATAINS JONCHERES JOURNADAS JUSTICES LANDES LANDES BOS CAUX LANDES CHERVEIX BAS LANDES JOUVEAU LANDES LACAUD VIOLEIZEIX LANDES ROUVEIX LANDES PLAISIRS LANDES EST LANDES LEBUIS LANDES PUY LANDES PUY ROUDIER LANDES TRASPONT LANDOUX LANVERS LATHIERES LATTES LAURENCIERES LENVERS LEVADES LEZES LIEVRES LIGNES LILAS LISSES LOGES LOTISSEMENTS LUNADES LYS MAISONNETTES MAISONS MAISONS NEUVES MAISONS NEUVES BOURG MAREUX MARGINIERES LESMARIERE MARIONNETTES MARJOUX MARRONNIERS MARSEILLES MAS MASVESIS MAYS MAZIAUDS MEANDRES MERGLERIES MESANGES SOUS METTES MEYNIEUX MEZIERES MINARDIERES MINES MONDOUX MONTADES MONTAGNERES MONTETS MONTS MOTTES MOULADES MOULADES BEAUSOLEIL MOULINS MOULLETTERIES MOUNIERES MOUSSEAUX MURAILLES MURES NARDIERES CURSAC NEGRES NEPLOUS NOCHES NOUAILLES NOUHAUDS OLLIERES ORS SOULIER OURGEAUX PAICEIX PAILLERES PALANGES VERTHAMONT PALENES PALENNES PALENNES BOISSAC PALLADAS PANISSIAS PAQUERETTES PASSADOUX PATAUDES PAUSES PAYAUX PECHAUDS CHATENET PELADES PELOUSES PENDANTS L ESPERANCE PEROUSES PERRIERES PERRIERES VERVIALLE PETITES BETOULLES PETITES BORDES PETITES FORETS PETITES LANDES PETITES LIGNES PETITES MAGNELLES PETITES MAISONS PETITES VIGNES PETITS 4 VENTS PETITS CAIRES PETITS MAGNEUX PETITS RIEUX PEYRICHOUX PEYROCHES PEYRONNELLES PIAULERIES PIAURES PICS PIECES LESSINE PIECES LESSINES PIECES MOREAU PIERRES PIERRES DOULINES LESPINAS LESPINASSE PINS PLACES PLACIEUX PLAIGNES PLAINES PLAINES REJATAS PLAISIRS PLANCHES PLANCHETTES PLANCHETTES VILLAGEAS PLASSES PLATANES PLATS POINTIERS POINTIS POMPERIES POUGES POUSSES POUYADES PRADEAUX PRADELLES PRADES PRADILLES PRADILLES PLACES PRAS PRES EVEQUAUX PRES GRAS PRUNES PRUNIERS PUYS PUYS CHABAUD QUATRE POTEAUX QUATRE VENTS QUATRE VENTS PERISSAT QUATRE VENTS VEYRINAS QUATRE VIAS QUENES RAMADES RANGEES RATELADIS RAVENEAUX RAYERES RAZES REBEIROUX REBRAS RECOUDES RENARDIERES RENNES RESERVES RESSAUDIS RIBIERES RIBIERES SILORD RIBIERES RICARDIES RICHARDS RICHARDS PUY FROID RIEUX RIEUX JEUNES RIEUX PEYROUX RIVAILLES RIVAUX ROCHES ROCHES VILLEBRANGE ROCHETTES RONGERES ROSES ROUCHILLOUX ROUCHOUX ROUILLES ROUMEJOUX ROYAUX SAGNES SAILLES SALESSES SALLES SANGSUES LESSARD LESSART SAULIERES SAUVAGES SAUZADES SECHERES SEPPES SERVENTIERES SESSAUDES LESSILLADE LESSINES SIREYJOLS SOULIERS SOULZORS SOUMAGNES SUBREUTTES STADE TAFFRES TAILLES TAILLIS TANNERIES TANTARIDIERS TARRIERES TENELLES TERMES TERRASSES TERRES CROIX TERRES PUY DIEU TERRES PUY DIEU TEXONNIERES THERMES TILLEULS VILLETTE LESTOR TOURETTES L ESTRADE TREMBLADES TRIBARDIERES TROIS ARBRES TROIS BORNES TROIS CERISIERS TROIS SAPINS TUFFAUTS TUILERIES TUILIERES TUILLIERES TUILLIERES CINQ SURZOL SUS VALADES VANNEAUX VAREILLES VASEIX VAURIES VERGES VERGNADES VERGNASSES VERGNES VERGNES BRAMAUD VERGNES MAS REVERY VERGNOLLES VERINES VERRINES VEYSSIERES VIALARDS VIAS VIGENAUDES VENTRE NOIR VIGERES VIGNES VIGNES DIEU VIGNES BUXEROLLES VIGNES BUXEROLLES VIGNES ROYERE VILLARS VILLETTES VITAILLES TABARLET TAILLADIS L ETANG TANIER TEILLOL TEILLOL MASDEHORS TEMPLE TERRASSON TERRIER TERTRE THEIL THERME TILLEUL L ETOILE LETRADE LETRANCI TRAUD TREUCH TREUIL TRIADOUR TRIANON L ETRILLE TRONC L ETRUCHAP TUQUET TUQUET BETOUR LETUQUET FOURNEAU LEUGE LEUGRATTE VALOIS VERDAUD VERDEAU VERDIER VERDOYER VERDURIER VERGER VERGER POULENAT VERGT VERT VERT COTEAU VESINIER VEYSSET VIADUC VIALLARD VIEUX BOISSEUIL VIEUX VIEUX CHENE CHAREILLES VIEUX CREZIN VIEUX MONT VIEUX VIEUX HILAIRE VIGNAUD VILLAGEAS VILLAGEAT VILLARD VILLEVIT VIRADIS VISTRAT VIVIER L EXPARDELIERE LEYBARDIE LEYCHOISIER LEYCOUSSINERIE LEYCURAS LEYCURAUDOUX LEYCURAUFAURE LEYCURAURIAUX LEYCURE LEYCURE CHATEILLIERE LEYGAT LEYGEAS LEYGOUTERIE LEYLUSSAC LEYMARIE LEYRAOUT LEYRAS LEYRAT LEYRAUD LEYSENNE LEYSSARD LEYSSENNE LEYSSERIE LEYTAT YUCCA FREGEFOND LEZIGNAC LEZIGNAT L HERITIERE L HERMITAGE L HERMITERIE ROUZEIX L HERMONT L HOPITAL L HOSNE LHOUILLET L HOUME LHOUME LIADET LIBAUDIERE LIBERTE LIBOUREIX LIBRE OREILLE ANGELAUD ANGLARD ANTARDIEU ARCHAYAUX ARFEUIL ARFEUILLE ARTHUGERAS BACHELLERIE MIDI BACHELLERIE NORD BALAN BALEZY BAS PUY NEIGE BASSE MAZELLE BEAUFORT BEAUPRE BEAUREGARD BEAUVAIS BEL AIR BELLEGARDE BELLEVUE BIAUGEAS BLANC FINET BRUJOUX D AMOUR JOLI LACROIX BOISSE BORD BOSMATHE BOS VIEUX BOUSSIGNAC BRET BRINDES BUSSIN BUXEROLLES CELICROUX CHABANAS CHAMBAREILLE CHAMBERET CHAMBOURSAT CHAMP CHAMPANEIX CHAMP JUGIE CHAMP RIBIERE CHAMPS CHANTECAILLE CHANTEGROS CHANTELAUVE CHARNAILLAS CHATREIX CHAVAGNAC CHENI CHEZ BEAUGY CHEZ BEILLOU CHEZ FOURNIER CHEZ MONDIE CHEZ PICAT CHEZ PICOCHE CHEZ VEVAUD CLOS BARRE CODILLE CORDELAS COUBRAS COUERASSAS COURBEIX COURBIAT COURTILLAS COUTURES LAJOUX COYOL CREZIN CROCHAT CROIX BLANCHE CROIX COULAUD CROIX COYOL CROIX GONDAT CROS NOYER CROUZEIX CROUZILLAT CROYER CUBERTAFOND DOUILLAC DROULY ECHAUDIERAS EMERAT EN GARENNE ENGUERNAUD FARGETAS FAUGERAS FAYE HAUTE FENEROLLES FERIGNAC FERMIGIER FIGEAS FONTCHABRIER FONTERNE FONTGEAUDRANT FONTSALADE FOREST FORGE BASSE FORGEIX FRAISSEIX FROUGEIX GABILLOU GATE SOLEIL GLANE GONDANDEIX GONDAT GORCEIX GOUPILLAC GOUPILLOUX BEAUNE GRANGES CHANTELAUVE GRIS GROSSEREIX HAUT MERIGNAC IMBOURDEIX MARLIAGUET JABALIE JABREILLES ROY JURIOL BACONIE BASSE GARDE BEAUBERIE BELLE BENECHIE BORDERIE BRETAGNE BRUNERIE CHABANNE CHABROULIE CHAUME CHAVADE CONDAMINE CROIX D ANGLARD CROIX BOURNAZEAU CROIX L AGE CROIX L ARBUSSEAU DEPESSE FAYOLLE FEUILLEE FORET FOUCAUDIE CHOME GROSSE BORNE HAUTE GARDE JALETTE LAJOUX JUGIE LANDE LINGAINE LONGERE MADONE MAISON NEUVE MAZELLE LAMBERTIE MOULINERIE LANDES GABILLOU NOUGERADE PERRIERE PRADE VERGNE LAPLAUD RENAISSANCE RIBIERE SABLIERE LAS RESERVAS LAS VIAS TAILLADISE LATHIERE TOUR TUILERIE TUILIERE LAUBANIE LAUBAUDIE LAUGERIE LAURIERAS LAURIERE LAURIERE BRUNE LAUTERIE LAVAUD BAS COUDERT BAS QUARTERON BOCAGE VIGNE BOST BOUCHET BOUQUET BOURNAZEAU CABARET CHAMPEIX CHATELARD CHATENET CHEVRIER DESERT DOGNON GOT LAVAUD GOTH CHATAIN CLOS GRAVIER KIOSQUE LANDOU MAINE MAINE BOST MALABRE MALUCHAT MAS L EMERIGIE MONTEIL MONTIN RABAUD PAVILLON BOURNAZEAU CLOS LAGE LANDOUGE LAVAUD POUDRIER PRADAUD PRE SEC PUY PUY DIEU PUY MOULINIER PUY NEIGE RACA ROCHELOT ROUVEILLOUX ROUZEIX BARRIERES BATIMENTS BESSIERES BIGOURIES BOUCHETTES BOUNEILLAS BROSSES BRUGES BRULADES CAMBUSES CAVES CHAMPS LES CHARREAUX CHEYROUX COMBES COUTURES COUTURES L HERMITERIE ESSARTS FARGES FAYES FRANCINES GOUPILLERES GOUTAILLES HAUTES PILATERIES HAUTS GENETS JOUBERTIES JUILLARDIES LACS LOGES MARTINES PALEINES PERRIERES PLANCHETTES POULETTES PRADES QUATRES PINS ROCHILLOUX SEGUINES LESSINES TAILLES THERMES VERGNES VICQUERIES VIRADIS L ETANCHOU TERME ROUGE TREUIL VIEUX JURIOL VILLAGEAS LEYCURAS LEYRISSAUDE L HERMITERIE L HOMME BOST MADERE MAGENTA MAISON NEUVE MALVOISINE MANDERESSE MANSAC MARCILLAC MARCOGNAC MARLIAT MARSAC MARSAGUET MAS BATIN MAZIERAS MECHENIE MERIGNAC MOISSAC MOISSAGUET MONTAUBAN MONTEVERT MONTIGNAC MONTJOVIS MONTLUC MONTPLAISIR MORTEROLLES MOULINARD BRULE FOLIE MURAT MURIOL NEGRELOUBE NOUAILHAS PEREIX BEAUNE MECHENIE GONDANDEIX MONTLUC PEYRAZET PIERREBRUNE PIERRECHAVE PILLET PLAISANCE PLAISANCE MOISSAC PLANCHE AUX FAUX PLAUD PONTEIX PORPHYRE POULENAT POURIOL PRENDS Y GARDE PRESSAC PRESSALEIX L OUTRE PUY ANNET PUY D ARTHUGERAS PUY BAS PUY HAUT PUY VALETTE PUY FAYARD PUY JOURDE PUY MAROT PUY ROCHER PUY VIGNAUD QUARTERON QUINSAC RAQUIAUD REIGNEFORT REPAYE ROCHE ROUMEGOUX SEZEILLAC SICIOREIX SOIRAT TEXONNIERAS TROUSSEGERAS TUQUET FOURNEAU UZURAT VALETTE VALPARAISO VENTENAT VERGNAUD VERGNE RIGAUD VERGNES CROCHAT VERT COTEAU VERTHAMONT VILLEBOIS VILLEBRANGE VILLEFELIX VIROLAS LIGNAC LIGONAT LIGOURE LILAS L ILE LIMON LINGAUD LINTIGNAT L ISLE LISONNE LOGE LOGES LOGES LOGIE LOGIS PAILLE LOGIS CHAMPAGNAC LOMAILLERIE LOMBARDIE LOMBRE LONCLETAS LONGE LONGE CHAUX LONGEPLANE LONGEQUEUE LONGERE LOR L ORANGERIE L OR PUY LORETTE LOSMONERIE LOTCHENES LOTISSEMENT 10 GENETIE LOTISSEMENT 129 PLANCHE LOTISSEMENT 12 GANNES LOTISSEMENT 14 4 SALINES LOTISSEMENT 17 FONTANELLE LOTISSEMENT 1 QUATRE VENTS LOTISSEMENT 1 VIEUX CREZIN LOTISSEMENT 23 AUPHELLE LOTISSEMENT 35 35 METAIRIE LOTISSEMENT 3 BOUQUET LOTISSEMENT 3 PRADILLES LOTISSEMENT 3 ROSEAU LOTISSEMENT 3 VIEUX BOISSEUIL LOTISSEMENT 4 JUDIES LOTISSEMENT 54 54 GARDE LOTISSEMENT 5 GATEBOURG LOTISSEMENT 5 SALINES LOTISSEMENT 64 BESSIERES LOTISSEMENT 6 PRADILLES LOTISSEMENT 8 CONDAMINE LOTISSEMENT 9 ETANG LOTISSEMENT ALLEES LOTISSEMENT AUPHELLE LOTISSEMENT AUPHELLE AUPHELLE LOTISSEMENT BARRE LOTISSEMENT BAUDY LOTISSEMENT BEAUMATHE LOTISSEMENT BEAUVAIS 15 BOURG LOTISSEMENT BEAUVAIS 6 BOURG LOTISSEMENT BEAUVAIS BOURG LOTISSEMENT BEIGES LOTISSEMENT BEL AIR LOTISSEMENT BELLEVUE 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LOTISSEMENT COMMUNAL 5 BOURG LOTISSEMENT COMMUNAL 6 BOURG LOTISSEMENT COMMUNAL 7 BOURG LOTISSEMENT COMMUNAL 8 BOURG LOTISSEMENT COMMUNAL 9 BOURG LOTISSEMENT COMMUNAL BOURG LOTISSEMENT COTEAUX METRO BOURG LOTISSEMENT COURDELLES LOTISSEMENT CROIX VERTE LOTISSEMENT D AUGERES LOTISSEMENT CHASSAGNAT LOTISSEMENT BARNIQUE BARNIQUE LOTISSEMENT CHABANIE LOTISSEMENT LOTISSEMENT VIGNE BOURG LOTISSEMENT L ETANG LOTISSEMENT CHENES LOTISSEMENT CROUZETTES LOTISSEMENT GANNES LOTISSEMENT VIGNES BOURG LOTISSEMENT TEXONNIERAS LOTISSEMENT DOMAINE GARDE LOTISSEMENT DOMAINE MERIGNAC LOTISSEMENT BONHEUR LOTISSEMENT CHEYRAUD LOTISSEMENT MAS L AURENCE LOTISSEMENT PARC LOTISSEMENT PAVE LOTISSEMENT PECHER LOTISSEMENT GRAMMONT LOTISSEMENT L AGE 3 LOTISSEMENT POUEIX LOTISSEMENT STADE LOTISSEMENT ECOLES LOTISSEMENT ECUREUILS BOURG LOTISSEMENT ETANG 10 BOURG LOTISSEMENT ETANG 11 BOURG LOTISSEMENT ETANG 14 BOURG LOTISSEMENT ETANG 3 BOURG LOTISSEMENT ETANG 4 BOURG LOTISSEMENT ETANG 9 BOURG LOTISSEMENT ETANG BOURG LOTISSEMENT EYCOUVEAU LOTISSEMENT EYCOUVEAUX RONGERES LOTISSEMENT FARGES LOTISSEMENT FERNAND FOUREAU LOTISSEMENT FONTAINE CAILLAUD LOTISSEMENT FONTAINES LOTISSEMENT FORET LOTISSEMENT FORET 1 BAS RAYNAUD LOTISSEMENT FORET ALLOIS LOTISSEMENT FOURNE LOTISSEMENT FRENES 4 BOURG LOTISSEMENT FRENES 7 BOURG LOTISSEMENT FRESNES BOURG LOTISSEMENT FRESSINGEAS LOTISSEMENT FRONTIGNAC BOURG LOTISSEMENT LOTISSEMENT GARENNE LOTISSEMENT GARENNES BOURG LOTISSEMENT GARTEMPE BOURG LOTISSEMENT GATTEBOURG LOTISSEMENT GENETS LOTISSEMENT CHENE LOTISSEMENT QUARTIER LOTISSEMENT GRANDS CLOS LOTISSEMENT GRAVATAUD LOTISSEMENT GRAVILLAS GRAVILLAS LOTISSEMENT HACHES LOTISSEMENT HAUTS BEAUFORT LOTISSEMENT HAUTS D ENVAUD 13 ENVAUD LOTISSEMENT HAUTS D ENVAUD 14 ENVAUD LOTISSEMENT HAUTS D ENVAUD 15 ENVAUD LOTISSEMENT HAUTS D ENVAUD 1 ENVAUD LOTISSEMENT HAUTS D ENVAUD 20 ENVAUD LOTISSEMENT HAUTS D ENVAUD 4 ENVAUD LOTISSEMENT HAUTS D ENVAUD 5 ENVAUD LOTISSEMENT HAUTS D ENVAUD ENVAUD LOTISSEMENT HORIZON CHABROULIE LOTISSEMENT JAMOTTES LOTISSEMENT JEANNE D ARC BOURG LOTISSEMENT BARRE LOTISSEMENT LAC LOTISSEMENT CHAPELLE 1 BOURG LOTISSEMENT CHATAIGNERAIE LOTISSEMENT FOURNE 2 FOURNE LOTISSEMENT GARTEMPE 10 BOURG LOTISSEMENT GARTEMPE 1 BOURG LOTISSEMENT GARTEMPE 3 BOURG LOTISSEMENT GARTEMPE 6 BOURG LOTISSEMENT GARTEMPE BOURG LOTISSEMENT PEPINIERE FONTS LOTISSEMENT PERRIERE 5 BOURG LOTISSEMENT PERRIERE BOURG LOTISSEMENT PEYRADE LOTISSEMENT PLANCHE LOTISSEMENT PRADE LOTISSEMENT RENARDIERE THIAS LOTISSEMENT ROCHE LOTISSEMENT LAS MINAUDAS LOTISSEMENT LATHIERES LOTISSEMENT TOUR LOTISSEMENT TREILLE LOTISSEMENT VERINAUDE VEYSSET LOTISSEMENT PECHER BOURG LOTISSEMENT PRE VIGNE LOTISSEMENT RIBARDY LOTISSEMENT AUGERES 2 LOTISSEMENT BESSIERES BESSIERES LOTISSEMENT BRUTINES LOTISSEMENT CHARRAUDS LOTISSEMENT CHENES BOURG LOTISSEMENT COTEAUX BOURG LOTISSEMENT FARGES LOTISSEMENT GOUTTES LOTISSEMENT GRILLONS 1 LOTISSEMENT GRILLONS 2 LOTISSEMENT PIERRES DOULINES LOTISSEMENT PINS LOTISSEMENT PLACES 17 BOURG LOTISSEMENT PLACES BOURG LOTISSEMENT THEIL LOTISSEMENT L HORIZON CHABROULIE LOTISSEMENT MACHETARD LOTISSEMENT MAISONS NEUVES LOTISSEMENT MAS BARETTE LOTISSEMENT MERCERIE LOTISSEMENT MIAUMANDE LOTISSEMENT MONT GERBASSOU LOTISSEMENT NICOULLAUD LOTISSEMENT OREE LOTISSEMENT PANORAMA LOTISSEMENT PARC LOTISSEMENT PECHER 1 BOURG LOTISSEMENT PECHER 4 BOURG LOTISSEMENT PEPINIERES LOTISSEMENT PERRIERE BOURG LOTISSEMENT PERRIERES BOURG LOTISSEMENT LANDE LOTISSEMENT ROCHER 1 BOURG LOTISSEMENT ROCHER 7 BOURG LOTISSEMENT PEYROT LOTISSEMENT PEYROT 15 BOURG LOTISSEMENT PEYROT 4 BOURG LOTISSEMENT PEYROT BOURG LOTISSEMENT PLACES LOTISSEMENT PLASSES LOTISSEMENT PLEIN CIEL 2 BOSMIE LOTISSEMENT ROCHES POULOUZAT LOTISSEMENT PRADILLES PRADILLES LOTISSEMENT PRESSOIR BUISSON LOTISSEMENT PUY CHATELARD LOTISSEMENT RENARDIERE LOTISSEMENT RENARDIERE THIAS LOTISSEMENT RIBARBY LOTISSEMENT RIBARDIS LOTISSEMENT RIBARDY LOTISSEMENT ROCHE LOTISSEMENT ROUCHILLOU LOTISSEMENT ELOI LOTISSEMENT SAINTE LOTISSEMENT SOURCES 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MENARDIE MENDIERAS MENEIREIX MENIERAS MENTEIX MENUSSAC MERCERIE MERGLERIES MERIGAUD MERIGNAC MERIGOT MERINAS MERLIS MERLY MERY MESNIL MESOUGUE MESSAC METRO METTES MEYGEAS MEYJONIAL MEYNIAL MEYNIEUX MIAUMANDE MILANGES MILHAC MILHAGUET MIMOLLE MINES MINGOUX MISERAT MOISSAC MOISSAGUET 3 MONTAUBAN MOISSUN MOLLE MOMBESSIER MONBEUGE MONCEAU MONCESSEAUX MONCHENON MONDESIR MONDON MONDOUX MONERIE MON IDEE MONISMES MONLANCHE MONLARGE MONNERIE MONPOUTIER MONS MONSAC MONS EST MONT MONTADES MONTAGRIER MONTAIGUT MONTANAUD MONTANTY MONTARGIS MONTASSAUD MONTAU MONTAUBAN MONTAUD MONTAUDET MONTAUDIE MONTAUMARCHAND MONTAUMART MONTAURAN MONTAUTRE MONTAZAUD MONTAZEAU MONTAZEAU LACAUD MONTAZOT MONTBESSIER MONTBON MONTBRANDEIX MONTBRUGNAUD MONTCALM MONT CALME MONTCHAUD MONTCHENON MONTCOCU MONT COCU MONTCOCU FAYANNE MONTCOUDIOUX MONTDOUHAUT MONT RABAZEIX MONTEAU MONTEE CHARRIERS MONTEE LAVAUD MONTEE MASBATIN MONTEE THEIL MONTEE TRACHERAS MONTEGUT MONTEIL MONTERNON MONTESOUR MONTET MONTEZOL MONTFAYON 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COMBESSE COURIEUX COYOL CROSSAS DIEULIDOU DOMPIERRE DROUX FAYE FIGEAS FIRBEIX FONFROIDE FOURNAUD FREGEAIGE FREGEAIGUE GENSAIS GERAL GOUTAY GRAFFEUIL GRATOULET GRINGALET GUILLAUMERIE JAMEYRAT BARBARIE BARRE BOILERIE BOUCOLE BOURETTE BREGERE BRIDERIE CHABASSIERE CHAIZE LACHENAUD COTE LACOUR LACOUR LACOUR LACOUX COUZE FORGE GALAMACHE GANCE GARDE LAGE L AGE GERBE LAJOUMARD JUGIE LANDE LOCHERIE LAMBAUDIE MIE MONTAGNE MOULINASSE PERDRIX PERRIERE PLANCHE POUGE RIBIERE LAROCHE ROCHE LARRON SALESSE LASCAUT LASCAUX LAS ROCHAS L ASSIER TACHE LATOUR L AUMONE LAURIE LAVAUD LAVAUGUYON LAVAUPOT VERGNE VILLETTE L EAU LEGAUD L ETANG L EYSSARD LEYSSERIE MADAME MAISONNAIS MASSALOUX MICHELE MONTANAUD MONTAUTRE NOUAILHAS NOUAILLAS PEYRAHOUT PEYTAVEAU PONTABRIER PREMARTIN PRESSAC PUYRASSOUS RANCON REBEYROLLES RICHEBOURG ROCHE ROUFFIGNAC ROUSSET SAGNAS AUVENT BAIGES BORDES CAILLOUX CHENAUX COLLES COMBES FEUILLES GRATTES GROLLES HEBRAS SILIEM JALADAS MONTS SOULIGNAC PERES PLANCHETTES PRATS RHUES ROCHES SANGLES SECHERES TARN TOUR TRANCHEPIE VAUDGEAI VERGNOLLE VIENNE VILLEBOIS VILLEVALEI VILLEVALEIX D OZIERS BECHAT BREUIL BURG CHATAIGNIER CHATAIN CHATENET CLAIR COURET DEROT DOGNON MAITRE MAS MASBERTIER MAS BLANC MAS GLANE MONTHEIL PIC MOULIN GORRE PREDEAU PRIEUR PUY PUY EMBARD PUY LAURENT RACA RAT REPAIRE ROC SAGE TEMPLE THEIL VERGER FOLLET GADY GALANT GERY GIRAUD GRAVATEAUD GUERINDOUX JAMMEYRAT JAPAUD JAUNIE JOYEUX BASSE PRADELLE BOUTEILLE LACOUR JUGIE L ARTIGE LAS LEBREIX LE AUTHIER BLANC PERRIN BREUIL BRULE DAUMAIL BOIROT CROS FIGEAS JUNIAT BARBARIE LACOUX LAJOUMARD JUDIE COTE LAURIE PUYMORY COTES PERES ROCHES ROCHES CONFOLENS VERNON DOUILLAC CROS PICQ LE MONTEIL PINTOU LE RATEAU REPAIRE LIEUTENANT LOMBRAS MARTINET MAS MATTEAU MAUMAUD MAUMOT MAZAUD MINET MINGOUX MONJONC MONTJONC MURAT RIVAILLES NEUF NOIR PARINET PAUTE PINTOU PLET NEDDE PONTY PUYMARTIN PUYROBI QUEYROUX RATEAU REFOUR RIBIERES RICHARD MOULINS SALAS SILORD SIQUET SIVERGNAS SOURUE SOUS CHAMPMAIN TEILLAUD THEILLAUD TREILLARD TRULY VAUGELADE VAUZELLE VERGNES MOUNIERES MOUNISME MOUNISMES MOURIOL MOUSSANAS MOUSTIERS MOUTIERS MURAT MURAT HAUT MURAUD NABOULIERAS NADALIE NAILHAC NAVALEUIL NAVALEUIL 5 RENARDIERES NAZAT NEGRELAT NEGRELOUBE NEGREMONT NEGRIGNAS NEGRIGNAT NEPLES NEPLOUX NEPLOUS NEPOULAS NEPOULAUD NEPOUX NERGOUT NEUPLANCHAS NEUVIALLE NEUVIC NEUVILLAS NEUVILLE NEYCHAT NEZIEZES NICOULLAUD NIGONNERIE NIOLLET NOAILLAS NOCHE NOGEAS NOLLET NONARDIE NONTYON NORD GARDE NORD NOUAILHAGUET NOUAILHAS NOUAILLAS NOUAILLE NOUAILLES NOUEIX NOUELLES NOUHAUD NOUHAUT NOUIT NOUVELLE NOUZIERE NOUZILLE NOUZILLERAS NOVA NOVAS NOYER NOYERAS NUMERO 8 CHAMPOUR OLLIERES ORADOUR ORBAGNAC OREIX ORGNAC ORS OVIERS OZIERS PABOT PACAGE MILIEU PACAILLE PAGENIE PAGES PAGNAC PAILLERAF PAILLERES PAINCOURT PAISSEIX PALAIS PALINIERE PALISSES PALLAND PALLOUX PAMPALY PANECHE PANGUET PANISSAC PANISSAT PANISSIAS PAPE SOLEIL PAQUERETTES PARADE PARADIS PARC THEULET PARENTE PARPAYAT PARSAC PASSADOUX PASSAGE ACHILLE FEVRE PASSAGE AGE 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PAUCHAVERIERAS PAUGNAT PAULHIAT PAULMET PAUZES PAVE PAVILLON ROCHE PAYAUD PAYAUX PAZAT PECHER PECHEREAU PEDAVIE PEGE PELADIS PELEGERIE PELLECHAVENT PELLECHEVENT PELLENIE PELOUNEIX PELOUSES PENAUDERIE PENAUX PENELIE PENITENTS PENY PEPINIAS PERANCHE PERANTONIE PERASSOUX PERBIN PEREIX PERET PERIAUD PERIGNAC PERIGNANAS PERIGORD PERISSAT PERISSAT ESSARTS PERISSET PERMINAUD PERNAUD PEROU PEROUSAS PEROUSES PERRASSADES PERRET PERRIER PERRIERE PERRIERES PERT PERTH PERTHUIS PERTUIS PERUSSAS PERUSSAT PETAVIGNE AU BAGNOL BASTIER BEAUBIAT BEAUNE BELLEGARDE BETOUR BILLAT PETIT CHALE BOISGIRAUD BOISSE BONNE BONNEFONT BORDEIX BOSVIEUX BOUCHET BOUQUET BUEIX BUISSON CHALEIX CHALIER CHAMPAGNAC CHAMPAGNAT CHARRAT GAILLARD CHAVANAT CHAVANNAT CHERVEIX COLOMBIER CONFOLENS COUDIER CREZIN CROS CROUZEIX BERTHE BOUEINE BOULESSIE BREGERE CHAISE ECHERAT COMBORNIE CORDELLE ECUBILLOUX FORET GAGNE GARDE GARDELLE GASNE GORCE GRANGE JOUVIE LANDE PARLIERE RENAUDIE RIBIERE PETITES BETOULLES PETITES BREGERES PETITES CAIRES PETITES CHEVAILLES PETITES FORETS PETITES FORGES PETITES GRANGES PETITES LOGES PETITES MAGNELLES PETITES MAISONS PETITES MAISONS ARFEUILLE PETITES MAISONS D ARFEUILLE PETITES POUSSES PETITES VIGNES VEYSSIERE VIOLETTE FENEROLE FONTGRENIER FRAUX GENSENAS GOUTTEPAGNON GRAMMONT GRANDMONT GROSSEREIX LAC L AGE LAGE LAVAUD LEIGNIAT LEYGAT LOUBIER LUZAT MACHETARD MACUREAU MANCHINAL MAS PUY MAS NEUF MASVAYRAUD MESURAT MEYNIEUX MONT MONTEIL MONTLUC MONTMERAUD MOULARD PETIT MURET NEPOULAS PAGNAC PEYROUTEAU PEYROUX PIC PLAUD POUZOL PUYCONNIEUX PUY MATHIEU ROUMILHAC ROUSSINGEAS ROYERE AUVENT SAUVOT SAZERAT PETITS HORS PETITS MONTS PETITS QUATRE VENTS PETITS RIEUX PETITS TROIS CERISIERS THEIL TOULONDIT VALEIX VEAUX VEDEIX VIGNAUD PETOURELLE PEUBUY PEU CHATENET PEUCHAUD PEUCHEAUD PEU CHAUDADE PEU TACHE PEUDRIX PEU PEUPITON PEU REGARD PEURLAND PEURUSSE PEURY PEU MARTIAL PEUTHIER PEUVINOU PEUVINOUX PEUX PEUX TACHE PEUX PAIX PEUX ROULIER PEYCURET PEYJARD PEYNES PEYRADE PEYRADIS PEYRAGOUT PEYRAHOUT PEYRAMONT PEYRASSOULAT PEYRAT PEYRATAUD PEYRAT MASSALOUX PEYRAZET PEYRELADE PEYRELEINE PEYRELENE PEYRE MAGNOLLE PEY RIBO PEYRICHOUX PEYRIERE PEYRINAUD PEYRISSAT PEYRONNELLES PEYRONNET PEYRONNIE PEYRONNIES PEYROU PEYROUX PEYRUCHE PEYTAVAUD BAS PEYTAVEAU PEYTAVIGNE PEYTAVINE PEZARD PIAUTREIX PIC PIC L AIGUILLE PICQ PIECE TEMPS PIECES PIECES LESSINES PIECES LOUIS PIECES MOREAU PIED VIGNE PIEGUT PIERRE BRUNE PIERREBRUNE CHAVALLE PIERRECHAVE NEUVIC PIERREFICHE PIERREFITTE PIERREFOLLE PIERREFOLLES GREFFIER LAINE PIERRES PIERRES DOULINES PIERRES MAS PIGEONNIER PIGNIER PIGNIER BELLEVUE PIGNOTERIE BROUSSE PIGNOUX PIGOT PILATERIES PILOUX PINATAUD PIN BERNARD PINGRELAUD PINQUELY PIOFFRET PIOFOUX PIQUETTE PISSARAT PISSAROTTE PISSAUD PISSELOUP 11 NOVEMBRE 1918 19 MARS 19 MARS 1962 63 EME REGIMENT INFANTERIE 8 MAI 8 MAI 1945 8 MAI BOURG ABBAYE ADRIEN GIRETTE ALBERT PESTOUR ALBERT SCHWEITZER AMPERE ANCIEN CAFE SISSAC ANNIE FRATELLINI ARAGO ATTANE AUGUSTE ROCHE AYMARD FAYARD BARBES BARRYS BAS BASCULE BEAUCLAIR BEAULIEU BERAUD BERNARD JUSSIEU BLAISE PASCAL BLANQUI BONNEFOND BRIANCE CARDAILHAC CARMES CARNOT CHAMP FOIRE CHAMP FOIRE BOURG CHANOINE FAYAUD CHANOINE MERLIN CHAPITRE CHARLES MICHELS CHATAIGNER CHATAIGNIER CHATAIGNIERS DUCS CHATEAUDUN CHATENET CLAUDE CHAPPE CLEMENT ADER COLLEGE COLLEGIALE COMMERCE COMMUNAL PUYBOURREAU COOPERATEURS D AINE D ALEMBERT DANIEL LAMAZIERE BABAUDUS BIENNAC COMPOSTELLE FARGEAS DEFFUAS BARREYRRETTE BASCULE BASTILLE PLACE COLLEGIALE FONTAINE L AIXETTE LIBERATION BOURG MOTTE NATION L ANCIENNE COMEDIE PAIX POSTE PREFECTURE PRUGNE REP GRANGE VILLEDON REPUBLIQUE ROYERE TOUR L AURENCE L L BOURG L CHRISTOPHE L ETOILE FONTAURY LEUN L EVECHE DENIS DUSSOUBS PIERREFOLLE BANCS BASSES VIGNES CARMES CARREAUX CASTORS CHARENTES COOPERATEURS GENEVRIERS HORTEILS JACOBINS LILAS STALINGRAD TILLEULS BLANC HUGONNEAU OCTAVE MARQUET 11 NOVEMBRE 1918 14 JUILLET 8 MAI 1945 BREUIL CHAMP FOIRE CHAMP MARS PLACE COMBORN COMMERCE DUCOUX ALBERT SCHWEITZER JUSTIN LABUZE FORUM GENERAL DELESTRAINT GENERAL THOUMAS MARCHE MARRONNIERS MONUMENT AUX MORTS MOUSTIER PALAIS PERIGORD POIDS PUBLIC PRESIDENT J F KENNEDY PRESIDIAL DUPUYTREN ECOLES BOURG EGLANTINES PLACE BOURG EMILE FOUSSAT ESPERANCE SISSAC PINCHAUD EUGENE DEGRASSAT EUROPE FLEURS FONTAINE FONTAINE BARRES FONTAINE FELIBRES FORGE FOSSES FOURNIER FRANKLIN ROOSEVELT FREDERIC MISTRAL FRESNEL GAY LUSSAC GENERAL BONNET CLAUDE DUBREUIL DUMAS GUSTAVE PHILIPPON GUY MOCQUET HAUTE VIENNE HENRI QUEUILLE HOTEL HOTEL NORD JACQUES GAREBEUF JACQUES GAREBOEUF BAPTISTE LAMARCK BATISTE BRACHET FONTAINE FAYAUD GIONO JAURES PLACE JOLIOT CURIE JOLIOT CURIE POULOUZAT JOSEPH DOMINIQUE JOSEPH LASVERGNAS JOSEPH LAVERGNAS JOURDAN JULES MICHELET JULIENNE BRUYERE LAVAL LAVANDIERES LAZARE CARNOT LENINE LEO LAGRANGE LEONARD ROUCHAUD LEON BETOULLE LEON LITAUD LEON ROCHE LEUN LIBERATION LIBERTE LOUIS TAILLEUR LUCIEN BERDASE MAIRIE BOURG MAISON D ANTAN MAISON DIEU MANEGE MANIGNE MARCEAU MARCELIN BERTHELOT MARCHE MATIVET MAZA MAZURIER MIDI MONTESQUIEU MOTHE MOTHERN MOTTE NOBLAT NORD BOURG OCTAVE MARQUET PARBELLE PENITENTS BARRY CREPIAT FLOURENS POIDS PUBLIC PONTVILLE POSTE PRIERES RENE REAUMUR REPUBLIQUE ROGER COUEGNAS ROUGET LISLE ROYALE PLACES SABINUS VALIERE SADI CARNOT SAINTE FELICITE SAINTE QUITTERIE FORTUNAT PLACE LEGER MAIXENT MARTIAL MAURICE MEDARD MICHEL PLACE THOMAS BECKET SALLE FETES SALMBACH SALVADOR ALLENDE SARRAZINS STALINGRAD TEMPLIERS MORTEROLLES TILLEUL TILLEULS TILLEULS PEURY TROIS TILLEULS VENTS VICTOR HUGO VIERGE VILLAGE WILSON WINSTON CHURCHILL XAVIER MAZURIER PLAGNE PLAGNES PLAIGNES PLAINARTIGE PLAINE PLAINES REJATAS PLAISANCE PLAISANCE BEAUNE PLAISANCE BOURGADE PLAISIRS PLAN PLANCHADEAU PLANCHAS PLANCHE PLANCHE LONGUE BUXEROLLES PLANCHEMOUTON PLANCHES PLANCHETTES PLANCHON PLANECHAUD PLANEIX PLANELLE PLANTADAS PLANTADE PLANTADIS PLANTE PLANTEDIT PLATEAU MARSAC PLATS PLAUD PLAUDEIX PLAUDERIE PLEINE MEYZE PLEIN JOUBAUD PLEQUEIX PLOMBARD PLONNEAUD PLUVIERS PN 20 LARTIGE PN 22 CHANDIAT PN 241 BLADS 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HAUT BESSOUS ROUTE LAC ROUTE LAC FONTBESSE ROUTE LOCHON ROUTE MANOT ROUTE MAS BUSSIERE ROUTE MASCROISIER ROUTE MAS GAUTHIER ROUTE MAZEAU ORADOUR ROUTE MONTET ROUTE MONTIN ROUTE ROUTE BOUSSIGNAC ROUTE JAURIE ROUTE RIBIERE ROUTE MUGUET ROUTE PALAIS ROUTE CHARANNAT ROUTE MEILLAT ROUTE MORTEROLLES ROUTE BARDE ROUTE POUYOULOU ROUTE PUY ROUTE PUY BOURG ROUTE PUY MALLET ROUTE PUY DIEU ROUTE PUY NEUVILLAS ROUTE PUY POUYADE NEUVILLAS ROUTE QUEYROIX ROUTE RELAIS MALAISE ROUTE DURIS ROUTE STADE ROUTE STADE BOURG ROUTE VIADUC ROUTE VILLAGEAS ROUTE ETANG ROUTE ETANG BOURG ROUTE ETANGS ROUTE EYMOUTIERS ROUTE EYMOUTIERS BOURG ROUTE FEUILLARDIERS ROUTE FONTAINE GOUAINEIX ROUTE FONTARNEAU ROUTE FONTBOUILLANT ROUTE FONTENILLE GLAIEULE ROUTE FORET ROUTE FORETS ROUTE FORGE ROUTE FOUGERAS ROUTE FRANCINES ROUTE GARDE ROUTE ROUTE BOURG ROUTE MAS ROUTE GARENNES ROUTE GARNAUDIE ROUTE GASSOTTE FOULVENTOUR ROUTE GATTEBOURG ROUTE GAUDEIX ROUTE GAY VERGNE ROUTE GENTES ROUTE GLAIEULE PUYBOURREAU ROUTE GORRE ROUTE GRAMMONT ROUTE CHALIER LANDES ROUTE COUDERT BOURG ROUTE GRANDMONT ROUTE PUYCONNIEUX ROUTE GRANDS CLOS ROUTE GRANGE BOEIL ROUTE GRATELOUBE ROUTE GREIGNAC ROUTE GREIGNAC LANDE ROUTE GUILLAT BOURG ROUTE GUILLOT ROUTE GUINAY ROUTE HAUTE VIENNE MERIGOT ROUTE HAUT FREGEFOND ROUTE HIPPODROME ROUTE IF ROUTE JAILLOUX ROUTE JALADE ROUTE JUSTICES ROUTE BORIE BOURG ROUTE LAC ROUTE CROISILLE BOURG ROUTE LACS ROUTE LAFARGE ROUTE ROUTE GRANGE ROUTE LANDE ROUTE LANDE NOYER ROUTE LANDES MORTEROLLES ROUTE POUGE ROUTE RIBIERE BOUCHET ROUTE LASCAUX ROUTE SOUTERRAINE BOURG ROUTE SOUTERRAIN PEURUSSE ROUTE LATTERIE ROUTE LAURIERE ROUTE LAURIERE BOURG ROUTE LAVALETTE BOURG ROUTE LAVAUD ROUTE LAVAUPOT BOURG ROUTE LAVIGNAC ROUTE VILLATE ROUTE LAVOIR ROUTE LEYCHOISSIER ROUTE LIEGEAUD ROUTE LILAS ROUTE LILAS CHAMPAGNAC ROUTE LIMOGES ROUTE LIMOURET ROUTE LONGEAIGUE ROUTE MAGNAC ROUTE MAGNAC LAVAL ROUTE MAISON NEUVE ROUTE MAISONNEUVE ROUTE MARMOULT GOUAINEIX ROUTE MARRONNIERS CHAMPAGNAC ROUTE MARVAL ROUTE MARVAL MONTAGNE ROUTE MAS ROUTE MASBATIN ROUTE MAS PUY ROUTE MASSAC PLACIEUX ROUTE MASSARD ROUTE MASSARD BEAUSEJOUR ROUTE MASSART ROUTE MASSEIX ROUTE MASSEIX CUREBOUTEILLE ROUTE MATELINES ROUTE MAUVE BOURG ROUTE MAZAURIE ROUTE MAZEAU ROUTE MEILHAC ROUTE METTES ROUTE MEYGEAS ROUTE MONS ROUTE MONT ROUTE MONTBRUN ROUTE MONTCIGOUX ROUTE MONTEIL ROUTE MONT GARGAN ROUTE MONTMORILLON ROUTE MONTS ROUTE MONTS BOURG ROUTE VILLEGOUREIX ROUTE BARRE ROUTE ROMPU ROUTE MOULINS ROUTE MOUNISMES ROUTE MOUSSANAS ROUTE NADALIE BOURG ROUTE NANTIAT ROUTE NANTIAT 1 BOURG ROUTE NATIONALE 147 ROUTE NATIONALE 20 ROUTE NEXON ROUTE NIEUL ROUTE NIEUL BARRE ROUTE NIOLLET ROUTE NOGEAS ROUTE NONTRON ROUTE NOUAILLAS ROUTE NOUIC ROUTE NOUVELLE ROUTE NOYER ROUTE OF BOISSE NEUVILLAS ROUTE ORADOUR SENON ROUTE ORADOUR SUR GLANE ROUTE PACAGES MASBATIN ROUTE PARADE ROUTE PARIS ROUTE PAYAUX ROUTE PAYSAC BOURG ROUTE PELON MASBATIN ROUTE PERIGUEUX ROUTE PEROU ROUTE BORT ROUTE MORELIE ROUTE MAS PUY ROUTE PEUX TERRADE ROUTE PEYRADE ROUTE PEYRILHAC CONORE ROUTE PEYRILHAC SENON ROUTE PIEGUT BOURG ROUTE BUFFIERE ROUTE PLAINARTIGE ROUTE PLAISANCE ROUTE PLANCHE ROUTE PLANCHE DAMES ROUTE PLAN D EAU BOURG ROUTE POINT JOUR ROUTE POITIERS ROUTE MELANIE ROUTE POUGE ROUTE PRADEAUX ROUTE PRE CLOS PEUTRU ROUTE PRE MADAME ROUTE PRE MADAME BRIGNAC ROUTE PRESSIGNAC ROUTE PUIFFERAT BOURG ROUTE PUIFFERAT PAINCOURT ROUTE PUITS FOULVENTOUR ROUTE PUYBOUREAU BASBONNAUD ROUTE PUYBOUREAU NOUHAUDS ROUTE PUYBOURREAU NOUHAUDS ROUTE PUY CHABROL BOURG ROUTE PUY MONT LAS TAUVEYRAS ROUTE PUY LOUP ROUTE PUYMERLE ROUTE PUY MONTEY PEUTRU ROUTE PUY NEIGE ROUTE QUEYROIX MARTIN ROUTE RABAUD SENON ROUTE RAGNAT ROUTE REPAIRE ROUTE RHIN DANUBE ROUTE RIBIERE ROUTE RIBIERE THEIL ROUTE RIBIERES SENON ROUTE RILHAC ROUTE RILHAC BEAUNE MINES ROUTE RILHAC LANDES ROUTE ROCHECHOUART ROUTE ROCHECHOUART BOURG ROUTE ROCHES ROUTE ROUGERIE ROUTE ROUMILHAC ROUTE ROUSSAC ROUTE SABLIERE ROUTE SAGNAT ROUTE SAILLAT ROUTE AUVENT BOURG ROUTE BARBANT ROUTE BRICE ROUTE DENIS ROUTE SAINTE ANNE ROUTE GENEST ROUTE GERVAIS BOURG ROUTE ROUTE LIGOURE ROUTE JOUVENT THEILLOL ROUTE JUNIEN ROUTE JUNIEN BARRE ROUTE LAURENT ROUTE LEGER BOURG ROUTE LEONARD ROUTE MARTIAL ROUTE MARTIN ROUTE MARTIN BOURG ROUTE MATHIEU BOURG ROUTE ROUTE PARDOUX ROUTE PARDOUX BOURG ROUTE ROUTE PRIEST BOURG ROUTE PRIEST TAURION ROUTE REMY ROUTE SORNIN CHAMPAGNAC ROUTE SULPICE ROUTE YRIEIX MOISSAC ROUTE SALESSE ROUTE SAPINIERE TERRADE ROUTE SEREILHAC ROUTE SERVES SECHES ROUTE SISSAC ROUTE SOUTERRAINE ROUTE SOUTERRAINE BOURG ROUTE SOUTERRAINE PEURUSSE ROUTE STADE ROUTE STADE 2 NOLETS ROUTE STADE 4 NOLETS ROUTE STADE BOURG ROUTE STATION ROUTE TAMISAC ROUTE TEILLOL FREGEFOND ROUTE THEIL ROUTE THIAT ROUTE TOULOUSE ROUTE TRACHERAS ROUTE TRAMONT ROUTE TRANCHARDIE ROUTE TREIGNAC ROUTE VALETTE ROUTE VALLETTE ROUTE VALLONS ROUTE VASSIVIERE ROUTE VAULRY ROUTE VAUZELLE ROUTE VEDRENNE ROUTE VEILLERIE COUTUCIE ROUTE VERDIERS ROUTE VERGNES ROUTE VERNEUIL ROUTE VERNEUIL BOURG ROUTE VERNEUIL MOUSTIERS BOURG ROUTE VERTIGUEUIL ROUTE VIALEBESOIN ROUTE VIALEIX ROUTE VIALOTTE ROUTE VIAUD ROUTE VIDEIX BOURG ROUTE VIEILLE CROUZILLE ROUTE VIGNAUD ROUTE VIGNE TERRADE ROUTE VIGNERAS ROUTE VIGNERIE ROUTE VIGNES ROUTE ROUTE VILLECHENOUX ROUTE VILLENEUVE ROUVEIX ROUVEIX HAUT ROUVELEADE LANDES FRESSINET ROUVERAT ROUVEREAUX ROUVERET ROUVETS ROUZEIX ROYER ROYERAS ROYERE ROYERES RTE ETANG BAUDY RTE VERGNES RTRAVERSIERE CHAPTERIE RUAS 10 JUIN 11 NOVEMBRE 11 NOVEMBRE 1918 19 JUILLET 1944 19 MARS 19 MARS 1962 19 MARS 1962 RIPPE 19 MARS 1962 POULOUZAT 4 EME ZOUAVE 4 SEPTEMBRE 63 EME REGIMENT D INFANTERIE 71 EME MOBILES 7 AOUT 7 AOUT 1944 8 MAI 8 MAI 1945 8 MAI 45 90 EME TERRITORIAL ABBAYE ABBE DAYRAT MORTEROLLES ABBE GALANT ABEILLES ABEL FAGOIS ACACIAS ACHILLE FEVRE ACHILLE ZAVATTA ADOLPHE MANDONNAUD ADRIEN DUBOUCHE ADRIEN DUGENY ADRIEN PRESSEMANE ADRIEN PRESSEMANNE ADRIEN TIXIER AGE AGES AGUA AIGUEPERSE AIME PATAUD AIXE AIXETTE ALAIN ALBERT CALMETTE ALBERT CAMUS ALBERT CHAUDIER ALBERT CHAULY ALBERT DUCHAIGNE ALBERT EINSTEIN ALBERT JOYEUX ALBERT PESTOUR ALBERT SAMAIN ALBERT SCHWEITZER ALEXANDRE BRONGNIART ALEXANDRE DUMAS ALEXANDRE DUMAS POULOUZAT ALEXANDRE RIBOT ALEXANDRE VIALATTE MUSSET VIGNY DREYFUS MEZIERES SISLEY ALLUAUD ALOUETTES ALPHONSE DAUDET ALPHONSE DUCHASTEAU ALPINIEN BOURDEAU ALSACE LORRAINE AMBROISE CROIZAT AMBROISE PARE AMEDEE BOLLEE AMEDEE BROUSSILLON AMPERE AMPHORES AMYOT ANATOLE FRANCE ANCIEN ABREUVOIR ANCIEN COUVENT ANCIENNE ANCIENNE FONTAINE ANCIENNE ANCIEN VILLAGE SENON ANDRE ANTOINE ANDRE BABLET ANDRE BORDESSOULE ANDRE BOUCHARECHAS ANDRE BOURVIL ANDRE BRISSIAUD ANDRE CHENIER ANDRE DEXET ANDRE JOSEPH MASSIE ANDRE FOURCADE ANDRE FURELAUD ANDRE GIDE ANDRE JOUHANNAUD ANDRE GENTILE ANDRE LERECLUS ANDRE MALRAUX ANDRE MAUROIS ANDRE MESSAGER ANDRE RAYNAUD ROUSSET ANET BEYRAND ANGOULEME ANTOINE BECQUEREL ANTOINE BLONDIN ANTOINE BOURDELLE ANTOINE BOURNAZAUD ANTOINE COYSEVOX ANTOINE LAVOISIER ANTOINE EXUPERY ANTOINE FOURCROY ANTOINE LAFARGE ANTOINE LOUIS BARYE ANTOINE EXUPERY ANTOINE TARRADE ARAGO ARAGON ARBONNEAU ARBORETUM ARCADE ARGANCY ARISTIDE BRIAND ARMAND BARBES ARMAND DUTREIX ARMAND MARSAUD ARSONVAL ARTHUR RIMBAUD ARTISANAT ARTRAS ARTRAT ATLANTIS AUBEPINE AUBER AUBOURGEOIS BOST AUBUGEAS AUBUGEOIS AUDIENCE AUGUSTE ARIDAS AUGUSTE GAGNE AUGUSTE PAILLIER AUGUSTE RENOIR AUGUSTE RODIN AUGUSTINS AUGUSTIN THIERRY AURENCE AURIERES AVENIR BACHERET BAD WINDSHEIM BALANDIE BALARD BALCONS BALLET BANC LEGER BANNEIX BARBES SISSAC BARDE BARDEAU BARGES BARLET BARNY BARRE BARRETS BARRETS PUYPICHAUD BARRY BASCULE BAS POULOUZAT BAS LEYCURAS BAS MERIGNAC BASSE BASSE COTE BASSE BASSE BABAUDUS BASSE CHABAUDIE BASSE COMEDIE BASSE ROYERE BASSE TROUPIN BASSE VIVIENAS BASSE GORCE BASSE MALAISE BASSES CARRIERS BATTES BAUDELAIRE BAUDIN BAUME BAYLES BAZINE BEAUFRANC BEAULIEU BEAUMARCHAIS BEAUMARD BEAUNE BEAUREGARD BEAUSEJOUR BEAUSOLEIL BEAUVAIS BECQUEREL BEIGE BEISSAT BELAIR BEL AIR BEL FONTAINE BELLAC BELLEVUE BELLEVUE POULOUZAT BENJAMIN DELESSERT BENOIST BUIS BENOIT DUBUIS BERANGER BERCAIL BERGERE BERGERIE BERGERIES BERLIOZ BERNARD VENTADOUR BERNARD PALISSY BERNART VENTADOUR BERTHELOT BERTRAND BERTRAND BOURDEAU BERTRAND BORN BERTY ALBRECHT BETOULLES BEYNAC BEYRAND BILLANGES BILL COLEMAN BIZET BLANZOU BLED BLERIOT BLEUETS BOBBY LAPOINTE BOBEROT BOBILLOT BOILEAU BOIS DAMES BONDY BORDERIE MORTEROLLES BORIE BORIS VIAN BOS BATY BOSCHAUDERIER BOS BOS BOS POULOUZAT BOSFRANC BOSFRANCS BOSFRAND BOSQUET BOSSUET BOTTEAUX BOTTEAUX HLM BOUCHERON BOUCHERS BOUGAINVILLE BOULEAUX BOULOU BOULOU VIBLAC BOULOUX BOURDON BOUVREUILS BRACONNE BRAILLE BRAMAUD BRANLY BRANTOME BRAQUE BREGUET BREMONTIER BREUIL BRIANCE BRIGITTE GUITARD BRIGNAC BRISSIAUD CHAPTERIE BROUILLET BROUSSEAU BROUSSES BRUYERES BUFFON BUGEAUD BUISSONS BUSSY CAIFFA CAILLAUDOU CALIFORNIE CALVAIRE CAMBACERES CAMELINAT CAMILLE CHABANEAU CAMILLE COROT CAMILLE FAURE CAMILLE FLAMMARION CAMILLE GRELIER CAMILLE GRELLIER CAMILLE GUERIN CAMILLE JULLIAN CAMILLE PELLETAN CAMILLE SAENS CAMILLE SAENS POULOUZAT CAMILLE SEE CAMILLE VARDELLE CAMP CESAR CANTINES CAPUCINES CARMES CARNOT CARRIERE CARRIERS CARRIERS MONDELET CASCADE CATHERINE DESCHAMPS CAVELIER SALLE CAVOU CELLE CERVIERES CEZANNE CEZANNE HAUTE GORCE CHABRETAIRE CHABROL CHAIGNAUD CHAMBEAU CHAMBONNEAU CHAMP FOIRE CHAMPLAIN CHAMP VIGNE BOURG CHAMPOLLION CHAMPOREES BEISSAT CHAMPS CHAMPS CHAMPS CHANOINE FAYAUD CHANTECAILLE CHANTECAILLE CROUZEIX CHANTECLAIR CHANTECLER CHANTECLERC CHANTEGRIS CHANTELAUVE CHANTS D OISEAUX CHANZY CHAPELLE CHAPELLE QUEYROI CHAPELLE SISSAC CHAPITEAU CHARLEMAGNE BACH BAUDELAIRE BLANCHAUD CROS CHARLES DENUELLE FERRAND FOURIER GIDE LAMY LEGENDRE LINDBERGH MICHELS NICOLLE PEGUY POITEVIN SILVESTRE SYLEVESTRE POULOUZAT SYLVESTRE SYLVESTRE POULOUZAT CHARLY CHARMILLES CHARPENTIER CHARRETIERE CHARREYRON CHARRIERS CHARRON CHARRONS CHARTREUX CHASSAGNE CHATAIGNERAIE CHATAIGNIER CHATAIGNIERE CHATAIGNIERS CHATAIGNOL CHATAIGNOLLES CHATEAU BRETIGNOLLES CHATEAUBRIAND D EAU ROCHER CHATEAUX CHATELARD CHATENET CHATRE CHAUDIERE CHAUDRON CHAUMETTES CHAVILLAT FER VILLAGEAS NEUF CHENES CHENES VERTS CHENE VERT CHEVAL BLANC CHEVRETTES CHEYROUX CHEZ CANARD MORTEROLLES CHEZ GALEIX CHEZ ROUX CHOMES CHRISTOPHE COLOMB CITES CIVOIR CLAIRE FONTAINE CLAIR LOGIS CLAUDE BERNARD CLAUDE DEBUSSY CLAUDE LORRAIN CLAUDE MONET CLEMENCEAU CLEMENT ADER CLEMENT MAROT CLOCHER CLOITRES CLOS CLOSES CLOS BREGERE CLOS NOBERT CLOS PLEIN SUD CLOUET CLOUS COLETTE COLLEGE COLLEGIALE COLLINE COLOMBIER COLOMBIERS COLONEL GARREAU COLONEL ROL TANGUY COMBAILLE COMBAUD COMBE AU COMBE AUX BOEUFS COMBE AUX LOUPS COMBELLES COMMANDANT DUGROS COMMANDANT L HERMINIER COMMERCE CONDADILLE CONDADILLE POULOUZAT CONDAMINES CONDORCET CONSERVERIES CONSTANCIN CONSULAT COQ COQUELICOTS COQUI CORGNAC CORNEBOEUF CORNEILLE CORNEILLE POULOUZAT COROT CORVETTE COSTRENAT COTE COTEAU COTEAUX BREUILH COTE BOURG COTILLES COTTAGE COUDAMY COULOMB COURBE COURBET COURBEYSSIE COURTELINE COURTOT COURTY COUTHEILLAS COUTURE COUVENT CRAMOULOU CRAMOULOU SUDRIE CROIX BILLARD CROIX BLANCHE CROIX BUCHILIEN CROIX L HOMME CROIX L HOZANNE CROIX CROIX CHARRIERS CROIX BAC CROIX PAROT CROIX CROIX VERTE CROS BLANC CRUCHE D OR CRUVEILHER CRUVEILHIER CUJAS CUVE TRILOBEE CYGNES DAGOBERT D AGUESSEAU D ALBRET D ALEMBERT DALESME D ALGER DALHIAS D ALLEMAGNE DALLOZ D ALSACE DANIEL LAMAZIERE DANIEL LATHIERE D ANJOU DANTE D ANTONY D AQUITAINE DARCY D ARGENTON D ARLIQUET DARNET D ARSONVAL D AUVIGNAC D AUZETTE BABAUDUS BABYLONE BAMBOURNET BEAUFORT BEAUGAILLARD BEAULIEU BEAUMONT BEAUPUY BEAUSEJOUR BEAUVAIS BEL AIR BELFONT BELFORT BELGIQUE BELLAC BELLEVUE BENEVENT BESSINES BEYNAC BIENNAC BORDEAUX BOURGOGNE BOURNEVILLE BRECHE BRETAGNE BRETTES BROUILLEBAS DEBUSSY DEBUSSY POULOUZAT BUXEROLLES CHAMBEAU CHAMPAGNAC CHANTEMERLE GAILLARD CHATEAUROUX D ECHAUDIERAS CHEZ FRUGIER CHEZ SADRY COGNAC CORGNAC COUERASSAS DECOUVERTE CREZIN DEDIN LAPORTE DOUAI FAUGERAS DEFAYE FENEROLLES FEYTIAT FLOREAL FONTAINEBLEAU FONTAUBERT FONTAURY FONTBONNE FRANCE FRESSIGNAT FURTH GAMBSHEIM DEGAS GATE SOLEIL GAUD GENEVE GERMINAL GORCEIX GORRE GRAVELLONA JUILLAC BACHELLERIE BACONIE BEAUSSERIE BOETIE BORIE BOUCHERIE BRASSERIE BREGERE L ABREUVOIR BRIANCE BROUSSE BUTTE CARAQUE CAVALERIE CERAMIQUE CHABAUDIE CHAPELLE CHASSAGNE CHATAIGNERAIE LA COLLINE CONQUE CONVENTION CORNUE COTE COURTINE COUTURE CROIX BLANCHE CROIX CRAMAUX CROIX MISSION CROIX ROUGE CROIX VERTE DELIADE FERME FIDELITE FILATURE FONDERIE FONTAINE FONTAINE VANTEAUX FONTAINE CELLIER FONTAINE PEYRE FONTAINE PINOT FONTANILLE FORGE FRATERNITE GALLIE GARDE GARENNE GARTEMPE GASNE GLANE GORRE GRANGE PATAUDE GRELE L AIGUILLETTE L AIXETTE JOCONDE LANDE LIBERTE LIGOURE L ALMA LOI L ALOUETTE MAIRIE BOURG MAITRISE MALAISE MARNE MAULDE MAUVENDIERE L AMPHITHEATRE NATION NAVETTE L ANCIEN ABATTOIR L ANCIENNE FORGE L ANCIENNE POSTE NEPLE PAIX PASSERELLE PECHERIE PEIRAUBADE PERDRIX PICAUDE POMPE POSTE POTERNE POUGE PREFECTURE PROCESSION PROVIDENCE PRUGNE QUINTAINE RAUGE REFORME REGLE RENAUDE REPUBLIQUE REVOLUTION REYNIE L ARGONNE ROCHE ROCHE AU GO ROSERAIE ROYERE SANTAUDE LASCAUX SOUTERRAINE LAS VIAS TERRASSE TOUR PLO TOUR NEGRE LAUBANIE L AURENCE L AURENCOUS L AUTIER VALLEE VALOINE VIALOUBE VIGE WANTZENEAU L EGALITE L L ELYSEE L ENEIDE L EQUERRE L ERMITAGE DELESCLUZE L ETOILE FONTAURY L EUROPE L HOPITAL LIEGE L INDUSTRIE LISBONNE L OBSERVATOIRE LOMENIE LONDRES L ORADOUR LORETTE L ORME LOSTENDE L UNIVERSITE MAGENTA MANDERESSE MARCILLAC MESSIDOR MESSINE METZ MOISSUN MONTAZEAU MONTLHERY MONTPLAISIR NAMUR NANCY NAZARETH NEW YORK NEXON DENFERT ROCHEREAU NICE DENIS DUSSOUBS DENIS PAPIN NONTRON NORMANDIE NOUAILHAS PANAZOL PARADIS PEYRAT PICANYA PICARDIE BLANCHE PIERREFOLLE PILLET PILSEN PLAISANCE POITIERS POMPADOUR DEPOT PROVENCE PROXIMART PUYJEAN PUY JOYEUX ROCHECHOUART ROMANET ROUMAGNAC 3 MAISONS ACACIAS GENCE ALLEES ALLOIS ALOUETTES ANGLAIS ARDILLOUX ARENES ARGENTIERS ARTS AUDOUINES AULNES SAUTE BERGERE SAUVIAT SAVOIE AZALEES BARBICHETS BARRIS BASSES PALISSES BELGES BIARDS BINLAUDES BLES NOIRS BLEUETS BOERS BOLETS BORDES BOUTONS D OR BOUVREUILS BROSSES BRUYERES CAMELIAS CAMINES CAPUCINES CARRIERS DESCARTES DESCARTES POULOUZAT CEDRES CERISIERS CEVENNES CHAMBONS CHAMPS CHARSEIX CHATAIGNIERS CHENEBEAUX CHENES SCHILTIGHEIM SCHLEITHAL CLAIRETTES CLISSES COLOMBES COMBES COOPERATEURS COQUELICOTS CROUZILLOUX ECOLES ECUREUILS EGLANTIERS EGLANTINES ENTOURS EPICEAS ESSARTS ETAGES ETANGS FAUVETTES FELINES FEUILLANTS FEUILLARDS FILLES NOTRE DAME FLANDRES FORGERONS FOSSES FOUGERES FOURNERIES FREGATES FRERES BONNEFF FRERES GRIMM FRERES LUMIERE FRERES RIBIERE FUCHSIAS GARENNES GENETS GENEVRIERS GIROFLEES GIROLLES GLAIEULS GLYCINES GOELANDS GRANDES POUSSES GRANGES GRANGES VIGNAUD GRANGETTES HALLES HAUTES PLACES HORTENSIAS HORTICULTEURS HOUMEAUX INSURGES DESIREE IRIS JARDINS JASMINS JONQUES JONQUILLES LANDES LATTES LAURIERS LAVEUSES LILAS LONGES LORIOTS LUCIOLES MAISONS NEUVES MARGINIERES MASSOTTES MELEZES MESANGES METAYERS MIMOSAS MINES MONTARAUDS MONTEES MOUETTES MURES MYOSOTIS NOISETIERS OEILLETS SOLIGNAC SOUDANAS D ESPAGNE PALMIERS PAPILLONS PAQUERETTES PAVES PAYS BAS PECHORELLES MICHEL PENDANTS PENITENTS PENITENTS BLANCS PENITENTS NOIRS PENITENTS ROUGES PENSEES PETITES MAISONS PETITES POUSSES PETITES TUILIERES PEUPLIERS PILATERIES PINS PINSONS PLACES PLAISANCES PLANCHES PLATANES PLATS POMMIERS PORTES FERREES PRADES PRAIRIES PRIMEVERES PRUNIERS RECOLLETS REMPARTS RIBIERES ROCHES ROCHES BLEUES ROCHETTES ROCHILLES ROSES ROSIERS ROSSIGNOLS SABLONS SAGNES SAIGNES SALINES SAPEURS SAPEURS POMPIERS SAPINS SITTELLES SIX CHATAIGNERS SOEURS RIVIERE SORBIERS SOUCIS TANNERIES TEMPLIERS TERMES THUYAS TONNELLES TOURTERELLES BOURG STRASBOURG TUILIERES TULIPES TULIPES MICHEL URSULINES VENITIENS VIATS VIEUX BLATS VIGNES VILLETTES BESSIERES VIOLETTES VIOLETTES BARRE DETAILLE TEXONNIERAS TIRGU NEAMT TOCQUEVILLE TOULOUSE TOURAINE TOURCOING TROUPIN TURENNE DEUX PONTS DEUX VILLAGES VALPARAISO VENDEMIAIRE VERDUN VERINAS DEVERRINE VERSAILLES VEYRAC VIVIENAS D EYJEAUX DIDEROT DIEUDONNE COSTES D IRLANDE D ISLE D ISLY DISMISSED JAUVION DISMISSED LIGOURE DISTOMON D ITALIE DJANGO REINHARDT BOURG BALET BALLET POITEVIN DELHOUME EMILE DOURDET FERDINAND RENAULT CHARBONNIER POITEVIN POUQUET RENAULT ROBERT PASCAUD ROUX TOURAILLE VETELEY DOMNOLET LAFARGE DONZELOT D ORLEANS DOUCHES 11 NOVEMBRE 11 NOVEMBRE 1918 19 MARS 1962 4 SEPTEMBRE 4 SEPTEMBRE 1870 71 EME MOBILES 8 MAI 1945 BALCON BARRY BAS BOURG BAS FARGEAS BASSIN BELLAY BELVEDERE BERRY CHARMANT MACHAUT BOS BOST HILAIRE BOUCHERON BREUIL GORRE BREUIL VAYRES BUIS BUISSON BUISSON BLANC CAILLAUD CALVAIRE CANAL CANTOU CAPITAINE DEMAISON CAPITAINE FONCK CAPITAINE VIGUIER CAVOU CERCLER CHAMP TIR CHAMP DORAT CHAMPFOUR CHANOINE CHALMETTE CHANOINE PENY CHANT L OISEAU CHANT OISEAUX CHAPELAIN CHATAIN CHATEAU D EAU CHATENET CHINCHAUVAUD CLOCHER CLOS ADRIEN CLOS AUGIER CLOS BELLEVUE CLOS VIGNES CLOS SARIS CLOS GASPARD CLOS JARGOT CLOS BRUNES CLOS LONDEIX CLOS MOREAU CLOS ROCHER CLOS SAINTE CLUZEAU COLLEGE COLONEL IMFELD COLONEL LEDOT COLONEL MANHES COLONEL RIVIER COLONEL SAUTEREAU COMMANDANT BARLIER COMMANDANT FELIX COMMANDANT GATARD COMMANDANT TULASNE CONSULAT COQ COUDERT CRUCIFIX DESERT BERGONIE BOULLAND DUVERGER JACQUET JAGOT LACOUSSIERE J ESQUIROL JOUHAUD LEMOYNE DESOURTEAUX RAYMOND RAYMOND MARCLAND ROUX SAMUEL MISES VETELAY FAUBOURG FOUR FRAUD DUGAY TROUIN GENERAL ARBELLOT GENERAL BARDET GENERAL CATROUX GENERAL CEREZ GENERAL GENERAL DELESTRAINT GENERAL MONSABERT GENERAL BESSOL GENERAL CRAY GENERAL MICHEL PRE THEIL TREUIL VIGNOBLE GUE VERTHAMONT GUERAUDIER DUGUESCLIN HAUT VIBLAC LAC LANDOU LANGUEDOC LAVOIR LIEUTENANT LIEUTENANT MENIEUX LIMOUSIN LUXEMBOURG MAINE MAJOR STAUNTON MANEGE MARCHE MARCHE VIEUX MARECHAL FAYOLLE MARECHAL FOCH MARECHAL GALLIENI MARECHAL JOFFRE MARECHAL JUIN MARECHAL MAS MASAN MAS BILIER MAS BLANC MAS BOURIANNE MAS BOUYOL MAS DAVIS MAS L AGE MAS GIGOU MASGOULET MAS LOGE MAS LOUBIER MAS NEUF MAUPAS DUMONT D URVILLE MONTIN MOULIN FERT L HOPITAL L OR TARN GUE RABAUD ROUX MURIER NEPLOT PARC BELLEGARDE LIMOGES MEILLAT ROYAN RUISSEAU TOUR TREUIL PIC DUPLEIX DUPLOYE POIDS ROI POINT JOUR DU GORRE LAS BORDAS LAS VEYRAS ANDRE MARTIAL PORTAIL PORTAIL IMBERT POUEIX PRADOU PRE AUX CHEVAUX PRE DAMES PRE MOROT PRE PEYROUX PRESIDENT RENE COTY PRIEUR PRINTEMPS PROFESSEUR JACQUES LEVEUF PROGRES PUITS PUY CHATU PUY LOUP PUY PIC PUY ROI PUY IMBERT PUY LANNAUD PUY LAROUSSE PUY LAS HAUTAS PUY LAS RODAS PUY NEIGE PUY PANARD PUY REGNIER PUY REYJAUD DUPUYTREN PUY VINCENT QUAI MILITAIRE DUQUESNE RAJAT RAUDOUX RELAIS DURIEUX ROYAUME UNI RUISSEAU CHANTRE SANCERROIS SERGENT MORANGE STADE TARN TAURION TEMPLE THEIL TILLEUL TRANSVAAL VELODROME DUVERGER VIEUX CHENE VIEUX CREZIN VIEUX POIRIER VIEUX VILLAGE FORET VINCOU ECHANIE ECHAUGUETTE ECOLES ECOLES BOURG ECOLIERS EDGAR QUINET EDISON EDITH PIAF EDME BOUCHARDON EDMOND ABOUT EDMOND GONDINET EDMOND ROSTAND EDOUARD BRANLY EDOUARD CHAULET EDOUARD CHOLET EDOUARD HERRIOT EDOUARD MANET EDOUARD MICHAUD EDOUARD MOURATILLE EDOUARD VAILLANT EGLANTINES ELIE BERTHET ELSA TRIOLET ELYSEE RECLUS EMAILLEURS EMETTEUR EMILE BAUDOT EMILE GIRARDIN EMILE DOURDET EMILE DURKHEIM EMILE FRANGE EMILE MONTEGUT EMILE ZOLA EMILIE FAURE EMMANUEL CHABRIER ENCOMBE VINEUSE E NORMALE INSTITUTEURS EPERON ERIC TABARLY ERIK SATIE ERNEST ROBERT BLANCHER ERNEST LAVISSE ERNEST MALLARD ERNEST RENAN ESPERANCE ESPERANTO ETANCHOUX ETANG ETANGS ETANGS BREUIL BAIGNOL DOLET JODELLE MALEU EUCLIDE EUGENE ALLUAUD EUGENE CHEVREUL EUGENE DELACROIX EUGENE GAILLEDRAT EUGENE JAMOT EUGENE JAMOT L ECHALLIER EUGENE ROY EUGENE LEROY EUGENE SUE EUGENE VARLIN EUROPE EUSEBE BOMBAL EVARISTE GALOIS EVECAUD EVEQUE FABRIQUE FANTAISIE FARGE FARGES FARMAN FAUCHERIE FAUCHERON LAROCHE FAUVETTES FELIX FAURE FELIX FENELON FERDINAND BUISSON FERDINAND LASSALLE FERDINAND MENU FERDINAND RENAULT FERNANDEL FERNAND FOUREAU FERNAND LAPEYRE FERNAND MALINVAUD FERNAND RAYNAUD FERRERIE FEU MORTEROLLES FEUILLADE FEUILLARDIERS FEUILLARDINE FICHES MONTEIL FIGUIER FILATURE FIRMIN DELAGE FIRMIN TARRADE FITZ JAMES FIZOT LAVERGNE FLEMING FLEURS FLORIAN FOND L HERBEIX FONTAINE FONTAINE CARS FONTAINE POULOUZAT FONTAINES FONTAINE MARTIN FONTAINE MICHEL FONTAINE FONTAINE SULPICE FONTAINES CARS FONTAINES ELYSEE FONTANELLE FONTAULARD FONTBOUILLANT FORET FORGE FORGES THEILLOL FORSFELD FORSTFELD BOST FORTS QUINSAC FOSSES FOUGERAS FOUGERES FOUGERON LAROCHE FOURIE FOURS FRAISSE FRANCINES FRANCIS BLANCHE FRANCIS CHIGOT FRANCISCO ZURBARAN FRANCIS PLANTE ALLUAUD ARAGO CHENIEUX COPPEE FRANCOISE DOLTO FRANCOISE GIROUD FRANCOISE ROSAY MAURIAC MILLET MITTERAND MITTERRAND MOURIOUX PERRIN RICHARD ROMAIN SARRE VILLON FRATERNITE FRAUD FREDERIC BASTIAT FREDERIC CHOPIN FREDERIC LEGRAND FREDERIC PLAY FREDERIC MISTRAL FREDERIC PASSY FRERES LUMIERE FRESNEL FRESSIGNAT FROIDE FROIDES BOURG FROISSART FROMENTAL FRONTIGNAC FRUGERIE FRUITS FUSILLES FUSTEL COULANGES FUTAIE GABRIEL DEBREGEAS GABRIEL FAURE GABRIEL PERI GABRIEL THAVENOT GABY MORLAY GAGNERIE GAIGNOLLE GALILEE GALLIENI GAMBETTA GAMBETTA POULOUZAT GANES THEIL GARDE GARENNE GARENNES GARRIGOU GRANGE GASNES THEIL GASTON CHARLET GASTON HYLLAIRE GATE SOLEIL GAUDINOTTES GAUGUIN GAUMOT GAY LUSSAC GEMINI GENERAL CHAMBE GENERAL GENERAL MONTREAL GENERAL GENERAL MARTIAL BARDET GENETS GENETS SENON GENEVRIERS GEOFFRES GEORGE SAND BIZET BONNIN BRASSEN BRASSENS BUFFON CHABRIER CLEMENCEAU CUVIER DUHAMEL DUMAS FOUREST LAPIERRE MAGADOUX PERIN PERRIN POMPIDOU GERARD GERARD PHILIPE GERARD PHILIPPE GERBASSOU GERMANET G J MARTIN PUYTISON GIONO GLANE GLUCK GO GONDINET GORCEIX GORGEIX GOT GOT MARCHE GOUFFIER LASTOUR GOUFFIER LASTOURS GOUNOD GOURDON GOURGAUDERIE GOYA GRAMMONTS CHAMP CHATENET PACAGE SENON PATURAGE RIEU ROUVRE ROYERE GRANDS FONDS GRANDS JARDINS GRANDS MARMIERS VIGNOBLE GRANGE BOEIL GRANGES GRANGES THEILLOL GRANGETTES GRANGE VIGNIAUD GRELLET GREVY GRIGNARD GRILLAS GRIPPE GRIVET GROS CHENE GUE VERTHAMONT GUE MARCHAND GUERINIERE APOLLINAIRE GUILLAUMIN GUILLOT GUIZIER GUSTAVE COURBET GUSTAVE DORE GUSTAVE EIFFEL GUSTAVE FLAUBERT GUSTAVE GLANGEAUD GUSTAVE NADAUD GUTENBERG GUY MAUPASSANT GUY MOQUET GUYNEMER HALARY HALEVY HARRY BAUR HAUTE HAUTE COMEDIE HAUTE BOURG HAUTE RIPPE HAUTE VIENNE HAUTS BONNAC HAUTS VIENNE HAUT VIBLAC HEA HECTOR BERLIOZ HELENE BOUCHER HELOISE HENRI AUTEF HENRI BARBUSSE HENRI BERGSON HENRI BONNET HENRI D AGUESSEAU HENRI BOURNAZEL HENRI MONTHERLANT HENRI NAVARRE HENRI DUMONT HENRI DUNANT HENRIETTE DOURDET HENRIETTE PERUCAUD HENRI FARMAN HENRI GAGNANT RIPPE HENRI GAGNAT RIPPE HENRI GIFFARD HENRI GRANGER HENRI IV HENRI JOURDAIN HENRI LAGRANGE HENRI MATISSE HENRI MEYRAT HENRI MOREAU HENRI MURGER HENRI SELLIER HENRI VERGNOLLE HENRI WALLON HENRY BATAILLE HERBERAL HOCHE HOMERE HONORE ARNOUL HONORE ARNOULD HONORE ARNOULT HONORE BALZAC HOPITAL HORACE VERNET HORS HORTILLIERS HOTEL HOTEL DIEU HOZANNE HUCHETTE HURLEVENT HYACINTHE FAURE I F JOLIOT CURIE IFS IMBERT INDUSTRIE INGOLSHEIM INGRES IRIS BARRE ISSOIRE JACQUARD JACQUELINE AURIOL JACQUES ANQUETIL JACQUES BLENY JACQUES BREL JACQUES CHARDONNE JACQUES DUCLOS JACQUES FONTANIER JACQUES HENRI PRADEAUX JACQUES MONOD JACQUES PREVERT JACQUES THENARD JACQUES YVES COUSTEAU JAILLOUX JARDINS JAUVION ANTOINE BAIF AUTHIER BAPTISTE BLANC BAPTISTE BOUZAT BAPTISTE CARPEAUX BAPTISTE CHEZE BAPTISTE CLEMENT BAPTISTE COUTY BAPTISTE KLEBER BAPTISTE LANDON BAPTISTE LAPLAGNE BAPTISTE MAPATAUD BAPTISTE MARCET BAPTISTE MARQUET BAPTISTE NANOT BAPTISTE PAUZET BAPTISTE RUCHAUD BAPTISTE SENELAS BART BAYLE CACAUD CHABAUDIE CHARCOT CLAUDE PAPON COCTEAU COQUILLAUD CRUVEILHER FRESSE BRUYERE FONTAINE VIENNE DORAT DORCY EFFEL FAUCHER FREDON GABIN GAGNANT GIONO GIRAUDOUX GOUJON JACQUES CAFFIERI JACQUES ROUSSEAU JAURES JAURES POULOUZAT LALUE BAIL LOUIS GAGNANT LOUIS PAGUENAUD LURCAT MACE MERMOZ MONNET JEANNE D ALBRET JEANNE D ARC NOUAILHER PARVY SARRE SARTRE PERRIN TIMBAUD POUYAT RACINE RATEAU REBIER REBIER POULOUZAT REDON RENAUDIE ROSTAND ROUGERIE SEBASTIEN BACH SEGUREL TEILLET TEILLIET TEXIER TISSIER JEANTY SARRE VARIN VERSPIEREN VILAR JENNER JEROME THARAUD JOE DASSIN JOINVILLE JOLIOT CURIE JONQUILLES JOSEPH CAZAUTETS JOSEPH GUILLEMOT JOSEPHINE BAKER JOSEPH BRIX JOSEPH LEYSSENE JOSEPH MAZABRAUD JOSEPH ROUMANILLE JOSEPH ROUX JOSEPH WRESINSKI JOSETTE CORNEC JOSNIERE JOULE JOURDAN JULES CLARETIE JULES COURIVAUD JULES DUMONT D URVILLE JULES DUPRE JULES FERRY JULES GUESDE JULES MASSENET JULES MICHELET JULES NOEL JULES NORIAC JULES RAIMU JULES RENARD JULES SANDEAU JULES VALLES JULES VEDRINES JULES VERNE JULIAN GRIMAUD JULIEN LAFERRIERE JUMILHAC JUNIEN RIGAUD JUSTINE TEILLET JUSTIN NINARD KARL MARX KLEBER KRUGER LABICHE LABORDERE BORDERIE BORIAS BORIE BRUYERE LAC CHAPELLE MONTBRANDEIX CONDAMINE LAENNEC FANTAISIE FAYETTE LAFAYETTE FONTAINE LAFONTAINE POULOUZAT FONTAINE PUYBOURREAU LAGARDE LAGRANGE LAKANAL LAFAYETTE LANDE LOMBERTIE LAMARGUERITE LAMARTINE METAIRIE LANDE LANSECOT LANTERNE PEROUSE PEYRADE LAPLACE ROCHE ROCHEFOUCAULD SENDILLE LAS PLANCHAS LATHIERE TRAPPE LAUBARD LAURENT ROBERT LAURIERS LAURIERS BREUIL LAUTERBOURG VALOINE VALOINE POULOUZAT LAVANDIERES LAVAUD LAVENAUD LAVERAN LAVEYSSIERE LAVOIR LAVOISIER CID LECONTE LISLE LEDRU ROLLIN LEGOUVE NOTRE LEO DELIBES LEO LAGRANGE LEONARD BRETAGNE LEONARD VINCI LEONARD JEANTON LEONARD LIMOSIN LEONARD SAMIE LEONARD TROMPILLON LEON BETOULLE LEON BLUM LEON BOURGEOIS LEONCE BOURLIAGUET LEON CLADEL LEON DHERALDE LEON FOUCAULT LEON GAMBETTA LEON JOUHAUD LEON JOUHAUX LEON MERCIER LEON MORANE LEON ROBY LEON SAZERAT LEON TOLSTOI LEON WALRAS LEOPOLD BOURDIN LESAGE PROVINCIALES SUEUR TITIEN TRANSLOY L HERMONT LIBERTE LIEUTENANT LACOSTE LIGURE LILAS LILAS PUY CHABROL LIMOGES LIMOGES POULOUZAT LIMOUSIN LITTRE LIZEN RUELLE BOSCHAUDERIER RUELLE CHARRIERS RUELLE BIENNAC RUELLE JUILLAC RUELLE FONTAINE MONTEIL RUELLE GRANGE BOEIL RUELLE JALLADEAU RUELLE JARDINS RUELLE LAVAUD RUELLE MASBATIN RUELLE NIOLLET RUELLE THEIL RUELLE VIEUX VILLAGE RUELLE VIOTA LOISEL LOU ENAS LOUIS ARAGON LOUIS ARDANT LOUIS ARMAND LOUIS ARMSTRONG LOUIS AYMAR LOUIS AYMARD LOUIS BLANC LOUIS BLERIOT LOUIS BRAILLE LOUIS CASIMIR RANSON LOUIS CHADOURNE LOUIS CODET LOUISE MICHEL LOUISE MICHEL CORNEDIE LOUIS GUIBERT LOUIS JALLADEAU LOUIS JOUVET LOUIS LOUCHEUR LOUIS PASTEUR LOUIS SEYVAUD LOUIS VALADAS LUCIEN BOSSOUTROT LUCIEN DUMAS LUCIEN FONTANAROSA LUCIENNE DELYLE LUCIEN POURET LUCIEN ROUGERIE LUCIEN THOMAS LULLI LUPPE MAGELLAN MAISON NEUVE MAISON ROUGE MALESHERBES MALHERBE MALIBATAS MALLEDENT SAVIGNAC MANDEL MAPPAS MAQUISARDS MARC ANTOINE MURET MARCEAU MARCEL MARCEL BOURCET MARCEL BOURSET MARCEL LALU MARCEL MADOUMIER MARCEL PAGNOL MARCEL MARCEL SEMBAT MARCEL VARDELLE MARCHANDS VIN MARCHE MARCHEDIEU MARCHE OVIN MARDALOUX MARDOCHEE MARECHAL LATTRE TASSIGNY MARECHAL JUIN MARECHAL MARGUERITE BURGUET MARGUERITE DURAS MARGUERITE MORENO LAURENCIN MARIETTE MARINETTES MORTEROLLES MARIVAUX MARRONNIERS MARTHE MORAND MARTIAL CHAPUT MARTIAL DROUET MARTIAL PRADET MARTIN CELERIER MARTINERIE MARTINES MARTIN FAURE MARTIN FREMINET MARTIN NADAUD MARX DORMOY MARYSE BASTIE MARYSE BASTIER MARYSE HILSZ MAS MASBARREAU MASBATIN MAS BOURIANE MAS DAVIS MASDEHORS THEILLOL MASGOULET MAS LOGE MASSENA MASSENET MATHURIN CROUZIL MATHURIN DELIRANT MAURICE BELLONTE MAURICE CAILLIS MAURICE CHEVALIER MAURICE RAVEL MAURICE ROLLINAT MAURICE THOREZ MAURICE UTRILLO MAX DIF MAX LINDER MAZAURIE MAZELLE MEISSONIER MELEZES MERIMEE MERMOZ MEROVEE MESANGES MEUQUET MICHEL ANGE MICHEL BOUTAUD MICHEL CHEVALIER MICHEL MONTAIGNE MICHEL DETROYAT MICHELET MICHEL GONDINET MICHEL JOURDE MICHEL SERVET MICHEL SIMON MIDI MIGNET MILLENAIRE MILLOUNE MIRABEAU MIREBOEUF MOISSAN MOLIERE MOLIERE POULOUZAT MONACO MONET MONGE MONNAIE MONPLAISIR MONTAIGNE MONTAIGNE CRASSAT MONTAIGNE POULOUZAT MONTALEMBERT MONTE MONTE REGRET MONTEIL MONTESQUIEU MONTESQUIEUX MONTGOLFIER MONTHYON MONTMAILLER MONTMORENCY MONTPLAISIR MONTROZIER MONTS MORTEMARE MORTEMART MOTHE MOTTE MOUILLERE MOUILLERE VEDRENNE MOULIN TOUR MONTEIL NEUF NEUF POULOUZAT VIEUX MOUNET SULLY MOUNISMES MOZART MOZART POULOUZAT M QUENTIN TOUR MUGUET MUGUET BARRE MYOSOTIS NATHALIE SARRAUTE NATIONALE NAZARETH MA MAISON NELSON MANDELA NEUVE NEUVE CARMES NEUVE NEWTON NEXON NEXON CHARROUX NICEPHORE NIEPCE APPERT DENISOT FROMENT MORTEROLLES NOBEL NOEL LAUDIN NOISETIERS NONTRON NORD NOTRE DAME NOUAILHAS NOUILHAS NOUSSAT NUMERO 1 OBSERVATOIRE OEILLETS OFFENBACH OFFENDORF OF THE SEWING OISEAUX OLIVIER SERRES ORADOUR SUR GLANE OREE OREE ORPHEROUX OSCAR NIEMEYER OTHELLO OTHON PECONNET OUTRE VIENNE PABLO PICASSO PAINLEVE PAISSES PAIX PALENE PALENNES PALINS SENON PALVEZY PANHARD LEVASSOR PANORAMA PAPESOLEIL PARC PARDOUX BORDAS PARMENTIER PASCAL PAS MULE PASSERELLE PASTEUR PASTOURELLE ANTOINE BONNAUD BERT CEZANNE CLAUDEL DERIGNAC DUBOIS DUCOURTIEUX DUKAS ELUARD EMILE VICTOR GAUDY GAVARNI PAULINE CARTON PAULIN TALABOT LAFARGUE LANGEVIN LOUIS COURIER LOUIS GRENIER MORAND RENAUDIE SAVIGNY VALERY VERLAINE VIGNERAS PAVEURS PAYAUX PAYS D OC PECHERIE PENITENTS PENITENTS BLANCS PENNEVAYRE PENSEES PERES DUCLOU PERICHON BEY PEROU PERRAULT PERRONS PERVENCHES PETANQUE PETINIAUD BEAUPEYRAT PETINIAUD DUBOS BARRY PETIT BONTEMPS CHATENET PETITE BRELAUDIE JUILLAC ROYERE VERINAS VIVIENAS PETITS PETITS MARMIERS PETITS PARCS P M CURIE P NATHALIE MARTROU PEU CHAUDADE PEUPLIERS PEYNEVERT PEYRADE PEYRICHON BEY PEYROU PEYRUSSON PHILIPPE COMMINES PHILIPPE LEBON PIC PICASSO ABELARD ASTIER BASILE BENOIT BRASSEUR BROSSOLETTE CHAMBORD CORNEILLE CORNEILLE POULOUZAT COUBERTIN COURTEYS CURIE DAC COUBERTIN L HOMME RONSARD DUDITLIEU DURANT CURIE FRESNAY HECTOR PIGNIER LAROUSSE LEROUX LOTI LOUIS CACALY MAISON MALLET MENDES FRANCE MERIGOU MERLIN MICHAUX MOUSNIER PAROT PARREAU BROCA RAGOT RAYMOND RIVIER ROSSIGNOL SEMARD VERGNIAUD PIGEONNIER PILATERIES PINIER PINSONS PISCINE PISSARRO PUYTIGNON PLAINE PLAISANCE PLANTIER PLANTUSE PLATANES PLATEAU PLATON PLEIQUEIX PLEQUEIX CASSEPIERRE PLIERE POIDS PUBLIC POINT JOUR POMMERAIE THEILLOL PONANT PONT GABIE GRAULE NEDDE PEYRAT GUE BARRE ROUGE PORCELAINE PORTE ARCHAMBAULT PORTE BERAUD PORTE BERGERE PORTE BONHEUR PORTE DINSAC PORTE PANET PORTE TRILLOUX POSTE POTHIER POULOUEIX POUSSIN PRADIER PRAIRIES PRALON PRASMOUNIER PRE CAPIROU PRE MOROT PRESBYTERE PRESIDENT BONNET MORTEROLLES PRES MOUS PRESSOIR PREVERT PRE VIEUX PREVOTE PREVOT LATHIERE PRIERES PRINCIPALE PRINCIPALE BONNEFOND PR JOLIET PROFESSEUR BERGONIE PROMENADE PROUDHON PRUGNE PRUNH PSALETTE PUITS PUITS CHATONNIER PUY BOURSAUD PUY CHARENTON PUY CHEVALIER PUY D AYEN PUY LOUP BOURDELAS PUY PUY MAUD PUYMONTEIL PUYPICHOT QUATRE QUATRE ROUTES MOISSUN QUATRE SAISONS QUERELLE QUEUILLES RABAUD RABELAIS RACINE RACINE POULOUZAT RAFILHOUX RAMPE RAMPE GAZ RANCONNIERE RAOUL DAUTRY RAOUL FOLLEREAU RAOUL MONRIBOT RAOUL VERGEZ RAPHAEL RASPAIL RAVEL RAYMOND CHAMEAU RAYMOND CHAMEAUX RAYMOND COUTY RAYMOND POULIDOR RAYMOND SOUPLEIX REAUMUR RECLAUDI REGNARD REINE RELAIS QUINSAC REMBLAI REMBRANDT REMPARTS RENAISSANCE RENAN RENAUDS RENE CAILLE RENE CASSIN RENE DESCARTES RENE FARNIER RENE LAENNEC MERIGNAC RENE LATHIERE RENE LOUIS LAFFORGUE RENE PECHIERAS RENOIR RENOIR BASSE GORCE REPUBLIQUE RESISTANCE LIMOUSINE RESISTANTS REVOLUTION RHIN DANUBE RHODES RIBARDY RIBER RIBIERE 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HONNEUR TOUCHE TREILLE LAURIERS LARE PELOUSES SAULES LOUIS GERMAIN MAINTENON MICHELIN MILLE VENTS OUDIN PARC PAVIERS PELOUSES CLOCHER CARREE PINS RIASSE ROCHAMBEAU ROCHES RUISSEAU 1 RUISSEAU 2 SABLES SABLONS CITEARMEL CITELAZARE SALPETRERIE SAULES SIGNORETS TILLEULS TONNELLES TOUCHE TOUCHES TREILLE TUILERIES VALLEE VALLON VAUZELLES VELOR VIGNES CIVRAY CLAIE CLAIREAUX CLAIREVILLE CLAVIERE CLERE CLERET AUX LOUPS BALAI BARBINS BEAUCE BEL AIR BELLEVUE CHANCELEE CHANONAISE CHARLOT CHEVALERIE BEL AIR CEMENT FORGES LANDE MIGNARDIERE L ETANG PAULMY ALOUETTES AMANDIERS BROSSEAUX CHENES GALLAIS GIRARDIERES HAUTES MAISONS LOUPS POMMIERS QUILLES BOURG PAVILLON PIN PRIEURE SAULE CLOSERIE BEL AIR GANIER GARNIER CHAPELLE MAZERE MARIFA MAURY MOISON MOURY NEUF NOYERS OLIVIER ROMAIN RONSARD CLOSDENIS CLOSGILLES CLOSNICOLAS SAUT LOUP SIMON VAUGIRARD VAUGRIGNON CODE COIN MUR COIN PARC COLLEAU COLLEGE G BRASSENS PIECES COLOMBRIOUX COLONNIERE COMMANDERIE VILLEJESUS COMMARUERE COMPLANS CONFLUENT CONNEUIL CONSTANTINE CONTANCIERE CONTRAY COQUETIERE CORBERY CORBLANDES CORMIER CORNILLE CORNUARD CORVIERS COSNIER COTEAU BARAUDIERES COTEAU CARROI PARIS COTEAU CHAUSSEE COTEAU FOLLET COTEAU BARDOUILLERE COTEAU ROCHERE COTEAU MALVAULT COTEAU MONAVILLE COTEAU POUPINE COTEAU ROCFORT COTEAU DERADEGONDE COTEAU MADERES COTEAU VERONS COTEAU VENISE COTEAU GUE COTEAU ROY COTEAU VIEUX BOURG COTEAU FOLLET COTEAU MORIN COTEAU MUNAT COTEAU PIOTTES COTEAURADEGONDE COTEAUMARTIN COTEAUVENANT COTEAU SEUILLY COTEAU TRAINEAU COTEAUX SONNAY COTE BARAUDIERES COTE BEAUVOISERIE COTE CARROI PARIS COTE CHABOISSIERE COTE CHAUSSEE COTE D OR COTE PEU COTE MORIN COTE PIOTTES COTES GRANGES COTE SEUILLY COUASNAY COUDRAY COUESME COULAINE COULANGE COULINGUES COUPURE PARC ABBAYE COURANT MARTEAU BRULOTS COURCHAMPS CHAUVEAUX CASTIGLIONE FERME FONTAINE PHILIPONIERE BLANCS FILEUSES MESANGES OISEAUX ROSSIGNOLS PUITS HERPIN SOLEIL 1 ER GABRIELLE D ESTREES GENERAL BERTRAND AUX MALLETS DOUARDS PAVEE COURLAURENT L ARMORIAL MADAME SEVIGNE COURTABON COURT COURT PARILLY COURTE MICHE COURTIL COURTINEAU COURTRIE TROIS ROIS VAN DER MEERCH COURVAULT COURVEAUX COUTARDERIE COUTAY COUTEAU COUTURE CRACHOUSES CRAPEAU CREABE CREMILLE CRENE CREUILLY CREUZOT CRIABE CROCHET CROIX CAMUS CROIXCHAUDRON CROIX CHESNEAU CROIX D ARGENSON CROIX CROIX BORDEBURE CROIX LACROIX L HERMITE CROIX MORIN CROIX DEJEAN CROIX DURETS CROIX VAUX CROIX GASCOGNERIE CROIX GRANDS MAISONS CROIX HABERT CLERE PIN CROIX CROIX PALLU CROIX QUILLARD CROIX ROBERT CROIX ROCHERE CROQUET CROTET CROUILLAS CUEILLE AUX PRETRES CUEILMINAULT CUFFOU CUVASSERIE DARGENTERIE DAUCONNAY DAUDERE DDEFAIX DEGRESMARC DENELLERIE DENEUSERIE DENILLIERE DERCE DEROUINERIE VICTOIRES DETILLY DEUX DEUX CROIX DEVANT L ARRE DIGUEJACQUES DOLBEAU DOMAINE BEAUFOU DOMAINE BEAUVOIS DOMAINE CHARTRE DOMAINE CORLOUET DOMAINE L AULEE DOMAINE ROCHEFORT DOMAINE LAC DOMAINE MINIERE DOMAINE NEGRON DOREE DOUCE DUCHE SERRAIN EBAUPIN EBEAUPIN ECHALLERIE ECLUSE LARCAY JUSPILLARD ECOLEGERMAIN EDMOND CHEDEHOUX ENCLOS L ABBAYE EPEIGNE EPINAY EPINETTE EPINIERE ERIPPES ESSARTS ETABLEAU ETANG SAULAIE ETANG MEES ETANG L BRULE ETANG L CASSE ETANG L L ILE ETANG L ETANG L MARNAY ETANG L MARRON ETANG L MARTIN ETANG L SAVIN ETANG MARTIN ETANG NEUF FALAISIERE FALECHE FAUBOURG BICHETERIE FAUBOURG BICHETTERIE FAUBOURG BUCHETTERIE FAUBOURG GIRAULT FAUBOURG BICHETTERIE FAUBOURGFAUCOUDE FAUTAUBOURG FAUX GIRAUD FAVIER FAVRIE FENETIERE FERME BEL AIR FERME BEL AIR BEL AIR FERME BOULAY FERME CANDE FERME FERME L ANGELIENNERIE FERME LESSAY FERME L ETANG FERME MADERE FERME MONTRUAND FERME GRANGES FERME D ESVES FERME TAILLE FERME THORIAU FERME BOULAY FERME FERME COUVENT L ENCLOITRE FERME DOUX FERME HAUT AUNAI FERME PRAY FERME TERTRE FERME GIVRY FERME HABARDERIE FERME LAITERIE FERME BOULAY FERME L HEBERDIERE FERME PARTHENAY FERME GRANGE FERREAU FERTAUDERIE FEUILLE D OR FEVRAIE FIEF BOUJU FILLONIERE FINELLERIE FLEURAU FLEURAY FLEURIAN FLONNIERE FOIX FOLANT FOLIE FOLLET FONCLUSE FONTAINE AUX OISEAUX FONTAINE BERTHON FONTAINE BESNON FONTAINE BONDREE FONTAINE BRETON FONTAINE L ORMEAU FONTAINE VIVIER FONTAINE PSYCHEE FONTAINECECILE FONTAINEMARTIN FONTAINES BLANCHES FONTENAILLES FONTENAY FONTENELLE FONTENELLES 1 FONTENILLE FORET PIQUANTE FORGE HUDEAU FORTIGNIERE FORTINIERE VENT FORVILLE FOSSE AUX SABLES FOSSE BERGEAULT FOSSE BESSE FOSSE BLANCHE FOSSE COURTOISE FOSSE AUX CHIENS FOSSE BERGEAU FOSSE BERGEAUD FOSSE CAILLOU FOSSE CARDIN FOSSE COURTOISE FOSSE LAURESTE FOSSE RATEAU FOSSE FOURRIER FOSSE MAURE FOSSEMBAULT FOSSE NEUVE FOSSE NOIRE FOSSE NOUE FOSSEROUX FOSSES BLANCHES FOSSE SECHE FOSSES LAURENT FOSSETIERE FOSSE TIERRE FOSSILLON FOUCHARE FOUCHAULT FOUGERAY FOUGEREAU FOUILLE FOUINAIS FOUR CHAUX FOURCHETTE FOURNEAU FOUX FRANCERIE FRANC JEU FRENE FRETAY FROGERIE FROMENTEAU FRONDENIERE FUYE GADOUILLERE GAITRIE GALICE GALISSON GALOCHERIE GALOPPERIE GARANDE BARRIERE FONDETTES GARENNE GARENNE BASSE GARENNE HAUTS GARGET GASTINES GATEBOURSE GATEFERS GATINE GATINES GAUBOURG GAUDINE GAUTRIE GEAY GENERAL GENES GENNEBERT GENTINIERE GERBERIE GERMINY GERON GIBAUDIERE GIDAIRON MARRAY GILBARDIERE GILOTTIERE GIMONT GIRARDET GIRARDIERE GIRARDIERE GIRONDE GIVRAIZAY GIVRAY GIVRE GODEFROY GONDOUBARD GOUELANDE GOUESERIE GOUGEONNIERE GOULEVENT GOULVENT GOURMOIS GOURY GRAMMONT BROSSAY GRANDGRAND CLERET COURCHAMP FOLIE GAUTELLERIE ROTTE GRANDEGRANDES MAISONS GRANDES RAIMBAUDIERES GRANDES VIGNES TOUCHE FOND GEAY GRAIS GRANDINIERE GRANDLAY MARRAY CHAMBOURG S I MONT MORIER MORTIER GRANDGRANDS VALLEE VAU VILLAGE GRANGE GRANGE 3 VAURICHARD GRANGE ACQUET GRANGE NEUVE GRANGER GRANGE ROUGE GRANGE TIPHAINE GRAPINEAU GRASSE COUE GRATTE CHIEN GRATTE PAILLE GRATTE PUITS GRAVOT GRAVOTS GRES GRIERE GRIGNON GRIGNY GRILLEMONT GRIMAUD GRISAY GRUTEAU GUE AUX MOINES GUE BLANDIN GUE GRE GUE MOSSON GUE BERTHE GUE LEGER GUENAY GUE RAIDE GUERETINIERE GUESNE GUET BICHE GUIAUGUIERE GUIFFARDIERE AVERRIES GUIFFAUDIERE GUIGNAUDERIE GUIGNE FOLLE GUILBERTIERE GUILLERY GUILLONNIERE GUILLOTIERE GUIMONT GUINAUDERIE HAIE HALLEBARDEAUX HALLES CENTRALES HALLOUSSIERE HAMARDIERES BRETONNIERE CANTERIES CARROI CAVE VAUDELET CHATAIGNIERS TREILLE MARIDENNEAUX VEAUX ENCLOITRE FLORAY FORTINIERES GUICHARDIERE MEQUILLIERE NITRAY ATHEE C PRATEAUX SIROTIERE HARAMBURE HARDELIERE HARDELIERES HARDONNIERE HAUT BOURG HAUT BRAY HAUT CHAMBOISSON HAUT CHANDON HAUT COULEUR HAUT CRIABE HAUTE BORNE HAUTE BOURGERE HAUTE CORMIERE HAUTEE HAUTEE FRANCS HAUTE JARDERIE HAUTE LANDE HAUTE MAISON HAUTE MAZERE HAUTE PINAUDIERE HAUTE RUCHELIERE HAUTEHAUTE RUISSELIERE HAUTES MAISONS HAUTES SEVAUDIERES HAUT FRESNAY HAUT PAVOT HAUT RONGERE HAUTS NOYERS HAUTS PIMBERTS HAUTTEES FRANCE HAUT VENEUIL HAUT VILLAGE HAUT PAYS HAYE MARTIN HEMOND HERAULT HERMITAGE HLM BAS HLM L ETANG HLM ETANG HLM L ECHENEAU HLM MARIAUDES HOMMES HOUDEAU HOUX HOYOU HUCHE PIE HUMEAU HUMEAUX HUPPE CHAT HUANT HUPPE LOUP HURLEVENT HUTTIERE HUTTIERES HUVETS HYS ILE AUCARD ILE AUGER ILE BARDE ILE TOURS ILEMARTIN ILE THIMEE IMAGEJEAN IMAGESJEAN IMP 20 11 NOVEMBRE 19 MARS 1962 1 CAVES BOUVRIERES 5 EME ABBE PIVET ABSONNERIE ACACIAS ADELAIDE RICHE ADOLPHE LANGLOIS ADRIEN DESLONDAINS AGNES SOREL AITRE ALBERT CAMUS ALEXIS MEUNIER AMBOISE AMBROISE CROIZAT ANATOLE ANATOLE FRANCE ANDRE BAUCHANT ANNE BRETAGNE ANNE NOAILLES ANNE FRANCK ANSAULTS ARCHE ARGENTERIE ARSONNIERE AUBEPINES AUBINS AUGUSTINS AUMONE AURELIE MARDELLE BAFAUDRIE BALTAZAR BERNARD BALZAC BARATIERE BARBOISE BARILLERIE BARRE BASSES RUES BASSES VIGNES BAZONNEAU BEAUREGARD BEAU VOILIER BECQUEREL BEGUINERIE BEL AIR BELLEVUE BENAIS BERANGER BERTHAISIERES BERTHE BESNARDIERE BESNERAIE BLAISE PASCAL BLANDINIERE BLANQUI BLARDIERE BLEUETS BLONDELLERIE BODINS BOGNOUX ROCHE BOISDENIER NEUF REGNIER BORDE BOUILLERE BOURDONNIERE BOURGNEUF BOUT MONDE BRAUDIERE BREVIANDE BRION BROCA BROUTIER BRUERE BUISSON BURONNIERE BUTTE CABUROCHE CALOSSERIE CAMILLE DESMOULINS CARCOULT CARROI FORET CARROI LAMBERT CAVE CAVES CAVES BODINES CAVES BOUVRIERES CEDRES CESAR CEZAR CHALONNIERE AUX MOINES PUITS CHAMPS PERRON CHANCANES CHANTERIE CHAPERONNIERE CHARBAUDIERE CHARBONNERIE CHARDIN CHARDONNERAIE PICART DOUX VIGREUX D EAU CHATIERE CHAVIS CHENES CHENIL CHENILLE CHEVRETTE CHINON CHOLETTERIE CHOLLETTERIE CIGALES CIGOGNE CLAUDE BERNARD CLAUDERIE CLONAS CLOS AUVRAY COLINETS COLOMBIERS COLONEL GOULIER COLS VERTS COQUELICOTS CORBINIERE COROT COSSONIERE COTEAU COTEAU POUAND COUDRAIE COUDRAY COURANCE COURCHAMP FERME MIRACLES COUTEAUDRIE COUTURE CROISSANT CROIX CROIX BAZOUILLE CROIX ROUGE CROIX VERTE CURE BIGNON CUVIER CUZE CYGNES DAMES DANTON DAVIEL BELLEVUE BOISDENIER BON REPOS BOURGOGNE CARCASSONNE DECHENE CHERIZY CHINON GRATTE SEMELLE DEJAUTERIE L ABBE CHASTEIGNIER BILLARDIERE BONNETIERE LABRANDONNE CAILLONNERIE CHABOTTIERE CHARTERIE CHENARDIERE COQUINIERE VERTE DAME EN NOIR DEVINIERE DIGUE FOSSE BOUCHER FOSSE LORETTE FRELONNERIE FRESLONNIERE GARGOUSSERIE GAUDELLERIE GIRONDE GRANDIERE GUYENNE HAUTE ARCHE HAUTE LANDE LIBERTE MAISON ROUGE MARCHANDERIE MERCANDERIE MIGNONNIERE NOUE PAILLONNERIE PASQUERAIE QUI TOURNE SOURDE PIETRIE POUDRIERE PRESLE RAGOTIERE ROCHE RONCE RONCIERE SAGERIE SAINTERIE SOURCE TERGATTERIE TOURMALINE L AUBERDIERE VALLEE RAINTRUE L EBEAUPIN L EGALITE L L ERABLE L HOSPITALITE L ILE D AMOUR L ORATOIRE L ORMEAU PARCAY PENAVET D EPIRAY PORTILLON PROVENCE RIDEL VUE RIGNY RIVOLI ROCHETTE DELEGER ALOUETTES AMANDIERS AUBEPINES AULNES BLEUETS BOISSELEES BORDIERS BOULEAUX BOURDAISIERES CAMELIAS DESCARTES CAVES CAVES VASLINS CERISIERS CHAINEAUX PERRONS CHARRONS CHATAIGNIERS CHESNAIES CHEVALERAIES CHEVALLERIES CLEMATITES CONFERENCES COQUELICOTS COURLIS ECUREUILS EPINETTES ERABLES ESSARTS FAUVETTES FIGUIERS FLEURETTES FONTAINES FOURS CHAUX GANAUDIERES GIROFLEES GLYCINES GOUBINS HERMITES HETRES HIRONDELLES HORTENSIAS HOUX IFS ILES IRIS JARDINS LILAS MAGNOLIAS MARGUERITES MARRONNIERS MESANGES MURIERS MYOSOTIS NENUPHARS NOYERS OUCHES PAPILLONS PERRETS PINSONS POMMIERS PRES PRIMEVERES PRUNUS REINETTES RELIEURS ROSIERS ROSSIGNOLS SABLES SANSONNETS SAPINS SORBIERS SOURCES TILLEULS STRASBOURG TULIPES VERRERIES VIGNES VILLAS THUISSEAU DIDEROT DIDIER DOUVES DRAON 1 ER MAI BOIS BOUTET JOLI BOISSEAU CERCELE D EAU CHATELET CHER MEZIERE NANETTE COLOMBIER COMMERCE FORGERON GAULT PRESSOIR GUE NOBLE JOUE NOUVEAU CALVAIRE PASSAGE NOTRE DAME SENTIER PIGEONNIER PLACIER PLATRIOU PLESSIS POITOU PRESSOIR PUITS BAS PUITS BANCS RACOUPEAU RAINEAU TEMPLE VAL FLEURI VERGER ECOLES EDMOND ROSTAND EDOUARD ANDRE ESPAGNE BUREAU EUGENE GOUIN FANTAISIE FARMAN FARTE FAUBOURG FAUBOURGPIERRE FEBVOTTE FERME COULOMMIERS FERMETTES FERMETTES FOURNEAUX FERRANDIERES FIGUIERS FLEURIE FLEURS FLORA TRISTAN FONTAINE FONTAINE VAUX FORET FORGE FORTUNE FOUGERAY FOUGERES FOUR PAIN FOUR BANAL FOURBISSERIE FOURNEAU MOREAU SICARD FREDONNIERE GABRIEL LAME GABRIELLE D ESTREES GABRIEL RICHAUD GABRIEL RICHAULT GADOUILLERE GALLIENI GALUCHES GAMBETTA GAY LUSSAC GENDARMERIE GENETIERE GENETS CARPENTIER CUVIER MAHOUDEAU MOURET GIRAUDIERES GIRONNETS GLAIEULS GLYCINES GOHUAU GRALLEPOIX BALLET GAUDREE PRAIRIE GRANDES BASTES PRAIRIE GRENOUILLERE GRES GRILLEMONT GUERINIERES GUE ROGER GUILLOTERIE GUY MOCQUET HAUT BOURG HAUT CHARRIERE HAUTE TISSERIE HAUT MELOTIN HAUT SALVERT HENRI DUNANT HENRI IV HENRI MARTIN HERMITAGE HERMITES HEURTELOUP HORTENSIAS HURAUDIERES IMPERIALE INDUSTRIE IRIS JAMINIERE JARDINS JARDINS CURE COLIN GITTON JACQUES ROUSSEAU JAURES MACE JEANNE D ARC PHILIPPE RAMEAU JENSON JEU PAUME JOACHIM BELLAY JONQUILLES JOSEPH BARA JOSEPHINE BAKER JOUBARDIERE JUCHE PERDRIX JULES SIBOT JULES VALLES L ABSONNERIE CALOSSERIE CHARBONNERIE CHOLLETERIE COSSONIERE CROIX ROUGE LAENNEC LAFAYETTE LIBERTE LISIERE LAMARK LAMARTINE LAMPE LANDE PRAIRIE LATHAM L AUMONE LAVOIR LEBRUN LEON BRULON LEPETIT LEZARDS LIBERTE LIEVRE D OR LILAS LIONEL TERRAY LISIERE LIVRE D OR LOIRE LONG LONGUETERIE LOUISE VALLIERE LOUIS LACHENAL LOUIS NOISETTE LUREAU BARBEREAU MADAME TALLIEN MADEMOISELLE MADO ROBIN MAILLAUDIERE MARAT MARCEAU MARCHE MARECHAL MARELLE ANTOINETTE TROCLET MARREY MARYSE BASTIE MATTEOTI MECANIQUE MENOUX MENUISIER MERLETS MESANGES METAIRIE MEUNIER MICHEL ANGE MICHEL DUCLOS MINOTIERE MIRABEAU MOLIERE MORIER MORT VOUS ETES MOTTE MOULIN VENT MOULINE ROUGE MOUTON MURIERS MYOSOTIS MYSTERE NEPER NEURAIE POTOCKI NIVERDIAIRES NOYER NOYER NOBLE OISEAUX OLIVET OLIVIER DAIM ORIGET ORTINIERES OUCHE RICHOT PANTAGRUEL PAPINIERE PARADIS PARC PASSOIR HUET LOUIS COURIER PAVILLERIE PAVILLON PELLERAULT PENDARDES PENSEES PERAUDIERES PERDRIAU PERON PERRIERES PERRONS CREUILLY PREE LIEU PRE RAIFAULT PHILIPEAUX PICHROCOLE PIC VERT CHAPELAIN CURIE VIGNON PILORY PINARDERIES PISSOT PIVERT PLANCHOURY PLANTES PLANTIN PLATANES PLESSIS PLOQUIN POIRIERS POMMERAIE POMMIERS PONCELET BOUCHER BOUCHET POSTE POULAINS PRAIRIE PRAIRIES PRE HAUT PRES PRES VIVIER PRESLAURENT PRIEURE PRIMEVERES PROUST PUITS CHAUVEAU PUITS ROCHE PUITS GUIBERT PUVIS CHAVANNES PUY BANCS PUY RIGAULT QUINCAMPOIX RABELAIS RASPAIL RAUDERIE REGAINS REJAUDRY RENAN RENARDIERES RENAUDIERE RENE PRIE REPUBLIQUE PARC ROBERT BARON ROBERT NADAUD ROC ROCHE ROCHE DONNET ROCHER ROCHES ROCHE THIBAULT ROCHETTES ROND POINT RONSARD ROQUILLE ROSA ROSES ROSIERS ROUILLERE ROUJOLLE IMPASSEMARTIN AUDENET RUETTES RUISSEAU SABLES SABOT SABOTIER IMPASSEANNE IMPASSEBARBE IMPASSERADEGONDE IMPASSEETIENNE IMPASSEEXUPERY IMPASSEGERMAIN IMPASSEGERVAIS IMPASSEJEAN IMPASSELADRE IMPASSELAZARE IMPASSELEUBAIS IMPASSEMARTIN IMPASSEMAURICE IMPASSENICOLAS IMPASSEPIERRE IMPASSEROCH IMPASSESAENS SANGUIER SAPINS SARAH BERNHARDT SARAH BERNHART SARCELLES SAUGE SAULAIE SAULTRIE SAVATERIE SERRANTES SEVERINE SINISTRES SOLEIL LEVANT SOUCHARDIERES VALLEES STADE STEPHANE PITARD SUFFREN TALLIEN TEMPLE THOMEAUX TISSERIE TIVOLI TONKIN TOUR TRAVERSIERE TREMBLAIE TREMBLERES TROIS ROIS TUILERIE TULIPES VALLEES VALLON VAN VOOREN VARENNE VARENNES VARIDAINE VAUGIRARD VAUMENAISE VAUX VERGEAT VERRIER VIALA VIANDAISES VIAULLIERES VICTOR HUGO VIEILLOIS VIENNE VIEUX BOURG VIGNES VIGNES BLANCHES VILAINES VILLATTE VILLENEUVE VILVENT VILVERT VIOLETTES VOLTAIRE WOLLE IMP R STE BARBE IMPINGRANDE INGRANDES ISEMBARDIERES ISLE AUGER ISSARD JAGEE JALLANGE JARCY JARDIN ANDREA GABRIELI JARDIN BEAUNE SEMBLANCAY JARDIN JEU PAUME JARDIN 1 ER JARDIN G BOUZIGNAC BTE 113 JARDIN BOUZIGNAC JARDIN OCKEGHEM JARDIN JEU PAUME JARDIN MONTAIGNE JARDIN MONTREUIL BELLAY JARDPIERRE JARRY JAUDELLERIE JAULNAY JAUTROU JEANBUAIRE JERUSALEM JONCHERAY JONCTEAU JUBERGEIN JUCHE PIE JUCHE POULE JUIVERIE JUMEAUX JUSSAY JUSTIERE KER GAEL KERLEROUX BABAUDERIE BABINIERE BADERIE BAGONNE BAGUETTERIE BAILLARDIERE BAILLAUDIERE BAISERIE BAISSE OREILLE BALASTIERE BALERIE BALEUZERIE BALLIERE BALLONIERE BALONNIERE BARANGERAIE BARANGERIE BARATERIE BARATIERE BARATTERIE BARAUDERIE BARAUDIERE BARBELLERIE BARBILLONNIERE BARBINIERE BARBOTINIERE BARBOTTERIE BARBOTTIERE BARBOTTINIERE BARBOTTINNIERE BARDOIRE BARDONNERIE BARDOUILLERE BARDOUSIERE BAREILLERIE BARELLERE BARETTE BARILLERAIE BARILLERE BARILLIERE BARNIERE BARONGERE BARONIERE BARONNERIE BARONNIERE BARRE BARREE BARRELLERIE BARRERIE BARRETTE BARRIERE BARURIE BASSE BERGE BASSE BERGERIE BASSE BESNARDIERE BASSE CALOGNIERE BASSE BASSE COURANCE BASSE GARDE BASSE JARDERIE BASSE JARNIERE BASSE LANDE BASSE MARTINIERE BASSE MOUSSARDIERE BASSE POMMERAIE BASSE RAIRIE BASSE SALLE BASSE VAU BASSE VERRERIE BATAILLERIE BATARDIERE BATARDRIE BATE BATISSE BAUBRIE BAUCHETIERE BAUDELIERE BAUDERIE BAUDICHONNERIE BAUDIERE LABAUDINIERE BAUDINIERE BAUGE BAUGELLERIE BAUGERIE L ABBAYE L ABBE L ABBEE BEAUCE BEAUCHETIERE BEAUDIERE BEAUGERIE BEAUJARDERIE BEAULOIRE BEAUVOISERIE BECASSERIE BECHETIERE BECTHIERE BEDASSERIE BEDELLERIE BEDINIERE BEHARIE BEJAUDERIE BELLASSERIE BELLAUDIERE BELLE ANGERIE BELLE CROIX BELLE ETOILE BELLE JONCHERE BELLERIE BELLETIERE BELLEVUE BELLE VUE BELLISSIERE BELLONNIERE BENAUDERIE BENERIE BENNERIE BERANGERIE BERDINIERE BERGEONNERIE BERGEOTTERIE BERGERAIE BERGERESSE BERGERIE BERGETIERE BERJEOTTIERE BERJETTIERE BERJOTTERIE BERLOTTIERE BERNARDIERE BERNASSIERE BERNELIERE BERNELLERIE BERRERIE BERRICHERE BERRURIE BERRUSERIE BERTAUDIERE BERTERIE BERTHELERIE BERTHELIERE BERTHELLERIE BERTHELOTIERE BERTHONNIERE BERTINIERE BERTONNERIE BERTONNIERE BERTOTTERIE BESCOLIERE BESNARDERIE BESNARDIERE BESNERIE BESSIERE BEURRERIE BEZARDERIE BEZARDIERE BEZAUDIERE BIAUDRIE BICANERIE BICANNERIE BICHERIE BICHETIERE BICHETTERIE BICHOTTIERE BIDAUDERIE BIDAUDIERE BIENVENUE BIGEONNERIE BIGONNIERE BIGOTIERE BIGOTTERIE BIGOTTIERE BILLARDERIE BILLARDIERE BILLETIERE BILLETRIE BILLETTE BILLONIERE BILLONNIERE BILLOTIERE L ABIME COTEAU BINELLERIE BINETTERIE BIONNERIE BIROTTE BIZARDIERE BIZAUDERIE BIZELLERIE BLAIVERIE BLANCHARDIERE BLANCHERE BLANCHERIE BLANCHETIERE BLARDIERE BLETIERE BLINIERE BLISSIERE BLONIERE BLONNERIE BLONNIERE BLOTELLIERE BLOTERIE BLOTTERIE BLOTTIERE BLUTERIE BOBINIERE BODINAIS BODINIERE BOIGARDIERE BOIRE MASSON BOISARDIERE BOISGARDIERE BOISSELIERE BOISSIERE BOITOULIERE BOLTIERE BONDE BONDONIERE BONDONNERIE BONDONNIERE BONDONNNIERE BONINIERE BONNE DAME BONNELIERE BONNERIE BORDE BORDE BERTRET BORDE BOULEAU BORDE BUISSON BORDE CORNUARD BORDE FRESNEAU BORDERIE BORDIERE BORDINIERE BOSNIERE BOTARDIERE BOUCAIRE BOUCARDERIE BOUCHARDIERE BOUCHERAIE BOUCHETTERIE BOUCHONNERIE BOUDINIERE BOUE BOUETTERIE BOUFFARDIERE BOUFFERIE BOUFFERIE BOUGAUBRIE BOUGE AU VIN BOUGESIERE BOUGRERIE BOUGRIE BOUHARDIERE BOUILLETERIE BOULAIE BOULAYE BOULERIE BOULIERE BOULINIERE BOULLONNERIE BOULOGNE BOUQUETIERE BOUQUINIERE BOURASSEE BOURDAISERIE BOURDAISIERE BOURDE BOURDILLERE BOURDILLERIE BOURDILLIERE BOURDONNIERE BOURELLERIE BOURGADE BOURGEAISIERE BOURGESIERE BOURGINIERE BOURGOGNERE BOURGOGNIERE BOURJALLIERE BOURLERIE BOURLIERE BOURNACHERIE BOURNEE BOURRELERIE BOURRELIERE BOURRELLERIE BOURRELLIERE BOURSAUDERIE BOURSETTRIE BOUSSAYE BOUTARDRIE BOUTEILLERIE BOUTIERE BOUTRIE BOUTTERIE BOUVALINIERE BRANCHE VERTE BRANGEAUDIERE BRANGERIE BRAUDERIE BRAUDIERE BRAVERIE BRAYE BRECHE BRECHOTIERE BRELIERE BREMAUDIERE BREMONDERIE BRENELLIERE BREPINIERE BRETAUDIERE BRETECHE BRETINIERE BRETONNERIE BRETONNIERE L ABREUVOIR BREZARDIERE BREZERIE BRIANDERIE BRIANDIERE BRIARDIERE BRICHE BRIFAUDIERE BRIMBALE BRINERAIE BRIOLLERIE BRIOLLETRIE BRIQUETERIE BRIQUETTERIE BRISSANDIERE BRISSETERIE BRISSONNIERE BRIVEDELLERIE BROCHARDIERE BROCHE BROCHETIERE BRODIGNIERE BROGONDIERE BROSSARDIERE BROSSE BROSSELERIE BROSSELLERIE BROSSERIE BROUETTE BROUSSE BROUSTILLERE BRUERE BRUERE CALOTIERE BRULEE BRUNETTERIE BRUYERE BRUZETTE BUCHERIE BUCHETTERIE BUFETRIE BUHARDIERE BUHETTERIE BUISSONNIERE BULOTIERE BURERIE BURETTE VEAU LIARD BUSCAUDIERE BUSTIERE BUTONNIERE BUTTE BUTTIERE BUVINIERE CABANE CABOTTERIE CABUROCHE CAGNAUDERIE CAHUTTE CAILLARDIERE CAILLAUDIERE CAILLERE CAILLETERIE CAILLETIERE CAILLIERE CALAISIE CALIFORNIE CALLERIE CALONNERIE CALONNIERE CALOURIE CALTIERE CAMUSARDERIE CANONNIERE CANTEE CANTELLERIE CANTERIE CANTINIERE CAPITAINERIE CARANDERIE CARDINIERE CARILLONNERIE CARIONNIERE CAROGNIERE VIEUX BOURG CARRELLERIE CARRIERE CARROITE CARTE CARTINIERE CARTONNIERE CASSE CASSINE CATAUDIERE CATINERIE CAUDIERE CAVE CAVE BASSE CAVE CAVE HERPIN CAVE MORINET CAVE PELLETIER CAVE PICHARD CAVE SALMON CELONNIERE CENETRIE CERISAIE CEVETERIE CHABOISSIERE CHABOTTIERE CHABRUERE CHACHIE PETITES BRUYERES CHACHITTE CHAINAIE CHAINE CHAINE SOULIERE CHAISE CHALANTONNERIE CHALLERIE CHALLONNIERE CHALONIE CHALONNERIE CHALONNIE CHALONNIERE CHALUERE CHAMARDIERE CHAMBAUDIERE CHAMBELLERIE CHAMBONNERIE CHAMBRIERE CHAMNILONERIE CHAMPEIGNE CHAMPIONNERIE CHAMPIONNIERE CHANTELEUSERIE CHANTELOUP CHANTEMELIERE CHANTERIE CHANTEVINIERE CHAPELIERE CHAPELLE 1 CHAPELLE CHAPELLE CHANDERY CHAPELLERIE CHAPELLEJULIEN CHAPERONNIERE CHAPPELLIERE CHARBONERIE CHARBONNERIE CHARBONNIERE CHARDERIE CHARDONNIERE CHARLOTTIERE CHARMERAIE CHAROLIERE CHAROLLIERE CHARPENTERIE CHARPENTRAIE CHARPRAIE CHARRIERE CHARRONNERIE CHARTERIE CHARTREUSE LIGET CHARTRIE CHASSE CHASSEIGNE CHASSETIERE CHATAIGNERAIE CHATELLERIE CHATIERE CHATONNIERE CHATRE AUX GROLES CHATRIE CHAUBERTON CHAUME CHAUME BLONDE CHAUME PUIT CHAUME FORTUNIERE CHAUMELLE CHAUMERAIE CHAUMERIE CHAUMEROIRE CHAUMETTE CHAUMIERE CHAUMINE CHAUSSEE CHAUVELIERE CHAUVELLERIE CHAUVELLIERE CHAUVINIERE CHEDAINIERE CHEDENNIERE CHENAIE CHENARDIERE CHENELIERE CHENEVAIE CHENUERE CHEROLE CHERRERIE CHESELLERIE CHESNAIE CHESNAYE CHESNERAIE CHESNERIE CHETARDIERE CHEVALERIE CHEVALETTE CHEVALLERIE CHEVRAIE CHEVRIERE CHEVROLIERE CHEVRONNIERE CHEZELLERIE CHICHARDIERE CHICHEE CHIFOULIERE CHILLERIE CHIPERIE CHOGUETTE CHOLETRIE CHOLLET CHOLTERIE CHOLTIERE CHOTARDERIE CHOUANNIERE CHOUETTERIE CHRISTINERIE CHENUSSON CHUPRAIE CICARDIERE CIGOGNE CIVERIE CIVRIE CLAIE CLAIRIERE CLAISIERE CLAMAVOIRE CLARCIERE CLARTRIE CLAVELLERIE CLAVETERIE CLEMENCERIE CLISSONNIERE CLOGELLERIE CLOSERIE CLOUSIERE CLOUTIERE COCANDERIE COCARDERIE COCARDIERE COCHARDIERE COCHERE COLADRIE COLARDRIE COLINETRIE COLLARDERIE COLLERIE COLLINIERE COLLONNIERE COLONNERIE COLTIERE COMAILLIERE COMBE COMMAILLIERE COMMANDERIE COMMANDERIE MALEMORT COMPINERIE COMTE COMTEE CONARDIERE CONTANCIERE CONTE CONTERIE CONTRAIE CONTRIE COQUELINIERE COQUERIE COQUILLERE COQUINIERE CORBINE CORBINIERE CORMELIERE CORMELLIERE CORNEE CORNETIERE CORNETTERIE CORNIERE CORROIRIE COSSAIE COSSERIE COSSONIERE BAS COSSONNIERE COTE COTELLERAIE COTINIERE COTTERIE COUARDE COUARDIERE COUBAUDIERE COUDRAIE COUDRAYE COUDRE COUE COULARDIERE COULEE COULOITERIE COULONNIERE COUPE COUPELLIERE COUPERIE COURANCE COURBE COURETTE COURRAIE COURROIERIE COURTAUDIERE COURTILLONNERIE COUSSAYE COUTANCIERE COUTELLERIE COUTERIE COUTURE CRAPAUDIERE CREPINIERE CROISANDIERE CROISETTE CROIX CROIX AUX RENARDS CROIX BAZOUILLE CROIX BODIN CROIX BOISSEE CROIX BOUCHERON CROIX CAMUS CROIX CHALON CROIX D AIX CROIX D ARGENT CROIX D AVON CROIX CROIX GRIERE CROIX MIAUZAY CROIX CROIX RIGNY CROIX BROSSES CROIX HABERS CROIX VAUX CROIX D OUAULT CROIX CHENE CROIX CHENE PILE CROIX NEUF CROIX PARADIS CROIX PIN CROIX GALIFERE CROIX GRYERE CROIX GUIMAS CROIX HABERT CROIX JARIA CROIX DELAHAYE CROIX JULIENNE CROIX CROIX MARION CROIX MAZARIN CROIX MORTE CROIX PARDIEU CROIX PATEE CROIX PERCEE CROIX ROLAYE CROIXJEAN CROIXMICHEL CROIX VANNIERE CROSSONNIERE CROULERIE CROUTELIERE CULODERIE CURE CUVASSERIE DACTIERE DANNERIE DARGENTERIE DASERIE DAUDERE DAUVERNERIE DAVAIRIE DAVELLERIE DAVIERE DAVILLIERE DEASSERIE DEGESSIERE DELAUNERIE DELUGRIE DEMANGELIERE DEMENNERIE DEMONNERIE DENIELLERIE DENILLERE DENISERIE DEROUETTERIE DETEURBE DETURBE DEVAUDERIE DEVINERIE DEVINIERE DIABLERIE DIGUE DINDASSIERE DOLINIERE DONNERIE DONNETERIE DORATTIERE DORDINIERE DOREE DORERIE DORETERIE DORINIERE DORMIERE DOUARERIE DOUCE DOUCIERE DOUE DOUZILLERIE DRAGONNIERE L ADRET DREUNIERE DREUSERIE DROLINIERE DUBE DUBOISERIE DUCHE DUFFERIE DUPONDERIE DUPORTERIE DUQUERIE DURANDIERE DURERIE DUTTERIE DUVALERIE DUVALLERIE FABRICE FAGOTIERE FAISANDERIE FAISANDIERE FALOTTIERE FAROIRE FASTIERE FAUCHERIE FAUPERIE LAFAYETTE FEERIE FELONNIERE FENAILLIERE FENETRE FENNETIERE FENNETRIE FERAUDIERE FERME BROSSES FERME THEILLAUT FERME FAY FERME NEUVE FERMONNERIE FERRANDERIE FERRANDIERE FERRIERE FERROUARDERIE FERTAUDERIE FERTONNERIE FETIERE FEUILLADE FEUILLAIE FEUILLEE FEUILLERAIE FEUILLERIE FEVERIE FEVRAIE FILAUDIERE FILLONNERIE FILLONNIERE FILONNIERE FIOLAIS FLATTERIE FLEURETTRIE FLEURIETTE FLEURISSERIE FLONIERE FOI FOLERIE FOLIE FOLLAINE FOLLETERIE LAFOND FOND LAFONT FONTAINE FONTENELLE FORCINE FORESTERIE FORET FORET PIQUANTE FORGE FORTERIE FORTUNERIE FOSSE FOSSELETTE FOSSETIERE FOUGERE FOUQUERIE FOUQUETIERE FOUQUETTERIE FOURNERIE FOURNIERE FOURRERIE FOUSSARDIERE FRAUCOISSERE FREDONNERIE FREDONNIERE FRELONNIERE FREMAUDIERE FRESNAYE FRETONNIERE FRIANDIERE FRILIERE FRILLIERE FRIPIERE FROGERIE FROMAGERIE FRUGERIE FUIE FUTAIE FUYE FUYE VAUX GABILLIERE GABLINERIE GABLINIERE GABLOTERIE GACERIE GACHERE GADOUILLERE GAGNERAIE GAGNERIE GAILLARDERIE GAILLARDIERE GAITRIE GALAISIERE GALANDERIE GALARDIERE GALECHERE GALERIE GALICHERIE GALIFERNIERE GALINIERE GALLAISIERE GALLARDIERE GALLECHERE GALLERIE GALLICHERIE GALLUERE GALTIERE GANACHERE GANAUDIERE GANDONNIERE GANERIE GANNERAIE GANNERIE GAPILLERE GARANCERIE GARANDE GARANDERIE GARDE GARDIERE GARENNE GARENNE BOURDES GARENNE CARTES GARGUILLIERE GARMAUZIERE GARNAUDERIE GAROCHERE GASNERIE GASPIERE GASPILLERE GATEAUDERIE GATE AU MARTEAU GATE AUX MARTEAUX GATINIERE GAUBERTELLE GAUBETTERIE GAUCHERAIE GAUCHERIE GAUDARDERIE GAUDIERE GAUDINE GAUDINIERE GAUDIONNERIE GAULERIE GAULTERIE GAUTELIERE GAUTELLERIE GAUTERAIE GAUTERIE GAUTHIERERIE GAUTRAIE GAUTRAYE GAUTRIE GAVELERIE GAVOTTE GAZILLIERE GENETELLE GENEVRAIE GENIERE GENTILLERIE GENTINIERE GEORGERE GEORGERIE GEORGETTIERE GERARDIERE GERBERIE GERBILIERE GERMONERIE GESNIERE GIBAUDERIE GIBAUDIERE GIBELLERIE GIBERDIERE GIBONNIERE GIGAUDIERE GILBARDIERE GILBERDERIE GILBERTIERE GILLETERIE GILLETIERE GILOTERIE GIRANDELIERE GIRARDERIE GIRARDIERE GIRAUDERIE GIRAUDIERE LAGIRAUDIERE GIRONDE GIRONNERIE GITIERE GITONNERIE GITONNIERE GITTOURIE GLANCHERE GLANDEE GLANDIERE GLARDONNIERE GLAUME GLOMETTERIE GODARDIERE GODETTERIE GODINIERE GOETIERE GOGUERIE GOINIERE GONDONNIERE GOTHERIE GOUARDERIE GOUARIE GOUBINERIE GOUDINE GOUELANDE GOUFFINIERE GOUGEONNERIE GOUGEONNIERE GOUJONNIERE GOULARDIERE GOUNIERE GOUPILLERE GOUPILLIERE GOURIE GOURONNERIE GOUSSARDIERE GOUSSERIE GRANDE AUBINIERE AUDEMOIRE AUDIANIERE AVALLOUX BALLOLIERE BARILLERAIE BENAUDRIE BOISSIERE BOURDE BRUERE CARRERIE CHAUME CHAUVERIE COUTURE CROIX FERME FOLIE FOSSE HARNACHERIE JOUMERAIE LAIZE LANDE LOGE MAIS JACQUELINIERE MAISON MARCHE METAIERIE METAIRIE MICHELLERIE NOUE PLANCHE ROCHE ROCHETTE ROUE GRANDELA SOURICIERE TOUCHE VALLEE VARENNE VIGNELLERIE GRANDINIERE MAISON VALLEE GRANGE GRANGE 7 CHATELIER GRANGE AUX MOINES GRANGE BARBIER GRANGE CHAMPION GRANGE NAIE GRANGE GRANGE LOURNAY GRANGE GLENARD GRANGE NEUVE GRANGE RENE GRANGERIE GRANGE ROUGE GRANGESENOCH GRANGETTE GRANGE VILLEMEREAU GRATELIERE GRATERIE GRAVELLE GRAVIERE GREFFERIE GRELETTE GRENAUDIERE GRENETERIE GRENETIERE GRENIERE GRENOUILLEAU GRENOUILLERE GRENOUILLIERE GRIFFONIERE GRILLE GRILLONNIERE GRINGOLERIE GRIPPERIE GRISARDIERE GRODE GROIE GROLERIE GROLETIERE GROLLETIERE GROLLIERE GROSEILLERE GROSSE BORNE GROSSE GROUAIE GRUAUDIERE GRUE GRUECHE GUENUCHERIE GUEPIERE GUERAUDIERE GUERCHE GUERINIERE GUERITAUDE GUERRERIE GUERRIERE GUESPIERE GUETAUDIERE GUETTERIE GUIBERDIERE GUICHARDIERE GUICHERAIE GUICHERIE GUICHETERIE GUIGNARDIERE GUIGNAUDIERE GUIGNERIE GUILBAUDERIE GUILLARDIN GUILLAUMERIE GUILLERAIE GUILLERIE GUILLETRIE GUILLONNIERE GUILLOTERIE GUILLOTIERE GUINCENDRIE GUITTIERE GUYONNIERE HACHERIE HAIE HAIE MARTIN HAIE MORONS HALLANDIERE HALLEGRENIERE HALLEGRENNIERE HALLOTERIE HALOUZIERE HAMONIERE HARDELIERE HARDELLIERE HARDONNERIE HARDONNIERE HARDRAIS HARLANDIERE HARPINERIE HATE HAUTE BARBOTIERE HAUTE BARDE HAUTE BARDIERE HAUTE BOURDE HAUTE BROSSE HAUTE BRUZETTE HAUTE BUTTE HAUTE CALOGNIERE HAUTE CHESNAYE HAUTE CORMIERE HAUTE COSSONNIERE HAUTE HAUTE COURANCE HAUTEE HAUTE GARANDERIE HAUTE JARNIERE HAUTE LANDE HAUTE MAISON HAUTE MARTINIERE HAUTE MAZERE HAUTE OLIVE HAUTE PINAUDIERE HAUTE POMMERIE HAUTE ROBERDIERE HAUTE RUCHELIERE HAUTELA HAUTE SOURCE HAUTE TOUCHE HAUTE VALLERIE HAYE HAYES HEGRONNIERE HERAUDERIE HERAUDIERE HERCELLIERE HERCERIE HERISSAUDIERE HERONNIERE HERPINIERE HERSE HERSERIE HINARDIERE HOGUE HORAIE HOTTIERE HOUCHINIERE HOUCHINIERE VILLEPRE HOUDRIERE HOUDRILLERE HOULERIE HOUQUINIERE HOUSSAIE HOUSSAYE HUAUDIERE HUBAILLERIE HUBERDIERE HUBERTIERE HUCHE HUCHERIE HUETERIE HUETTERIE HUETTIERE HUGUETAUDERIE HULAUDIERE HUMELLERIE HURAUDIERE HURTAUDIERE HUSSARDIERE HUTIERE HUTTE HUTTIERE L AIGREMONT L AIGUILLON L AIRAULT MARQUETS L AIRAULT LUCAS L AIRE LAIREAU L AIREAU L AIREAU BESNIERS L AIREAU GARRAULTS L AIREAU SOUDET L AISANCE L AITRE ADAM L AITRE AU GODET L AITRE BARBIER L AITRE BODIER L AITRE COCHER L AITRE LEGERS L AITRE GAUTHIERS JABOTERIE JACQUELINIERE JACQUEMINIERE JALTIERE JAMETIERE JAMINERIE JANIERE JANVRIE JAQUETIERE JARNIERE JARRIE JASNIERE JAUNAIE JAUNERAIE JAUNERIE JEANNIERE JEUNERIE JIRAUDIERE JOACHINERIE JOINIERE JOLINIERE JOLIVERIE JOLIVIERE L AJONC JONCHERIE JOSSERAYE JOTTERIE JOUBARDIERE JOUBERDIERE JOULINIERE JOURDINERIE JOURDINIERE JOUSSERIE JUBARDIERE JUBERDIERE JUCAUDRIE JUCHERIE JUDELLIERE JUDERIE JUGERAIE JUILLOTERIE JUIVERIE JUQUELLERIE JUSTICE LAISSONIERE LAITIERE LAMBARDERIE LAMPE LANCONNIERE LANDE LANDE THEILLE LANDE FLEURAY LANDEJACQUES LANDRIE LANTONNIERE LAPINIERE LAUDIERE LAURENCERIE LAURENCIERE LAURIERE LALAY LAYE LALEU LEUZIERE LEVERIE LIBERTE LIBORIE LICORNE LIEE LIEUBARDIERE LIEUTENANDERIE LIGOTTIERE LIMETRIE LIMOUGERE LIONNERIE LIONNIERE LIZARDIERE L ALLIER L ALOEUF LOGE LOGERIE LOITIERE LONDONNIERE LONGUE ECHELLE LONGUE PREE LONGUERAIE LONGUETANDIERE LORINIERE LOUACHINERIE LOUDIERE LOUERE L ALOUETTE LOUETTERIE L ALOUETTIERE LOUISATIERE LOUISIERE LOUISOTTIERE LOURIOTERIE LOURIOTTERIE LOUTIERE LOUZARDIERE LOUZERIE LOUZIERE LUCARCRIE LUCAZIERE LUCTERIE LUSSARDIERE LYE LYONNERIE MACONNERIE MADELEINE MAGNANERIE VAUX MAHONNERIE MAILLARDIERE MAILLEE MAILLETTERIE MAILLOTTERIE MAINGAUTIERE MAIS JARDIN CANTINNIERE MAISON BLANCHE MAISON BRULE MAISON BRULEE MAISON COLIN MAISON D ARDOISE MAISON JARDINIER MAISON LUREAU MAISON MULET MAISON NEUVE MAISON ROSE MAISON ROUGE MAISON VIEILLE MALADRIE MALERIE MALGACHE MALICORNIERE MALOIRE MALOURIE MALOUSIERE MALTIERE MANCELLERIE MANCELLIERE MANERIE MANOEUVRERIE MAQUINIERE MARAUDIERE MARCADERIE MARCECHERE MARCHANDERIE MARCHAUDERIE MARCHE MARDELLE MARE MARE AUX BICHES MARGALLERIE MARGAUDIERE MARIETTE MARIGNE MARINERIE MARINIERE MARLATIERE MARMELINIERE MARNE MARNIERE MARQUETERIE MARQUETTERIE MARQUISERIE MARSAUDERIE MARTELLIERE MARTIERE MARTIN MARTINIERE MASSEILIERE MASSICAUDIERE MASSONNIERE MAUBENNERIE MAUDIENCE MAUGERIE MAURELLERIE MAURICIERE MAUVERNIERE MAUVINIERE MAUVISSIERE MEE MELAUDIERE MELINIERE MEMERIE MENAGERE MENARDE MENARDERIE MENARDIERE MENARDIERE GRANDS MENAUDIERE MENEGENTERIE MERANDIERE MERCERIE MERLAUDIERE MESLAYE MESLERIE MESLUERE MESNEE MESSANDIERE MESSARDIERE MESSIRIE MESSONNIERE METAIRIE METAIRIE NEUVE METAIRIE ROBERT METAISSONIERE METIVERIE MIBELLERIE MICHAUDERIE MICHELIERE MICHELLERIE MICHELLIERE MICHENNERIE MICHINIERE MIENNERIE MIGEONNERIE MIGNONNERIE MILCENDIERE MILLANDERIE MILLANDRIE MILLERAIE MILLETERIE MILLETIERE MILLETRIE MILLONNIERE MILTERIE MILTIERE MINARDIERE MINE MINERAIE MINIERE MINOTIERE MIRAUDIERE MISSONNELLERIE MISTIGNIERE MITAUDERIE MITAUDIERE MOINERIE MOISANDIERE MONERIE MONNAIE MONTAGNE MONTETERIE MONTROTERIE MONTRUCHE MORANDIERE MORCELLIERE MORCHOISNIERE MORCIERE MORELLERIE MORELLIERE MORICAIE MORIERE MORIETTERIE MORILLIERE MORILLONNIERE MORINIERE MORISSIERE MORNIERE MORRELIERE MORTELLERIE MORTIERE MOSELLERIE MOSTIERE MOTTE MOTTE GEMMES MOTTERIE MOUCHERIE MOURIERE MOUSSARDIERE MOZELLERIE MUANNE MULOCHERE MULONNERIE MULOTIERE MULTONNERIE MUSARDIERE MUSSE NALLERIE NAUDAIS NAULIERE NAURELLERIE LANCON NEGOCE L ANERIE NERONNERIE NEUPTIERE NEURAIE LANGENNERIE LANGEVILLE NICOLLERIE NIVERDERIE NIVERDIERE NOELLE NOGUERIE NORAIE NORANDIERE NORGELLERIE NOUE NOUE VILLIERS NOUEFRAULT NOUE RONDE LANTIGNY L ANTIVRAIE PAGEAISSE PAGERIE PAGESSERIE PAILLARDERIE PAILLASSE PAIRAUDERIE PAIREAUDERIE PALISIERE PANTOUCHERE PAQUERAIE PAQUERIE PARASSIERE PARAUDERIE PARAUDIERE PARENTERIE PARENTIERE PASBRUNIERE PASTIERE PATAUDIERE PATELLERIE PATERNELLE PATRIE PATTERIE PAUMIERE PEAUDIERE PECHAUDIERE PEIGNERE PEIGNERIE PEIGNIERE PELLEGOUSSIERE PELLETERIE PELOTTERIE PELOUSE PELTIERE PELUCHE PENETRIE PENETTERIE PENNETERIE PENTE GRILLONS PENTINIERE PEODIERE PEPINIERE PERAUDERIE PERAUDIERE PERCALINE PERCHE PEREE PERFETTERIE PEROUSE PEROUZE PERRE PERREE PERREE AUX NAUX PERREE GROSLOT PERRERIE PERRIERE PERRINIERE PERROTIERE PERRUCHE PERRUCHONNIERE PERSILLERE PERSILLERIE PERSILLIERE PESANDIERE PETARDIERE PETITELA AUBINIERE AUDIANIERE AUDIANNIERE AVALOUX BALLOLIERE BARDOUILLERE BARRE BINELLIERE BOISSIERE BONNE BORDE BOURDE BROSSE BRUYERE CAILLIERE CHAMBONNERIE CHAMPIONNERIE CHAPOTIERE CHARLOTTIERE CHAUME CHAUVELIERE CHAUVELLIERE CHAUVINIERE CHOTARDIERE COLLINIERE COUTURE CROIX CROIX PATTEE CUINIERE PETITE EPINIERE FAGOTIERE FAISANDIERE FERRANDIERE FOLIE FUTAIE GACHE GANERIE GARDE PETITE GAUBERTELLE GIRAUDIERE GUERIE GUINAUDIERE HARNACHERIE HARTELOIRE HUBAUDIERE HUTTIERE JONQUERE LOGE LOGERIE LOITERIE MAISON METAIRIE MICHELLERIE MORINIERE MUSSE NORMANDIE OISILLIERE PLAINE PLANTE POSTE POUZIERE RABLETTE RAMPEE RENARDIERE RICHERIE RIPAUDIERE ROCHE ROUCHERIE ROUERE PETITELA TOUCHE VACHERIE VALLEE VARENNE VIOLIERE PETITIERE PETOIRE PEUVERIE PEUVRIE PHILIPIERE PHILLONNERIE PIARDIERE PIAUDIERE PICARDERIE PICARDIE PICARDIERE PICAUDIERE PICHARDIERE PICHAUDERIE PICHONNIERE PICTIERE PIDELLERIE PIECE RAIES PIEILLERIE PIE NOIRE PIERRE BIDAULT CARREE PIERRIERE PIGEONNIERE PIGERIE PILAUDIERE PILETTE PILLATRIERE PILLAUDIERE PILLETIERE PILONIERE PILTIERE PIMPARERIE PINARDIERE PINCELLIERE PINCONNIERE PINERIE PINIERE PINNETRIE PINONERIE PINONNERIE PINOTIERE PINOTTIERE PINSONNERIE PINSONNIERE PINTRAY PIOCHERIE PIOFAYTERIE PIPAUDIERE PIQUIERE PIRAIDIERE PIRAUDIERE PISSEUSE PITANCERIE PITELLERIE PITONNERIE PIVERDIERE PLACETTE PLACIERE PLAINE PLAINE FOLIE PLAINE VAUX PLANCHE PLANCHE CHAQUENEAUX PLANCHE VALLIERES PLANCHETTE PLARDERIE PLARDRIE PLATRERIE PLAUDERIE PLEE PLESSE PLONIERE PLOTERIE PLOTTERIE PLUCHE POCHETTERIE POELE POINTE POINTRODIERE POIRIERE POISSONNERIE POITEVINIERE POIVRERIE POMMERAIE POMMERAYE PONTONNERIE PORCHERIE PORCHETTERIE PORERIE PORRERIE PORTE POSTE BEAUVAIS POTAGERIE POTERAIS POTERIE POTETERIE POTIERE POUGE POUILLERE POULAISE POULETIERE POULETTERIE POULINIERE POUPELIERE POUSSARDIERE POUZIERE POVINIERE POVIVIERE PRAGUE PRAIRIE LUYNES PREVAUDERIE PREVENDERIE PRIMAUDIERE PROMENADE PROPHETERIE PROUSTIERE PROUTERIE PROUTIERE PROVENDRIE PRUD HOMMIERE PSONNERIE PUCE PUCHERE PUCHETIERE QUANTINIERE QUEBRIE QUELLERIE QUENARDIERE QUEUE GATINE QUEUE MERLUCHE QUILLOTTERIE QUINTAINE QUINTAINE NOYERS QUOMARUERE RABARONNERIE RABATE RABATERIE RABAUDIERE RABELIERE RABELLERIE RABINIERE RABLETTE RABOTIERE RABOTTIERE RACINIERE RAGAUTERIE RAGONDERIE RAGONNERIE RAGUINIERE RAIE RAILLERE RAIMBARDIERE RAIMBAUDIERE RAINERIE RAINIERE RAITRAYE RALLIERE RAMEE RAMONERIE RANCONNERIE RANGEARDIERE RANGEE RANVERIE RAPINERIE L ARARIE RATERIE RATINIERE RATRIE RAUDERIE RAUDIERE RAUDINIERE L ARCHE L ARCHERIE L ARDILLE L ARDILLON REBOCQUERIE REBUFIERE RECHARDERIE RECHAUDERIE RECORDELIERE RECORDIERE RECOULLERE REGNERE REGNIERE REINERIE REJAUDERIE RELANDIERE RELLERIE REMBOURDERIE REMERIE RENAISSANCE RENARDIERE RENAUDERIE RENAUDIERE RENNERIE REPTIERE RERAIE RERAIS RESNIERE RESPINIERE RETARDIERE RETAUDIERE REUNIERE REUZERIE REVAUDIERE REVEILLERE REVEILLERIE REVELERIE REVERDIERE REVERSIERE REVOISERIE LARGERIE RIANDIERE RIBELLERIE RICARDIERE RICASSIERE RICENDELIERE RICHARDIERE RICHETIERE AVRILLE PONC RIDERIE RIDERIE BROSSE RIFFAUDIERE RIFFOISE RIGAUDIERE RIGOISIERE RILLONNIERE RIMONERIE RIMONIERE RIPAUDIERE RIVIERE L ARMANCAY L ARMANDIERE L ARMEE L ARMONERIE L ARNERIE L ARNON ROBERDERIE ROBERDIERE ROBICHERE ROBILLIERE ROBINERIE ROBINIERE ROCHE ROCHE BERLAND ROCHE BERTAULT ROCHE BOBEREAU ROCHE COMUSELIERE ROCHE D ALES ROCHE D AMBILLE ROCHE DENIAU ROCHE DONNET ROCHE FOULON ROCHE GUENNET ROCHE MABILLEAU ROCHE MORIN ROCHE PICHON ROCHEPINEAU ROCHE PIPARD ROCHE RACAN ROCHERIE ROCHETTE ROCHINERIE RODAIE RODERIE ROITIERE ROLETIERE RONCERIE RONCHERAIE RONDE RONDELLERIE ROQUILLE ROSERAIE ROSIERE ROSIERE SICAULT ROSSIGNOLIERE ROTTE FORTUNIERES ROUARIE ROUASERIE ROUCHERIE ROUCHOUZE ROUERIE ROUGELLERIE ROUGERIE ROUGETTERIE ROUILLERE ROUILLERIE ROULETTERIE ROULIERE ROULINIERE ROULONNIERE ROUSSELIERE ROUSSELLERIE ROUSSIERE ROUVERIE ROUZERIE ROYAUTE ROYAUTEE L ARPENT JEANDON LARRE L ARSANDRIE L ARTIVIERE RUCHERIE LALA RUEE RUELLERIE RUERIE RUETTE RUSSOTIERE SABARDIERE SABLERE SABLIERE SABLONNIERE SABOTERIE SACRISTIE SAINSONNIERE SAISIE SALAMANDE SALAMANDRE SALLE SALMONIERE SALONNIERE SALPETRERIE SALVARDERIE SANSONNIERE SAPINIERE SARDINASSE SASSERIE SAUCRAIE SAUDRAIE SAULAIE SAULAIS SAULNERIE SAUNERAIE SAUNERIE SAUNIERE SAURIE SAUVAGERE SAUVAGINE SECHERESSE SECRETERIE SEDINIERE SEGUINIERE SELLERIE SENAUDIERE SERCLERE SERINIERE SERMENTAYE SERMONIERE SERPINERIE SERRAUDERIE SETTERIE SEVENNIERE SEVRANDIERE SEVRAUDIERE SIBYLLERIE SICARDIERE SILONNIERE SILOTERIE SIMARDIERE SIMAUDIERE SIMONAIE SIMONNIERE SINSONNIERE SIROTERIE SIROTIERE SIROTTIERE SOLIVIERE SOMMERIE SONNETTE SORINIERE SORNIERE SOUDELLE SOUDRERIE SOUDRIE SOULETTERIE SOULTIERE SOUPIQUERIE SOURDERIE SOURDIERE SOURICIERE SOURNAS LASSAY LASSY SUCRERIE SUISONNIERE TABARDERIE TABOURDIERE TACHELLERIE TACONNETTERIE TAILLE TAILLE BRUERE TAILLE BEAUCE TAILLE BEAUCES TAILLE TAILLE ROIS TAILLE CHAUDRON TAILLE PIED D OR TAILLE PIEDOR TAILLERIE TAISSERIE TALBOTERIE TAMBRE TANCHONNERIE TANNERIE TARDIVIERE TAVINIERE TERMELIERE TESSERIE TESTUERE TETE FORTIERE TETE NOIRE TEURTERIE THIBARDIERE THIBAUDELIERE THIBAUDIERE THIBAUDRIE THIERRIERE THILLOIRE THILOIRE THOMASSIERE THUAUDIERE TIERCERIE TINELLIERE TINSONNIERE TINTINIERE TIRELIERE TIROLE TIROLIERE TOITTERIE TOMASSIERE TOMBE TONELLERIE TONNELLE TORTILLIERE TORTINIERE TOUCHAREAU TOUCHE TOUCHE BELLEVUE TOUCHE HULIN TOUCHE NEUVE TOUCHERIE TOUCHES LIGNES TOUMINIERE TOURETTE TOURMELLIERE TOURNELLERIE TOURNIERE TOURRELIERE TOURTELERIE TOURTELLERIE TRANCHAUDIERE TRANCHEE TRAVAILLARDIERE TRAVAILLERE TREFAUDIERE TREILLE TREMBLAIE TREMBLAY TREMBLAYE TREMELIERE TREMELLERIE TRENARDIERE TREURIE TREVENDIERE TREZILLIERE TRICHERIE TRIGALIERE TRILLONNIERE TRIPIERE TRIQUETIERE TROCHETIERE TROCHOIRE TROMPETTE TROTTERIE TRUTTE TUAUDIERE TUFFE TUFFELIERE TUFFIERE TUILERIE TUILERIE LIGET TURBELLIERE TURMELLIERE L AUBARDERIE L AUBARDIERE L AUBEPIN L AUBERDERIE L AUBERDIERE L AUBIER L AUBINIERE L AUBONNIERE L AUBRIERE L AUBUIS LAUCONNIERE LAUDERIE L AUDEVERDIERE L AUGEREAU L AUGERIE L AULNAIE L AULNAY L AULNE LAUNAY L AUNAY L AUNAYE LAURAY L AURIERE LAUSANNE L AUSILLET L AUVERDIERE L AUVERGNIERE L AUVERNIERE L AUVINIERE VACHE VELEE VAILLANDERIE VALIGON VALINERIE VALINIERE VALLEE VALLERIE VALLIERE VARANTERIE VARENNE L AVARICE VASLINIERE LAVAU VAUBERDE VAUMENERIE VAUVELERIE VAUZELLE LAVEAU VELLERIE VELOPPERIE VENERIE VERDERIE VERDINIERE VERDONNERIE VERGLACIERE VERNATERIE VERNELLE VERNELLERIE VERONIQUE VERRERIE VERRONERIE VESNIERE VEZIERE VIAUDIERE VIDONNIERE VIEILLE JOUMERAIE RACOUPEAU VIEILLERE VIENNE VIENNERIE VIGETTERIE VIGNE HOUDIN VIGNELLERIE VIGNERIE LAVIGNON VIGNOTTE VILAINE VILLA VILLAINE VILLATE VILLATTE DAVEAU PLATE VILLIERE VINARDERIE VINCEDERIE VINCENDERIE VINDERNIERE VINERIE VINOGERIE VINOTERIE VIOLERIE VIOLIERE VOIRIE VOISINIERE VOLANDRIE VOLETTERIE VOLIERE VOLIERE BEAUVOIS VOLLIERE VOLTIERE VOVELLERIE VRILLE VRILLERE VRILLETTRIE L AZAIN L EAU MORTE BAILLIN BANNI BANY BARON BARREAU BAS BOISRIMONT BAS BONCHAMP BAS BONNET BAS BOURG BAS BRY BAS CHELOT BASLE BAS CHEZELLES BAS COULOMMIER BAS COUSSE BAS DEGAUDIERE BAS DERCE BAS VAU BAS VAULT BAS LIMERAY BAS MONTEIL BAS MONTMARTRE BAS MORTIER BAS NOISAY BAS PAVOT BAS PERROCHET BAS PEU BAS PUITS BASPAUL BASTEREAU BAS VALLIERE BAS VAU BAS VENEUIL BAS VENEUIL BAS VENEUIL BATEAU LAUNAY LAUNAY BATIMENT BATTEREAU L EBAUPIN L EBAUPINAYE L EBEAUPIN BEAUSEJOUR L EBELLEE BERTEAU BERTHENAY BESNIER BEUGNON BEY BION BLUET LE BARDOU BARRAULT BOURDIN BOURREAU CHANTREAU CHAUDRON CHEVET CLAIR COLAS CORNU D AZAY D EAU CHANCAY BROSSE L ARCHE RE L ORMEAU PLANTE LE HAIES PLANTES LEBOIS SAVATIERS LE PAVILLON PUITS FARAUD FARRAUD FRELON GELIF GIRAULT GODEAU GUENAND HARDEAU JESUS JOLI JOUBERT MESLIN MIGNE PIAIS PILET PINEAU PRETRE REGARD PARILLY ROBERT ROUGE BOISMARTIN BOISMAURICE SEME SMITH THOREAU TURBET VAUROUX VERT VIOLET BOMMELIERE BON TEMPS BORDAGE BORNAIS BOUCHET BOUFFAY BOUILLON BOULARDIN BOULAY BOUQUET BOURG BOURG JOLI BOURG NEUF BOURNAIS D AIME BOUT BOUT BOUTON BOUTREUIL BRAY BRETEAU BRETONNET BREUIL BROSSEAU BROUARD BROUILLARD BROUTIER BUCHER BUCHET BUISSON BUISSONNET CABINET CABOT CAFE BRULE CALVAIRE L ECARLATE CAROI GUERIN CAROI MAUVIERES CARREFOUR CARROI CARROI RIOU CARROI AU LOUP CARROI BODIN CARROI BOUCHER CARROI MUSE CARROI JOUTES CARROI VARENNES CARROI D HUISMES CARROI JAUNE CARROI JODEL CARROI JONC CARROIR CARROIR LOUCHE CARROIR MONTQUEIL CARROIR VIGNES CARROIR JODEL CARROI TAVEAU CASSEREAU CASSOIR CATICHON LECE CELERY CHAILLOU CHALENTIER CHALET CHALET FORGES LE CHAMPBERTIN DURAND CHAMPGAULT CHAPELET CHAPITRE L ECHARDERIE CHARDONNERET L ECHARDONNERIE L ECHARDONNET CHARDONNET CHARLEMAGNE CHARME CHARRIOT CHATAIGNER CHATAIGNIER LE D AZAY BAGNEUX PAULMY BREUIL ROUGE CHATELET CHATELIER CHATELLIER CHAUCHE L ECHAUDE CHAUMENIER CHEMET CHENE CHENE ARRAULT CHENE AUGER CHENE BAUDET CHENE BEAUDET CHENE BILLAULT CHENE CHARPENTIER CHENE LANDES CHENE D ORLIN CHENE DOUCIER CHENE GUE CHENE FEUILLET CHENE GIRAULT CHENE PENDU CHENE PERCE CHENE ROND CHENE VERT CHENIL CHEREAU CHEROU CHERPRE CHESNEAU CHEVAL BLANC CHEVALET CHILLOU CHIVART L ECHO CLAIR SOLEIL LE SIMON CLOU COCHEREAU COIN COIN VAUJOURS COIN MUR COIN TAFFONNEAU COLLIER COLOMBIER COLOMBIER CHIZERAY COMPAS COMTE CONFLUENT L ECONOMIE CORMIER CORNIER COSSIER COTEAU COTTAGE COTTEREAU COUDRAY COURBAT COUREAU COUVENT CREUSERON CREUZEAU CROIX GREDON CROQUET CROULAY CROULE CRUCHERON CRUCHET L ECUREUIL DAUPHIN DEFA DEFAIX DEFFAIX L EDELWEISS DOIGT NEUF DOLMEN DONJON FAUBOURG FAY FEUILLET FIEF L ABBE FONDIS FORGET FOSSE FOUCHAINAY FOUGERAY FOULON FOUR CHAUX FOUR AUX NOIR FOURNEAU FOUTEAU FRENE FRESNE GAILLARD GAST GATEAU GATE SOIE LEGE GEAY GENETAY GENETE GERFAULT GILLET GITOURIE GLAND GLANDIER GODARD GODET GOURBIER GOURET GOURNAY GRAIS AIR ARRET AULNAY BELVEAU BERCHAINE BIARD JAMET BONCHAMP BOULAY BOUQUETEAU BRAY BROSSAY CALVAIRE CARROI CARTADEAU CEDRE LE GRANDLE CHERAMANT LE CLERE LE CONAI COURCHAMP CRANCAY DOMAINE DOUCET FALAISE FOSSE GOUARD HOUTEAU LIARD LOCHEREAU MAIL LE MARTIGNY MENASSON MESNIL MOREAU PICOU LE MOURE NEMBON ORME ORMEAU PARET POIRIER LE PREAU PREVOST PUITS PUYRENAULT RECHAUSSE RELAIS RIZ ROUCHOUX ROUVRE SALE SAVIGNY TEMPLE THURE VARNELLE VAUSDANIERE VAUX VICQ VILLAGE GRAVIER GREFFIER GRIGNON GROLLAI L EGRONNIERE GROS BUISSON GROS CHENE GROS CHILLOU GROS ORMEAU GROS TREMBLE GROUAIS JOLIVERIE GUE GUE ANDREAU GUE BLANDIN GUE GRE GUE GUE MALADRIE GUE L AUMONE GUE PRES GUE QUATRE SOURIS GUE LOUP GUE ROI GUE GAULTIER GUE RAIDE GUERET GUIGNER GUIGNIER GUIGNIER BENI L EGUILLERE GUINIER HACHEREAU HALLIER HAUT BOUCHER HAUT BOUCHET HAUT BOURG HAUT BROUTIER HAUT BRY HAUT CHARCAY HAUT DEGAUDIERE HAUT BOURGGERMAIN HAUT BAS MONTEIL HAUT FOURNEAU HAUT FRESNAY HAUT HALLAUD HAUT LIEU HAUT LIMERAY HAUT LUAIN HAUT MARTRAY HAUT MERE HAUT MONTAS HAUT MONTAT HAUT MONTMARTRE HAUT NOISAY HAUT PAIMBAULT HAUT PERCHENE HAUT PERTHUIS HAUT PLESSIS HAUT PUITS HAUT PUITS BERTIN HAUT RACAN HAUT RONGERE HAUT VAU HAUT VERGER HAUT VILLAGE HAUT VILLAUMAY HAUT HAUT VILLEE HAUT VILLENEUVE HAUT VONTE HAY HOUDEAU HOUSSARD HOUSSAT HOUX JANOT JARDIN JARDINET JARROSSAY JAUNAY JAUNEAU JONCHER JONCHERAY JONCHET JOUR JUBERT LAC LACHE LAC LACUNE LAITIER LAURIER LAVOIR LECHEREAU LIGNON LIGNOU LION D OR LISA LIZON LOCHEREAU LONG LONG LUMINAIRE L ELYSEE MAGE MAI LE MARCHAIS MARCHAIS ROCHE MARCHAIS RUISSEAU MARCHAISSATURNIN MARGA MARIDONNEAU MARIGNY MARTRAY MAUPAS MAY MAZEREAU MEE LEMEE MESNIL MILAN MILLERY MONTACHAMPS MONT DORE MONT OMER MORIBOT MORIER MORILLON MORTIER MORTIER BADEAU MORTIER BELLE EAU MORTIER ROUE MORTIER DEROUET MORTIER PROFOND MAUDOU MOTTAGE LE VENT MOULINET MOUSSEAU MOUSSOU L EMPOCHERIE MUR VAL MURGER MURIER L ENCLOITRE L ENCLOS NEMAN L ENFERTERIE L ENFUME NOYER NOYER VERT PAIN L EPAISSE PANIER PAQUET CHENES PARADIS PARC PATIS PATIS GRANDIN PATOIS PATUREAU PAVILLON PENETY BEL AIR PEPELERIN PERE L EPERON PEROU PERRAULT PERRE PERREE PERRET PERRIN PERRON PERTHUIS PERTHUS PERTUIS PET FOURCHE AULNAY BARD BARDINE BAS MORTIER BAUGE BEAUCHENE BEUSSAC BIARD LE MOTTE LEE BOISSE BOULAY BOUQUETEAU BOURNAIS BRAY CESNAY CHAMPGAULT CHAUVIGNY CHENE CHERAY CHERBOURG CHESNEAU CHOUZE CLERE LE COINAY CONAI CORNILLOU COUDRAY COUDREAU COURCHAMP COUTAY CRANCAY CREUILLY CROULE DOUCET FAISAN FALAISE FEU FORGET LE GAILLARD GALISSON GOUARD GRAIS GUE HETRE LAUNAY LIGNOU LOCHEREAU LUAIN LE MARIAUDE MARIGNY MARNAY MARRAY MARTIGNY MENIL MESLAY MESNIL MOLEON MONTS MORIER MORTIER LE MOURE NEMBON PARIS PERRE POUILLE PREVOST PRIEURE PRIX PUIT PUY PUYRENAULT RELAIS RIZ ROUVRE SAVIGNY SOUPE THEILLE TRAVESAY VAIRIN VARENNE VARENNES VARNELLE VAUCOULON VAUDANIERE VAUSDANIERE VAUX VENDOME VERSAILLES VILLAGE VILLEFRAULT VILLIER VILLIERS VOUVRAY PEU PEU BLANC PEY GROLLE PIGEON BLANC PIGNON VERT PILIER PIN L EPINAY L EPINE L EPINIERE PIPELINE PISSOT PLESSIS PLESSIS ORMEAU PLESSIS ROSE POINCON POINT JOUR POINTEAU JACQUET POIRE POIRET POIRIER POIRIER AU LOUP POIRIER LOUP POIRIER VERT POISOU POITEVIN POMMIER AMARD POMMIER VERT POMPIER PONCEAU LE PONTCECOIN PONTILON PONTOU PONTREAU PORTAIL PORTEAU PORTULAN POTAGER POTARD POTEAU POTERNE PRE PRE BORDET PRECLOS PRE GUE PRE MAINE PREAUBIN PRESBYTERE PRESSOIR PRESSOIR PRESSOIR MAY PRE VERT PRIEURE PROGRES PUET PUISARD PUIT GUIBAUX PUIT LIVET PUITS PUITS BRUNET PUITS D ABBAS PUITS ENAULT PUITS GIRARD PUITS MOREAU PUY PUYBERT PUY D ARTIGNY PUY D ASSAY PUY TAMBRE PUY ENGELIERS PUY GALANT PUY ROUJOU PUY SERPAULT QUARTERON QUARTIER QUELLAY L ERABLE RABORIEN RAGOT RAINGE RANG L EREAUBRECHOT REIN RIBARD RIBOUILLET RICHEBOURG RIS RIVAGE VIENNE RIVALIER RIVEAU ROCHEREAU RONCERAY ROND ROND MONSIEUR RONDIN ROSSIGNOU ROUCHOUX ROUGET ROULET ROULLET ROUSSAY ROUVE ROUVRE RUAU RUDANAY RUISSEAU RUISSEAU POUILLET RUISSEAU GAUTIER RULON 3 ARPENTS 3 CHEMINEES 3 CHENES SABLONNET ACACIAS AJONCS ALLEES ALLETS ALLOUAUX SALVERT AMANDIERS ANNEAUX ANSAULTS SAPIN NORD ARCHAMBAULT ARCHAMBAULTS ARCHAMBEAULTS ARDONNIERES ARGOUGES ARMUSERIES ASSIS AUBIERS AUBOEUFS AUBREES AUBUIS AUBUS AUDRIES AUGEREAUX AUGERIES AUGERS AUJOUX SAULE SAULE DURAND AULNES AUNAIES AUNILS SAUT LOUP AVAILLONS AVANRIES AVANRYS BABAUDIERES BAFFARDS BAFFAULTS BAILS BALAIS BALLANDAIS BARANGERAIES BARANGERS BARDINS BARDONS BARONNIERES BARREAUX BAS CHAUVAUX BAS BAS CLOSEAUX BAS QUARTS BASSES BALUERES BASSES BORDES BASSES BASSES PELOUSES BASSES POITEVINIERES BASSES POTERIES BASSES RENAUDRIES BASSES TAILLES LESBASSES THURINIERES BASSES VIGNES BAS SICOTS BAS THOMEAUX BATAILLAUX BATAILLEAUX BAUCHETIERES BAUDES BAUDITS BAUGES BAZINIERES BAZYUNIERES BEAUCES BEAUGERIES BEAUREGARDS BEAUVAIS BEAUX CHATS BEAUX CHENES BEAUX MORTIERS BEDONNIERES BEIGNETS BEILLEAUX BELLES CAVES BELLES RURIES BENARDIERES BERGEAUNEAUX BERGEONNAUX BERGEONNEAUX BERGEONNIERES BERGEONS BERGERIES BERGERS BERGES BERNARDS BERNELLERIES BERRUCHONNERIES BERRURIES BERSAUDIERES BERTHELOMMIERES BERTHELONNIERES BERTHELOTS BERTHOLLIERES BERTINIERES BESNARDIERES BESNAULT BESNEAUX BESNIERS BIGOTTIERES BILLANGES BILLOTS BLANCHARDIERES BLANCHARDS BLANCHES BRUYERES BLANCHETIERES BLANCHETS BLANCHETTIERES BLAVETIERES BODINIERES BOIS BRULES ROUARDS BOISSEAUX BOISSES BOISSIERES BOISSONNERIES BOMMIERS BONINS BONNEAUX BONNES GRENIERS BONS ENFANTS BONS TEMPS BORDEBURES BORDERIES BORDES BORDS BOSSARDS BOUCHERS BOUCTONNIERES BOUDGOURDS BOUERES BOUGAUDIERES BOUGERIES BOUGREAUX BOULAIRIES BOULEVRIES BOUQUERERIES BOURDEAUX BOURDELIERES BOURDELLIERES BOURDES BOURELLES BOURNAICHERES BOURNAIS BOURNAIS PLESSIS BOUROFLERES BOUROFLERS BOURREAUX BOUTINIERES BOUTINS BRANDELLES BRANDES BRAUDIERES BREDANNIERES BREDONNIERES BRIANDS BRIANTS BRILLONIERES BRILLONS BRIQUERIES BRIQUIONS BRISACIERS BRISASSIERS BROSSEAUX BROSSES BROUILLERES BROUTES VIGNES BRUASSERIES BRUERES BRULEES BRUNEAUX BRUNETS BRUNS BRUYERES BUCHERONS BUCHERS BUISSONNETS BUISSONS BUTTES CAILLAUDERIES CAILLAUX CAILLERES CAILLOUX FOSSE CANTERIES CARRES CARRIERES CHAUVIN CARROIRS CARROIS CARTES CASSARDIERES CATHELINETTES CAVES CAVES D AMONT CAVES D AUMONT CAVES BLAVETIERES CAVES BOUTEVELLIERE CAVES FEU CAVES L HUILERIE CAVES FURETTES CAVES JAILLIERES CAVES POUPIERES CAVES SAUTYS CENDRIERES CENDRIERS CERISIERS CHAILLOUX CHAINTRES CHALES CHALLONGES CHALOTTIERES CHAMPAGNES BOUREAUX DANNERIE MEUNIER MILLARDS RONDEAUX CHANTE MUALLE CHANTREAUX CHAPELLES CHARBONNIERES CHARDONNIERS CHARPEREAUX CHARRUES CHATEAUX CHATELIERS CHAUFFEAUX CHAUFFOURDS CHAUMES CHAUMETTES CHAUVAUX CHAUVRAUX CHEMINIERES CHENEAUX CHENES CHENEVIERES CHERAUX CHESNEAUX CHEVALIERS CHEVAUPIES CHIRONS CHOLLIERES CIGOGNIERES CINQ CINQ POIRIERS CLAIRAIES CLAIRGERIES CLAIRS CLERGERIES CLES CLOSEAUX CLOSEAUX TURPENAY CLOSIERS CLOTONS COCHETIERES COLLINIERES COLOMBELLES COMAILLERES COMMUNS CONTENTS COPEAUX COPINIERES COQUETTES CORBEAUX CORMIERS CORNETTERIES CORNUES CORONS CORVIERS COSSES COSSES BORNAY COSSES BOURNAIS COSSONNIERES COTEAUX COTEAUX GUE COTEAUX FOLLET COTTES BLANCHES COUDES COUDRAIES COUDREAUX COUDRIERES COULONNERIES COURANCES COURTINAIS COURTINIERES COURTIS COURTRIES COURVAUX COUTANTS COUTIERES COUTTIERES COUTURES CRACHOUSES CRENES CUFFAUX DAVIAILLES DAVIERES DEFENDS DELAROCHES DELICES DENEUX DENIS DESFORGES DESSUS DEBLAISE DESSUS VIENNE DEUX ARPENTS DEVANTS CLERETS DIVARDS DOGUINS DOILES DOMINES DOREES DOUBLEAUX DOUCETS DOUEES DOUETTES DOUX DRAGEONS D HIVER DUBES DUBOIS DUMUS DUPORTS DURETTES ECHARLOTTES SECHOIR ECOINS EEES EES EFFES EGUETS EMOTTOUSES ENGREFFIERES SENTIER EPINETTES EPINETTES REPOS ST MARTIN EPIOTS EQUARRIES ERAULTS SERRAIN SERRIN ESSARDS ESSERTS ETANGS EVOIS FALAISES FAUCHERIES FAUDIERES FAUX GIRAUDS FERRANDIERES FERRANDS FERRUS FERS FEUILLETERIES FILONNIERES FLAMANDS FLEURETTES FONTAINES FONTENEAUX FONTENELLES 1 FONTENELLES FONTENILS FORESTIERES FORGES FORTIGNIERES FOSSES FOSSETTES FOUGERES FOUGERETS FOUINIERES FOULOND FOULONS FOUQUETS FOURNEAUX FOURNEAUX PELOUSES FOURREAUX FOURS FOURS CHAUX FOURS DOREE FOURS DOREES FRAICHAURES FRANDINIERES FRELANDERIES FREMONDIERES FRENERIES FRIPPIERES FROGERIES FROMENTEAUX GABLOTIERES GACHAUX GAGNERIES GAILLARDS GAINS GALLES PIERRES GALOCHERES GALTIERES GARENNES GARNERAIES GARONNIERES GAS GASCOGNERIES GASNIERS GATINES GATINIERES GATS GAUDEBERTS GAUDIERES GAUDINES GAUDINIERES GAUTIERS GENCINIERES GENDREAUX GENETAIES GENETRAIS GENETTERIES GENIEVRES GERMAINS GIGOTS GILLETERIES GILLETTERIES GIRARDIERES GIRARS GIRAUDIERES GLANDINS GLAUMEAUX GLYCINES GODEAUX GODERIES GODETS GODINS GORONNIERES GOUPILLERES GOUPILLIERES GOURBIERS GOUSILS GOUTTIERES GOUZETS GRANDERIES GRANDES BARDONNIERES GRANDES BERTINIERES GRANDES FOSSIERES GRANDES FRICHES GRANDES GROMMERELLES GRANDES MAISONS GRANDES ROUGES GRANDES RUES GRANDES VARENNES GRANDIERES GRANDS GRANDS L OURS GRANDS GRANDS FOULONS GRANDS MOLEONS GRANDS MOREAUX GRANDS MOULINS GRANDS ORMEAUX GRANDS PATIS GRANDS PONTS GRANDS PRES GRANDS TAILLIS GRANDS VILLEPINS GRANGERS GRANGES GRANGES ROUGES GRANGES FRAULT GRANLINERIES GRELETS GRENOUILLERES GRESILLONS GREVES GRILLES GRONDELLIERES GROUSSINS GUERINIERES GUIBOURGS GUILLEMETS GUILLOTEAUX GUINDERIES HAIES HAIES BODINEAUX HALBARDEAUX HALBARDERIES HALLEBARDEAUX HALLEBARDIERES HAMARDIERES HARDELIERES HARDIS HARRANGES HAUTES BABINIERES HAUTES BERSAUDIERES HAUTES BORDES HAUTES BORNES HAUTES BOURDAISIERES HAUTES HAUTES MAISONS HAUTES POTERIES HAUTES REMETTIERES HAUTES RIVES HAUTES ROCHES HAUTES SEVAUDIERES HAUTES TUILERIES HAUTES VARENNES HAUTS HAUTS HAUTS CLOSEAUX HAUTS CRUCHONS HAUTS MILLETS HAUTS MONTAS HAUTS NOYERS HAUTS QUARTS HAUTS VIVIERS HAYES HELAS HEMERIS HERAULT HERAULTS HERBES BLANCHES HERONNIERES HERSES HOUDRIS HOUTES HOUTTES HUAUDERIES HUCHEROLLES HUNAUDIERES HUTTIERES HUYS SICOT SIGNAL ILES ISLES 11 ISLES JACCOTINS JAROSSAIS JAROSSIERES JAUNEAU JAUNEAUX JOINDIERES JOLINIERES JOLLETIERES JONCHERIES JONCS JOUANETS JOUANNETS JOUARDIERES JUMEAUX JUSSIAUMES JUSTICES LACAS LACS D AMOUR LAMBERTS LANDELS LANDES LAUDERIES LESINIERES LIGNERIES LIGNES LILAS LIMBARDIERES LIMETTES PROURAY LIOTS LISATS LISONS LITARDIERES LIZIERS LOGERIES LOGES LOITERIES LOUETTES LOUINES LOUPS LOURIOUX LUATS LUTINIERES LYS MACHETIERES MADRAUX MADREAUX MAGNOLIAS MAINGOTTIERES MAISONS MAISONS BLANCHES MAISONS BOURDEAUX MAISONS BRULEES MAISONS NEUVES MAISONS ROUGES MALHUREAUX MALPIECES MALVAUX MAQUINIERES FLAMBERT MARCHAIS MARCHAIS LONG MARCHAIS LONGS MARCHAUX MARCHES NOIRS MARDELONS MARES MARIAUX MARIDONNEAUX MARIONNAUX MARIONNEAUX MARNIERES MARQUETS MARTEAUX MARTINEAUX MARTINIERES MASNIERES MASNIERS MATTES MAUDIANCES MAUGIS MAUGUERETS MAUVAISINIERES MAZAUDIERES MAZELLES MECHINIERES MENARDS MERCIERS MEREAUX MERIS MERONS MERRIERES MERYS METAIRIES MILLE VENTS MINETS MINIERES MITARDIERES MITELLERIES MOCQUETS MOINEAUDIERES MOLLIERES MOMPAS MONJALLONS MONTEAUX MONTOUS MONTPOINTIERES MONTRAIS MONTREES MONTROTIERES MONTS MOREAUX MORILLIERES MORILLONS MORINERES MORINIERES MORINS MORISSETS MORTIERS MOTTAIS MOUCHETIERES MOUILLERES MOULINES MOUSSEAUX MUSETTERIES NARDISONS NARICOTS NAUZEROLLES NAVETS NIVERDERIES NIVERDIERES NOCEREAUX NOELS NOIRATS NOISILLERS NOISILLES NOIZIELLES NONGRENIERES NORAIES LESNOUERS NOUES NOUES BOUCHEES NOUIS NOYERS OBUS OISELIERES OISERIES ONZE ARPENTS BATIMENT 3 4 MAIL ONZE ARPENTS BATIMENT 4 8 MAIL ORMEAUX ORMES OUCHES SOUFFLET OULDES SOULIER SOURD OURILLAUX VALLET PACAUDERIES PAINS BENIS PAIRONS PALETS PALUDS PASSAGE PASSEURS PASSOIRES PATIS PATOUILLEAUX PATUREAUX PAUVRETES PAZINIERES PELOUSES PENAUDIERES PENETS PENLOUPS PENTES PENTES L ESPERANCE PERAUDIERES PERCHES PERNELLES PERONNES PEROUSES PERRAULTS PERREES PERRES PERRETS PERRIERES PERROTINS PERRUCHES PESNAULTS PESNEAUX PETITES PETITES BELLES RURIES PETITES BORDES PETITES BROSSES PETITES BROUILLERES PETITES CORBINIERES PETITES CROIX PETITES FRICHES PETITES INGUENNIERES PETITES LANDES PETITES MAISONS PETITES MAURICERIES PETITES MORISSERIES PETITES MOTTES PETITES PATURES PETITES PIECES PETITES RAIMBAUDIERES PETITES RICODRIERES PETITES ROCHES PETITS ALLOUAUX PETITS BOURNAIS PETITS PETITS PETITS DAVIALLES PETITS FOULONS PETITS FOURREAUX PETITS MOREAUX PETITS NOYERS PETITS PRES PETS GROLES PIAUX PICAULTS PICHONNIERES PIECES HAUTE PIERRES PIERRES CARREES PIGEONNELLES PIGOTS PILETS PILLARDIERES PIMBERTS PINARDIERES PINS PINSARDIERES PINSONNIERES PIQUANTES PIRAUDIERES PIVARDIERES PIVOTIERES PIVOTS PLACES PLACIERS PLAINES PLAINES VAUX PLANCHES PLANDRIONS PLANTES PLATES PLAUDIERES PLAUX POELONS POITEVINIERES POMMEREAUX PONTINS PORCHERS PORTES PORTES ROUGES POTERIES POULINERIES POULINIERES POUSSINERIES PRATEAUX PRES PRES BAYARD PRES CHABAULT PRES L ETANG PRES MAINE PRES HAUTS PRES POTETS PRESTREAUX PRES VERTS PRIOLESSES PRISEAUX PROUTS PUITS QUARTIERS QUARTS QUARTS FLEURIS QUARTZ QUATRE BORNES QUATRE CARROIS QUATRE QUATRE NOIRS QUATRE PORTES D AGEAIS QUATRE VENTS QUENTINS RABATTERIES RABAUDIERES RABONNIERES RABOTTES RACINAUX RACINES RAGUINS RAIMBAUDIERES RAIMBAULT RAIS RAMEAUX RANGERS RAUDERIES RAUDIERES RAVELONNES 3 REAUX RECAIS REGLACIERES REIGNIERES REMETTIERES REMISES RENARDES RENARDIERES RENAUDIERES RENAULTS REUILLES REVEILLAUX RIAUX RIBERGERES RICHARDIERES RICORDIERES RIGOLLETS RIMBAUDIERES RIMBERGERES RIOMS RIVAUX ROBERTS ROBICHONNES ROBINIERES ROBINS ROCHARDIERES ROCHES ROCHES TROIS MARIES ROCS ROLINS RONDEAUX ROSEES ROSIERS ROTIS ROTTIERS ROUCHERES ROUCHERS ROUERES ROUETS ROUGERIES ROUILLERS ROUILLES ROUJOUX ROULEAUX ROUX ROYERS ROYS RUAUX RUBANNIERES RUELLES RUETTES RUSTAUDRIES SABLES SABLES DEBLAISE SABLES TARTIFUME SABLESBLAISE SABLONNIERES SABLONS SAIN FOINS SANGUIERS SAPINS SAPINS BRAY SARRAZINS L ESSART SARTIERS SAULAIES SAULES SAULQUINS SAUTINIERES SAVATIERS SENTIERS SERRAULTS SERRUERES L ESSERT SETIERS SEVAUDIERES L ESSEY SICOTS SIMONEAUX SOUCHES SOUCHOTS TABARDIERES TAGAULTS TAILLES TAILLES LOURIS TAILLES PLAISANCE TAILLES BEAUCES TAILLES GRELETS TAILLIS PAYS LESTANG TARDINES TEILS TENIERES TERMELLES TERRAGES TERRAGES MEIGNEUX TERRASSONS TERREAUX TERRES BLANCHES TERRES LOINTAINES TERRES ROUGES TESNIERES TESSEREAUX TETES NOIRES THEILS THIBAUDELLES THIBAULTS THIERS TIMBRELLES TITIS TOMBES TORTES EN TOUCHES TOURNEES TRANCHANDIERES TRANCHANTS TREILLES TREMBLAIRES TREMBLAIS TREMBLEAUX TRESORIERES TREUILLETS TRIZAIES TROIS CHEMINEES TROIS CHENES TROIS COLOMBES CHEPTELLIERE TROIS MARCHAIS TROIS MARIS TROIS POIRIERS TROIS TILLEULS TROIS VOLETS TUFFAUX TUFFEAUX TUILERIES USAGES VALLEES VALLEES BASSE VALLEES BASSES VALLIERES VARENNES 1 VARENNES ROCHES VARENNES VARENNES CLERET VARENNES D ETABLEAU VAUBAINES PARILLY VAUX VAUZELLES VEES VEILLARDIERES VENAUDIERES VERINERIES VERNEAUX VERNERIES VERSSEES VEZARDIERES VIEILLES AITRES VIEILLES CARTES VIEILLES LIGNERIES VIEILLES RUES VIEUX PARCS VIGEANTS VIGNEAUX VIGNES VIGNES BLANCHES VIGNES BRETON VIGNES GRANDLES VIGNES TEMPLE VILLAUDIERES VOISINES YUTIERS YUTRIES YVONS L ETANCHEREAU TANCHET L ETANG TEDEUM TEMPLE TERRIER TERTRE TERTREAU THAURON THENAY THOU TILLAUD VENEUIL TILLEUL TISON TIVOLI TOURNESOL L ETRAPE TRAVAIL RIBAULT L ETRE BOSSE TREMBLAIS TREMBLAY TREMBLE TREUIL TRINCU TRONCHET TUFFEAU LEUGNY VALAY VALET VAU VAU GELE VAUGUIGNIER VAUROUX VAU ROZET VAUSAINTON VAUSSERIN VAUTHIBAULT VAUX VEAU LEVEE CHER LEVEE LOIRE LEVEE CHER LEVEE LOIRE VERDET VERDET DUPUY VERDET PERDRIAU VERGER VERGERBLAISE VER LUISANT VIENNET VIEUX BOURG VIEUX GERFAULT VIEUX VIGNEAU VIGNEAU BLANC VIGNOU VILLAGE VILLAGE AUX MOREAUX VILLAGE D ABAS VILLAGE ROIS VILLAGE VILLAGE GUE VILLAGE PUITS VILLERET VILLIER VIORNAY VIVIER VOU L HABIT L HARDELLIERE L HARDIONNERIE L HARDONNIERE L HARICOTERIE L HERAUDIERE L HERAULT MEUNIER L HERAU PICHARD L HERAUT PICHARD L HEREAU PICHARD L HERMINIERE L HERMITAGE L HERMITAGE 49 ROUCHOUZE L HERPINIERE L HETRE GOUJON L HIVERNIERE L HOMME L HOPITAU L HOPITEAU L HORTIER L HORTIERE L HOUDINERIE L HOUSTIERE L HUBERDIERE L HUILERIE L HUISSERIE L IDEEANGENEAUAVANTIGNYBARRIERESBAUDRYBAULAYBEAUCHENEBEAUGERBEAUJARDINBEAUREGARDSYMPHORIENBEAUVAISBEAU VALLONBECHENOIXBEL AIRBELLE VUEBELLEVUEBERNETTERIEBOIS BRULEBOIS CLAIRBOIS D ARDREEBOIS BOURGBOIS GIBERTBOIS HERYBOIS NEUFBOIS RENAULTBOIS RIPAULTBOIS RIVIEREBOIS SOULAGEBORDEBUREBOURDAREAUBOURGSYMPHORIENBOUROTBOUTINEAUBOUZIEREBRESSEAUBRULOTSCARREFOUR PRISONSCHABROLCHAMP BARONCHANTELOUPCHATEAU CHEVRIERCHATEAU CHENIERCHATEAU GALLECHATENAYCHAUMETTE BLANCCHOPETCITE POPLURUCITE ACACIASCITE TILLEULSCLOS BABINIERECLOS GARDIERECOMMANDINCONNEUILCORSELLIERECOTEAU GUEPIERECOULEE GUECOULEURCOURBOINSYMPHORIENCRAYCRETINAYCROIX DEGESSIERECRUCIFIX VERTCUSSECUZE PEUILDE BEAUREGARDDEFROCS COLOMBIERECOMARDECURIES TRIGANIEREEN SUEURSYMPHORIENFAITESFERME ROCHECAVEFERME GATINEAUFORET LARCAYFOSSE SECHEFOUASSEEGARDERELLEGAREGRAND COURSGRANDELIEU DIT MOULINGRANDLIEU DIT GRENETGUE LOUISHAUT GALMAINILE BOURDONJOUBERDERIE METAIRIELA ACHELLERIELA BAILLARDIERELA BAILLIERELA BALTIERELA BARAUDIERELA BARDINIERELA BAREUSIELA BARRURIELA BASSE BERGERIELA BASSE CARTELA BASSELLERIELA BASSE METAIRIELA BAUDRAIELA BEAUPINIERELA BELLANGERIELA BELLE CAVELA BELLE ETOILELA BELLERIELA BERDAQUERIELA BERNETTIERELA BESNARDIERELA BESNERIELA BIGOTIERELA BILLE PERDUELA BILLETTELA BINETTERIELA BLOTTERIELA BLOTTIERELA BOIRE FUTEMBRELA BOITIERELA BONNELIERELA BONNETTERIELA BORDELA BORDICHELA BOUBINIERE COTTEREAULA BOUCHARDIERELA BOUCHERIELA BOULASLA BOULAYLA BOURDAIRIELA BOURICHONNERIELA BRANCHERIELA BRAUDIERELA BRETONNIERELA BREZARDIERELA BRICHELA BRIQUETERIELA BRISSONIERELA BROSSELA BUFFETERIELA BUISSONNIERELA BUTTE MALABRISLA BUVINIERELA CAGELA CANTINIERELA CARTELA CAUTIERELA CHABANNERIELA CHAMBAUDIERELA CHAPELLELA CHAPERONNIERELA CHARBONNIERELA CHARDRIELA CHAUMINELA CHAUVELLERIELA CHAUVELLIERELA CHAUVINIERELA CHEMINEE RONDELA CHENARDIERELA CHESNAIELA CHESNAYELA CHESNERAYELA CHEVALERIELA CHUTELA COLINIERELA COMMANDERIELA COTONNERIELA COUDRAYELA COULOTERIESYMPHORIELA DIMIERELA CROIXLA CROIX BRISSETLA CROIX CHAUFFOURLA CROIX GEAYLA CROIX HABERTSLA CROIX MONTRAIXLA CROIX SUDLA CROIX FRELONLA CROIX GALIFERLA CROIX DENISLA CROIX LISETTELA CROIX PAGEAULA CROIX PERRETTELA CROIX ROUGELA CROIX SENARDLA CURELA DAGNELLERIESYMPHORILA DELLETIERELA DEMENNERIELA DIABLERIELA DITARDIERELA DOUZILLERELA DRAGEONNIERELA DUBOISERIELA DURANDERIELA DURANDIERELA DUVELLERIELA FAUCILLONNERIELA FERMALETRIELA FERRONNERIELA FEVERAIELA FLEURISSERIELA FLORIERELA FOIRERIELA FOLIELA FOLTIERELAFONDLA FONTAINELA FORGESYMPHORIENLA FOUQUERIELA FUIELA GAGNERAIELA GAGNERIELA GAILLARDIERELA GARENNESYMPHORIENLA GASNOIRELA GASPIERELA GASTIERELA GATINIERELA GAUDRAYELA GAUTERAIELA GELINIERELA GIRAUDERIESYMPHORIELA GIRAUDIERELA GODINIERELA GOUPILLERELA GOUPILLERESYMPHORIELA GRABOLLIERELA COURLA BARRELA BORDELA LIELA RIBOTELLIERELA GRANGE AUX BOISLA GRANGE AUX MOINESLA GRANGEMARTINLA GRANGE TIPHAINELA GRENERAYELA GRENOUILLERELA GRILLELA GUIGNIERELA GUILLOTIERELA HARDIONNERIELA HAUTE BERGERIELA HAUTE BRETONNIERELA HAUTE JONCHERELA HAUTE VENNETIERELA HUAULERIELA HUBERDIERELA HURONNIEREL AIREAU BERGEONSL AIREAU HERPINSL AIREAU POULETSLA JARRIERELA JOUSSINIERELA JUGERAIELA LARDIERELA LUZIERELA MAILLELA MAISON BLANCHELA MAISON BRULEELA MAISON LUREAULA MAISON NEUVESYMPHORLA MAISON ROUGELA MAISON ROUINEAULA MALCHUTELA MANSELLIERELA MARCHANDERIELA MARIETTELA MAZERAIELA MERILLELA MEUNERIELA MIDYLA MILLETIERELA MOLETRIELA MORANDERIELA MOULINERIELA MULONNIERELA MUSARDIERELANDES CHARLEMAGNEL ANGUICHERIELA NOUELA NOUE GUERINETLA PACOTERIELA PAILLETERIELA PAILLETRIELA PATOUILLELA PAUMERIELA PEIGNANTERIELA PERSILLIERELA PERTUISIERESYMPHORILA BELLE ETOILELA BROCHETIERELA CARTELA CHAUMERAYELA CHAUSSEELA CHAUVELIERELA FRECHELA GUICHERIELA MONNAIELA PIOTTERIESYMLA RIBOTELLIERELA TOUCHELA PICHARDERIESYMPHORILA PICHARDERIE ST SYMPHORILA PICHETIERELA PICHOTERIELA PIECE BASSELA COUVERTELA PIETRIELA PILONNIERELA PINALIERELA PINELLERIELA PINSONNERIELA PIOTTERIELA PIOTTERIESYMPHORIENLA PLANCHELA PLAUDERIELA PLOUTRIELA POIFILIERELA POISSONNIERELA POMMELIERIELA PONTONNIERELA POQUERIELA PROTAIRIELA PROVERDERIELA PUISERIELA QUINTAINELA RABAROIRELA RAJACELA RANVERIELA RAPINIEREL ARARIEL ARCHE DESYMPHORIENLA REBECATERIELA RIBAMBELLELA RIBAULLERIELA RICHARDIERELA RIVIERELA ROBINIERELA ROCHERIELA RODERIELA RONGERESYMPHORIENLA ROUSSELIERELANEUVELANOIRELA SAGOTIERELA SAIMBAUDERIELA SAINTONNIERELA SILLONNIERELA SINSARDIERELA SURPRISELA TABOURDIERELA TAILLELA TAILLE HUETSLA TARDIVIERELA THIBAUDIERELA THIERRIERELA TOUCHELA TRUISSARDIERELA TUILERIELA TURPINIEREL AUBERDIEREL AUBRAYEL AUNAYLAUNAYL AUNAY BERTINSLA VALLARDERIELA VALLEELA VALLEE GERMAINLA VALTRIELA VAUGUYONLA VIEILLE CARTELA VIEILLE MAISONLA VILLATTELA VINIERELA VISTROUILLERELA VOIELA VOVELLERIELE BARRAGELE BAS ROCHECAVELE BAS ROCHEDAISLE BAS VILLAGELE BEAUBELINLE BELVEDERELE BILLOITLE BISSETLE BONNEVIELE PLANTELE PLANTESLE D ESVESLE FARRAULTLE LEVELE SEMELE BORDEAULE BOURGLE BOURG OUESTLE BOURGSYMPHORIENLE BREUILLE BRIDOULE BUISSONLE CARROILE CARROI MEUNIERLE CARROISYMPHORIENL ECEUILLELE MASSELE PERRAULTLE VERTLE CHAPEAU GROLLELE CHATAIGNIERLE CHAT COURANTLE VERTLE CHENE GUELE CHENE PENDULE CHESNEAULE CEMENTLE LULULE MICHETL ECOINLE COLLIERLE COLOMBIERLE COTEAU VAUGELELE COUCOULE FAUVINLE FOURNEAULE FOUTEAULE CLOSLE L ESSIGNYLE MAZEROLLELE MORTIERLE PORTEAULE TRIZAYLE VILLIERSLE GUE COPEAUXLE GUE PIERRESLE HALLIERLE HAUT MUNATLE HAUT RULONLE HOMELE HOUSSALE LARVOIRLE LISALE MARCHAISLE MESNILLE MORTIERLE MORTIER ROUELE VENTLE CORMIERSLE BOURGLE NEUFLE PLANCHETLE RENOUARDLE NOIR GUEUXLE NOYER VERTLE PARCLE PASSAGE AUX CHEVAUXLE PAVILLONL EPENDLE PERREAULE BARRELE BEAUMONTLE BERGERESSELE BONNEVIELE FORTLE MAREUILLE MAZEROLLELE POIZAYLE PORTEAULE PEU D OLIVETLE PEUXLE PICHOISONLE PIED FLEURYLE PILORILE PLELYLE PLESSISLE POIRIER MOREAULE PONTLE AUX OIESLE VOULE PORTLE PORTAILLE PORTEAULE PRE VIGNELE PRESSOIR VAULE PRIEURELE PUITS LALIEU DIT PUY CHAUVEAULE PUY GRAZAYLE QUEBLELE RECOINLE RESERVEL ERMITAGELE SABLONNETLES ALLAISLES AUBUISLES AULNAIESLES AUMONESLES BARILLERSLES BAS NAINSLES BASSES RENTRIESLES BASSES TUILERIESLES BATISSESLES BAUDINERIESLES BAUDINIERESLES BAUDITSLES BERCELLERIESLES BERGEONNERIESSYMPHLES BERNARDIERESLES BESNARDERIESLES BILLETTESLES BLARDIERESLES BOIREAUXLES QUARRESLES BOURDEAUXLES BOURNAISLES BROSSESLES CARROIRSLES CARROISLES CAVES MACORSLES CAZEAUXLES COUELES CHAPELLESLES CORBIERESLES COSSESLES COUDREAUXLES CROIXLES DAVIDLES DEVANTS RUPUANNELES ECHALIERSLES EGUILLERSLES EMONIERESLES ENAUXLES ETANGSLES FONDS BARRELES FORGESLES FOURNEAUXLES GATES BOURSELES GRANDS BOISLES GRANDS CHAMPSLES GRANDS GARSLES GRANGESLES GRATIENSLES GUILLETSLES HAUTES FAITURESLES HAUTES HARSESLES HAUTS BERGEONSLES HAUTS BOEUFSLES HAUTS L EGLISELES HESNAULTSLES HUCAUDIERESLES ILES NOIRESLES ISLESLES 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AU BLE MARCHE AUX LEGUMES MARECHAL NORD PRESIDENT COTY ECOLES EDISON EDMOND ROSTAND PLACE EGLISEVENANT ELVIRE EMILE ZOLA ERIC TABARLY ETOILE EUGENE LABICHE EUGENE VIDELOUP EUROPE FOIRE ROI FRAMBOISIERS RABELAIS RUDE SICARD TRUFFAUT FREDERIC CHOPIN GAIETE GALLO ROMAINE GAMBETTA GASTON PAILLHOU GENERAL GENERAL COURTELINE L HERMITE GOYA GREGOIRE TOURS HALLES HENRI LANGLOIS HOLNON HONORE BALZAC HONORINE HOTEL HUBLIN HUCHETTE INGRES IRIS JACQUES BEAUNE JACQUES DRAKE JACQUES BELLEY JACQUES PREVERT JASNIN ANTOINE HOUDON ANTOINE NICOD AUDEBERT BAPTISTE CARPEAUX BOUIN BUEIL CONINGHAM FONTAINE DUVAL JACQUES SUZANNE JAURES MEUNIER PLACE JEANNE D ARC BOUILLY ROUSSEAU JEHAN D ALLUYE JOHANN STRAUSS JOLIVET JOSEPH BOURREAU JULES MASSENET JULIEN AUDEBERT L ABBAYE HUCHETTE POSTE REPUBLIQUE L LEGLISE LELU LEO LAGRANGE LEONARD VINCI LEON BOYER LEOPOLD SENGHOR L ETOILE LIBERATION LOGETTE LOISEAU D ENTRAIGUES LOUISE MICHEL LOUIS XIII LOUVOIS LUDOVIC VENEAU LUTINS MADO ROBIN MAIL MAILLE MALLARME MARCELIN 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COULANGE ROUTE COURCAY ROUTE COURCELLE ROUTE COUZIERS ROUTE CRAVANT ROUTE CREZE ROUTE CRISSAY ROUTE CROTELLES ROUTE CUEILLEMINAULT ROUTE D AMBOISE ROUTE DAME ROUTE DAMES TOURAINE ROUTE DAUDERE ROUTE BALLAN ROUTE BELLEVUE ROUTE BERTHENAY ROUTE BLERE ROUTE BORDEAUX ROUTE BOURGUEIL ROUTE BREHEMONT ROUTE CHANNAY ROUTE GAILLARD ROUTE VALLIERE ROUTE RENAULT ROUTE CHENONCEAUX ROUTE CHINON ROUTE COURCELLES REGNERE ROUTE DRUYES ROUTE FONDETTES ROUTE HUISMES ROUTE HUSSEAU ROUTE BASSELLERIE ROUTE BOISSIERE ROUTE BRECHE ROUTE BRETONNIERE ROUTE CHEMINEE RONDE ROUTE FOSSE AUX BRAY ROUTE FOSSE BOUCHER ROUTE FRILLERE ROUTE ROUTE GUILLONNIERE ROUTE MARTINIERE ROUTE MONTEE JAUNE ROUTE PLANCHE ROUTE ROCHE ROUTE L AUDEVERDIERE ROUTE VALLEE BOURCIER ROUTE L ORMEAU VIGNEAU ROUTE L OUCHERIE ROUTE LOUDUN ROUTE METTRAY ROUTE MONNAIE ROUTE MONTILS ROUTE MONTLOUIS ROUTE MONTS ROUTE NAZELLES ROUTE PERNAY ROUTE ROUZIERS ROUTE DEAIGNAN ROUTE DEAVERTIN ROUTE DEGENOUPH ROUTE DEMARTIN BEAU ROUTE DEMAURICE ROUTE DEROCH 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GAUDREE ROUTE GIZEUX ROUTE GLAUME ROUTE GRAMMONT ROUTE BALLET ROUTE PRESSIGNY ROUTE VAUJOINT ROUTE VILLAGE ROUTE GRAVIERE ROUTE GRAVOIS ROUTE GRAVOTS ROUTE GREGOSSIERES ROUTE GRIVELLERIE ROUTE GROTTES PETRIFIANTES ROUTE GUE DROIT ROUTE GUERCHE ROUTE HAIES ROUTE HAUT BAIGNEUX ROUTE HAUT BOURG ROUTE HAUT ROUTE HAUTES MAISONS ROUTE HEMOND ROUTE HERMITES ROUTE HOTTIERE ROUTE ILE BARDE ROUTE ILE BOUCHARD ROUTE INDUSTRIES ROUTE JAVELLE ROUTE JUDERIE ROUTE BECELLIERE ROUTE CELLE GUENAND ROUTE CHAPELLE ROUTE CHAPPE ROUTE LACS D AMOUR ROUTE FERRIERE ROUTE FLECHE GRANGE ROUTE ROUTE GUERCHE ROUTE JAVELLE ROUTE LANDE ROUTE LANDES ROUTE LANDESMARTIN ROUTE LANE ROUTE LANGEAIS ROUTE NOIRAIE ROUTE POULETTRIE ROUTE ROCHE POSAY ROUTE ROCHE POZAY ROUTE LEMERE ROUTE LERNE ROUTE LESIGNY ROUTE LEUGNY ROUTE LIBERTE ROUTE LIEGE ROUTE LIGNIERES ROUTE LIGUEIL ROUTE LIMERAY ROUTE LIZIERS ROUTE LOCHES ROUTE LOGES ROUTE LOIRE ROUTE LOUANS ROUTE LOUDUN ROUTE LOUESTAULT ROUTE LOUESTAUT ROUTE LOUROUX ROUTE LUBLE ROUTE LUREUIL ROUTE LUSSARDIERE ROUTE LUZE ROUTE MAILLE ROUTE MANS ROUTE ROUTE GIBERNES ROUTE MARCAY ROUTE MARCE ROUTE MARCHAIS ROUTE MARNAY ROUTE MARTINIERE ROUTE MAUPAS ROUTE MAUVIERE ROUTE MAZIERES ROUTE MENUET ROUTE MESLIERE ROUTE METAIRIE ROUTE MONNAIE ROUTE MONTIGNY ROUTE MONTILS ROUTE MONTILS MAILLARDIERE ROUTE MONTOIRE ROUTE MONTRICHARD ROUTE MONTS ROUTE MORINIERE ROUTE ROUTE BLEU ROUTE FROMENTIERE ROUTE POUJARD ROUTE CHENE ROUTE ROUTE GLABERT ROUTE NEUF ROUTE POTTIERS ROUTE MOULINS ROUTE MOUSSEAUX ROUTE MOUZILLY ROUTE MUR ROUTE NANTES ROUTE NATIOALE 76 ROUTE NATIONALE 10 ROUTE NATIONALE 143 ROUTE NATIONALE 152 ROUTE NATIONALE ROUTE NATIONALE 76 ROUTE NATIONALE ARCIS ROUTE NATIONALE ARCIS ROUTE NATIONALEMARTIN ROUTE NAZELLES ROUTE NEGRON ROUTE NEMAN ROUTE NEUILLE ROUTE NEUILLE LIERRE ROUTE NEUILLY BRIGNON ROUTE NEUVILLE ROUTE NOIRAIE ROUTE NOIRE ROUTE NOIZAY ROUTE NOYERS ROUTE ORMEAU ROUTE ORMEAUX ROUTE OUCHE AUX SOIES ROUTE OUZY ROUTE PARCAY VIENNE ROUTE PARIS BUTON ROUTE PAULMY ROUTE PERNAY ROUTE PERREE ROUTE PERRES ROUTE PERREUX ROUTE PERRUCHES ROUTE ROUTE FLANIERE ROUTE PETITES LANDES ROUTE ROUTE PRESSIGNY ROUTE VAUJOINT ROUTE PEU MULEAU ROUTE PILONNIERE ROUTE PISSOT ROUTE PLACES ROUTE PLESSIS ROUTE POCE ROUTE ROUTE PONTCHALET ROUTE L ARCEAU ROUTE ROY ROUTE PORTE JOIE ROUTE POUET ROUTE PRES ROUTE PREUILLY ROUTE QUARTS ROUTE QUATRE ALOUETTES ROUTE QUEBRIE ROUTE RASSAY ROUTE RECAIS ROUTE REIGNAC ROUTE REIGNERE ROUTE RENAUDERIE ROUTE RETAUDIERE ROUTE REUGNY ROUTE REUNIERE ROUTE RIBOT ROUTE RIBOTTIERES ROUTE RICHELIEU ROUTE RICHELIEUE ROUTE RIGNY USSE ROUTE RIPARFOND ROUTE RIPAULT ROUTE RIVARENNES ROUTE ROBERGES ROUTE ROCHE ALLARD ROUTE ROCHES ROUTE ROUCHOUZE ROUTE ROUZIERS ROUTE SABLONNIERE ROUTEANTOINE ROUTEBAULD ROUTEBRANCHS ROUTEMAURE ROUTEEPAIN ROUTEETIENNE ROUTEFLOVIER ROUTEGEORGES ROUTEGILLES ROUTEMARTIN BEAU ROUTEMICHEL ROUTEOUEN ROUTEOUEN VIGNES ROUTEPATERNE ROUTESENOCH ROUTE SAUMUR ROUTE SAVIGNE ROUTE SAZILLY ROUTE SEMBLANCAY ROUTE SEPMES ROUTE SERRAIN ROUTE SICOTS ROUTE SILEX ROUTE SOLEIL LEVANT ROUTE SONZAY ROUTE SOUVENIR ROUTE SOUVIGNE ROUTE STADE MUNICIPAL ROUTE ST MARTIN BEAU CHANTELOUP ROUTE STOUEN ROUTE TAPIS ROUTE TAUXIGNY ROUTE TAVANT ROUTE TEMPLE ROUTE THENEUIL ROUTE THILOUZE ROUTE THUET ROUTE TOUCHE ROUTE TOURNON ROUTE TOURS ROUTE TROIS MOUTIERS ROUTE TURPINIERE ROUTE VAL CLAISE ROUTE VALLEE LYS ROUTE VALLEE LYS GIRARDIERE ROUTE VALLEES ROUTE VALLERES ROUTE VALLIERE ROUTE VALLIERES GRANDES ROUTE VAU ROUTE VAUX ROUTE VELANTAN ROUTE VELLECHES ROUTE VERGERS ROUTE VERNANTES ROUTE VERNEUIL ROUTE VEZIERES ROUTE VIAULLIERES ROUTE VIEUX PLESSIS ROUTE VIGNEAU ROUTE VIGNES ROUTE VIGNOBLE ROUTE VILLANDRY ROUTE AU MAIRE ROUTE VILLEPERDUE ROUTE PLATE ROUTE VILLEPOU ROUTE VILLEVERT ROUTE VOU ROUTE VOUVRAY ROUTE YZEURES ROUVRES ROUX ROUZERIE ROUZOU ROZENDAL RTANNE RTE LIGUEIL RTE L ILE BARDE RTE L ILE BOUCHARD RTE MONTILS VILLAGE RUAUX RUBLIN RUDANAY11 NOVEMBRE 191811 NOVEMBRE14 JUILLET 178914 JUILLET18 JUIN 194018 JUIN18 JUIN 4019 MARS 196219 MARS25 AOUT2 PORTS32 REGIMENT INFANTERIE4 SEPTEMBRE4 VENTS8 MAI 19458 MAI8 MAI 45ABATTOIRABBAYEABBE BAUDRYABBE BOURASSEABBE BOURRASSEABBE L EPEEABBE EBRARDABBE FAVOREAUABBE HARDIONABBE HUBERDEAUABBE L HERMITEABBE PIERRERUE ABBE PRUE ABBE SIMONINABBE UBERDEAUABEILLESABEL GANCEABEL GANGEABEL MARINIERABRAHAM BOSSEABRAHAM COURTEMANCHEABREUVOIRACACIASACACIAS SABLESACACIASMARTINACADEMIEACHELLERIEADAM FUMEADAM FUMEEADDI BAADRIEN DESLONDAINSADRIENNE BOLLANDADRIENNE LECOUVREURA M NADAILLACAFNAGNES SORELAGRICULTUREAIME BARDOUAIREAU BRIZARDAIREAU CAILLEAIREAU BODINSAIREAU DOUETAITREALAIN CHARTIERALAIN FOURNIERALAMBICALBERT 1 ERALBERT BERGERAULTALBERT CAMUSALBERT LEBRUNALBERT MASSONALBERT RUELLEALBERT THOMASALCUINALENESALEXANDER CALDERALEXANDRE CALDERALEXANDRE CHARPENTIERALEXANDRE DUMASALEXANDRE RENE VERONALFRED BAUGEALFRED MUSSETALFRED VIGNYALFRED DRIANCOURTALFRED GALLAISALFRED MAMEALFRED NOBELALFRED TIPHAINEALFRED TYPHAINEALGERALIENOR D 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MONCONTOURDE MONDOUXDE MONREPOSDE MONSOUDUNDE MONTAURANDE MONTBAZONDE MONT PLAISIRDE MONTRESORDE MONTRICHARDDE MONTSDE MOSNYDE MUNATDE NAMURDE NANTESDE NAZELLESDE NEGRONDENEUXDENFERT ROCHEREAUDE NIMEGUEDENIS PAPINDE NOHANTDE NOIZAYDE NORMANDIED ENTRAIGUESDE PALLUAUDE PARCAYDE PENAVETDE PERIGOURDDE PICARDIEDE GALLED EPIRAYDE PLEIN CHAMPDE CHERDEPORTATIONDEPORTESDE PORTILLONDE POURTALESDE PRENEYDE PREUILLYDE PROVENCEDE PUYGIBAULTDE RANCEDE REAUMURDE REIMSDE RENNESDE RICHELIEUDE RIGNY USSEDE RILLEDE RIVOLIDE ROCHECORBONDE ROCHEFURETDE ROCHEPINARDDE ROCHETTEDE ROSNAYDE ROTTERDAMDE ROUGEMONTDES 11 ARPENTSDES 3 CHEMINEESDES 3 GUIGNIERSDES 3 PAVES RONDSDES 4 VENTSDES ABEILLESDES ABRICOTIERSDES ACACIASDE SACHEDES AFFLUENTSDE SAIGNESDES AILESDERADEGONDEDEGOBAINDELEGERDEMALODEVENANTDES AITRESDESAIXDES AJONCSDES ALOUETTESDES AMANDIERSDES AMERICAINSDES AMOURS D HELENEDES ANCIENNES ECOLESDES ANCIENS D AFNDE SAN FRANCISCODE SANZELLEDE SAPAILLETDES ARTISANSDES ATELIERSDES AUBEPINESDES AUBUISDES 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DEVALLERIESDES DOUETSDES DOUVESDES ECOLESDES ECUREUILSDES EGLANTIERSDE SENSDES EPINES FORTESDES EPINETTESDES ERABLESDE SERVOLETDE SEUILLYDES FAUVETTESDES FERRONNIERSDES FLEURSDES FONTAINESDES FONTENAILLESDES FONTENELLESDES FONTENILSDES FOSSES BOUTEILLESDES FOSSES MERCIERDES FRENESDES FRERES LUMIEREDES FRERES WRIGHTDES FUSILLESDES GALBRUNESDES GANAUDIERESDES GENETSDES GIRARDIERESDES GLYCINESDES GOELANDSDES GOUBINSDES GOUGETSDES GRANDES VIGNESDES GRANDS CHAMPSDES GRANDS MAITRESDES GRANDS MORTIERSDES GRANGESDES GRANGES GALANDDES GRAVIERSDES GRAVINIERESDES GRILLESDES GRILLONNIERESDES GRIPPEAUXDES GROSEILLIERSDES GUERIDONSDES GUESSIERESDES GUETTERIESDES GUILLETSDES HALLESDES HATESDES HAUTES GATINIERESDES HAUTES MARCHESDES HAUTES ROCHESDES HAUTES VARENNESDES HAUTS CRENEDES HAUTS LOIREDES HAUTS LUBINAISDES HERAULTSDES HERISSIERESDES HETRESDES HIRONDELLESDES HOUXDES HUVETSDESIRE GANNAYDES JACOBINSDES JARDINSDES JARDSDES JEUNESDES JEUXDES JONQUILLESDES JUMEAUXDES JUSTICESDESLANDESDES LANDESDES LAURIERSDES LEVEESDES LEZARDSDES LILASDES LOCQUETSDES LOGESDES LOISIRSDES LYSDES MAGASINS GENERAUXDES MAILLERIESDES MAISONS ROUGESDES MARAICHERSDES MARAISDES MARRONNIERSDES MARTYRSDES MAURESDES MESANGESDES MESLIERSDES MILLEPERTUISDES MIMOSASDES MINIMESDES MOLIERESDES MONTAINSDES MONTILSDES MOUETTESDES MOULINSDES MURIERSDES NARCISSESDES NAUDINIERESDES NIGOUILLETSDES NOISETIERSDES NOUIESDES OISEAUXDES OLIVIERSDES ORFEVRESDES ORMEAUXDES ORMESDES OUCHESDES PASSEURSDES PATISDES PATUREAUXDES PATYSDES PAVILLONSDES PECHERSDES PELERINSDES PELUSDES PERCE NEIGEDES PERRIERSDES PERRUCHESDES PERVENCHESDES PETITES BOISSESDES PETITES COURSDES PETITES MAISONSDES PETITS PRESDES PEUPLIERSDES PHALENESDES PIERRES PLATESDES PILIERSDES PINSDES PINSONNIERESDES PINSONSDES PITOCHESDES PIVOTTIERESDES PLATANESDES POETESDES POMMIERSDES PONTSDES PREBENDESDES PRIMEVERESDES PROVINCESDES PRUNELLIERSDES QUARTSDES QUATRE ARPENTSDES QUATRE CHEMINSDES RABATTERIESDES RAGEAUXDES REAUXDES RELIEURSDES RESERVOIRSDES RIBAINSDES RIBELLERIESDES RIMONEAUXDES ROCHEROUXDES ROCHESDES ROITELETSDES RONCIERESDES ROSESDES ROSIERSDES ROSSIGNOLIERESDES ROSSIGNOLSDES ROULETSDES RUERSDES RUISSEAUXDES SABLESDES SABLONNIERESDES SABLONSDES SAPINSDES SAULESDES SORBIERSDES SOUCHETTESDES SOURCESDES SPORTSDES SUREAUXDES TAILLESDES TAMARISDES TANNERIESDES TANNEURSDES TEMPLIERSDES TERRAGESDES TERRES BLANCHESDES TERRES FORTESDESTEURDES THOMEAUXDES TIERSDES TILLEULSDES TISSERANDSDESTOUCHESDES TOURELLESDES TROGLODYTESDES TROIS ECRITOIRESDES TROIS GUIGNIERSDES TROIS MARIEDES TULIPESDE SUEDEDE SULLYDES URSULINESDES VALLEESDES VANNEAUXDES VANNIERSDES VARENNESDES VAUXDES VERGERSDES VERRERIESDES VIGNESDES VIOLETTESDE TARTIFUMEDE THUISSEAUDETILLYDE TOLBIACDE TORCHANAISDE TOURCOINGDE TOURNAIDE TOURSDE TRECHEDE TRIANONDE TROTBRIANDDE TURENNEDE TURPENAYDE VALENCAYDE VALLIERESDE VALMYDE VAUBRAHANDE VAUCANSSONDE VAUCHEVRIERDE VAUGIRARDDE VAUGONDYDE VAUMORINDE VAU MORONDE VAUTROMPEAUDE VAUXIBAULTDE VERDUNDEVERSOIRDE VIGNEMONTDEVILDEDE VILLANDRYDE 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VAREILLE VARENNE VARENNES VARENNES CLERET VARENNES CHARTRIE VARENNES POSTE VARENNES ROCHES GASNIERS VAUBARDE VAUBRAHAN VAUBRAULT VAUCHENU VAUCHEVRE VAUCHIGNARD VAUCOULON VAUDESIR VAUGARAUD VAUGARNI VAUGRIGNON VAUGUERIN VAU GUERIN VAUGUINIER VAUHARDY VAUJOURS VAULANDRY VAUMAIN VAUMARTIN VAUMORIN 1 VAUMORIN VAU PRECIEUX VAU RENOU VAURICHARD VAUROBRAY VAUSAINTON VAUSSOUVIN VAUSSUBLEAU VAU TOURNEUX VAUTRUCHOT VAUVELLE VAUVERT VAUX VAUX CHOUAN VAUZELLE VAUZELLES VEAUGAUDET VELANTAN VELLECHES VENELLE GAILLARD VENELLE LAURIERS VENELLE COMPAGNON BOURRELIER VENELLE EGLISES VENELLE VERON VENEUIL VENIEL VENISE VERDET VERGER COTINEAU VERNAY VERNELLE VERNEUIL VERRIERES VERTE VERTENAY VIAUVY VIEUX BOURG VIEUX VIGNELLERIE VIGNEMONT VIGNES SAULAIE VIGNES CROIX VIGNOLES VIGNOUX VILLA CHANTAL CLOSEAUX VILLA BEAUPUITS VILLAGE AU GUE VILLAGE AUX POTIERS VILLAGE AUX VACHES VILLAGE BABAULT VILLAGE VILLAGE BUISSON VILLAGE D ABAS VILLAGE RE VILLAGE VILLAGE HAIES VILLAGE JEUNES VILLAGE ROUX VILLAGE VILLAGE GUE VILLA CHESNAIE VILLA HYBRIDES VILLA ROBESPIERRE VILLAJEAN CAVES FEU VILLEBASLIN BLANCHE VILLECHAISE VILLECHENARD EN VILLEFRAULT VILLEGRIGNON VILLEJESUS VILLENEUVE VILLEPAYS PLATE VILLEPOU VILLEPRE VILLEPREE VILLEPROVEL VILLERET VILLESEPTIER SETIER VILLETISSARD VILLETIVRAIN VILLEVERT VILLIER VILLIERS VILMIER VINCEUIL VIRAGEPIERRE VIRFOLLET VITRAY VIVIERS VOIE COMMUNALE 20 SINSONNIERE VOIE CREUSE VOIE D AZAY VOIE DIEU VOIE ROMAINE VOIE ROMAINEVOIZERAY VONNE VONTES VOSLOIS VRILLE VRILLY XAINTRAILLES YUTIERS ZAC CROIX AUX JEAUX LOTISSEMENT 98 3 BL ZAC VRILLONNERIE ZA MONPLAISIR GRIMAUDERIE ZI NONGRENIERES ZONE ARTISANALE ZONE ARTISANALE TALVOIS ZONE INDUSTRIELLE BEL AIR ZONE INDUSTRIELLE MURIERSAdresse ABATTEES ABBAYE ABBAYEPIERRE ABOTTERIES ACACIAS ACCORD PARFAIT ACEQUIA GUE AFRIQUE AGENAISE AIGLETIERE AIGNEAUX AIGREFFINS AIGREFIN AIGREMONT AIGREMONTS AIGREVILLE AIGUEBELLES AIGUILLE AIGUILLEE AIGUILLON AIGUILLONNERIE AIGUIONNIERE AILBERT AILLANDES AILLERIE AIR TEMPS AIRELLES AIRES AIRS AISES AISNIERE AITRE AITRE REINE AITRE AU BERNEUX AITRE AU ROI AITRE AUX MAIRES AITRE BOURGINE AITRE BOUTAIN AITRE CAYET AITRE CHARRON AITRE CHOTARD AITRE AITRE VEAU AITRE GAUTHIER AITRE GAUTIER AITRE HERAULT AITRE HULAIN AITRE OISEAU AITRE PASQUIER AITRE RATEAU AITRES AITRES AITRE TOUZE AJONCS ALBATROS ALBERT MOUETTE ALENCON ALFRAY ALICANTE ALIGNES ALIZE ALIZES ALIZIERS ALLAIS ALLAYSACACIASAGRIPPA D AUBIGNEALFRED NOBELALLENDEALOUETTESALSACEAMOUREUXANJOUARTHUR RIMBAUDAUBEPINESAUGUSTE LAMBERTAULNESBEAUREGARDBEAUREGARD L EPAUBECHEREAUBEETHOVENBEL AIRBELLEVUEBERGERONNETTESBERLIOZBERNINBESLINIEREBEURICHOTTERIEBEURICHOTTERIE CHANVRERIEBLEUETSBOCAGEBODERIEBOHEMEBONNE FONTAINEBOSQUETSBOULEAUXBOURDASIEREBOURGBOURGOGNEBRUYERESCAMELIASCARIBOUSCEDRECEDRESCERISIERSCESAR FRANCKCHAINTRESCHAMPAGNECHAMP AUX LOUPSCHAMP FLEURICHAMP ROUXCHAMPSCHANTERELLESCHANTERIECHARBONNIERECHARDONNERETSCHARLEMAGNECHARLES GODEFROYCHARLES PEGUYCHARMESCHARMILLECHARNIECHATAIGNIERCHATAIGNIERSCHATEAU D 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NITRAYDE NORMANDIEDE PONCEDEPORTESDE PROVENCEDE ROUENDES ABEILLESDES ACACIASDE SACHEDES AGAPORNISDES ALBATROSDES ANEMONESDES AULNAYSDES AULNESDES BELUGASDES BERGERONNETTESDES BIGNONSDES BLEUETSDES BOISDES BORDS SARTHEDES BOULEAUXDES BOUVREUILSDES BRUYERESDES CERISIERSDES CHAMOISDES CHARMESDES CHATAIGNIERSDES CHENESDES CHENES VERTSDES CIGOGNESDES COLCHIQUESDES COQUELICOTSDES COURLISDES EGLANTIERSDES ERABLESDE SERRANTDES FAUVETTESDES FORGESDES FOUGERESDES GENETSDES GENTIANESDES GIROFLEESDES GLYCINESDES GOELANDSDES HIRONDELLESDES HORTENSIASDES IRISDES JONQUILLESDES LAURENTIDESDES LAVANDESDES LICORNESDES LILASDES LOGISDES LYSDES MACAREUXDES MAGNOLIASDES MESANGESDES MIMOSASDES MONTFORTSDES MYOSOTISDES NARCISSESDES NOISETIERSDES PAQUERETTESDES PECHEURSDES PETITES RUESDES PETITS CHAMPSDES PETITS DESHAIESDES PINSDES PINSONSDES POMMIERSDES POMPESDES PRAIRIES FUNAYDES PRIMEVERESDES ROCHESDES ROITELETSDES ROSESDES ROUGES GORGESDES RUAUXDES SAULESDES SONNETSDES SORBIERSDES TILLEULSDES 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ALLONNES ALLOUETTES ALLOURDES ALLUMIERS ALMADA ALMANDIER ALOUETTE ALOUETTES ALOUGERES AMARYLLIS AMBROISE AMIGNE ANAUDIERES ANCIEN ETANG ANCIEN ANCIENNE ANCIENNE ANCIENNE LIGNE TRAMWAY ANCIEN PRESBYTERE ANCINNETTE ANDONNERIE ANDOUARDIERE ANDOUILLETTERIE ANDROSACE ANELLES ANERIE ANGEBAUDIERE ANGEBERDIERE ANGELERIE ANGELLERIE ANGE ANGENARDIERE ANGERIE ANGIZIERE ANGLECHERE ANGLEE ANGLICHERIE ANGOTTIERE ANGUILOUX ANJOUBERT ANJUBAUDERIE ANJUBAUDIERE ANNERIES ANNONCIATION ANTE ANTELLERIE ANTINIERE ANTONNIERE APPRETS AQUARIUM ARAUCARIA ARBONDIERE ARCAILLERE ARCHE ARCHE FERRIERE ARCHE VALLEE ARCHE D ENFERNAY ARCHE PASSAGE BRUN ARCHE EN VALLEE ARCHE NEUVE ARCHER ARCHERIE ARCHES ARCHES BENEHARD ARCHES ARCHES ARCIF ARCIFS ARCIS ARCY ARCY BRULE ARCY D AUBTERRE ARCY ROCHE ARDENTS ARDOIRIE ARDOISE ARDRIER ARDRIERS ARDRILLE ARDRILLERE ARENCAY ARGERAY ARIS ARMENAUDIERE ARPENT ARPENT BLANC ARPENTEAU ARPENTS ARQUENAY ARRACHEE ARRACHERIE ARRONNIERES ARTAY ARTENIERE ARTHE ARTHUISIERE ARTIGNE ARTIVERIE ARTOIS ARTUSIERE ASNERIE ASNEROLLES ASNILLE ASPERGERIE ASSE ASSELIERE ASSIERES ASSIS ATELLES ATHENAY ATOUR AUBE AUBEE AUBEPIN AUBEPINE AUBERDERIE AUBERDIERE AUBERGELLERIE AUBEVOIES AUBIGNE AUBIGNY AUBINIERE AUBISAIE AUBOINS AUBRAYE AUBRIERE AUBRINS AUBRULIERE AUBTERRE AUBUE AUBUS AUBUSSON AUCHELLERIE AUDELLERIE AUDIGEOIS AUDINIERE AUDIONNIERE AUDISIERES AUDIZIERES AUDURON AUGARIS AUGE AUGEARDERIE AUGERAIES AUGERIES AUGONAY AUGOTERIE AUGUISIERE AUGUSTE NOURRY AULAINES AULNAIE AULNAIES AULNAY AULNAY GUILLIER AULNAYS AULNES AULNETTES AUMINIERE AUMONDE AUMONE AUMONERIE AUMONERIES AUMONES AUMONETTE AUNAIE AUNAIE MORIN AUNAIES AUNAIS AUNARIE AUNAY AUNAY BAS AUNAY GUILLU AUNAY PION AUNAYS AUNE AUNEAU AUPINAY AURORE AU SOLEIL LEVANT AUTELS AUVRIE AVALOIR AVEL BREIZ AVELDRO AVELINES AVENRY AVENTURE11 NOVEMBRE14 JUILLET8 MAI8 MAI 1945ABEL TIRANDANDRE CERISAYANTARESAUGUSTE BARTHOLDIBEAULIEUBOLLEECARNOTCHARLES GAULLECLAUDE DEBUSSYCOURSIMAULTCOURTANVAUXDE BELGRADEDE BONENDE BRETAGNEDE BRUXELLESDE BUCKEBURGDE CLOSERIEDE GAUTRIEDE CHAMPAGNEDE LIBERATIONDE LIBERTEDE PAIXDE PEUPLERAIEDE REPUBLIQUEDE L EGALITEDE L ETANGDE MADRIDDE MARSEILLEDE MONTARGISDE MONTREALDE MONTREUXDE NETTLEHAMDE PADERBORNDE PERSEIGNEDEPORTESDES ACACIASDES BAZINIERESDES FLEURSDES LILASDES PLATANESDES POMMIERSDE THURY HARCOURTDE TOULOUSEDE VERDUNDE VERRONDE WELTONDIVISION LECLERCD OBERNKIRCHENDOCTEUR BAUDRILLARTDOCTEUR GALLOUEDECDOCTEUR LEROYDOCTEUR RIANTDU 19 MARS 1962DU 8 MAI 1945DU BOCAGEDU MACDU ZAMENHOFDU GENERAL GAULLEDU GENERAL LECLERCDU MAINEDU MANSDU MARECHAL LYAUTEYDU PANORAMAESTOURNELLES CONSTANTEUGENE GOURDEAUEUROPEFELIX GENESLAYFORETFRANCOIS CHANCELFRANCOIS NICOLAYFRANCOIS MITTERANDFRANCOIS MITTERRANDGAREGENERAL GAULLEGENERAL LECLERCGEORGES DESNOSGEORGES DUHAMELGEORGES DURANDGRECOGRILLONSGUY BOURIATHENRI LEFEUVREHENRI KLOTZJEAN JAURESJEAN LOISEAUJOEL THEULEJONCHERAYKIRCHDORFLAENNECLIBERATIONLIBERTELOUIS CORDELETLOUIS CORDELET FOYER LOGEMENTLOUIS GAUDINEAULOUIS LEGROSMAINEMANSMARECHAL FOCHMARECHAL LIAUTEYMARECHAL LIAUTEY 5 EMEMARECHAL LYAUTEYMAX BOYERMICHEL VERDIERMONTREALMOZARTNATIONALENOGUESOLIVIER HEUZEOLIVIER MESSIAENPAIXPANORAMAPASTEURPAUL LOUIS JACQUESPECARDIEREPELOUSEPIERRE BRULEPIERRE COUBERTINPIERRE MENDES FRANCEPIERRE PIFFAULTPIERRE SEMARDPISCINEPLAISANCEPLEIADEPLESSIS BOURREPOINT JOURPOIRIER ROUGEPROSPER DAUDIBONREMBRANDTRENE LAENNECRHIN DANUBERODINROSTOV DONRUBILLARDSAUMURSAVORGNAN BRAZZATALHOUETTOURSVENDOMEVERON FORTBONNAISVICTOR HUGOVOLOSYZEUX AVOINERIES AVRILLERE BABIERE BABILLE BABILLONNIERE BABINIERE BACHELOTIERE BACHOIRE BADERIE BADEUSSIERE BAGATELLE BAGERE BAGUERIE BAHINE BAHURIE BAIGNEUX BAILLANDIERES BAILLE BAILLEE BAILLEES BAILLIE BAILLIS BAILLOTAGE BAJOTIERES BALANCE BALANCON BALCON FLEURI BALEINERIE BALEINES BALET BALISIERE BALLET BALLUERE BALONNIERE BALONNIERES BALOTTERIE BALTIERE BANCE BANDES BANNE BANNERIE BANNIERE BANOS BANQUES BAQUET BARAGUAY BARAGUERIE BARANDIERE BARATERIE BARATIERE BARATINIERE BARATTERIE BARATTIERE BARAUDIERE BARBAUDERIE BARBEGAULT BARBELIERES BARBERIE BARBES FAILLIS BARBIERE BARBIERS BARBIONNIERE BARBOINAY BARBOISEAU BARBOISEAU MONTPLAISIR BARBOTERIE BARBOTIERE BARBOTTIERE BARBOTTIERE HYERRAY BARBOTTIERES BARBOUERES BARDETIERE BARDINNERIE BARDON BARDOULE BARENTON BARETRIE BARIGNE BARILIERE BARILLE BARILLERIE BAROCHERE BARONNERIE BARONNIERE BARQUE BARRADIGUE BARRE BARREAU BARRE AUX ODEAUX BARREE BARRE MARION BARRERIE BARRES BARRICADE BARRIERERIE BARRIERES BARROIS BARSONNIERE BARY BAS BAS BAGNOLET BAS BALLON BAS BEAUMONT BAS BERTEAU BAS BIGNON BAS BAS BORDAGE BAS BOULAY BAS BOURG BAS BRAY BAS BRETEAUX BAS BRETOIN BAS BROSSERIL BAS BROUSSIN BAS BUISSONS BAS BURON BAS CASSOIRS BAS BAS CHANSORT BAS CHAPEAU BAS CHAPLET BAS CHARME BAS CHATONS BAS CHAUSSINS BAS CHENE BAS CHESNAY BAS CHEVIGNE BAS CHOQUETS BAS CLACAUX BASCONNIERE BAS COUDREUSE BAS COURFESSON BAS COURTEILLE BAS COURTIMONT BAS CROUTON BAS BUTTE BAS COTES BAS LANDES BAS MONTS CREAUX BAS ROTTES BAS BAS BOURG BAS CREUX BAS TERTRE BASFANY BAS FOURNEAUX BAS FRAY BAS FRENE BAS FRETE BAS GADOIS BAS GAUDU BAS GRATAIS BAS HOMMAS BAS HONGRIE BASHOURDES BAS IVAY BAS JAMBETTE BAS JARDINS BAS JAUNAY BAS JONCHERAY BAS JOUFFRAY BAS JOUSSE BAS LIVRE BAS MARCHAIS BAS MESNIL BAS MIAULES BAS MOLLANS BAS MONTJOIE BAS MONTSUREAU BAS MORENTAIS BAS MORIERS BAS MORTRIE BAS MORTRY BAS BAS NOUYS BAS NOYEAU BAS NOYERS BAS ORMEAUX BAS PLESSIS BAS POMMEREUIL BAS PRESSOIR BAS PRIEURE BAS PUITS BAS RASNE BAS RHEM BAS RIDOIRE BAS RIVERELLE BAS RIVIERE BAS ROCHER BAS RONCERAY BAS ROSSAY BAS ROUILLIS BAS ROUSSET BASSACHERIE BAS SARTHON BASSE BASSE ALOUETTE BASSE ARMOIRE BASSE BARRE BASSE BERTRIE BASSE BLANCHERIE BASSE BLONNIERE BASSE BOSSE BASSE BOUFFETIERE BASSE BOURDINNIERE BASSE BRIERE BASSE BROSSE BASSE BROUILLERE BASSE CHANTELLIERE BASSE CHIFFIERE BASSE CORMERIE BASSE BASSE COUSINIERE BASSE CRONERIE BASSE DAVIERE BASSE EPINE BASSE ETURCIE BASSE FLORISIERE BASSE FOLIE BASSE FORET BASSE FOSSE BASSE FOUBAYERE BASSE FOULIERE BASSE FROGERIE BASSE GAUMERIE BASSE GAUTIERE BASSE GENESIERE BASSE GOHONNIERE BASSE GOUBINIERE BASSE GOULANDIERE BASSE GROIE BASSE GUIBERDIERE BASSE GUIERCERIE BASSE GUIMONDIERE BASSE HERPINIERE BASSE HOUSSAYE BASSE JOUANNIERE BASSE JOUBARDIERE BASSE LANDE BASSELLERIE BASSE LOGERIE BASSE MARE BASSE MARTINIERE BASSE MERCERAIE BASSE MERCERIE BASSE METAIRIE BASSE MINISTRIE BASSE MOLIERE BASSE MORINIERE BASSE MOTTE BASSE PANCHERIE BASSE PATURIERE BASSE PERCHAIE BASSE PERROCHERIE BASSE PILTIERE BASSE PLOUTERIE BASSE ROBINIERE BASSE ROCHE BASSE RONGERE BASSE ROUGESSE BASSE ROUSSARDIERE BASSE SAULAIS BASSES BARRES BASSES BENARDIERES BASSES BEURICHONNIERES BASSES BLANCHARDIERES BASSES BONOTIERES BASSES BROSSES BASSES CHAUVIERES BASSES COURTOISIERES BASSES CROSSONNIERES BASSES FERRIERES BASSES FINARDIERES BASSES FONTENELLES BASSES FORGES BASSES GACHERES BASSES GUETTES BASSES HUAUDIERES BASSE SICOTTERIE BASSES MORICIERES BASSES MORLETTES BASSES MORTONNIERES BASSES NOES BASSES PERRIERES BASSES PILAVINIERES BASSES PLANTES BASSES QUANTIERES BASSES RACINAIES BASSES RAINEBAUDIERES BASSES RICOSSERIES BASSES ROCHES BASSES RUAUDIERES BASSESSE BASSES TOUCHES BASSES TOUCHETTES BASSES VALLEES BASSES VARENNES BASSE TERCHERIE BASSE TERMELIERE BASSETIERES BASSE TOUCHE BASSE TUILERIE BASSE VALLEE BASSE VENTE BASSE BASSIERE BASSINS BAS TERTRE BAS THEIL BASTIENNERIE BAS TROGNE BAS VEAU BAS VERNET BAS VILLIERS BAS VINAY BAS VINCENNES BATAILLERE BATAILLERES BATAILLERIE BATAILLIERE BATANCON BATASARDERIE BATE BATES BATIMENT MARTINIERE BATIMENT HERON BATIMENT ARGANCIERE L ARGANCIERE BATIMENT BATC BATIMENT GITE 2 CORMIER BATIMENT PN 2 TRAVERS BATISSE BAUCERIE BAUDIERE BAUDINIERE BAUDINIERES BAUDOUR BAUDRAIRIE BAUDRAY BAUDRIERE BAUDRY BAUGE BAUGERIE BAULERIE BAUMELIERE BAUMERIE BAUMERIES BAUREGARD BAUSSERAIE BAUSSONNIERE BAUVAIS BAVERIE BAVETIERES BAY BAYARDIERE BAYLES BAYOLLIERE BAZILE BAZILLERE BAZINIERE BAZINIERES BAZOLIERE BAZONNERIE BEARDIERE BEASNERIE BEATIERE BEATISSERIE BEAUBERIE BEAUBOIS BEAU BUISSON BEAUCE BEAUCERIE BEAUCERIES BEAUCHAMP BEAUCHENE BEAU CHENE BEAUCHENE CUVY BEAUCIERE BEAUCOUDRAY BEAUDIERE BEAUDISSERIE BEAUDONNIERE BEAUFEU BEAUFLEURY BEAUFOURIE BEAUFRENE BEAUGE BEAUGENDRIE BEAUGEONNIERE BEAUJARDERIE BEAULIEU BEAULORIER BEAUMANOIR BEAUMEILLERE BEAUMELLIERE BEAUMERAIE BEAUMERIE BEAUMONT BEAUMONTCEL BEAUMONTEEL BEAUMORTIER BEAU MORTIER BEAUMOYERES BEAUREGARD BEAUREPAIRE BEAUREPAIRES BEAU RIVAGE BEAUSEJOUR BEAU SEJOUR BEAUSOLEIL BEAU SOLEIL BEAUSSANT BEAUSSENTS BEAUSSERIE BEAUSSONNIERE BEAUTRUERE BEAUVAIS BEAUVAL BEAUVERGER BEAUVOIS BEAUX JOURS BECANE BECANNE BECANNERIE BECHEPAY BECHEREAU BECHETIERE BECHIE BECHOTIERE BECHUERE BECQUERIE BECQUETTE BECRE BEDANIERE BEDELLERIE BEDENIER BEDIVIERE BEDOINERIE BEDOUINIERE BEGAUX BEGLAUDIERE BEGRON BEGROS BEGUINERIE BEILLOT BEL AIR BELAIR BELAIRE BEL AMOUR BELANJARD BELANNIERES BEL ASILE BEL BEURE BEL EBAT BELEBAT BEL ECLAIR BEL EGOUT BEL ESSART BELEVENT BEL BELIN BELINERIE BELINOISE BELIVIERE BELLANDERIE BELLANDIERE BELLANGERAIE BELLANGERIE BELLANGERIES BELLAUBRIE BELLE BELLE AUBRY BELLE BARBE BELLEBAT BELLE BRANCHE BELLE CHASSE BELLECHASSE BELLE CROIX BELLE ENTREPRISE BELLE ERONCE BELLE ETOILE BELLE FILLE BELLE FONTAINE BELLEFONTAINE BELLE FRAISE BELLE FRAYERE BELLE FRESNE BELLEGARDE BELLE GARDE BELLE HOIRIE BELLE HOIRIE ECURIE BELLE HOTELLERIE BELLE INUTILE BELLE INUTILE BELLE MAISON BELLE METAIRIE BELLE NOE BELLE OUVRIERE BELLE BELLE PROMENADE BELLE RIVE BELLE RIVIERE BELLES BOUCHES BELLE SELLERIE BELLES POULES BELLESSARD BELLESUSE BELLES Y VIENNENT BELLE TOTERIE BELLETROTIERE BELLEVILLE BELLEVUE BELLE VUE BELLE VUE BALLERIE BELLEVUE SOURCES BELLEVUE TAMARINS BELLEVUEGERMAIN VAL BELLIERES BELLIVIERE BELLONNIERE BELLOSSY BELLOUTES BELLURIE BELOTTIERE BELOUZE BELVEDERE BENARD BENARDIER BENARDIERE BENEHARD BENER BENERIE BENER RUISSEAU BENESTIERE BENETRIE BENOISEAU BENOITIERE BEQUILLE BEQUINIERE BERCAIL BERCE BERCEGE BERCELLERIE BERCELLIERES BERCERIES BERCON BERESINA BERGEOTTIERE BERGEOTTIERES BERGERIE BERGERONNETTES BERGES L ORNE BERGEVINIERE BERICHONNERIE BERLOQUET BERNARGERIE BEROIRIE BERRY BERSANDIERE BERSAUDIERE BERSAY BERSONNERIE BERSONNERIES BERTELLIERE BERTHELLIERE BERTHELOTTIERE BERTHERIE BERTHES BERTIERE BERTIN BERTINIERE BERTRAIE BERTRAS BERTRE BERTRIES BERTRONNIERE BERVILLE BESASSE BESASSIERE BESCHERAU BESLAY BESLERIE BESLIERES BESLINIERE BESLON BESNARDERIE BESNARDIERE BESNARDIERES BESNERIE BESNERIE NORD BESNERIES BESSIERE BETE BETHETE BETHUNS BETTELOTTIERE BETTERIES BETUNE BEUCHER BEUCHERIE BEUCHERIES BEUCHET BEUCHETIERE BEUCHETIERES BEUGLAIE BEUGUELIERE BEULIERE BEUMERIE BEUNARDIERES BEUNECHE BEUNOTTERIE BEURALLERIE BEURELIERE BEURELLIERE BEURESSARD BEURRE BEURRELOUP BEURRERIE BEURREROUX BEURY BEUTIERES BEUVERIE BEUVINIERE BEUVRIE BEVERIE BEZANNE BEZARDIERE BEZINIERE BEZONNAIS BEZORY BIANS BIARD BIARDS BIAS BIAUDERIE BICETERIE BICHES BICHET BICHETIERE BICHONS BIDAUDIERE BIDEAULT BIDORIERE BIEN ETRE BIENVENUE BIENVILLE BIENYVIENNE BIERGERIES BIETTERIE BIFFAUDIERE BIGARREAUX BIGLERIE BIGNOLAS BIGNON BIGNONNIERE BIGNONS BIGORERIE BIGORRERIE BIGOTERIE BIGOTIERE BIGOTIERRE BIGOTTERIE BIGOTTIERE BIGOTTIERE GARENNES AUX CERFS BIGOTTIERES BIGUERIE BIJOTTIERE BIJOU BILANGE BILBAUDIERE BILLARD BILLETTE BILLETTE PIERRES BILLOT BIMERIES BINAUDIERE BINELLIERE BINETIERE BINETIERES BINETTERIE BINIERE BIONNERIE BIOTTIERE BIOU BIQUE ROUGE BIRE BIRETTE BIRONNERIE BIROTTERIE BISARDIERE BISCALLERIE BISSAC BISSAITRE BISSEZE BIZARDIERE BLAIRIE BLANC COTTAGE BLANCHARD BLANCHARDIERE BLANCHARDIERES BLANCHARDON BLANCHE BLANCHE LANDE BLANCHERE BLANCHERIE BLANCHETIERE BLANCHETIERES BLANCHISSERIE BLANDELIERE BLANDELLERIE BLANDELLIERE BLANDINERIE BLANDINIERE BLASOTTERIE BLATIERE BLATONNIERE BLAZOTERIE BLAZOTTIERES BLEINNERIE BLETINIERE BLETTERIE BLEUDIERE BLEUETS BLEURIE BLEUTIERE BLINIERE BLINIERES BLONNIERES BLOSSERIE BLOSSERIES BLOSSIERS BLOTERIE BLOTIERE BLOTINIERE BLOTINIERES BLOTTERIES BLOTTIERE BLOTTINIERE BLOUSIERE BLOUSIERES BLUETTIERE BLUSSIERE BLUTEAU BLUTIERE BLUTIERES BOCTIERE BODERIES BODINERIE BODINES BODINIERE BODINIERES BODRAIE BOELLE BOETE BOHORDIERE BOIDONNIERE BOIDRONNERIE BOIRES BOIRIE BOISAILLE APRES BOISARDIERE ARMAND AUX LOUPS AUX MOINES BATAILLE BECHETERIE BELLAND BESNARD BESNIER BEZARD BOUCHARD BOUQUET BOUTE BUISSON CAMUS CHABOT CHARMANT CHAUDE BOISCLAIREAU BOIS COCHET COCHIN COLOMBE COMMISSAIRE CONNUAU CORBONS D ANGERS D ASSE DAUPHIN D AUVERS BOIRE BRAYE BRICE FAYE D EFFE FUTAIE GERANS GUEMANCAIS JOIE JOUE BARONNIE BLOTTIERE BUTTE CROIX CURE PIECE LANDE MARCOTTIERE MARE MOTTE PICANNE PAPILLONNIERE PICASSERIE POINTE ROCHE SOU LINTHE L ISLE MINCLOU DEMOISELLE MONTAGENET MORTEVEILLE D EN HAUT DROITE PINCE ROUEZ ROUZIERS DERRIERE DEANNE BROSSES HOULBERDIERES HOUSSAIS PERRAIS VIGNES VAURENIER VAUX D HEURE D HIER DOUBLE D OURNE BOISDRONNAGE BON REFUIE BREUIL BUISSON CLERAY DEFAIS FOURNIER JARRIER LUCAS MILIEU BOISERIE FAGLIN FERME FLAMBERT FRANCHET GAMARD GAREAU GARS BOISGARS GAUDIN GENCE GENCY GESLIN GODIN BOISGONTIER GOUFFIER GRAMMONT GUET GUILLON BOISGUINANT HEBERT HU HUNAULT HUNAUT JOLI JOLI HARAS JOLY JOLYS L ABBE LANDON LAUNAIE BOISLENFRAY L EVEQUE LOUTRE LOUVEAU LOUVEL LUNAY MACE MARRON MARTEAU MARTIN MARTIN LOGE MAUCLAIR MESLIER MIALE MINOT MOISANT MOLLET MOQUET MORTIER BOISNET NEUF NEUF PIVARDIERE OLIVIER OLIVIER L ESPERANCE BOISORAN ORY BOISOUGE PIAU PICAULT PORTAIL MARIONNERIE PROVOST QUARTIER QUETIN RASOIR RENAULT RIBOT RICHARD RIGAULT ROBERT RONDEAU ROUGE ROUTE DAUVERT ROVERT BOISSAIE BOISANDRE BOISJEAN BOISJULIEN BOISMARTIN BOISSE BOISSELEE BOISSELIERE BOISSELLIERE SEME SERIN SERINE SIBYLLE BOISSIERE BOISSILIERE SINON CHERRE TAILLE TORCHE TRONCHE VERMEIL VERNEUIL VIEIL VIEILLIERE BOISVIER YVON BOITARDIERES BOITELLERIE BOITERIES BOIVENT BOIVINERIE BOIVINIERE BOIZARDIERE BOLINIERE BOLLEE BOMBARDIERES BOMBOIRIE BOMERIES BOMMERIE BON AIR BONDE BONDES BONDONNIERE BONHOMIERES BONHOMMERIES BONHOMMIERE BONLIEU BONNARDIERE BONNE FARIE BONNE FILIERE BONNE FONTAINE BONNETIERE BONNETTERIE BONNIVENTS BONOTIERE BONOTIERES BON PAIN BON REFUIE BONS ENFANTS BONS ROUIS BONTANGERIES BONVOULOIR BOORIERE BOOS BOQUETIERE BORDAGE BORDAGE VILLIERS BORDAGE GUE BORDAGE EPI FLEURI BORDAGES BORDASSE BORDE BORDE ANJUBAULT BORDEAU BORDE AU LIEVRE BORDEAUX BORDE AUX MOINES BORDE AUX PEAUX BORDE BEURRE BORDEBEURRE BORDE BOYER BORDE BRAYE BORDE BRISSAY BORDE CHAILLOUX BORDE CHANGE BORDE COQUETIERE BORDE CORMERY BORDE COEUR BORDE CHENES BORDE NOYERS BORDE GUE BORDE FRANCELIERE BORDE GAUDIN BORDE HALLAIS BORDE HEURE BORDE JANVIER BORDE JOUBERT BORDELIERE BORDELLERIES BORDE MECHINE BORDE MENIL BORDE MOREAU BORDE NAIL BORDE NEUVE BORDE PLEUVRY BORDE POTEAU BORDE RAGOT BORDERIE BORDERIES BORDE ROUSSEAU BORDE RUBIS BORDES BORDES BEURRE BORDE TRUDEAU BORDIERE BORDIGNE BORNE BORNO BOSQUET BOSSE BOSSELIERE BOSSELLERIE BOSSERIE BOSSERIES BOSSES BOSSIERE BOSSIFIERE BOSSONNIERE BOSSUE BOSTIERES BOTINEAUX BOTTELLERIE BOUAR BOUBIERS BOUCAN BOUCANE BOUCASSIERE BOUCHARDIERE BOUCHARDIERE SOURCHE BOUCHAUDERIE BOUCHAUSSIERE BOUCHELOUVE BOUCHER BOUCHERES BOUCHERIE BOUCHERIES BOUCHER LOUIS BOUCHER PERRICHER BOUCHETIERE BOUCHEVEREAU BOUCHEVREAU BOUCHONNIERE BOUCTEAU BOUDAN BOUDELLERIE BOUDEVINIERE BOUDIN SON BOUDINIERES BOUER BOUERE BOUESSAY BOUET BOUETEAU BOUFAUDIERE BOUFFETIERE BOUFFIERE BOUGARDIERE BOUGASIERE BOUGASSIERE BOUGE BOUGERIE BOUGLERIE BOUGRIERE BOUGRIERES BOUGRONNIERE BOUGRONNIERES BOUGUELIERE BOUGUELIERES BOUGUERIE BOUHARDIERE BOUHEREES BOUHOULIERE BOUILLANTS BOUILLAUT BOUILLE BOUILLERE BOUILLET BOUILLON BOUILLONNAY BOUILLONS BOUJAILLE BOUJARDIERES BOUJONTIERE BOULAI BOULAIE BOULAIE MONT BOULAIES BOULAIRE BOULAIRIE BOULAIS BOULANGERIE BOULARDERIE BOULARDIERE BOULAS BOULASSIERE BOULAY BOULAYE BOULAYS BOULEAU BOULEAUX BOULE D OR BOULERIE BOULERIES BOULETTERIE ALEXANDRE OYON ANATOLE FRANCE BELLEVUE BOBBY SANDS CARNOT NICOLLE CLEMENCEAU CURIE D ALGER D ANJOU BUTTE FRESNELLERIE VITESSE PRIMAUDIERE REPUBLIQUE L EUROPE MAULE MONTREAL DEMORIEUX AVOCATS GLONNIERES GRANDS ROTIS D ESTIENNE D ORVES DEUX JULIEN VENDEE GIGON COLONEL QUERU GENERAL NEGRIER EMILE PLET EMILE ZOLA FISSON FROMENTEAU GAMBETTA GEORGE SAND MELIES HENRI WILLE HORTON HOSPICE JARDINIERS JACQUES ROUSSEAU SEBASTIEN BACH YVES CHAPALAIN YVES CHAPALAIN MAISON LAMARTINE LATOUCHE MATHEWS MUTUEL CUGNOT PAIXHANS PASTEUR CHANTREL DOUMER VITESSE BROSSOLETTE LEFAUCHEUX QUEBEC RABIGOT RAVALIERES RENE LEVASSEUR REPUBLIQUE RICHMOND ROBERT SCHUMAN ROGER BOUVET BOULEVARDGERMAIN BOULEVARDMICHEL BOULEVARDNICOLAS TOURELLES VICTOR HUGO WINSTON CHURCHILL WINWSIANSKI BOULEVERT BOULIERE BOULIERES BOULIFERIE BOULIFFERIE BOULLERIE BOULOIRE BOULONNIERE BOULOTTERIE BOULVERT BOUQUE BOUQUET BOUQUETEAU BOUQUETERIE BOUQUETTEAU BOUQUETTERIE BOURBEILLON BOURBICIERE BOURCAILLON BOURCERIE BOURCHELIN BOUR BOURDAINES BOURDAIS BOURDAISERIE BOURDASERIE BOURDELECHE BOURDIGAL BOURDIGALE BOURDIGALLE BOURDIGNE BOURDINIERE BOURDINIERES BOURDINNERIE BOURDINNIERE BOURDONNERIE BOURDUE BOURELIERE BOURENARD BOURG BOURGANNIERE BOURG BELE BOURG BOURG CHEVREUIL BOURG D ANFRAY BOURG D ANGUY BOURG CHIEN BOURGEOISERIE BOURGEON BOURGEONS BOURGEOT BOURGEOTTIERE BOURGERIE BOURGETTIERE BOURG GUICHARD BOURG JOLI BOURG JOLY BOURG LUISANT BOURGNEUF BOURG NEUF BOURGOGNE BOURGOGNERIE BOURGOIGNERIE BOURG ROUTE AVOISE BOURGUIGNON BOURIANNE BOURICHOTIERE BOURJASE BOURLERIES BOURLIERE BOURMAUDIERE BOURMERIE BOURNAIS BOURNEUF BOURQUETIERE BOURRAVET BOURRAY BOURRELIERE BOURRELIERES BOURRIERES BOURSEAU BOURSOUL BOUSAILLERIE BOUSSARDIERE BOUSSARDRIES BOUSSAYERE BOUSSE BOUSSELIERE BOUSSELIERES BOUSSES BOUSSINIERE BOUSSIONS BOUT BOUTARBERIE BOUT LABOUT MOZE BOUT LANDES BOUTDEVILLE BOUT BOUT BOUT MONDE BOUTEILLERES BOUTEILLERIE BOUTEILUERE BOUTELOUP BOUTERIE BOUTEUSERIE BOUTEVINIERE BOUTIGNY BOUTILLONNIERE BOUTINIERES BOUTOIR BOUTONNERIE BOUTONNIERES BOUTREUX BOUTS BOUTTERIES BOUVARDIERE BOUVATERIE BOUVERIE BOUVERIES BOUVETIERE BOUVETS BOUVETTERIE BOUVIER BOUVINIERE BOUVRIE BOUZEAU BOYERE BOZE BRADEMER BRAHAIGNE BRAHANIERE BRAIERIE BRAILE BRANCHARDIERE BRANCHERIES BRANCHU BRANCHUERE BRANDARIE BRANDELLE BRANDINIERE BRANLARDIERE BRANLARDIERES BRANLE BRANLES BRANTALON BRARDERIE BRARDIERE BRAS MONTS BRASLON BRASSE BRASSEUR BRATELET BRAUDIERE BRAUDIERES BRAY BRAYE BRAZE BRAZONNIERE BREACHERE BREAULTS BREBIONNIERE BREBON BREBONNIERE BRECHE BRECHUERE BREDOUILLERE BREELLERIE BREGEON BREGEONS BREHANNIERE BREHAUDIERES BREHAUT BREHAVAL BREHERMONT BREIL BREIL GRUE BREIL METIER BREJEONS BREJON BREJONCS BREJON MUSARDIERE BREJONNIERE BREJONS BREJUS BRELEUDIERES BREMANCIERE BREMARDINS BRENON BREONNIERE BRESLAY BRESLERIE BRESSARDIERE BRESSAUDIERE BRESTEAU BRETEAU BRETECHE BRETELIERE BRETELLERIE BRETIGNOLE BRETOISIERE BRETONNERIE BRETONNIERE BRETONNIERE MARNAY BRETONNIERES BRETTES FOIN BREUILLES BREUNIERE BREVIERE BRIANDIERES BRICHANGE BRICHETIERE BRICOIN BRICOUGERES BRIDET BRIDOLAIE BRIERE BRIERE HOUSSINE BRIERE DRON BRIEUX BRIFFARDIERES BRIFFOLET BRIGNE BRIMAUDIERES BRIN D EAU BRINDELIERE BRINDILLES BRIOCHE BRIOLAY BRIOLETTE BRIOLON BRIQUETERIE BRIQUETTERIE BRISARDIERE BRISETTES BRISONNIERE BRISTIERE BRIVES BRIZARDIERE BROCELIANDE BROCHARDIERE BROCHE CREUSE BROCHERE BROCHERIE BROIS BROQUETIERE BROSSARDIERE BROSSAY BROSSE BROSSE PIN BROSSE PIGEON BROSSERIE BROSSE RONDE BROSSES BROSSILLOTS BROSSILLOTS LANDE NORD BROSSILS BROUARDIERE BROUARIE BROUASSIN BROUET BROUILLARD BROUILLETTE BROUSSES BROUSSIL BROUSSIN BRUANDIERE BRUELLERIE BRUER BRUERE BRUERES BRULANDIERE BRULAY BRULAY BRUYERE BRULE BRULEE BRULEES BRULERIE BRULIES BRULON BRULONNIERE BRULONNIERES BRUNELIERE BRUNELLERIE BRUNELLIERE BRUNERIE BRUNETIERE BRUNETIERES BRUNETTERIE BRUNIERE BRUNNERIE BRUON BRUYERE BRUYERE GUETTELOUP BRUYERE L ETANG BRUYERE FOURNEAUX BRUYERE TONNERIES BRUYERES BRYERE BUARDERIE BUBERONNERIE BUCHAILLE BUCHARD BUCHETERIE BUCHETIERES BUFARDIERE BUFFARDIERE BUFFARIE BUFFART BUFFAY BUGLERE BUINIERE BUIS BUISSAY BUISSON BUISSON BERGER BUISSON FLEURI BUISSONNIERE BUISSON ROND BUISSONS BULANDIERE BULARDIERE BULONNIERE BUNGALOW BUNGALOW POCKET BUNGALOW MOUCHENOU BUOT BURAISERIE BURANDIERE BUREAU BURES BURETIERE BURETTERIE BURON BURONNERIE BURONNIERE BUSSE CARREE BUSSON BUSSONNE BUSSONNERIE BUSSONNIERE BUSTIERE BUTACOL BUTONNIERE BUTTE GUINARD BUTTE AU RAY BUTTE BONNE NOUVELLE BUTTE L ARCHE BUTTE LUERES BUTTE D ENFER BUTTE FERMES BUTTE GATINES BUTTE NOYERS BUTTE PERRIERES BUTTE SOUCIS BUTTE VALIFER BUTTE D OIGNY BUTTE FOUR CARRE BUTTE JARRIER BUTTE MORMONT BUTTE BUTTE CHESNAIE BUTTE CAMP BUTTE VIEUX MANS BUTTE JARRIERES BUTTE NOIRE BUTTERIE BUTTES BUTTE VOYERE BUVINIERES BUZARDIERE BUZINIERE CABARET CABARETTERIE CABASSE CABAT CABESTON CABOCHE CABRIAU CABRIERES CACHE BOUTON CADOIRE CADOLIERE CADORIE CADORIERE CAFFERNANT CAILLARDIERE CAILLAUDERIE CAILLERE CAILLERES CAILLERIE CAILLETERIE CAILLETIERE CAILLONNIERE CAILLOTERIE CAILLOTIERE CAILLOTTERIE CALENDRIERE CALHEE CALIBROCHET CALICE CALIVONNIERE CALOTTERIE CALUYERE CALVAIRE CAMBRAY CAMELIAS CAMELIAS CHENE CAMP CAMP D AUVOURS CAMP THOREE CAMUSIERE CANNERIE CANNETIERE CANNOIS CANTER CANTIERE CANTINIERE CANVILIERE CAPINIERE CAPITAINERIE CARADOIRE CARCAHU CARCALINE CARDINIERE CARDUN CAREILLERE CARELIERE CAREME PRENANT CAREMIERE CARLERIE CARNOT CARONNIERE CARREAUX CARREFOUR BERTINS CARREFOUR BRUNEAU CARREFOUR CROIX ROUGE CARREFOUR CHATTE CARREFOUR AUBRINS CARREFOUR CHERES CARREFOUR LORIOTS CARREFOUR PILANS CARREFOUR RANDONNAYS CARREFOUR BREIL GENDARMERIE CARREFOUR GENEVRAY CARREFOUR ESPERANCE CARREFOUR JARDIN CARREFOUR LION D OR CARREFOUR LOGE CARREFOUR LOUDONNEAUX CARREFOUR PAIX CARREFOUR POSSET CARREFOUR ROCHE CARREFOUR RONCE CARREFOUR ROTTERIE CARREFOUR ROUTE MINOT CARREFOURHONORE CARREFOURNICOLAS CARREFOUR THIBAULT CARREFOUR TROIS POIRIERS CARREFOUR VERT CARRELIERE CARRELIERES CARRETIERE CARRIERES CARRIES CARRIOTIERE CARRIS CARROI CARROI BINET CARROI CITROUILLE CARROI HUGERIES CARROI PICHONS CARROIR CARROY GARANGER CARROY PONTEAU CARTE CARTELLERIE CARTILLON CARTRIE CASERNE ROUGET CASIER FORGES CASSE CASSEAU CASSE CORMIERS CASSE MOTTE CASSE MOTTES CASSEPOT CASSEREAU CASSERIE CASSIERE CASSIERES CASSINE CASTEL CASTEL JOLI CASTELLY CASTILLERIE CASTINE CATILLIERS CATINERIE CAUFETIERE CAUSSERAIES CAVE CAVE LANDES CAVE FUSEAU CAVE LANDAIS CAVE NEUVE CAVEREAU CAVES CAVES CHOLET CAVES GUINET CAVES PICHON CAVES RIBERDIERES CAVES ROCHETTES CAVIERS CAZEAU CEDRE CEDRE BLEU CELLE CELLERIE CELLIERS CENDRINERIE CENE CENOUZIERES CENSIE CENSIVE CENT CETIERS SOCIAL CENT VIGNES CERCLERIE CERCUEIL CERELLES CERENDIERES CERISAIE CERISERAY CERISIER CERISIERS CESINIERE CESSON CETON CEUX CEVERIE CHAANAY CHABOISSIERE CHABOSSIERE CHABOSSIERES CHABOTIERE CHABOUILLE CHAIGNE CHAIGNONNERIE CHAIGNONNIERES CHAILLERIES CHAILLEUX CHAILLOIS CHAILLOU CHAINAIE CHAINERIE CHAINES CHAINTRE CHAINTRES CHAINTRONNIERES CHAISE CHAISERAY CHALAMBERDERIE CHALAN CHALERIE CHALET CHALET SILARDIERE CHALET L ECOTE CHALET PLESSIS CHALET ROCHER CHALET NORMAND CHALET YVONNE CHALLERIE CHALOIGNE CHALONGE CHALONGES CHALONNIERE CHALOPINIERE CHALOTTIERES CHALOUGERES CHALOUSIERES CHALUAU CHALUERE CHALUMELIERES CHALUMELLERIE CHAMADE CHAMBON CHAMBORD CHAMBOUDE CHAMBOUILLET CHAMBOURG CHAMBRIN CHAMBRIOUX CHAMBRON CHAMBRONNERIE CHAMFORDIERE CHAMOIS CHAMPAGNE CHAMPAGNERES CHAMPAISSANT ANDRE CHAMPARDIERE AU FEUVRE AU MOINE AUX DAMES AUX LAPINS AUX LOUPS BARDIN BAS BELLE FONTAINE BERGER BIDAULT BINET BLANC BLANCHET BOISSEAU CHAMPBORDEAUX BOULAY BOUQUET BOUVET BRIOLET BRULE BUTTE CABARET CAILLON CAUDET CHAPEAU CHATAIGNIERS CHAMPCHER CHEVRIER CORNET COSME COSSONNEAU COURT CREUX CROCHE D ALOUETTE DAMONT BERNAY BOESSE BOUER CHALLES CHATEAUROUX DERRIERE DEVANT FOIRE GATINES BARRE BOUQUETIERE BUTTE CROIX CURE FERME FONTAINE FORGE FUIE GITONNIERE GRANGE GRELE GUIBRAY HAIE HAISE HAUTE PELOUSE L AIRE LANDE LCHAMP LOGE MAISON MARE MARNIERE PLANCHE POINTE L ARDOISE RIVIERE ROCHE ROUTE LACROCHE TARE L AUBEPIN L AUBUE VIGNE VINAUDIERE L ECOTAY L L EGUILLON L EPINE L ETANG L ORMEAU D EN BAS DENIAU PARENCE PLAISANCE PLAISIR ROSE DEDENIS BROSSES BRUYERES CAVES CHAUSSEAUX CHOUANNIERES CIGALES CONILLERES COURTILS DOUVES SEIGLE GAS GENETS SILLE MAIERES OISEAUX 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COURTIER COURTIERE COURTIERS COURTIGAULTS COURTIGNE COURTIL COURTILLE TILLEULS COURTILLON COURTILS COURTILSBENOIT COURTIMBOEUF COURTIMONT COURTINERIE COURTIRAN COURTIRON COURT JARRET COURTOISERIE TONNELIER COURTRIES COURTRUS COURTS COURTS LAYONS COURTY URBAINE 3 VALLEES COURVALIN COURVAU COURVAULERIE VERGERS VIEUX VINCENT MUSELLI COUSINIERE COUTANCIERE COUTARDIERE COUTIERE COUTIERE BROSSE COUTURE COUTURELLE COUTURES COUVELLERIE COUVENT COUVILLON COZE CRABASSE CRANNES CRAPAUDEAU CREANS CREMAILLERIE CRENAISERIE CRENAISIERE CRENON CREPELLIERE CREPERIE CREPILLON CREPINIERE CREPINIERES CREPONNIERE CRESSENTON CRESSONNIERE CRETINIERES CRETONNIERE CREULAIS CREUSAT CREUSE CREUSERIE CREUSECREUSOT CREUX CREVE L OEIL CRIBLERIE CRILOUX CROALIN CROC CROCHARDIERE CROCHARDIERES CROCHERE CROCHERIE CROCHETIERE CROCHETS CROHIN CROIE CROINAILLE CROISELLES CROISERIE CROISETTE CROISETTES CROISIES CROISILLES CROIX ALINE CROIX ALLAINE CROIX ARCHAMBAULT CROIX ARDRILLERS CROIX AUDET CROIX AU GARS CROIX BARBAULT CROIX BARDET CROIX BASSE VENTE CROIX BAUDIERE CROIX BECHIS CROIX BERTEVERT CROIX BETE CROIX BEUDET CROIX BIGOT CROIX BILLARD CROIX BOULARD CROIX BOURDON CROIX BRARD CROIX BRETTE CROIX BRIN CROIX BRULEE CROIX CASEAU CROIX CERIZAYE CROIX CHAMBEAUX CROIX CHAMPAGNE CROIX CROIX CHAPLIN CROIX CHARBONNIERE CROIX CROIX CHEVALIER CROIX CORNUEL CROIX COUVERTE CROIX D AURAY CROIX DAVOIS CROIX BEAUMERIE CROIX BONLIEU CROIX BOUILLARD CROIX CHERRE CROIX CLERMONT CROIX COURGAINS CROIX CUINIER CROIX FER CROIX GENIEVRE CROIX GLATIGNE CROIX GUITTION CROIX BATE CROIX BILLETTE CROIX FOULLIERE CROIX GRANGE CROIX JUSTICE CROIX LANDE CROIX MARE CROIX MORICELLIERE CROIX CROIX TOUCHE CROIX L AUBEPIN CROIX VAUDERE CROIX L EPINE CROIX L ORMEAU CROIX MALESSARD CROIX MARBRE CROIX MENUAU CROIX MISSION CROIX MONTARGIS CROIX MONTBRAY CROIX MONTIGNE CROIX MORIERS CROIX PAILLE CROIX CROIX ROINE CROIX DEAPOLLINE CROIX SAUSSAY CROIX BEAUSSENTS CROIX BERCONS CROIX BRETONNIERES CROIX CROIX ECONARDS CROIX EPINETTES CROIX FERMES CROIX FONTAINES CROIX GUETTES CROIX LANDES CROIX MONDUSSIERES CROIX MURS CROIX ORMEAUX CROIX SIX CROIX VERNELLES CROIX TROGNE CROIX VINAY CROIX DOLONNIER CROIX D ORIETTE CROIX BOULAIS CROIX CHAPLE CROIX CHATELET CROIX CHENE CROIX CORMIER CROIX GENET CROIX CROIX LIBOIS CROIX NEUF CROIX PATIS CROIX PERRAY CROIX PIN CROIX PLESSIS CROIX CROIX SABLON CROIX TAILLIS CROIX TERTRE CROIX TOURNAY CROIX FLEURIE CROIX FOUQUET CROIX GALLAS CROIX GASTINES CROIX GILETTE CROIX GIRARD CROIX GORGEAT CROIX LEONARD CROIX HATE CROIX HATET CROIX HOIST CROIX ISOLEE CROIX JANGUY CROIX CROIX CROIX LEVEE CROIX LORINE CROIX MALINGRE CROIX MANCEAU CROIX CROIX MARCE CROIX MARGOT CROIX CROIX MARION CROIX MIGNON CROIX MILET CROIX MILLET CROIX MINGUE CROIX MONGAULT CROIX MOREAU CROIX MORINEAU CROIX PAR DIEU CROIX PEAN CROIX PERCEE CROIX POMMIER CROIX PUCELLE CROIX QUARTEREAU CROIX ROBERT CROIX ROUSSE CROIX ROUSSIERE CROIX ROUTE SUZE CROIXANNE CROIXMARC CROIX SAUSSAIE CROIX SERPETTE GUE CROIX SIBILLE CROIX VALEPEE CROIX VALLIENNE CROIX VERRAS CROIX VOISINE CROLLE CROMAR CRONERIE CRONNES CROPES CROPET CROPIGNON CROPPES CROSET CROSNERIE CROSNERIES CROSPIERRE CROSSON CROSSONNIERE CROTTES CROUPE CROUPIERES SALVERT CROUSILLE CROUSILLES CROUZILLE CROYERE CROZET CRUCE CRUCHET CRUCHETIERE CRUCHETS CRUCHETTERIE CRUCHIERE CRUCHON CRUCHOT CRUET CRX BLANCHE ST DENIS TERTRE CUISINIERE CUISSE CUL CHESNEAU CULOUANIERE CURE CURE COURBES CURET CUTESSON CUTIBALE CUVY CYTISES DABOISIERE DADINIERES DAGUENETIERES DAGUERIE DALMINE DALOUSERIE DAMES DAMOISERIE DANDANNERIE DANIELLERIE DARAGON DAUBERDIERE DAUDINIERE DAUFINIERE DAUMOUCHE DAUNAYERE DAUVELLIERE DAUVERS DAUVERSIERE DAUVERT DAVIERE DAVILLIERE DEDOUERIE DEFAIS DEGOUTIERS DEHALLAIS DEHASERIE DEHEZERIE DEILLERIE DELINIERE DEMEDIERES DEMEE DENAZERIE DENETTERIE DENIAUTIERE DENICIERE DENILLERE DENISIERE DENISIERES DENT DEPELLERIE DEPISSERIE DERAZERIE DERCELLIERE DEROUETTERIE DEROUETTIERE DEROUINIERE DERREU DERREUX DERRIERE LUNE DESSUS ROTTES CLAIRIERE DETOURBE DEUNERIE DEUX EVES DEUX FOURCHETS DEUX JOURNAUX DEUX REBOURSIERES DEUX TILLEULS DEVANT DEVANT NIELLERIES DEVANTS DEVAUDE DEVINERIE DEVINIERE DEVISE DEVISES DIABIERES DIANN DIBONNIERE DIDIER DIGEONNERIE DIGITALES DIMAR DIMETIERES DINIERE DINON DIRONNIERE DISSE DITTIERES DIX BORNES DIX JOURNAUX DIX PERDRIX DODINIERE DOGERIES DOIGT FRESNE DOLBERDIERE DOLERIE DOLIERE DOLINIERE DOLOIRE DOLONNIERE DOMAINE PLANCHE DOMAINE BIARDS DOMAINE DOMAINE CHESNAY DOMAINE NARAIS DOMAINE GRASSINIERE DOMAINE POINTE POINTE DOMAINE PESCHERAY DOMAINE PRAIRIE DOMAINERIE DONNERIE DORAIRIE DORBELIERE DORERIE DORISSIERES DORMERIES DORRERES DOUAIRE DOUAIS DOUBARDIERES DOUBLE SIX DOUBRIS DOUCAIS DOUCAY DOUCELLE DOUCELLES DOUCET DOUCIN DOUCINIERE DOUE DOUE MURE DOUET DOULTIERE DOUSSAY DOUSSERIE DOUSSIERE DOUVE DOUVES DOUZE DOUZERIES DRAGONNIERE DRAGONNIERES DRAKKAR DREURIE DREUSERIE DREUTIERE DRONNIERE DROUARDIERE DROUARDIERES DROUELLERIE DROUERIE DROUETTERIE DROUINIERE DROULINIERE DROUSIL DROYERES DRUGEON DRUILLAIS DRUYAIS DRUYERES DRUYERS DUBARDIERE DUBOISERIE DUBOISIERE DUCHE DUCHERIE DUGASERIE DUIT DULTIERE DUONARDIERE DURAISERIE DURANDERIE DURANDIERE DURANDIERES DURANDRIE DUREAU DUREAUX DURELLERIE DURETIERE DURTIERE DUSSETIERE DUVALERIE DUVOLIERE DYNASTIE EBRARDRIE EBUFFOIRS ECHALLERIE ECHANGE ECHANGEES ECHANGES ECHARDIERE ECHARMEAUX ECHELARDIERE ECHIQUIER ECHOISEAU ECLAIRCIE ECLECHE ECLICHERIE ECLOPAS ECLUSE COURTIGNE ECLUSE HIERE ECLUSES ECOBU ECOBUE ECOBUEE ECOBUES BOURG PRIMAIRE COURTILLE ECOLLERIE ECONARDS ECOSSE ECOTAIS ECOTAY ECOTAYS ECOTTAY ECOUBEAUX ECOUBERIES ECOULEES ECOUTE GRENOUILLE ECOUTE S IL PLEUT ECOUVE ECRESSOIRS ECUELLERIE ECULIERE ECURE ECURETTES ECUREUIL ECUREUILS ECUREUILS SAPINIERE ECURIE ECURIES MOTTE ECUSSON ECUSSONNIERE EDELWEISS EFFRAYERE EGAIL EGLANS EGLANTIERS EGREVECHES EGUILLE EGUILLER EGUILLON EGUILLONNIERE ELANCHELLERIES ELANCOURTOISE ELENDENNES ELOUSIERES ELUYERES EMBOURCERIE EMERILLONNIERE EMILERIE EMILERIE EMILERIE EMONDIERE EMOTTERIE ENAUDERIES ENCHERIER ENCLOS VERDELLES ENCLOSES ENCLOS L JANVERIE ENCLOS L HUNAUDIERES ENCLOS L BOURG ENCOLIERE ENFER ENFERMERIE ENFERNEL ENFOURNERIE ENFOURNOIRE ENFUMERAIE ENGADINE ENGINS ENTE ENTERIES ENTOYERES ENTRE ENTRE DEUX EPAGNEUL EPAIGNE EPAILLARD EPARGNE EPAU EPAULARD EPAUTIERES EPEIGNES EPERNU EPESLAS EPICERIE EPICERIES EPICHELIERES EPIERRE EPI FLEURI EPIL FROMENT EPINAIS EPINARDIERE EPINARDIERES EPINARI EPINAY EPINAYS EPINE EPINEAUX EPINE BLANCHE EPINE DOLEE EPINE NOIRE EPINERIE EPINETTES EPINE VERTE EPINOIRE EPINOYEAUX EPIVENT EPIVENTS EPORCE EPRONNIERES EPRONS ERABLE ERALLAIS ERIDANT ERMITAGE ERVERIE ESCALE ESCALIER POTERNE ESCALIER TUCE ESCOLE CORBIN ESNAUDERIE ESNAUDIERE ESNAUDIERES ESNAY ESPERANCE ESPLANADE BAPTISTE BOURGOIN ESPLANADE LIBERTE ESSARD ESSARDS ESSARIGNE ESSART ESSE ESSEMPIEDS ESSORT ESTIERE ETABLES ETACHERE ETALOIR ETANG BECCANNE ETANG CHAUD ETANG GUIBERT ETANG LANDE ETANG PRALE ETANG L EVEILLE ETANG LANDES ETANG DREUX ETANG CHENE VERT ETANG CRUCHET ETANG PARC ETANG L LANDES ETANG L CHENE VERT ETANG NEUF ETANG RENARD ETANGS ETANG SALLE ETANG SEC ETANGS NEUFS ETERREAU ETINIERE ETIVAL EN CHARNIE ETOILE ETOURNEAU ETRE ETRE GARREAU CONGE ETRE JOLIVEAU ETRE MACON ETRE MELIERE ETRE MENARD ETREMETTES ETRE MOISY ETRE PILET ETRE PUCELLE ETRES ETRES MOULEES ETRE VERRIER ETRICHER ETRICHERIES ETRISAIE ETRIVIERES EUCHE EUCHES EUGENE VAUGRIVAUD EUVASERIE EVARDERIE EVASELLERIE EVASERIE EVAUX EVECHE EVENTAIL EVENTERIE EVERDERIE EVINIERES EXEMPLES EYGLANS EYGLETIERES FABRICE FABRIQUE FAGAUDIERE FAISANDERIE FAIVERIE FALAISE FALBARDIERE FALLOT FANEU FANIERE FARFOUILLERE FARIGOULETTE FARINEAUX FASSE FASSIERE FASSOTIERES FATELLERIE FATRAS FATTERIE FAUBOURG PITOULIERE FAUBOURGMICHEL FAUBRETEAU FAUCHERIE FAUCHERIES FAUSSES GAUDES FAUVELIERE FAUX FAUX LEVEE FAUX ROUGE FAVELIERE FAVERIE FAVEROLLES FAVROLLE FAY FAYAU FAZELIERE FECUVIERE FEFUERE FEIGNE FEIGNIER FELARDE FEMUSSON FENELLERIE FER CHEVAL FERCOCHERE FERDERIE FERME FERME DOBERT FERME MONTREUX FERME PARIS FERME PLESSIS FERME FONTENELLE FERME PROUILLERES FERME NEUVE FERMES FERMEDENIS FERME VOISIN FERM VILLAINES FERRANDERIE FERRANDIERE FERRERIE FERRIERE FERRIERE PLANTES FERRIERES FERRONNERIE FERRONNIERE FERROUELLES FERTE FERTELLIERE FERTIERE FERTINERIE FERTINIERE FESSARD FETE FORAINE QUINCONCE FETRIE FEUILLAGE FEUILLARDERIE FEUILLETTE FEUILLUS FEUMUSSON FEUPERIE FEU RICHARD FEUX FIALIERE FIARACHE FIARY VINAY FICHARDIERE FICHE PALLIERE FIEF AUX MOINES FIEF BAZILE FIEFSAULAY FIEFS MAHEST FIELLERIE FIERE FILATRIERE FILATURE FILATURES FILLERIES FILLETIERES FILLIERES FILLONNERIE FILLOTS FILONNIERE FILOUSIERE FILOUSIERES FINARDERIE FINARDIERES FINDAISE FINERIE FION FISSON FISSONNIERE FIZARD FLACE FLACHIERE FLAIRIEDENIS TERTRE FLANERIE BEL AIR FLARIES FLECHETS FLECHIN FLEUR FLEUR D ETE FLEUR SOLEIL FLEUR VENT FLEURET FLEURIAIS FLEURIER FLEURIERE FLEURIERES FLEURIONNIERE FLEURONNIERE FLEURS FLEURS D AJONCS FLEURTIERE FLORALIES FLORE FLOREAL FLORIDES FLORIERE FLORISIERE FLOSSELIERE FOCTIERE FOIE FOIRE FOLIE FOLIETTES FOLLERIE FONCEE FONCIERE FONDELIERE FONDRIERE FONDS FONELIERE FONTAINE ALLARD FONTAINE ALLIER FONTAINE BARRE FONTAINE BOUILLANT FONTAINE CORNUE FONTAINE BOUDAN FONTAINE CHAMPROND FONTAINE GUETTELOUP FONTAINE TERMELIE FONTAINE RHEM FONTAINE BOUES FONTAINE RUELLES FONTAINE SAULES FONTAINE BIGNON FONTAINE BOUT FONTAINE CHENE FONTAINE FEU FONTAINE FROIDE FONTAINE HARDY FONTAINE LIGOT FONTAINE FONTAINE MAROT FONTAINE MOUSSON FONTAINE NAVEAU FONTAINE PICOT FONTAINE PUTET FONTAINES FONTAINEAIGNAN FONTAINEMARTIN FONTAINEPERE FONTAINE VICOMTE FONTAY FONTENAILLES FONTENAY FONTENEILLE FONTENELLE FONTENELLES FONTENIS FOREAU FORESTIERE FORET FORET LAVARDIN FORET DOUVRES FORETERIE FORETS FORET TUCE FORGE FORGEAIS FORGEARDIERE FORGE COLLET FORGE L AUNE FORGE RHEM FORGE FORGE PICHER FORGERON FORGES FORGETIERE FORGETTE FORJARDIERE FORTAPORE ECU FORTERIE FORTERIES FORTRIE FOSSE AUX LOUPS FOSSE AUX MINES FOSSE AUX TIMONS FOSSE BODIN FOSSE BOUIN FOSSE BRAULT FOSSE COPIN FOSSE COULON FOSSE CROIX CARREFOUR FOSSE DENIS FOSSE FOSSE LANDES FOSSE FONDUE FOSSE GARNIER FOSSE GAUDE FOSSE GAULT FOSSE GIRAULT FOSSE JOUANETTE FOSSE MARION FOSSE RABINEAU FOSSE TAUREAU FOSSE BOUTINIERES FOSSE LONGUE FOSSE MERIENNE FOSSE ABRANNIERE FOSSE AUX LOUPS FOSSE ROBERT FOSSE ROBINEAU FOSSE ROUAUX FOSSE ROUGE FOSSES CORNUES FOSSES CHARENCE FOSSES ROBERT FOSSES ROBERTS FOSSES ROUGES FOSSE TREPIED FOSSETTES FOSSE VIEILLE FOSSE VINEUSE FOUARDIERE FOUARDIERES FOUAS FOUASNERIE FOUASSERIE FOUASTERIE FOUCARIE FOUCAUDIERE FOUCAUDIERE PARCELLE 1 FOUCAUDIERE FOUCAUDIERE FOUCHERIE FOUCHERIES FOUGEASSIERE FOUGERAIE FOUGERAIES FOUGERAIS FOUGERAY FOUGERE FOUGERES FOUGEROLE FOUGEROLLES FOUILLARDET FOUILLET FOUILLUS FOUINE FOUJASSIERE FOULAUDERIE FOULERAY FOULERETS FOULERIE FOULIERE FOULLERAY FOULTIERE FOUQUELLERIE FOUQUERIE FOUQUETTERIE FOUR FOURBINET FOURCHAUDIERE FOURCHE FOURCHE D AUVOURS FOURCHE EN FOURCHES FOUR GONTHIER FOURLETTE FOURMANDIERE FOURMENTERIE FOURMILLERE FOURMILLERIE FOURMONIERE FOURMONNIERES FOURMONT FOURNEAU FOURNEAU FONTAINE FOURNEAU PATISSEAUX FOURNEAUX FOURNERIE FOURNIERE FOURNIQUET FOURRERIE FOURRIER FOUSSARD FOUSSARDERIE FOUTAIES FOUTEAU FOUTIERE FOUTIERES FOYER LOGEMENT FOYER P PARADIS FOYER PERSONNES AGEES FOYERS FOYERS FOYERS FOYER ST THIBAULT ST THIBAULT FPAR HORNCASTLE FRAICHE FRAICHES FRAIN FRAIRIE FRAISERAIES FRAISIERE FRAMBOISIERE FRANC BARON FRANCELIERE FRANCHAISE FRANCHERIE FRANCHETIERE FRANCOISIERE FRANCOURT FRANSOLLIERE FRAPILLIERES FRARACHE FRAY FRAZELIERE FREBOURG FRECHE FRECUL FREDERIE FREDONE FREDONNIERE FRELONNIERE FREMISSON FREMONDIERES FREMOUSE FREMUSSON FRENE FREPINIERES FRESLONNIERE FRESNAIE FRESNAY FRESNAYE FRESNAY VILAINES CHATTERIE FRESNE FRESNELAIS FRESNELLE FRESNELLERIE FRESNES FRETAY FRETINIERE FRETTE FRETTERAI FREU FREULONNIERES FREUTIERE FREUX FREVAUDIERES FRIAISE FRIBAUDIERE FRIBOURG FRICHES FRILOU FRILOUX FRINGUERIE FROBERT FROGERIE FROGERIE FROGERIES FROIDOUSTAU FROLE FROMAGER FROMAGERIE FROMANDIERE FROMANGERIE FROMENTAGE FROMENTAIS FROMENTAYS FROMENTEAU FROMENTEAUX FROMENTELLE FROMENTERIE FROMENTIERE FROMENTIERES FROMENTIEUX FROMENTINIERE FRONTIERE FRONTINIERE FRONTINIERES FROTTERIE FROU FUIE FUIE AUX MOUCHES FUIE GARNIER FUIE POUPEE FUMERAIE FUMETAIRIE FURETERIES FURETIERE FURTIERE FUTAIE FUTAYE FUYE FUZELLERIE GABELLE GABINIERE GABONNAY GABRIELLERIE GABRONES GACHETIERE GACHETRIE GACHETTE GADELIERE GADOISERIE GADVINIERE GAGE GAGNARDERIE GAGNE GAGNERIE GAGNOTERIE GAIETE GAIGNARDERIE GAIGNER GAIGNONNIERE GAIGUENET GAILLARDERIE GAILLARDERIES GAILLARDIERE GAILLOT GAINERIE GAINIERE GAIRE GAISERIE GAITE GAITRIE GALANDERIES GALANDES GALARDON GALASIERE GALAXIE MONTAGENETS GALBRUNIERE GALBRUNNIERE GALECHERES GALERNE GALESNIERES GALETTE GALETTERIE GALICERIE GALICHET GALIERE GALIERES GALIFERT GALILEE GALINEE GALINIERES GALLATIERES GALLERANDE GALLERIE GALLETS GALLIERE GALLIERES GALLOISIERE GALMEZE GALON GALONNIERE GALOPIERE GALOUGERE GALOYERE GALPINIERE GALTIERE GALTIERES GAMBAUDERIE GAMBETTA GANAUDERIE GANCHES GANDELIERE GANDELIN GANDOLIERE GANDONNERIES GANDONNIERE GANDOUIN GANERIE GANNERIE GANNETIERE GANNETRIE GARAUDIERE GARDE GARDINIERES GARDON GARDS GARE CHIEN GARELLE GARENNE GARENNE ARPENTS VERTS GARENNES GARGANNE GARGOTTIERES GARGOUILLERIE GARNACHE GARNIER GARNISONS GARONNIERE GAROTTIERE GARREAU GARROTIERE GARS NOYER GAS GASCHEAU GASCOGNE GAS DALLE GAS D ANE GASNERAIE GASNERIE GASNERIES GASPERI GASSEAU GASSERIE GAST GASTES GASTINE GASTINES GATE GATELLERIE GATELLES GATERIE GATES GATINE GATINEAU GATINES GATINETTE GATS GAUBERDIERES GAUBERTS GAUBIER GAUCHER GAUCHERIE GAUCHIS GAUDARDERIES GAUDES GAUDIERE GAUDINE GAUDINET GAUDINIERE GAUDINIERES GAUDINNE GAUDION GAUDRAIZE GAUDREE GAUDRIERE GAUDRONNIERE GAUGUENIERE GAUGUSSE GAULAIS GAULARDIERES GAULERAIES GAULERIE GAULLETIERE GAUMERIE GAUMONT GAUTELLERIE GAUTERIE GAUTIERS GAUTRAIE GAUTRIE GAUTRIES GAVELLERIE GAZON GEBERDIERE GEIGNE GELERIE GELINIERE GELOPPIERE GEMASSE GEMETIERES GEMMERIE GEMMERIES GEMMETIERES GENAY GENDRIE GENDRINIERES GENDRONNIERE GENERE GENES GENESLAY GENET GENETAIL GENETAIS GENETAY GENETEIL GENET FLEURI GENETIERE GENETROLLE GENETS GENETS FONTENEILLE GENETTE GENEVRAIE GENEVRAY GENIE GENIEVRE GENIEVRES GENNEAUX GENNEBRAYE GENNETAY GENNETIERE GENNETTIERE GENTIANES GENTIERE GENTILLERE GENTILLERIE GENTROTIERE GENVERIE BRASSENS GEORGETIERE GERARDIERE GERBAUDIERE GERBEAUDERIE GERBEAUX GERMAINERIE GERMANDIERE GERMANIERE GERMENIERE GERMENIERES GERMINIERE GESBERDIERE GESLINIERE GESTE GESTIERE GEVAISEAU GEVEAU GIBAUDIERE GIBECIERES GIBLOTTIERE GIBRALTAR GIBRON GIDONNIERES GIGONNIERE GIGOTIERE GIGOULIERES GIGOULLIERES GIGOUS GILARDIERE GILBERDIERE GILETTRIE GILLARDS GILLETERIE GILLETIERE GILLOTTERIE GIMBERDIERES GIMBRUNS GIONNAI GIRAFE GIRARDERIE GIRARDIERE GIRAUDEAU GIRAUDET GIRAUDIERE GIRAUDIERES GIROIR GIROUARDIERE GIROUARDIERE GIROUARDIERE GLACE GLACIERE GLACIS GLAIEULS GLAN GLANDAIL GLANDIER GLANDINIERE GLANERIE GLANERIES GLATIGNE GLATIGNY GLONIERE GLONNIERES GLORIETTES GLOTTERIE GLOTTINIERE GLYCINES GLYCINES FRANC BARON GOBERIE GOBERIES GOBILLARDIERE GOBILLONNIERE GOBLERIE GODAIRIE GODARDIERES GODEFRAISE GODERIES GODET GODFRAINE GODINEAU GODINIERE GODONNE GOGRIERE GOGUETIERES GOHAN GOIRIE GOISLARDERIE GOIVERIE GOMBAULT GOMBEAUDIERE GOMER GOMMERIES GONACHERES GONAZIERE GONTELLERIE GONTERIE GONTERIES GONTHERIE GONTRIE GORD GORETIERE GORGETTERIE GOTTANGERE GOUAFFRIES GOUALARDERIES GOUALLERIE GOUALONNIERE GOUASLARDIERE GOUASLONNIERE GOUDE GOUDERIE GOUDONNERIES GOUETERIE GOUFFARDIERE GOUFFERIES GOUGER GOUGERIE GOUILLARDISE GOUJONNIERE GOULAISES GOULANDIERE GOULARD GOULARDIERE GOULET GOULETS GOULETTERIE GOULOIRE GOUMARDIERE GOUMNAUDIERE GOUPILLERE GOUPILLERES GOUPILLERIE GOUPILLIERE GOUPILLIERE D ATHENAY GOUPILLIERES GOUPILLON GOURBEAUDIERES GOURDIERE GOURDINELLERIE GOURDINIERE GOURGAIGNE GOURGOUSSE GOURIE GOURRIES GOUSSARDIERE GOUSSERIE GOUSSIERES GOUSSINIERE GOUSTIERE GOUT GOUTARDERIE GOUTE GOUTELLE GOUTIER GOUTTE GOUTTE D OR GOUVERIE GOUYELLERIE GOYER GOYETTE GOYONNIERE GRACE GRACIENNERIE GRAFFARDIERES GRAILAYERE GRAIMBAULT GRAIN FORET GRAIS GRAMMONT GRANAIE AIR AUBEPIN AULNAY AUMINIER AUNAY AUTREVILLE BAGNEUX BARGE BAULIN BAURAY BEAUCHENE BEAUMONT BEAUREGARD BERCON BIARD BOIS L EVEQUE BOISGARD DURANDIERE BORDAGE BOUILLON BOULAY BOURG BOURNAIS BOUT DEGRAND BOUTRY BRAULT BREIL BRESLAY BRETOIN BREUIL BRICOIN BROSSAY BROUET BROUSSIL BUIS BUISSON CAREMUS CARREFOUR CASSEREAU CHAILLOU CHALUMEAU CHAMAILLARD GRANDCHAMP CHAMPAGNE BEL AIR POMMIERS PAVILLON LONG CHANSONNAY CHARDONNERET CHATEAUFOU CHAVIGNE CHEMILLE GRANDGRAND CHENAY CHENE CHENE ARTUIS CHENON CHERRE CHESNAY CHESNEAU CHEVAIGNE CHEVRE CLAIRAY CLOTEAU CLOUET CORLEVE CORMIER COUCHETRAN COUDRAY COULOMBRAY COURCHALON COURGENOU COURPARENT COUVENT COYER CUTESSON DAUVERS DENNERAY DISSE DOMAINE DOUET GRANDE ALINIERE GRANDEGRANDE ARTUISIERE AUBERDIERE BAIE BARDIERE BARRE BAZOLIERE BEAUCE BEAUCERIE BEAUMERIE BERMONDIERE BEUCHETIERE BIGOTTERIE BIONNIERE BLAIRIE BODINNERIE BOELLE BOISSIERE BORDE BOTTERIE BOUILLANDIERE BOULARDIERE BOUSSE BOUVERIE BRANCHERIE BRETONNIERE LANDES BRIERE BRIOCHE BROSSE BRUYEREQUENTIN BUCHAILLE BUFFARDIERE BUSSONNIERE BUTTERIE CADOIRE CAILLETIERE CANCERIE CANNETIERE CENERIE CHAMPAGNE CHARRETIERE CHAUVINIERE CHENAIE CHEROUVRIE CHEVRIERE ECLECHE CLEMENCIERE COGNERIE COLLINIERE CORBIERE CORBINIERE CORBONNIERE CORBUCHERE CORNE ECOTTAY COULEE COUPELLIERE COUPERIE COURBE COURTINIERE COUTIERE COUTURE CROIXJOUIN DOIGTEE DURANDIERE FERME FERRIERE FILIEU FLEURIERE FORET FORTERIE FOSSE FOUBAYERE FOUCAUDIERE FOUCHERIE FOULIERE FOULTIERE FOURRERIE FROGERIE GAGNERIE GALIERE GARENNE GASNERIE GAUDIERE GEMMERIE GODEFRAYERE GODINIERE GOUAZERIE GOUPILLERE GOURIE GROUAS GUIBERDIERE GUICHARDIERE GUIGNARDIERE GUIGNIERE GUITTONNIERE HAIE HERBAUDIERE HERIPIERE HERTAUDIERE HEZE HILTIERE HOUSSAIE HOUSSAYE JAMELLIERE JANVRIE JARDIERE JARDINIERE JATERIE JEAUNIERE LANDE LEVRAIS LOCHERIE LOUVRAIS MABILIERE MACONNIERE MAISON MAISON NEUVE MALPOUGERE MARCHAIS CHERE MARE MARTINIERE MERIE METAIERIE METAIRIE MOINERIE MOLIERE MONGENDRIERE MORANDIERE MORICIERE MORINIERE MOUSSE MUZARDIERE OISELLERIE PAGERIE PATURE PELOUSE PERCHE PERRIERE PESNIERE PILLIERE EPINAY EPINEAU PINTIERE PLAINE PLANTE POMMERAIE PORCHERIE POTERIE PREE PRIAUTE PROUTIERE QUERLIERE RAGE RATERIE RIVIERE ROCHE ROCHELLE ROUCHE ROUILLARDIERE ROUSSELLIERE ROUSSIERE GRANDEGRANDERTE LOUE GRANDES AITRES SAPIANCIERE SAPINIERE SAPINIERE LANDES SAULAIE SAUVAGERE GRANDES BARRES GRANDES BARRIERES GRANDES BLOTTERIES GRANDES BORDES SUD GRANDES BOURSELIERES GRANDES BRAUDIERES GRANDES BRETONNIERES GRANDES BROSSES GRANDES BRUMALLES GRANDES BRUYERES GRANDES BUTTES GRANDES CHANTERELLES GRANDES CHERES GRANDES COULOIRES GRANDES COULVENDRIES GRANDES COURBES GRANDES GRANDES CROIX SELLE GRANDES EPINETTES GRANDES EPINIERES GRANDES ESNAUDRIES GRANDES FOSSES GRANDES GALINIERES GRANDES GUILARDIERES GRANDES HAIES GRANDES HELINIERES GRANDES HOMMEES GRANDES HOUSSAIS GRANDES HUNAUDIERES GRANDES ILES GRANDES LAMES GRANDES LANDES GRANDES LOUPES GRANDES MAISONS GRANDES MARES GRANDES MOLLIERES GRANDES MORCINES GRANDES MOULINES GRANDES NOES SOUDINIERE SOURVANNIERE GRANDES PICAUDIERES GRANDES PICORIERES GRANDES PIECES GRANDES PLANCHETTES GRANDES PLANTES GRANDES PLAUDERIES GRANDES POTERIES GRANDES POTINIERES GRANDES POULONNIERES GRANDES POUSSIERES GRANDES PRISES GRANDES PROUILLERES GRANDES QUINTES GRANDES RABONNIERES GRANDES RAINIERES GRANDES REUCHERES GRANDES ROCHES GRANDES ROPTIERES ESSARD ESSART GRANDES SAULNIERES GRANDES SENCIES GRANDES SOUCHES GRANDES TORCHONNIERES GRANDES TOUCHES GRANDES TROUTTIERES GRANDES TUILERIES GRANDES VALLEES GRANDES VEDIERES GRANDES VENTES GRANDES VIGNES GRANDES YVORIES ETALOIR ETANG TAUPE THIBERDIERE TOUCHE TOUTE FOUGERE ETRICHER TROCHE TROUESSE VALLEE VERONNIERE VICULIERE VIETTERIE VIGNE VOLANDIERE ZEZIERE FESSARD FEU FEUILLARD FIEF FOUGERAY FOUILLET FOURNIQUET GAIGNE GASSEAU GAULT GENETAY GLATIGNE GRAVIER GRENOUILLET GUE GUE PERRAY GUERAULT GYE HARRIER HAUT HETRE HOUX HUNE GRANDINIERE JARDIN JARRIER JARSAY LEARD L HOMMAS LIBOIS LIVERNOIS LOISEMENT LUART LUTH MAISON MANONE MAROLLE MAUNY MAUTOUCHET MAVRAIS MENIAU MENIL MOHAISON MOIRE MOLAND MONBROSSE MONCELAT MONT MONTAFRAY MONTANGENAUX MONTBRAY MONTCHEVRIER MORAY MORTIER MORTIER PINEAU MORTRAI MORTRE MOUSSU MUSSEROTTE NUISEMENT ORMEAU OUALET OZE PAIN PARC PERRAY PERRICHET PERRIN AUBE PIN PINEAU PLESSIS PONTFOUR PORTAL POSSET POYER PRE PRE DEVANT MARTI PRE PRE GUE RANCE RANSOU RAPEILLARD RESSORT REUCHOU REVEILLON RIBEMONT RIOLET ROSIER ROSSIGNOL ROUGEBEC ROUZIER ROY GRANDGRAND RUISSEAU SABLON GRANDANTOINE GRANDCHEREAU GRANDOUEN GRANDS AITRES GRANDS ALLAIS GRANDS ARPENTS GRANDSART GRANDS AUBOINS GRANDS GRANDS D ANGERS GRANDS BOULAS GRANDS BURONS GRANDS GRANDS CHENES GRANDS COLLINS GRANDS CORMIERS GRANDS COURTILS GRANDS DINONS GRANDS ECULAYES GRANDS ETANGS GRANDS FOUGERAYS GRANDS GENETS GRANDS HETRES GRANDS JARDINS GRANDS GRANDS MARTRAIS GRANDS MATELAS GRANDS MONTAIGUS GRANDS MONTMARS GRANDS NOYERS GRANDS ORMEAUX SOURDON GRANDS PATIS GRANDS POIRIERS GRANDS PRES GRANDS RIMBERTS GRANDS ROSEAUX GRANDS SABLONS GRANDS SAULAIS GRANDS VAUX TAILLIS TANGUEUL TEIL TERTIFUME TERTRE TESSE THOMAS THORIGNE THOUARS TIMON TIRE TIRON TREMBLAY TRONCHET TRUIGNE VALLIERE VAUBOUILLE VAUGEOIS VAUMORIN VAUNAVAL VAUX VERGER VERT VIHIERS VILETTE VILLENEUVE VILLEPEINTE YVAY GRANGE GRANGE PILLETIERE GRANGERAIS GRANGES GRANGEVEAU GRANITS GRANVILLE GRAS BUSSON GRASILLERE GRASSE TEILLE GRASSIERE GRASSINIERE GRASTIERE GRATTE LOUP GRAVE GRAVELLE GRAVELLES GRAVES GRAVET GRAVIER GRAVIERE GRAVIERS GRAVOIS GRAVY GREANDIERE GREDINERIE GRE VENT GREEZ GREFFIER GRELETTERIE GRELEYERES GRELON GREMELLIERE GRENANIERE GRENET GRENETEMY GRENETIERE GRENETTE GRENETTERIE GRENIERE GRENOUILLE GRENOUILLERE GRENOUILLERIE GRENOUILLERS GRENOUILLES GRENOUILLET GRENOUSIERE GRESAULES GRES VENT GRESILLE GRESILLON GREVE GREZ GRIDIERES GRIECHE GRIFFERIE GRIGNARDERIE GRIGNON GRIGNONS GRILLE GRILLE PARC GRILLEMONT GRILLERIES GRILLON GRILLONNIERE GRIMAUDIERE GRIMAULT GRIMAUX GRIMORDIERES GRINDERIE GRINGUENIERES GRIPONNIERE GRIPOUZE GRIPPERIES GRISEMONT GRISERAIE GRISETTE GRISON GRISSONNIERE GROIE GROIE BRIARD GROIE FRILOU GROIE D ENFER GROIE HALLES GROIE MIRAIL GROIE PARC GROIES GROIGNE GROIRIE GROISLARDIERE GROLLERIE GROS GROS BUISSON GROS CHENE GROSEILLIER GROS FOUTEAU GROS GESLIN GROSSE CROIX GROSSE GROSSES SOUCHES GROSSINIERE GROTEAU GROUAIS GROUAS GROUAS ADET GROUAS CHALIGNE GROUAS CASSINE GROUAS GUERRERIE GROUAS POIX GROUASLARD GROUIN GROULIERE GROUSSINIERE GROUTEL GROUZILLE GRUAU GRUCHES GRUDELLERIE GRUE GRUELLERIE GRUGINIERE GRUSSON GRUTIERES GUE GUE ANDRE GUE AU DRU GUE AU LOUP GUE AUX BIQUES GUE BERNISSON GUE BLANCHE GUE BOURDELAIS GUE BOURVILLE GUE BOUSSEAUX GUE BRETON GUE BRUNE GUE BRUNET GUEBRUNETS GUE CHARTIER GUE CUREAU GUE D ARRANCE GUE D ASSE GUE D AULAINES GUE CHIGNE GUE COURTEE GUE FORGE GUE PIE GUE L AULNE GUE L AUNAY GUE LAUNAY EST GUE LAUNAY OUEST GUE L OISELIERE GUE L ORTIER GUE MARCE GUE MEZIERES CAVES PEULE GUE MEZIERES GD VEUVES GUE ROUEN GUE BONDES GUE MONTREES GUE SOURDON GUE THOUARS GUEDEUX GUE VEAU GUEDON GUEDONNIERE GUEDRAY GUE D URTEBISE GUE VAL GUE GUE JOREAU GUE HART GUELDON GUE LIAN GUELLERIE GUE LONG GUELTON GUEMARDIERES GUE GUE MARTIN GUE MOELE GUE MORIN GUENADEUX GUENAIRIE GUENARDIERES GUENARY GUENAUDET GUENAUDIERES GUENEES GUENELIERE GUENETIERE GUENIVIERE GUE ORY GUE PERRAI GUE PERROUX GUE PIERREUX GUE PLARD GUERACHEUX GUERAME GUERANGERAIE GUERAUDIERE GUERAULT GUERBOEUF GUERCHES GUERET GUERETS GUE RICHARD GUERIDE GUERINET GUERINIERE GUERINIERES GUERITE GUERLEDAN GUERMERIE GUERMONT GUERNASSE GUEROTTE GUEROUILLERE GUERREAU GUERRERIE GUERRIERE GUERRIERES GUERRINIERE GUERROYERES GUES GUEVAST GUESMERIE GUESNERIE GUESNIERES GUE TETARD GUETRIERE GUETROUVE GUETTE GUETTE LOUP GUETTELOUP GUETTE MIDI GUETTERIE GUETTES GUEULE GUGERES GUIANDASSIERE GUIARDIERE GUIBAZERIE GUIBELLERIE GUIBERDERIE GUIBERDIERE GUIBERDIERES GUIBERT GUIBONNIERE GUIBOURGERE GUIBRAY GUIBRY GUICHARDERIE GUICHARDIERE GUICHELLERIE GUICHONNEAU GUIDRI GUIERCHE GUIERCHETTE GUIGERAULT GUIGNARDIERE GUIGNERIE GUIGNIER GUIGNIERS GUIGNOCHERE GUIGNOTERIE GUILARDIERES GUILBEDIERES GUILBERDIERE GUILETIERES GUILLARDIERES GUILLAUDIERE GUILLAUMERIE GUILLAUMERIES GUILLAUMIERE GUILLERES GUILLERIE GUILLETIERES GUILLONNIERE GUILLOREE GUILLOT GUILLOTIERE GUILLOTTIERE GUILMAIME GUILMAUDIERE GUILMAUDIERES GUILMETERIE GUILMETTIERE GUILOIRE GUILOISIERE GUIMANDIERE GUIMARDIERE GUIMERIES GUIMINERIE GUIMINIERES GUIMOCHERE GUIMONDIERES GUIMONIERE GUIMPELERIE GUIMPERIE GUINANDIERE GUINASSIERES GUINEBAUDIERE GUINEBAUDIERES GUINEPOT GUINETRIE GUINETTERIE GUINEUX GUINEVAUDIERE GUINOCHERE GUINSELLERIE GUIONNIERE GUIOTTIERE GUIPORON GUIROUET GUISSANDIERE GUISSINERIE GUISSINIERE GUISTIERE GUITIERE GUITONNIERE GUITTARDIERE GUITTELIERE GUITTER GUITTERE GUITTIERE GUITTIERES GUITTION GUITTONNIERE GUIVIER GUIZARDIERE GUSTAVE GUYARDERIE GUYARDIERE GUYNAUDIERE GUYOCHERE GUYONNERIE GUYONNIERE GUYONNIERES GUYONNIERES ST GERMAIN VAL HABERDERIES HABIT HABLERIE HACHE HACHERON HACHONNIERE HACIENDA HADERIES HAGUERIES HAIE HAIE BARRE HAIE BERT HAIE BOISSEAU HAIE BUIS HAIE GUIGNES HAIE GUERRIERE HAIES HAIES BRARD HAIES BRAY HAILERIE HAILLONNIERES HAIRAUDRIE HAISE HAITRES HAIZE HAIZE ROUGE HAIZETTES HALANDIERE HALETIERE HALGRIERE HALGUERIERE HALLAI HALLAY HALLEBARDERIE HALLE BROU HALLERIE HALLES FAZELIERE HALLIER HALLOUERES HALOPIERE HALOUSIERE HALTE HALTIERE HAMARDERIE BECQUET BRARDIERE BRAY BROSSES CHAPUIS HAMEAU CLOSEAU COURTEILLON D ALSACE DAUVELLIERE D AUVERGNE BRETAGNE CHANTEMESLE GASCOGNE BECHOTTIERE BOUILLERE BRETONNIERE CHAUSSELIERE CLAIRIERE DAUVELIERE FOUCAUDIERE FOULTIERE FOUSSARDIERE FREARDIERE LANDREL MARDONNIERE MARTINIERE MICHELIERE TORRINIERE LORRAINE NORMANDIE PARENCE PICARDIE PROVENCE ROME SAVOIE ESSARTS FLANDRES LOUDONNEAUX MARCHES SOURCES VERZE DHUISSE BAILLIF CALVAIRE CORMIER DAUPHINE LANGUEDOC AULNAY POITOU TREMBLAY VERGER FOUSSARDIERE POTERIE GRANDES BROSSES LIJONNIERE PLANTE LOUDONNEAUX LOUVERIES MAISONS ROUGES MONT NOE ORMEAUX PANORAMA PATIS PERRIERES BRAYE PONTHOUIN ROCHER SABLES TREMBLAY VIGNES VILLEDIEU VILLENEUVE HAMELIERE HAMELIERES HAMELINETTE HAMELINIERE HAMELINIERES HAMETIERE HAM ILE FRANCE HAMINIERE HAMMONIERE HAMON HAMONERIE HAMONIER HAMONNIERE HANDI HANDONNIERE HANNERIE HANNETERIE HANTELLE HANTELLES HANTENAISE HAPPELIERES HARANCE HARANCHERE HARARIE HARAS HARAS AULNAY HARAS CHAUSSEE HARAS BONLIEU BONLIEU HARAS MONTIGNY HARAS MORTRIE HARAS BRAYE HARAS HUNAUDIERES HARAUDIERE HARDANGE HARDANGERE HARDANGERES HARDANGERIES HARDIERE HARDIERES HARDINOIS HARDOIS HARDONNIERE HARDONNIERES HARENCHERIES HARES HARMONIERES HARMONNIERES HARNIERE HAROUARDIERE HARRARIE HARRY HASARD HASARDERIE HATELLE HATONNIERE HATTERIE HATTIERE HAUDRIERE HAULMES HAUTARY HAUT AULNAIE HAUT BAGNOLET HAUT BAIGNEUX HAUT BEAUMONT HAUT BERTEAU HAUT BIGNON HAUT HAUT BOULAY HAUT BOURG HAUT BOURRAY HAUT BRAY HAUT BREIL HAUT BROUSSIN HAUT BUISSON HAUT CERVIGNY HAUT CHANSORT HAUT CHANTELOUP HAUT CHANTELOUVE HAUT CHANTEPIE HAUT CHAPEAU HAUTHAUT CHEVIGNE HAUT CHEVRE HAUT CLACAUX HAUT COUPAILLE HAUT COURTIMONT HAUT COUTIL HAUT CRAMPAUX HAUT CUILLE HAUT LACREUSE HAUT MURS HAUT RAVALIERES HAUT ROTTES HAUT DIVE HAUT HAUT BOURG HAUT TERTRE HAUTE HAUTE BARBOUERE HAUTE BARDOUILLERE HAUTE BARRE HAUTE BECHE HAUTE BELLE CHERE HAUTE BENERIE HAUTE BERGERE HAUTE BLANCHERIE HAUTE BLOTTERIE HAUTE BORDE HAUTE BOULAIE HAUTE BOULAIRIE HAUTE BRIOCHE HAUTE BROSSE HAUTE BROUILLERE HAUTE BRUYERE HAUTE BUISSONNIERE HAUTE BULARDIERE HAUTE CHALOUZIERE HAUTE CHAMPARDIERE HAUTE CHENAYE HAUTE CLAIE HAUT ECLAIR HAUTE CONTRIE HAUTE COULOIRE HAUTE COUPARIE HAUTE HAUTE COUTURE HAUTE CRANE HAUTE DAVIERE HAUTE ETURCIE HAUTE FOLIE HAUTE FONTAINE HAUTE FORGE HAUTE FOSSE HAUTE FOURMONNIERE HAUTE FRESNAYE HAUTE FRESNE HAUTE FRETTE HAUTE GARENNE HAUTE GATANGE HAUTE GAUMERIE HAUTE GAUTIERE HAUTE GOHONNIERE HAUTE GUIERCERIE HAUTE JOUANNIERE HAUTE LANDE HAUTELLES HAUTE LOGERIE HAUTE MADELEINE HAUTE MARTINIERE HAUTE MORINIERE HAUTE MOTTE HAUTE NERAIS HAUTE NOE VAU HAUTE PAILLE HAUTE PAILLERIE HAUTE PASQUERIE HAUTE PATURIERE HAUTE PEINNIERE HAUTE PERCHE HAUTE PERDRIX HAUTE PERROCHERIE HAUTE PILETIERE HAUTE PORTE HAUTE POTTERIE HAUTE POULTIERE HAUTE PRISE HAUTE RENAUDIERE HAUTE ROBINIERE HAUTE ROCHE HAUTE ROUGESSE HAUTERUE HAUTEHAUTES ALLEES HAUTE SAUVAGERE HAUTE SAVONNIERE HAUTES BARRES HAUTES BAUMERIES HAUTES BELLES HAUTES BOITERIES HAUTES BROSSES HAUTES CHAUVIERES HAUTES CHEVRIES HAUTES CROIX HAUTES EPINES HAUTES FERRIERES HAUTES FONTENELLES HAUTES FUTAIES HAUTES GACHERES HAUTES GESLERIES HAUTES GROIES HAUTES HAIES HAUTES HERVALLES HAUTES HUAUDIERES HAUTES MANIERES HAUTES MENARDIERES HAUTES METAIRIES HAUTES MORLETTES HAUTES ORRIERES HAUTE SOURCE HAUTES PORTES HAUTES QUANTIERES HAUTES RACINAIES HAUTES REINIERES HAUTES ROUTES HAUTES TOUCHES HAUTES VALLEES HAUT CLAIR HAUTE TOUCHE HAUTE TOUVARIE HAUTE TROTTERIE HAUTE VEGRIERE HAUTE VENTE HAUTE HAUT FOLLET HAUT FONTENAY HAUT FREU HAUT GABRONE HAUT GAUDU HAUT GONAS HAUT GRATAIS HAUT GUERAME HAUT GUERINET HAUT GUISIER HAUT HONGRIE HAUTIERES HAUTINIERE HAUT IVAY HAUT JAGUENET HAUT JAUNAY HAUT JONCHERAY HAUT LAUNAY HAUT LEVRIGNE HAUT LIEU HAUT LIVRE HAUT LOUP HAUT MADRELLE HAUT MANONE HAUT MARCHAIS HAUT MENIL HAUT MESNIL HAUT MONTJOIE HAUT MONTSUREAU HAUT MORE HAUT MORIERS HAUT MORTIER HAUT HAUT PARILLE HAUT PATIS HAUT PAVILLON HAUT PERRAY HAUT PERRIN HAUT PERRY HAUT PESSIEUX HAUT PEZEREUIL HAUT PIN HAUT PINEAU HAUT POMMEREUIL HAUT PRE HAUT PRESSOIR HAUT PUITS HAUT RANCE HAUT RASNE HAUTREUX HAUT RIVERELLE HAUT ROCHER HAUT ROSSAY HAUT ROUILLIS HAUT ROUSSET HAUT SABLON HAUTS ANSUISIERES HAUTS BIARDS HAUTS BRETEAUX HAUTS CASSOIRS HAUTS HURLEVENTS HAUTS MONTAIGU HAUTS MONTS CREAUX HAUTS ROBINIERS HAUTS DINONS HAUTS MEZIERES HAUTS MONCEAUX HAUTS NOUYS HAUTS PRES HAUTS TUFFAUX HAUT TERTRE HAUT THURET HAUT TOUCHEBOEUF HAUT TROGNE HAUT TUCE HAUT VALET HAUT VAUX HAUT VILLEE HAUT VILLENETTE HAUT VILLIERS HAVARDERIE HAVARDIERE HAYE HAYE CHAPRON HAYES HAYES PARENTEAUX HAYETTE HECHERETTE HEILLIERE HELERIE HELINIERES HELLERIE HELOT HEN AUMANA HENRIERE HENTERIES HERANGERIE HERAUDIERE HERAULT HERBAUDIERES HERBE BLANCHE HERBECHERE HERBECHERIE HERBELOTTIERE HERBERDERIE HERBERDIERE HERBERIE HERBERIE VERRON HERCE HERCULES HERIBOULE HERISSERIE HERISSIERES HERISSONNIERE HERISSONS HERITIERE HERMANGERIE HERMELIERE HERMELIERES HERMENAUDIERE HERMENIER HERMERIE HERMITAGE HERMITAGE AUBEVOIES HERMITAGE ROCHERS HERMITE HERMITIERE HERNERIE HERODIERE HERONNIERE HERONNIERES HERON PISSOT HERPIGNIERES HERPIN HERPINIERE HERRAUDIERE HERRAUDIERES HERRAULT HERRIERE HERRIERES HERRIET HERSANDERIE HERSE HERSERIES HERSES HERTAUDERIE HERTAUDIERE HERTAUDIERES HERTERIE HERTEUDIERE HERTUISIERES HERUERE HERVETIERE HETIERE HETRE BOISSEAU HETRE BRULE HETRE DAVID HETRE CHARTIERS HETRE ROUSSEAU HETRES HETRES VALAUDUN HEUCHERAIE HEUCHES HEUDIERE HEUDRISSON HEULIERE HEULIN HEURIES HEURLIERE HEURTAUDIERE HEURTEBELIER HEUSERIE HEUZARDIERE HEUZARDIERES HEUZIERE HEZARDIERE HIERRAY HIERRAY FORGE HIMERIERE HINEUDIERE HINIERES HIRONDELLE HIRONDELLES HIRONDELLES HOUSSAIE HIVER HLM LEARD HLM DOUAI HLM BROSSE HLM RACAN HLM ROUTE MELLERAY HLMSAINTE HOCHETIERE HOLIERE HOLLIERE HOMAT HOMMAGES HOMMAS HOMMEAU HOMMEAU BLANC HOMMEAU MILLE HOMMEDAIRE HOMMEDIAIRE HOMMEE HOMMEES HOMMELAS HOMMELET HOMMELLERIE HOMME MORT HOMMERIE HONGRIERE HOPITALERIE HOPITAU HOPITAUX HOPITEAU HORIZON HORLIERE HORTENSIAS HORTON HOTEL BARBIN HOTELLERIE HOTTEREAU HOTTERIE HOUBLON HOUDAIRIE HOUDARIE HOUDINIERE HOUDONNIERE HOUDRAY HOUDRIE HOUDRIERE HOUELLERIE HOULAIES HOULAS HOULBRAN HOUSSAIE HOUSSAIS HOUSSASSERIE SUD HOUSSAY HOUSSAYE HOUSSAYES HOUSSELIERE HOUSSEMAINE HOUSSERIE HOUSSIERE HOUSTRIE HOUX HOUZIERE HUARDIERE HUAUDIERE HUAUDIERES HUBERDERIE HUBERDIERE HUBERDIERES HUBERT LATHAN HUBINIERE HUCHE CORNEILLE HUCHEE HUCHELOUP HUCHEPIE HUCHEPOCHE HUCHERIE HUCHETTE HUCHOT HUELLERIE HUERDIERE HUETRIE HUETTERIE HUGERIE HUGERIES HUGOTIERE HUGOTTIERE HUGUENOTERIE HUGUENOTTIERE HUGUERIE HUILERIE HULAIS HULIERE HULLOTERIE HULOTIERE HULOTTERIE HULOUP HUMELIERE HUMOTERIE HUNAUDERIE HUNAUDIERE HUNAUDIERES HUNE HUNNIERES HUONNIERE HUPELOUP HUPE NOIRE HUPET HUPPELIERE HUPPERIE HUPPINIERES HURANDIERE HURAUDIERES HURIE HURLIERES HURONNERIE HURTEBICHE HUSSAUDIERE HUTIERE HUTONNIERE HUTTE HUTTELIERES HUTTIERE HYBAUDIERE HYRE IFS ILE ILE BORDIERE ILES ILES MILIEU ILETTE ILOTS IMBAUDIERE IMBERGERES 8 MAI ABBE LOUIS ROUSSEL ABEILLES ACACIAS AISTRES ALAIN COLAS ALBERT CAMUS ALEXANDRE BELLANGER MUSSET ROCHER ALOUETTES ANCIENNES TANNERIES ANDRE DEROUET ANDRE SERAIS ANGENARDIERE ANJOU AQUITAINE ARCHE ARDOISE ARMAND SAFFRAY AUBEPINE AUBRIERES BASSE BEAUFRENE BEAUSEJOUR BEGUIN BEL AIR BELENOS BELIER BELLEGARDE BELLEVUE BERARD BERNARD LYOT BICETRE BIEF BLANCHETIERE BLAVETTE BLES D OR BLEUETS BLOSSERIE BOSSERIE BOUDVINIERE BOURBIONNE BOURDON BOURGES BOURGNEUF BOURRAY BOUTINIERES BRIFFAULT BRIQUES TOUCHE BRIQUET BRUNETIERE BUISSONNETS CAILLES CAMELIAS CANTINES CAPITAINE MAHOU CARNOT CARROSSE CARTIER CAVE PIE FLEUR LYS CENTRE SECOURS CERANS HAUT GARREAU ROUX CHAPEAU ROUGE CHAPELLE CHAPELLES FOUQUET FOURNIER CHARMES CHARMILLE CHATAIGNIERS GIRARD MOUCHE CHENE CHENE AU SOURD CHENES CHENES TOUR CHENE VERT CHERES CHESNAY CHESNAYE CHEVAL NOIR CHINTRES CHIVARS CLARGERIE CLERGERIE BOUGAS BRETON CLOSBENOIST COEMONTS COEUR D OISON COINTISE COLOMBES COQUELICOTS CORMIER COTEAU COTIERE COUDRAIE MIRACLES HAUTE COURTRIE COUTARDIERE CROIX AUX PRETRES CROIX BLANCHE CYTISES DAGOREAU DAHLIAS D ALMADA DANIEL CASANOVA DAUPHIN BALLON BALYVER BELFORT BELLE CROIX BELLEVUE DEGRE FINLANDE DEFORGE DEGAS BALLONNIERE BASCULE BODONNERIE BORDE BOUILLERE BUTTE CARRIERE CARROSSERIE CHARBOTTIERE CHARMILLE CROIX MARION CULTERIE DERAZERIE FONDERIE FORGE L AIR PUR L AITRE LUCAS MADELEINE MARE MATHIAS MONGE NOE NOUE L ANTONNIERE PAIX PERDRIX PERRIERE PLEIADE PORCHERE POUDRIERE PROMENADE L ARDOISE RIVIERE TIMONIERE VEQUERIE VIGNE BRULEE L ECLECHE L ETOILE L HERBERIE L IRE L ORMEAU L OUEST L UNION MARTFELD PORTAVEAU PREAU 4 VENTS ACACIAS DESAIX ALOUETTES AMANDINES ANEMONES ARBOUSIERS ARUMS AUBEPINES AULNAYS BAILLEES BAINS ROMAINS BEGONIAS BLEUETS BOULEAUX BOUVREUILS CASTORS CEDRES CERISIERS CHAPELIERS CHARMES CHATAIGNIERS CHENES CIGOGNES COLOMBES COQUELICOTS CORMIERS COUDRES CYCLAMENS ECUREUILS EGLANTIERS ELODEES ERABLES ETAMINES FAUVETTES FOURS FRESNES GENETS HAUTES FONTAINES HETRES IRIS JONQUILLES LAURIERS LILAS MARGUERITES MARONNIERS MARRONNIERS MERISIERS MESANGES MIMOSAS MYOSOTIS NENUPHARS NOISETIERS NOYERS OEILLETS ORCHIDEES OUCHES PAQUERETTES PECHEURS PEUPLIERS PINSONS PIVOINES PLAINS PLATANES POMMIERS POMPES PORTEAUX PORTES CIRCUIT PRAIRIES PRIMEVERES PRUNUS QUATRE RACAUX ROSEAUX ROSES ROSIERS ROSSIGNOLS ROUGES GORGES RUELLES SABLONS SAUGES SAULES SERINGAS SORBIERS SOURCES TAMBOURS TUILERIES TULIPES VERGNES VERTUS VIGNES VIOLETTES VALMY DIDEROT DOUCE AMIE DOUVES BIEF COUTANT BOCAGE DUBOIS BOIS RAYMOND BOSQUET BOULAY BOURG BELE CALVAIRE CROIX L AIR LONG CHAPEAU ROUGE DU CHENE CORMIER COTEAU CYGNE DONJON DUE FONCIER FOUR DOUAI PRE HARAS HOUX LAVOIR DULCIE SEPTEMBER LORIER MONT CERVIN MUGUET PASSAGE D ETE PATISLAZARE MIDI PAVILLON SEUX TERTRE PLAN D EAU PLESSIS POLYGONE PONCEAU PORT PRE VIGNE PRESBYTERE PRE VERT RENOUVEAU RHONNE RUISSEAU SAVETIER SORT TERTRE TERTRELAURENT TYROL VAL LOIR VERGER VIEIL HETRE VIEUX PUITS ECOLES ECOLES FILLES EGALITE ETOURNEAUX FAUVETTES FERNAND CRAPEZ FIZEAU FLEURS FLEURY FLOREAL FONTAINE MAROT FONTAINES FONTAINEMARTIN FOULAUDERIE FOURMILIERE FRANCIS CLEMENT FREDERIC CHOPIN FREDERIC DARD FUIE GAIETE GAMBETTA GARENNE GEAI GENETAY GLYCINES GOMBAULT MAISON GRANDS GRANGE GRANGE PIOT GRAVELLE GRIVES GROIE GROSSE PLANCHE GUE GUE BUSSON GUETTE BIEN GUIBERT GUICHARDIERE GUILLOTIERE GUSTAVE COSSON HAUTECLAIR HAUTE MERCERIE HELENE BOUCHER HELLE DESJARDINS HETRES HIRONDELLES HONORE BROUTELLE HORTENSIAS HORTON HOTEL IRIS JACQUES DANTON JACQUES IBERT JALAIS JARDINS BART BOUIN COUSIN DUCLOS JAURES MACE MONNET JERUSALEM JOLIE JOLIOT CURIE JOUVIGNE JULES FERRY JULES LAMBERT KAPELLE LACHENAL LAMARTINE LANDE LAUNAY L AURORE LAVANDIERES LAVOIR LAYON LEARD LEDRU ROLLIN LEO DELIBES LEONARD VINCI LEON GAMBETTA LILAS LIMON LISBORDE L LEROUGE LOGIS LOGIVAC LOIR LOISIRS LOUCHE LOUIS ARAGON LOUIS CRETOIS LOUIS LEROUGE LOUIS PASTEUR LOUIS ROSIER LOUVRE LYS LYZARDIERE MADELEINE MAINE MAISON BRULEE MALRUE MARCEL JOUSSE MARCEL PAGNOL MARCOTTIERE MARECHALERIE MARECHAL JOFFRE MARTELLERIE MARYSE BASTIE MASNIERE MATHURIN JOUSSE MAUPERTUIS MAURY MERCURE MERIDIEN MERISIERS MESANGES MESSAGER METAIRIE MIMOSAS MODISTES MONGIGON MONTBARBET MORANDERIE MORINERE MORINIERE MOULIN BANAL MOUREUIL MURIERS NEPTUNE NIOX NOE NORD NOTRE DAME FRESNE NOYERS OCTAVE POUSSIN OISELIERE OLIVIER ORME ORMEAUX OUCHE OUCHES PAMPLEMOUSSE PARC PARCS PARIS PARQUET PASTEUR PELTERIE PERCHE CHALET BEAU PETITS D ANGERS PEUPLIERS PIECE LOTI PINCE ALOUETTE PIPECHE PLACITRE PLAINE POINT JOUR PONT POSTE PRE CROIX PRES PRESBYTERE PRIEURE PRIMEVERES PRUNERIE PUISATIER PUITS PUITS FORGET QUATRE VENTS QUINTAINE RANGEVEILLE RAVAULT RAYMOND KOPA RENAUDEAU RENE BAGLAN RENOIR RENONCULES RICHMOND ROBIDAS ROCHER RODIVEAUX ROSEAU ROSES ROSIERS ROTE ROULIERE ROULLEE RUBESQUET RUISSEAU SABLONS SABOTIERE IMPASSEBLAISE IMPASSECHRISTOPHE IMPASSECATHERINE IMPASSEGENEVIEVE IMPASSEELOI IMPASSEEXUPERY IMPASSEGILLES IMPASSEJACQUES IMPASSEJEAN IMPASSEJUST IMPASSESAUVEUR SANCISS SAPINS SCARRON PARC SORBIERS SORT SOURCE SPORTS STADE SUIFFERIE TAMARIS TENNIS TERRASSES TERREAU TERRE ROUGE TERTRE TISSERANDS TIVOLI TOUR TOURAINE TRAM TROIS VALLEES TULIPES VALLEE VAUGERMAIN VICOMTE VICTOR HUGO VIEUX TILLEULS VIGNE VIGNEAU VIGNES VINOIS VIOLETTES VIRREFOLLET VIVALDI VOLTAIRE INARDERIE INDEPENDANCE INFIRMERIE INGLINIERE INIERE INSTITUTION ST MICHEL PERRAIS IRIS ISAMBARDIERE ISLE ISLE BORDIERE ISLES IVARDIERE JACOB JACOBINS JACQUETTE JACQUINIERES JACTIERE JAGAUDIERE JAGEOLET JAGLOTTIERE JAGUENET JAILLE JALETRE JALINIERE JALOUSIE JAMBIERE JAMELLIERE JAMELOTIERE JANIEL JANJARDIERES JANVERIE JANVRIE JARDIERE JARDIN JARDIN HERRAULT JARDIN BRETONNIERE JARDIN BRIERE JARDIN GRANGE JARDIN NOE JARDIN PERCHE JARDIN PIECE JARDIN ROSIERS JARDIN JARDIN JARDINETS JARDINS JARDINS LIVET JARDRUN JARIAS JARIE JARMINE GALOPPIERES JAROSSAY JARRETIERE JARRIAIS JARRIAS JARRIER JARRIERE JARROSSAIS JARROSSAY JARROTTIERE JARRY JARTHE JARTIERE JASMINS JASNIERES JASNIERES CAVIERS JATRIE JAUCOULLERE JAUGE JAUGES JAUNAIS JAUNAS JAUNAY JAUNEAU JAUNELIERE JAUNIERES JAUTERIE JEALIANE JEANNODIERE JEAUNIERE JENVRIES JERICHO JERUSALEM JEU JEU PAUME JEUILLERE JEULINIERE JEULINIERES JEUNAISIERE JEUNERIE JEUNESSE JEUNEVRAIE JEUNOIRES JOBINIERES JOCAILLE JOCHETIERE JOCTIERE JODASERIE JODELYNE JOINVILLE JOISIERE JOISIERES JOIZIERES JOLIETTE JOLIETTES JOLIFIERE JOLIFIERES JOLINE JOLIVERIE JOLIVERIES JOLIVIERE JOLIVIERES JOLLES JOLLIERES JONCHEE JONCHERAY JONCHERE JONCHERIES JONCHET JONCS JONQUILLES JOQUELINE JORIETTES JOUANETTE JOUANETTE JOUANNAIS JOUANNERIE JOUANNERIES JOUANNETERIE JOUANNIERE JOUARDIERE JOUBARDIERE JOUBARDIERES JOUBERDIER JOUBERDIERE JOUBERTIERE JOUFFROY JOUG JOURDINIERE JOUSSANDIERE JOUSSE JOUSSERIE JOUSSES JOUVENCE JOUVIN JOUVINIERE JOYANT JOYAU JOYEUSE JUBAUDIERES JUBERDIERES JUBINIERE JUBLINE JUFFLAUDERIE JUGANNERIE JUGERIE JUGUENIERES JUIGNERE JUIGNERES JUILLERIE JUILLET JUINIERE JUIVERIE JUIVERIES JULES FERRY JULIENNE JULIENNIERES JULINIERE JULIOTTERIE JUMEAUX JUMELIERE JUMELLEE JUMELLERIE JUPE JOUR JUPILLERIE JUPILLES JUPILLIERE JUPINIERE JUPTIERE JUQUELIERE JUREE JUSSAUMERIES JUSTICE JUTIERE KAABERT KAPELLE KER HELENE KER JANNICK KERLAND KER MARIA KER TOUTIC KLEDY BAILLEULERIE BAISSIERE BALLIERE BARBANNIERE BARBERIE BARONNIERE BARRE BARRIERE BASSE CHESNAIE BASSE COQUERIE BASSE BASSE NOE VAU BASSE ORANGERIE BASSE TECHERIE BATTEE BAUDONNERIE BAUDUSSERAIE BAUGE D 16 DENISIERES BAUGENDRERIE BAUSSONNIERE BAZILLIERE BEAUDOUINIERE BEAUSSONNIERE BECHEPESIERE EST BELHOUTIERE BELINOISE BELISIERE BELLANGERIE BELLE ETOILE BELLE ETOILE N 23 BELLE INUTILE BELLEMENERIE BELLE METAIRIE BELLE NOE BELLE PROMENADE BENOTIERE BERARDIERE BERCONNIERE BERGEOTTE BERJOLIE BERTELIERE BERTERIE BERTHELOTERIE BERTRONNIERE BESLIERE BESNIERE BESSIERE YVANDEAU BEURTIERE BEZARDIERE BEZINIERE BIASERIE BICHE BIGOTTIERE BIGRIE BILLENDIERE BINIERE BIRETTE BLANCHETIERE BLANDINIERE BLINIERE BLINNERIE BLINNIERE BLOSSERIE BLOTTERIE BODERIE BODINIERE BOHEME BOISNIERE BOISSIERE BOITELIERE BOIVINIERE BOMMERIE LABORD BORDE BORDE MOREAU BORDE RICAN BORNE BOSSE BOTELLERIE BOUBELLIERE BOUFFIERE BOUGELIERE BOUGENTIERE BOUGERIE BOUGRIE BOUGRIERE BOUJONTIERE BOULAIE BOULAYE BOULLANDIERE BOURDIGALE BOURDONNIERE BOURGAINNIERE BOURGALERIE LABOURGAUDERIE BOURGAUDIERE BOURGEOISERIE BOURGOIGNERIE BOURLERIE BOURRELIERE BOUSSELLE BOUSSELLIERE BOUSSINARDERIE BOUTRIE BOUVERIE BRACONNIERE BRARDIERE BRASSERIE BRELOQUERIE BRESDINIERE BRETECHE BRETONNIERE BRILLARDIERE BRIOLETTE BROSSE BRULETTE BRUNIERE BRUYERE BUGERIE BUSSONNIERE BUTINIERE BUTTE CAILLETIERE CALANDRE CANNE CANTINIERE CARREYRE CARRIERE LACART CAVE CAVE CAVE NEUVE CENTONNIERE CERISAIE CHACUNIERE CHAMAILLERE CHAMPAGNE CHAMPFORRIERE CHAMPFORTIERE CHANCHEVRIE CHAPELLE AUX GRENEAUX CHAPELLE CHENE CHAPELLERIE CHAPOISSIERE TERPELLIERE CHARDONNIERE CHARMILLE CHARPENTERIE CHARRAIE CHARTERIE CHATAIGNERAIE CHATAIGNERIE CHATELLERAIE CHAUFFANIERE CHAUMIERE CHAUSSEE CHAUVELIERE CHAUVERIE CHELLERIE CHENAIE CHENERIE CHERISAIE CHESLIERE CHESNAIE CHESNAIE HAUT ROTTES CHESNAYE CHEVALERIE CHEVALLERIE CHEVAUDIERE CHEVRERIE CHICOTIERE CHIFFLERIE CHOPLINIERE CHOUETTIERE CHRISTOPHIERE CIGOGNE CITRIE CLAIE CLAIRE FONTAINE CLAIRIAIS CLAIRONNEE CLERGERIE CLOUTERIE COCHERIE COCHINIERE COGNERE COISMELIERE COISNERIE COLETTERIE COLINIERE COLLINE COLLINIERE COLOMBE COMMUNE CONILLE CONILLIERE CONTOUDIERE COQUERIE COQUETIERE CORBINIERE CORDEE CORDELLERIE CORDONNIERE CORNILLIERE COSNILLIERE COSSASIERE COTTIERE COTTINIERE COUARDE COUDRE COULEE COUPELIERE COURBAULINE COURBE COURCIERE COURSIERE COURSURE COURTILLE COUSINIERE COUTERIE COUTIERE COUTURE CRAPAUDIERE CRENAISERIE CRENETTE CREUSIE CRIBLERIE CROCHARDIERE CROISETTE CROIX CROIX AU DOYEN CROIX AUX GARS CROIX BEAURY CROIX BET CROIX BOISEE CROIX CLAVET CROIX BRAYE CROIX JUIGNE CROIX CROIX LAUNAY CROIX MIREE CROIX PARIS CROIX CROIX PIERRES CROIX BUIS CROIX CINQ CHARMES CROIX LANDES CROIX SOURCES CROIX NEUF CROIX PARC CROIX GALOISIERE CROIX GEORGETTE CROIX MONQUIN CROIX NIQUEUX CROIXBENOIT CROIX VERON CULOTTERIE CUQUE CURE DAGUERIE DAUMERIE DAVIERE DEILLERIE DELINIERE DEPENDANCE DEPRAIRIE DERDENNERIE DEVALLIERE DEVESIERE DEVISE DIJONNERIE DIVERSIERE DIVOTERIE DODINIERE DOLBELLIERE DOMAINERIE DORIE DORISONNIERE DROURIE DUCHEE DUGASSE FABRICE FABRIQUE FARFOLIERE LAFAT FAUVELLIERE FERAUDIERE FERME FERME BOURG FERME BRISSON FERME PRIEURE FERME JOLIE FERRANDERIE FERRANDIERE FERRIERE FERRONNERIE FESSARDIERE FEUILLERE FILOUSIERE FINAUDIERE FLEMARDIERE FONTAINE FONTENELLE FORET FORET MONTAUBAN FORGE FORGETTE FOSSE FOUCAUDIERE FOUCHERAIE FOUGERAY FOULIERE FOUQUERAIE FOURMILLERIE FRANCOLLIERE FRELONNIERE FRENELLERIE FRESNAYE FRETERIE FRICHE FROIDE FONTAINE FUIE FUIE VIEUX LAVARDIN GABINIERE GADELIERE GAINELIERE GAITE GALOPIERE GALOPINIERE GAMBAUDIERE GANDELEE GANDONNIERE GANNETERIE GARDERIE GARENCIERE GARENNE GARENNE NOELS GARONNE GASSERIE GATE GATELLERIE GAULARDIERE GAUMUSSON GAUTRIE GAUVANNERIE GELANDERIE GELOPIERE GEMERIE GENAIZIERE GENDIERE GENETTIERE GENEVRAIE GENNETIERE GENNETRIE GESNAIE GESTIERE LAGETIERE GIBARDIERE GIDONNIERE GILLARDIERE GIRARDIERE GIRAUDIERE GIRONNIERE GOBTIERE GODARDERIE GOINCERIE GOUDARDIERE GOULANDIERE GOULINIERE GOUPILLERE GOURDINIERE GOUSTIERE BARATIERE BARRE BAUSSERIE BEDOUAUDIERE BEULIERE BRUYERE BUFFARDIERE CADOIRE CHANTRELLE CHAPINIERE CHESNAIE CROCHERE DENISIERE ECOTTERIE GENITIERE GRENOUILLERIE GRUELLE HOUSSIERE LANDE MAISON MALADRIE MAUVAISINIERE MOTTE PATURE AUX BOEUFS PERAUDIERE POTERIE RAISANDIERE ROBINIERE ROUILLE SALLE SAPINIERE SAPINIERE LANDES SAULAIE SIMONIERE TAILLE TARGERIE THIBAUDIERE TOUCHE VIEILLIERE ZEZIAIRE GRANDINIERE GRANGE GRANGE AUX GRANGE MOREAU GRANGE ROUGE GRANGEDENIS GRASSINIERE GRIGNARDERIE GRIPETTE GRISONNIERE GROGNERIE GROIE GROIE L HOMMEE GROIE FRILOU GROISTIERE GROUAS GROUJARDIERE GUERANDIERE GUERINIERE GUERINIERE CORBINE GUEROIS GUEROISIERE GUETTIERE GUIBERDIERE GUICHARDIERE GUILLOISIERE GUIMONDIERE GUINAUDIERE GUITIERE GUITONNERIE GUITONNIERE GUITTONNIERE GUIZARDIERE GUYONNIERE HAIE HAIE BARRE HAIE CERF HAILERIE HAISE HALTE HAMELOTIERE HANTELLE HARDANGERE HARDONNIERE EST HASERIE HAUTE BOSSERIE HAUTE CHAMPARDIERE HAUTE CHESNAIE HAUTE COURBE HAUTE FOLIE HAUTE MADELEINE HAUTE MOULINIERE HAUTE PATURERIE HAUTE PICAUDIERE HAUTE ROCHE HAUTE ROUSSIERE HAUTE SICOTERIE HERAUDIERE HERISSONNIERE HERMONNERIE HILIERE HOCTIERE HOUASLERIE HOUDAIRIE HOUDENNERIE HOULBARDIERE HOUSSAIE HOUSSAYE HUBERDERIE HUCHETTE HUETTERIE HULOTERIE HULOTTERIE HUNOTIERE HUPERIE HUTINIERE HUTTE HUTTIERE LAIGERIE LAIGLERIE LAIGNE L AIGNERIE L AIGUILLEE L AIGUSSON LAILLE L AIMERAIE LAINES INIERE LAIRE LAIRES LAITERIE LAITERIES LAITIER LAITRE LAITRE LANDES L AITRE ROIS L AITRE TESSIERS L AITRE VAU JADE JANIVIERE JANVERIE JARCE JARRIA JASNIERE JAUNAS JOLIVETIERE JOLIVIERE JONCHERAIE JOUALTIERE JOUANNIERE JOUFFETIERE JOUSSINIERE JUISSELERIE JUIVERIE JUSTICE LAKELAND LAIDRAIE LAIRE LAMANDIERE LANDE LANDE CLAIRE SEMAINE LANDE FEUMUSSON LANDE PETAY LANDE DEPIERRE LANDRAIZE LANTE FRENE LALAS LATRONNIERE LAURETTERIE L ALBIZIA LEU LIEGETERIE LIGERAIE LIGOTTERIE LINIERE LISIERE LIVAUDIERE LOGE LONGERE LONGRAIE LONGUERAIE LONGUE ROCHE LOUISIERE LOUPTIERE LOUVARDIERE LOUVE LOUVERIE LUARDIERE LUGUERIE MACONNERIE MAILLETERIE MAILLOCHERE MAISON D ARLENE MAISON JARDIN MAISON NEUVE MAISON SEULE MALADRERIE MALADRIE MALDOTIERE MALICE MALICERIE MALTIERE MAMOUILLERE MANIERE MARAUDIERE MARE MARE AUX ANES MARECHALERIE MARGEOTTE LAMARGOTTE LAILLE MARGOTTIERE PATIS MARIERE MARINERIE MARSOLIERE MARTINERIE MARTINIERE MASSONNIERE MASSUERE MAUGATELIERE MAUGERIE MAZURE LAMBAL LAMBERDERIE LAMBERDIERE LAMBRUCHET MEGRETIERE MEGRIERE MELOUSIERE MELUSINE MENERIE MERCERIE LAMERIE LAMERIES MERILLIERE LAMES JOYAUX MESSANDERIE METAIRIE METAIRIE TROGNE MEULE D ELOI MICHARDIERE MICHONNIERE MIJEYO MIOTISE MIOTTIERE MISSION MODEUSERIE MOINERIE MOISANDIERE MOISIERE MOLIERE MONNERIE POTERAU MONTREE MONTROLIERE MORINIERE MOTTE MOTTERIE MOTTE ROUGE MOUETTE MOUSSERIE MOUTELIERE L ANCELLIERE L ANCOLIE LANDAICHERIE LANDE LANDE BOISSEE LANDE CHALUBOT LANDE BELROT LANDE CERANS LANDE CLAIR SEMAINE LANDE CRANNE LANDE CHOUANNE LANDE COURANT LANDE MARNIERE LANDE LUERE LANDE D EN HAUT LANDE RIGAUSSONNET LANDE ROUILLON LANDE GIGOUS LANDE SOUCIS LANDE TERRIERS LANDE VAUGAUTIER LANDE VEREILLES LANDE VIHIERS LANDE BRAS MER LANDE CAMP LANDE FRENE LANDE LAPIN LANDE RIBERT LANDE FLEURIE LANDELIERE LANDE PICHONNEAU LANDEREAU LANDES LANDES BASSES LANDES CONILLES LANDES BECANNE LANDES FERRONNERIE LANDES MONTSORT LANDES FLEURIES LANDEVY LANDINIERE LANDIRON LANDON LANDORMIERE LANDRIAS LANDRIE LANDRIERE LANDRONNIERE NESSLIERE L ANGERIE L ANGINIERE LANGRIE LANGUECHERIE LANGUICHERIE L ANILLE NOE NOE GEIGNE NOEE NOELLERIE NOIRMORDIERE NONIVE NOUE LANSCAILLE LANSEAU L ANSUISIERE LANTE FRENE L ANTESIERE LANTINIERE PAGERIE PAILLERIE PAILLETIERE PALAINE PALESTINE PAPILLONNIERE PAQUERAIE PARISIERE PASTIERE PATISSERIE PAVATERIE PECANNIERE PECARDIERE PECHERIE PELLERIE PELLUETTE PELOTERIE PELOUASERIE PELOUSE PELOUSE GOUDERIE PELTERIE PEPINIERE PERAUDIERE PERICHE MAILLARDIERE PERRICHOTIERE PERRIERE PERRIGNE PERRINE PERRUCHE PERUCHE PERVENCHE PERVENDIERE PETITE BAILLEULERIE BALLIERE BARBIERE BAUDUSSERAIE BELLANGERIE BERGERIE BEUCHETIERE BODINIERE BOUGRIE BRETONNERIE BROCHERIE BROSSE BRUYERE BUCHAILLE CADORIE CARRELIERE CHARBONNIERE CHARBOTIER CHAUSSELIERE CHENAIE CHESNAIE COCHINIERE COLASIERE COSMERIE CROIX CROIX GANIER CURE FERME FONTAINE FORTERIE FOUCAUDIERE FOUGEROLLE GALICHERIE PETITE GARENNE GATE GATINE GAULARDIERE GRENADE GROIE GRUELLE GUICHARDIERE HAIE HARDANGERE HILTIERE HUCHE CORNEILLE HURTEBICHE JOLIVIERE LANDE LARDIERE LARDIERE LARDIERE LIEUE MAISON MAISON NEUVE MAISON ROUGE MARTINIERE MASSONNIERE METAIRIE MONGE MONTABONNIERE MONTREE MONTROLIERE MORINIERE MOTTE OUCHETTE OUSERIE PERDRIERE PORCHERIE POTENCE POTERIE RENARDIERE RIBOTIERE RIGANNERIE ROCHE TOUCHE TOUCHE LANDES VACHERIE VALLEE VIEILLIERE VIGNE PHILIPPOTERIE PIARDERIE PICARDIERE PICHARDIERE PICHOIRIE PIEDORERIE PIEMORDIERE VALAUDUN PIETINIERE PIFRE PIGORERIE PILARDIERE PILLERIE PILLETERIE LAPIN MAIGRE PINOISIERE PINSARDIERE PINTERIE PIROUSE PITRIE PIVARDIERE PLAINE PLAINE BAUDET PLAINIERE PLANCHE PLANTE PLARDE PLATELLIERE PLATERIE CROIX PLINIERE POCHERIE POINTE POISSONNERIE POMMERAIE POMMERAY PORJONNIERE POTENCE POTERIE POULAINERIE POULAISIERE POULIE POISSARD POUPARDRIE POUSSINIERE PRAIRIE PREHUTTERIE PRIMAUDIERE PROMENADE PROVOTERIE PRUNERIE QUESMERIE RACINETTE RACINIERE RACOISIERE RADONNIERE RAFFINIERE RAGEE RAGOTIERE RALUERE RATEE RATELIERE RATERIE RATIERE RATTERIE L ARBRARDIERE L ARCHE L ARCHETTE L ARCIS L ARCY LARDERIE LARDIERE LARDONNIERE RECOMPENSE FORET RECULERIE RENARDIERE RENAUDINIERE RENOUILLIERE RENTIERE REQUENOUARIE RETALLIERE RIAUTE RIBORDIERE RIBOULIERE RICHARDIERE RIGAUDERIE RIGOULIERE RIMBAUDIERE RIVIERE L ARLYSSON ROCANTINIERE ROCHE ROCHE CIMBAULT ROCHE DAVY ROCHE VENTS ROCHE SEBASTOPOL ROCHE TABARY ROIERIE ROIRIE ROMERIE RONCE RONCERAIE RONDELLERIE ROSE ROTERIE ROTERUERE ROTTELIERE ROTTERIE ROUABIERE ROUGERIE ROUMELLERIE ROUSSELIERE ROUSSIERE ROUTE L ARPENT L ARRACHEE RUAUDIERE RUCHE LALA SABOTTERIE SALLE SANGUINIERE SAPINIERE SAPINIERE MOULIERE SAPINIERE L ECU SAUGE SAUSSERAIE SAUVAGERE SCUDERIA ST DENIS TERTRE SECHETIERE SELLE SENETIERE SEVERIE CEVERIE SINELIERE L ASNERIE SORIE SOUCHARDIERE SOURCE SOURICERIE SOURICIERE LASSAY LASSIERE TABALERIE TACHERIE TAHERIE TAHINIERE TALONNERIE TANNERIE TARONNIERE TARTARASIERE TASSE TEILLAIE TEILLERAIS TEISSERIE TERRE NOIRE TERRE ROUGE TESSERIE TETUILLERE TEUTHAIRE THEBAUDIERE THEUTAIRE THIBAUDIERE THIELLEE THOMMERIE THUILERIE TIBAUDIERE TONNELIERE TONNELLERIE TOUCHARDIERE TOUCHE TOUCHE ARAMBOY TOUCHE TOUCHETTE TOUR TOURNERIE TOURTELLERIE TOUZELIERE TOUZERIE TREFLERIE TREMBLAIE TRICOTIERE TROVIATA LATTAIS TUILE LOUPS TUILERIE TURPENNE TUSSONNIERE L AUBE L AUBEPINE L AUBERDIERE L AUBERDRIE L AUBET L AUBET VALLEES L AUBET RUISSEAU LAUBRIERE L AUBRIERE L AUDINIERE L AUDIONNIERE L AUDONNIERE L AUDURGERIE L AULNAY LAUMAILLET L AUMONE L AUMONERIE LAUNARIE LAUNAY L AUNAY LAUNAY PERCE LAUNAY ROND L AUNAY TRUCHET LAURENCELIERE LAURENCIERES LAURIER L AURIERE LAURIERE LAURIERES LAURIERS L AUTEL GOYER VALINIERE VALLEE VALLIERE VALTIERE LAVANDES LAVARE LAVAREE VENILLERIE VERGERE VERRERIE VERRONIERE VERSERIE VETILLERE VICOMTE VIEILLE VIEILLE PELLIERE VIGNE VILLANELLE VILLE GAGNEE VILTERIE VINETTE VIVE JAGEOLAY VOIERIE LAVOIR VOISINE VOLERIE L AVRILLIERE LAYARD LEAN LEARD LEARD CHAT BUISSON LEARDS BALCON FLEURI CHICOTIERE BARATTON BAS BOURG BAS BAS CORMIER BAS MESLIER BAS TERTRE BAS LARRE BAS MESNIL BAS MINERAI BAS PEZEREUIL BAS PLESSIS BAS POUPLIN BAS VALLAS BAUCHET BEAUMORTIER BEAU VALLON BEDAUDIER BEL AIR BELLESSARD BENARDIER BERRY BIGNON LEBLANC BLUTEAU BOCAGE FLEURI LE FAY BUTTE FLEUR LYS PAPILLONNIERE MOULINS HOGUES ROGERS COLINET EMERY GUILLON JOLI LONG LOUVEL MARGOT MORIN NEUF POULARD RIBOT BORDAGE BORDAGE L ARGENTERIE BORDAGE PISSOT BOULAY LE BOULIDARD BOURBIER BOURG BOURG D ANFRAY BOURG GUE LIAN BOURG MAISON BOURGNEUF BOURRAY BOUT PARC BRAY BREGEON BRINDEAU BUIS BUISSON BURON BUSSON FLEURI CAILLEREAU CALME CALVAIRE CARREFOUR CARROI CASTILLIER CEDRE ROUGE BOUGRIERES CHALET CHALET ENSOLEILLE LE BAS BLANC BLOSSIER BOISSIAU BRIN COLLET BOUJONTIERE COMMUNE CROIX CURE DOUSSAYE FONTAINE FORGE GRANGE LALEAU LLE LOGE LE MARE MURE LALE TERRE NEUVE L AUBIER VOYE L ECORCE L INDENNE DEJEAN CINQ QUARTS ETOILES GARENNES HOGUES MAISONS MURIERS MURS SORT RUES TOUCHERIES VIGNES VOIE BIGNON LE MONTSORT BOURG CARREFOUR CHATAIGNIER CHATELET CHENE LE COQ CORMIER FEU FOUR GUIGNIER NOYER POMMIER PRESSOIR ROY VERGER VIVIER CHAMPERDU FRESNEAU GARREAU GUIMARD HAUT MALABRY MAUDET MEUCLA MORICHON PENCHE PIERRAT POIRIER ROBLOT ROUABLE TOLLET TRONCHET VERETTE VILAIN L ECHANIERE CHAPELET CHARDONNERET CHATAIGNIER CHATAIGNIER CHEFRAISON LE CHEVALLERIE D AMBRIERES COURTILLOLE L ETANG DEPAUL VAUX PATY GAILLARD CHATELET CHENAIE CHENAY CHENE CHENE CREUX CHENE FEUILLU CHENE CHENE ROND CHENE VERT CHEVAL L ECHO CHESNAIE LE CLOSAGE L ECLOSE CLOSEAU FABRICE CLOTEAU CLOTEAU GUE L ECLUSE L ECOBUE COIN COIN BEURRE COING COLOMBIE COLOMBIER CONILLEAU CORMAN CORMIER CORMIER MORLETTES COSNILLOU L ECOTAY COTEAU COUDRAY COURTY POIRRIER CREUX VALLEES CROISSANT L ECUSSON DEGOUTTIER L EDELWEISS DEMI JOUR DERRAY DESSUS FONTAINES DOMAINE DOUET LEDRU ROLLIN FOUDROU FOUILLE FOURNEAU FOUROLET FRENE FRESNE FROMENTEAU GALET GASSIOT GAT GAUTRAY GAZON GEAY GENETAY GENIEVRE GENNETAY GENNETIER GENNETREUILLE LEGERIE GESMIER GEVEAU GINKGE BILOBA L EGLANTINE GOULET GOUTIER L EGOUTIER GOUTTIER GRAIS LEGRAND AIR AUNAY BEAUVAL BERU BOULAY BRAY BRAYE BREUIL BRUSSON CARREAU LE CHARDONNERET CHATELET CHAUVEL GRANDLE CHENE LE CORNOUX COUDRAY COURTILLON GATE GUENAUDET HERMITAGE HOUX JAUNAY LARRE L HOMMAS LIMON MALACHE MENUAU MESNIL MORTIER MORTRAY LE NICOU OZE PLESSIS POIRIER PRE PRE AUNAIS PRESSOIR RIOUSSE ROULOIR SABLON TERTRE VEAU VERGER VIVIER VOISINE GRAVIER GREEMENT GREEZ GREFFIER GREMIL GRENODE GROS CHENE GROS CHESNAY GUE GUE AUX CENEAUX GUE D ABZELLE GUE CLAIE GUE L AULNE GUE L AUNE GUE GUE ANGES GUE SORT GUE FRANCON GUEROUET GUELAURENT GUIGNIER L EGUILLE L EGUILLEE HARAS L ETRIE HASARD HAUDON HAUT ARDRIER HAUT BARRE HAUT BINET HAUT BINET HAUT BOULAY HAUT BURON BURON HAUT CHERPICHET HAUT D ARIS HAUT PIN HAUT TERTRE HAUT FRESNE HAUT PALIS HAUT PEZEREUIL HAUT PIN HAUT RIDOIRE HAUT ROJEATELLE HAUT VERDELLE HAUT VOLAY HERON HERY HOUDOU HOUSSAY HOUX JANDRY JARD JARDIN JARDIN D EDEN JARDIN CHESNAIE JARDIN L ORTHIAU JARDIN LOGES JARDIN QUATRE CURES JARDIN TREILLES JARDIN CLOTEAU JARDIN PERRAY JAUNAY JOURNIEUX JOUSSE KERSAINT LAMPRON LANDEREAU LAVOIR LEAN LEMEULIER LEZARD LIARD LIBOIS LONG RIAGE MAIOLIER AITRE ORPHELIN MANAIS MANOIR LE MAS PINS MEBRON MERSAT MESLIER MESLIER ETOILE MESNIL MILLERY MINERAI MINERAY MIRAIL MIRE BEAU MOHAIN MONAVRIL LEMONT LEMONTEAU MONT FRUITE MORTIER MORTIER AU CHAT MORTRAY LE VENT MOULINET MUGUET L ENCOLLIERE LENDEREAU L ENFANTILLAGE L ENFER L ENGUIECHERIE NID HIRONDELLES NID TERTRE NORD NOYER NOYER L HABIT LENTE LEON BARBE L EPAGNEUL PARADIS PARC PARCHET BAS BOUCHAGE PASSAGE PATIFROID PATIS PATIS FAYOU PATIS RENTES PATIS VILLENEUVE PATIS HALBRON PATISCHRISTOPHE PATISSEAU PATISSEAU APRES EMMAUS PATIS VERT PAVILLON PERCEAU PERIGAULT PERRAY PERRAY NEUF PERRON PERRUCHET AUNAY AVENRY BARDEAU BERU BEURY LE BOISAILLE DELEGER FRANCHET MARGOT MAUCLAIR MOREAU BORDAGE BREIL BRIE BUISSON CASSEAU CHAIGNE CHAILLOIR CHAILLOU CHAMBRON LE RAFFON CHARME CHATELET CHAUVEL CHEVIGNE LE CORMIER CORNOUX COUDRAY COURBAULIN COURMEANNE COURVARIN ENFER EPINAY ERABLE FOURNIQUET FREU GAGE GAIGNIER GANDELIN GANDIER GARANCIERE GENET GROUAS GUE GUEROIS GUETTE LOUP HARRIER HERMITAGE HETRE HOUX JUPILLES MESLIER MESNIL MIDI MINERAI MORTRAY LE ORMEAU PARADIS PARC PLESSIS LE PONTFOUR PORT POYER PRE CROIX PRESSOIR PRE WHO DARE WHINS RONCERAY RONCHERAY SABLON PETITROCH SOURCHE TERTRE TREMBLAIS VALBRAY VERGER VIGNEAU PEUPLIER PIGEON BLANC PIGEONNIER PIGEONNIER FOUSSARD PIN L EPINAY L EPINE L EPINE HERBILLE PIOLEY PIQUET PITRIE PLAN PLESSIS PLESSIS GASTINES PLESSIS JOBART POINT JOUR POIRIER POLIGNERAIS LE PORTAIL PORTEAU POTERNE PRE PREAURAY PRE BEZONNAIS PRE CHARBONNEAU PRE DANAIN PRE D AVRIL PRE DERRIERE PRE BONDE PRE FONTAINE PRE L AIRE PRE LANDE PRE SOURCE PRE VALLEE PRE L PRE L OUCHE PRE AUBIERS PRE VAUX PRE CALVAIRE PRE DOUE PRE FREU PRE PORT PREMARE PRE ROBERT PRESBYTERE PRESSOIR PRE ST ALOURDES PRE VERDELLES PRIEURE PRIEURE VIGNOLLES PRIEURESYMPHORIEN P TIT TAMBOUR PUITS PUITS LAURIAU RANCHER REFOUR REFUGE REPOS LONGRAIS RHONNEAU RIALAUME RIBERT RIDRAY ROCHER RONCERAY RONDIN ROSIER RUAU RUCHER BEAUSOLEIL RUISSEAU 2 TILLEULS 4 VENTS SABLON SABLONAY ACACIAS ACRES AIGREMONTS AITRES ALLEUX ALOUETTES ALOURDES ANTES SAPHIR SAPIN APOYES ARCHES ARCIS ARDENTS ARDRIERS ARNAULTS ARPENTS VERTS ARRACHEES SARTHON ASSIS AUDIZIERES SAULNAY AUNAIES AUNAIS AUNAYS BAILLEES BARBEREAUX BAS BAS BOULAYS BAS MORIERS BASSES HAIES BASSES LOUVERIES BASSES MESSANDIERES BASSES MORLETTES BASSES PERETTES BASSES PERRETTES BASSES ROBINIERES BASSES VALLEES BASSES VIGNES BATES BEAUX ENFANTS BEAUX MANTEAUX BELLANGERIES BELLONNIERES BESNEDIERES BESNERIES BESNIERES BEURRERIES BIARBIERES CHANTELOUP BIARS BIAS BIGNONS BILLOTIERES BINOTTIERES BLEUETS BLOTTERIES BLOTTES BLUTTERIES BONDES BOUGERIES BOULAIES BOULAIS BOULEAUX BOURDAINES BOURGEONS BOURGEYERES BOUVRIERES BREBIONIERES BREGEONS BREJONS BRETONNIERES BRISSONS BROSSES BRUNETIERES BRUYERES BUSSES BUTTES BUTTES MARCOUE CANIERES CATERNES CAVES CEDRES BLEUS CELLIERS CENT SETTIERS CERISIERS CHABERDIERES CHALLONGES LEVREAU ROUX CHANSORIERES CHAPELLERIES CHAPELLES CHARMES CHARMILLES CHARROTS CHARTREUX CHATAIGNIERS CHATELIERES CHAURIES GD THIBAUDIERE CHAUVIERES CHEMINEES CHOUANAS CINQ CHARMES CLAIES COHERNIERES COINTELLERIES COLINIERES COLLIERES COMMUNES COMMUNS COMMUNS CONCHEES CORDELIERES CORDELIERS CORMERIES CORNILLIERES COSNUERES COTEAUX COTEAUX COTTEREAUX COURBES COURDAVIDS COURTRUS COUTILS COUTURES CROIX CROIX ROGERS CROIX VERRAS CYCLAMENS CYPRES DAHLIAS DATURAS DAUVAIS DEFAIS DEUX JOURS DEUX JUMEAUX DEUX TILLEULS DINONS DOUBLES CROCHES ECOMEAUX ECOTAIS ECOULEES ECUREUILS ST DENIS TERTRE ECURIES ECURIES CHAMPLINGE EDELWEISS EGREMONDIERES EPERVIERS EPINOIS SEPULCRE ESSARTS SEUNAY FAULX FERRERIES FILIERES FINARDIERES FLEURETTES FONDUS FONTAINES FONTENIS FONTENUES FORGES FOSSES FOUGERAIS FOUGEROLLES FOUQUETS FOURCHES FOURNEAUX FRAMBOISIERS FRENES FRICHES GABILLES GALBRUNIERES GALIPAUDIERES GARDONNIERES GASCONNIERES GATS GAULARDIERES GAULETTERIES GAULTIERS GAUTELLERIES GENETS GLOTTIERES GLYCINES GODERIES GODRIES GOUARDIERES GOUILLARDIERES GOUTIERES GRAIS GRANDES AEREES GRANDES AITRES GRANDES BARRES GRANDES BROSSES GRANDES CANLOIRES GRANDES CHALLONGES GRANDES FOSSES GRANDES GROIES GRANDES HAIES GRANDES HOUDAIRIES GRANDES JAVARIERES GRANDES LANDES GRANDES LEVERIES GRANDES LIGNEROTTES GRANDES MAISONS GRANDS BERTRAS GRANDS GRANDS DEVANTS GRANDS ERABLES GRANDS GENETS GRANDS JARDINS GROSCOLLIERES GROSEILLES GROS OS GROUAS GUILARDIERES GUILLEBEAUDIERES GUPPYS GUYOTTERIES HAIES HAIES FLEURIES HAIES GASTINES HAILLIERES HALLES HANTELLES HARMANDIERES HAUTES ANGEVINIERES HAUTES FERRIERES HAUTES HUAUDIERES HAUTES MORINIERES HAUTES PERETTES HAUTS BLOSSIERS HAUTS BRETEAUX HAUTS GREEZ HAUTS VERGER HERROUIES HETRES HEUZARDIERES HIRONDELLES HUGAUX HUTTIERES IRIS JAMBONNERIES JANJARDIERES JARDINS JARDINS GUE JARRAIS JAUNAIS JECULIERES JOLIETTES JUBAUDIERES JUBEAUDIERES JUGUENIERES LANDES LANDES VILLIERS LAURIERS LENEILLIERES LIBERDIERES LIBERRIERES LILAS LILAS RUAU LIOTIERES LOGES LOGETTES LOIGNES LONGUERAIES LONGUES MEZIERES LONGUES NOES LOUVERIES MAGNOLIAS MAISONS BLANCHES MAISONS NEUVES MAISONS ROUGES MARCHAIS MARCHES MARDELLES MARECHAUX MARRES MARSOLLIERES MASSICAUDIERES MAUGUINIERES MEFFRAYERES MENESTRELS YVANDEAU MESANGEAUX MESANGES MEZIERES MILANDES MIMOSAS MONDETOURS MONGASONS MONTFORTS MORTIERS MORTRAITS MURETEAUX MYOSOTIS NANCOLLIERES NARGEOTTIERES NERDOUCIERES NOELS NOES NOS NOUETTES NOUIS NOVALLES OISONNERIES ONGRAIS ORANGERS ORMEAUX OUCHES PALIS PANCHEVRIES PARCS PATIS PATOUREAUX PATURERIES PECHARDIERES PELLERIES PELOUSES L ESPERANCE PEROTTIERES PERRINIERES PERRUCHES PETITES BELLANGERES PETITES BELLEVUES PETITES BLOTTERIES PETITES BOTTERIES PETITES CHARRONNIERES PETITES FRAIRIES PETITES HAIES PETITES HOUDAIRIES PETITES JOISIERES PETITES LANDES PETITES LIGNEROTTES PETITES LOGES PETITES LUERES PETITES PORTES PETITES RENAUDES PETITES ROTES PETITES ROTTES PETITES RUES PETITES SOUVRES PETITES VEDIERES PETITES VENTES PETITS ATHEES PETITS BEAUVAIS PETITS PETITS BOULAY PETITS EVAUX PETITS MINERAIS PETITS PARCS PETITS RIAUX PETITS ROSIERS PETITS ROUFRANGEAUX PETITS TEILLAIS PETITS VAUMONCAIS PETITS VAUX PHILARDIERES PILAVINIERES PINARDIERES PLANCHES PLANCHETTES PLOUSIERES PLOUZIERES POIRIERS POMMIERS PORTES ROUGES POTERIES PRES QUATORZES QUATRE JOURNAUX QUATRE VENTS RAGOTTIERES RAGUENIERES RENAUDERIES RENAUDIERES RETRAITS RIAUX RICHOTTIERES RIGAUDIERES RIGODIERES ROBINIERES ROCHEREAUX ROCHERS ROCHES ROCS ROGERS RONDAINS ROSEAUX ROSES ROSIERS ROTTES ROUCHERES ROUGEMONTS ROULLIERES ROUSSELIERES ROUSSES ROUSSIERES RUAIS RUAUX RUELLES RUES SABLONS SAINFOINS SAINTONNIERES SAULAIES SAULLAIS SAULNERIES JUIGNE SARTHE SEGUINIERES SELLIERS SENCIES SIMONNIERES SOURCES SOUVRES SUHARDIERES SYRINGAS TABOUS TAILLES TAILLIS TAINNIERES TALUS TEILLAIS TERRES BLANCHES TERRES VENT TERTRES THEBAUDINS THUAUDIERES TILLEULS TOMBES TOUCHES TOURETS TOUZELIERES TREMBLAYS TREMELIERES TROIS CHENES TROIS COULEURS TROIS ETOILES TROIS MOUETTES TROIS PAUX TROIS POIRIERS TROTTINIERES TUILERIES VALLEES VAULARONS VAUX VAUX PUITS VENELLES VENTES VERGERS VIAUNOUX VIEILLES VIGNEAUX VIGNES ZENASTAS TALUS L ETANG TERTRE THEIL TOURNEILLAGE LETOUZE L ETRE GALLIER L ETRE TREMBLAY L ETRIER MONTINAULT LETTRE LEU LEURAY VALDARONE LEVANT VAU VAU PUITS VAUPERE VEAU FOLLET VERDEAU VERGER VERGER LEARD VERGER LEVERIE LEVERIES VERMONT VIEUX BOURG VIEUXLE VIEUX CHENE VIEUX VIEUX MANOIR VIEUX VIEUX PRESSOIR VIEUX RAFFUT VIHIERS VIVIER VOISIN VOLLIER LEVRAUDIERE LEVRAUDIERES LEVRETTIERES LEVRIE LEVRIGNE LEZARD LEZARDIERE LEZERIE L HAITRE TESSIERS L HERMENAUDIERE L HERMITAGE L HERTERIE L HETRE L HETRE ROIS L HETRE FLEURIOT L HOMMEAU L HOMMOIS L HOPITEAU LIARD LIARDERIES LIARES LIAUDIERE LIBAUDIERE LIBERATION LIBERDIERE LIBERDIERES LIBERTE LIBOIS LICHE LICORNE LIEFF LIEN LIERRERIEA CASAILLOLAAGLETA BELLE ETOILEAMBRIERESANTE ROUGEARCHEARCHETTEARGENSELASSEBALANCONBANETTEBARBE D ORGEBARDEAUXBAS BOISBAS MONTJOSSONBAS MOULINBAS ROSEEBAUGEBEAU BUISSONBEAUBURETBEAU CHENEBEAUCHENEBEAUFEUBEAULIEUBEAUMANOIRBEAUMONCELBEAUREGARDBEAUREPAIREBEAUSEJOURBEAU SOLEILBEAUSOLEILBEAUVAISBEAUVERGERBECHERELBEDANEBEL 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RETRAITE MAISON RETRAITE PRIEURE MAISON RETRAITE TOUCHE MAISON ARCHES MAISON VEAU MAISON DIEU MAISON DIMANCHE MAISON PUITS MAISON EDIN MAISON FLEURIE MAISON FORESTIERE MAISON FORET MAISONNAIS MAISONNETS MAISONNETTE MAISONNETTE BROSSIL MAISONNETTE FOUTAIES MAISON NEUVE MAISON NEUVE PAUFILS MAISON NEUVE ROSIERS MAISON PAILLE MAISON RETRAITE PRIEURE MAISON ROUAUX MAISON ROUGE MAISONS BLANCHES MAISONS BRULEES MAISONS DIMANCHE MAISONS GARES MAISONS JAUNES MAISONS NEUVES MAISONS ROUGES MAISONS VERTES MAISON VERTE MAITRIE MALABORD MALABRY MALADERIE MALADRERIE MALADRIE MALADRIES MALAGUET MALANDRIE MALARDERIE MALATTEINTE MALBROUE MALDOTIERE MALEFFRE MALEFRE MALESSARD MALFORGE MALFRAIRIE MALGRASSES MALHERBE MALHEURIES MALHONTE MALIDOR MALITIERE MALITOURNE MALLANDE MALLEVILLE MALLOIRE MALMARE MALORIE MALOURDIERE MALPAIRE MALPART MALRUE MALTEROIS MALTIERE MALTOTERIE MALTOUCHES MALUERTE MALVA MALVALERIE MALVALETTE MALVAUDRIE MALVAUX MALVOISINE MANADE MANCAIS MANCELLERIE MANCELLIERE MANCHONNIERE MANDEE MANDINIERE MANDOUILLERE MANDROUSIERE MANDROUZIERE MANEES MANGUENERIE MANIERE MANOIR MANOIR D ANTOIGNE MANOIR BERNAY MANOIR GROIGNY MANOIR LINTHE MANOIR RIABLAY MANOIR FONTENELLE MANS MANTEAU MA PRAIRIE MAQUERE MAQUERE DAUSERIE MAQUIGNONNIERE MARAISIERES MARAUDIERE MARCADRIE MARCAIS MARCAISERIE MARCE MARCELLIERE MARCHAINEAU MARCHAIS MARCHANDERIE MARCHANDIERE MARCHANDIERES MARCHE AUX BESTIAUX MARCHESAS MARCHESSOIR MARCHEVERT MARCONNIERES MARCOTTIERE MARDELLE MARE MARE FEU MARE AUX LOUPS MARE BERGERE MARE BESNARD MARE BIGOT MARECHAL EQUARRI MARECHAL FOCH MARECHALIERE MARE CIREUX MARE L AUNAY MARE DENISE MARE GUERINIERES MARE PRES MARE FLEURIE MAREIL MARE MOREAU MARE NOIRE MARE PINEAU MARE RONDE MARE ROTIE MARES MARETTES MAREUIL MARGALLIER MARGEOTTE MARGOTIERE MARGOTIERES MARGUERITE MARGUERITIERES MARIANNIERE MARIENNIERES MARIERE MARIERES MARIETTE MARIETTERIE MARINE MARINERIE MARINIERE MARIONNERIE MARJOLAINE MINE MARJOTTERIE MARMOT MARMOUCHERE MARMOUSET MARNAY MARNE MARNIERE MAROC MAROCON MAROLLES MAROMANY MAROTTE MAROTTIERE MARQUADIERE MARQUERIE MARQUERIES MARRONNIERS MARSAISSIERE MARSAUDIERE MARSE MARSET MARSILLE MARSOLLERIE MARSOLLIERE MARSOTTIERE MARSUARDIERE MARTELIERE MARTELLERIE MARTELLIERE MARTHE MARTIGNE MARTINERIE MARTINET MARTINIERE MARTINIERES MARTIZIERE MARTRAIS MARTRAIS MILIEU MARVALIERE MAS MASCAREIGNES MASNIERE MASNIERES MASPRE MASSONNIERE MASSOTIERES MASSOTTIERES MASSUERE MASSUERES MASSUIN MASURE MATIERE MATRAS MAUBERDIERE MAUBETS MAUCARTIER MAUCHEVALERIE MAUCHIENNERIES MAUFAYERE MAUFIERE MAUGERIE MAUGONNET MAUGONNIERE MAUGUINIERE MAUGUINIERES MAULE MAULIERE MAULNIERE MAULNY MAUMUSSON MAUNON MAUNY MAUPAS MAUPERTUIS MAUPERTUY MAUPINIERE MAUPOINTIERE MAUPORCHERIE MAUQUARTIERS MAUREPAS MAURERIE MAURICIERE MAUROUSERIE MAURUERE MAUSSIDE MAUSSONIERE MAUSSONNIERE MAUVIERE MAUVILIERE MAUVINIERE MAVRE MAYANNE MAYET MAZOUET MAZOUILLET MAZURE MAZURERIES MAZURES MEABERTS MEALERIES MEALTIERE MEAU MEAUSSERIE MECHINIERE MECORBON MEDMANCHE MEE MEFOSSES MEGRETIERE MEHISSON MELEVE MELIER MELIERS MELINAIS MELINIERE MELION MELLETIERE MELUERE MELUSINE MEMBRIE MENAGERIE MENAGERIES OUEST MENANDERIE MENANTERIE MENANTIERE MENARDERIE MENARDIERE MENARDIERES MENASSIERE MENATIERE MENEME MENERIE MENETRIE MENIL MENROSE MENTRIE MENUAU MERCERIE MERCURIE MERDOISIERES MERE MERE ANGUILLE MEREAU MEREUVE MERGEOIRE MERGER MERI MERIANNIERE MERIBLON MERISIER MERIZE MERLE BLANC MERLIERE MEROINCON MEROLLE MERUAU MERVE MESANGE MESANGEAUX MESANGELLIERE MESANGERE MESANGERES MESANGERIE MESANGERIES MESANGES MESLAY MESLERIES MESLIER MESLIERS MESNIL MESNIL ECURIES MESNIL MESNILS MESSANDIERES MESURERIE METAIRIE METAIRIE HAUT METAIRIE VAL METAIRIE GRASSIN METAIRIES METRIE MEUNERIE MEURLIERE MEVAIS MEVALETRIE MEVOIE MEZAIN MEZEAUX MEZEROLLE MEZIENNE MEZIERE MEZIERES MIALERIE MIALETIERE MIALETIERES MIAULES MICHALERIE MICHARDIERE MICHELIERE MICHELLERIE MICHETIERE MICHOLIERE MI MIDEROUERE MIDI MIELTAS MIGEONERIE MIGNEROLLES MIGNONNIERE MIGNONS MIHEROU MIHORIE MILAN MILARDERIE MILARDON MILLERIES MILLERY MILLETERIE MIMBRE MIMOSA MIMOSAS MINARDIERE MINARDIERES MINCLOU MINEE MINERAI MINERAY MINERET MINERIE MINERIES MINETTE MINI MINIERES MINISTRIE MINOT MINSONNIERE MIOTTIERE MIOTTIERES MIRAIL MIREBEAU MIROIR MIROLETTES MIRSON MITAUDIERE MITONNIERE MOCHERIE MOCQUETIERE MODETTERIE MODEUSERIE MODOT MOHARDIERE MOINERIE MOIRE MOIRE HAUTE MOIRON MOISEMARE MOISE MARE MOISIERE MOISNET MOITERIE MOLIERE MOLIERES MOLINERET MOLLANS MOLLEE MOLLIERES MOLON MOMENTINIERE MON ABRI MONCEAUX MONCELLERIE MONCHATRE MONCHEVALERIE MONCONSEIL MONDALLIERE MONDAN MON DESIR MONDETOUR MONDON MONDOUBLEAU MONDOUZERIES MONDRAGON MONDUSSIERES MONE MONERIE MONFRESLON MONFROU MONGAUDIN MONGIE MONGONDOUIN MONGRIGNON MONGUION MON IDEE MONJOINTIERE MONLIERAY MON LOISIR MONNAVRIL MONNERAIE MONNERIE MONNERIES MONPERTUIS MON PLAISIR MONPLAISIR MON PLAISIR BRULAY MONPOULE MONQUIN MON REFUGE MON REVE MON SECRET MONSOLEIL MONSOREAU MONT MONTABET MONTABON MONTABONNIERE MONTAFOIN MONTAFRAY MONTAGNE MONTAGNES MONTAGNEUX MONTAIGU MONTAINTIERE MONTALAUME MONTALLAUME MONTANGER MONTANGERIE MONTANGIS MONT REGRET MONTAREUX MONTARGIS MONTAUPIN MONTAUVENT MONTBAUGE MONTBAZON MONT BEAU MONTBELLIN MONTCAULIN MONTCHARRIERE MONTCHENOU MONTCHEVEREAU MONTCORBIER MONTERNEAU MONTFELE MONT FLEURY MONTFORT MONTFOULON MONTFRAISIERE MONTFREIN MONTFRESLON MONTFRILOUX MONTGATEAU MONTGUERRES MONTGUILLON MONTHARD MONTHEBERT MONTHEYER MONTHORIN MONTIFAUT MONTIGNES MONTIGNY MONTILLES MONTINGRAND MONTIOFRAY MONT JALU MONTJEAN MONTJOIE MONT JOLY MONTLAI MONTLEARD MONTLEVEAU MONTLEVENT MONT LEVRIER MONTLEVROUX MONTLIERRET MONT LIVOIS MONTLONGIS MONTLOUE MONTLOUIS MONT LOUVEAU MONT LOUVRE MONTMACON MONTMARTIN MONTMOURAULT MONTMOUSSEAU MONTOISSON MONTOUR MONTPERRIN MONTPLAISIR MONT PORCHER MONTREE MONTREES MONTREGNIER MONTREIGNIER MONTRENAULT MONTRENAULT VALET MONTRETEAUX MONTREUIL MONTREUIL MONTREUIL MONTROLIERE MONTRUBERT MONTRUCHON MONTRY MONTS BEAUX MONTSORT MONTUMIER MONT VAILLANT MON Y MOQUESOURIS MOQUE SOURIS MOQUESOURY MOQUETIERE MORAILLERIE MORAISIERE MORANDIERE MORANDIERES MORATIERE MORELLERIE MORENNERIE MORICELLIERE MORICERIE MORICIERE MORICIERES MORIER MORIERS MORILLON MORILLONNERIE MORILLONNIERE MORILLONNIERES MORINETTE MORINIERE MORINIERE BRULEE MORINIERES MORIONNIERE MORISTIERE MORLETTES MORLIERE MORMONT MORNIERE MOROLAN MORRIER MORRIERES MORTEFONTAINE MORTELIERE MORTELLERIE MORTERIE MORTEUVRE MORTEVEILLE MORTIER MORTIER AUGER MORTIER AUX OIES MORTIER AUX PIES MORTIER CHARMANT MORTIER CREUX MORTIER PIN MORTIERE MORTIER FOURMI MORTIER NOIR MORTIER POURRI MORTIER ROND MORTIERS MORTIERS BLANCS MORTIERS JUMEAUX MORTONNIERES MORTRAIS MORTRAY MORTRIE MOTHELLERIE MOTINTERIE MOTRAY MOTTAGE MOTTAIE MOTTAY MOTTE MOTTEAU MOTTEAUX MOTTE FERMEE MOTTE LANDRY MOTTELERIE MOTTERIE MOTTERIES MOTTES MOTTE SAYE MOTTE THIBERGEAU MOTTE TIBERGEAU MOTTEZ MOUCHARDIERE MOUCHENOU MOUCHETIERE MOUCHETTERIE MOUCHONNIERES MOUELLERIE MOUETTE MOUILLEPIED MOUILLETTES MOULAINERIE MOULIERE MOULIERES FOULON MOULINAS TAN AUBRY VENT BARBIER MOULINBERT D ALFRAY D ANET D ANJUBERT D ARTHE D AUVERS BANNES BARIGNE BEFFES BENEHARD BERNAY BLAIRE BLAIREAU BONNIEUX BRIAUDIN BRIVES BRUANT CERVEAU CHALUAU CHANGE CHASSE CHENON CHEVET CLUANNE CONTREL COUDEREAU COULENNES COULEVE COURCELLES COURGADY COURTANGIS COURTARVEL COURTEILLON COURTIRAN CRAMPAUX CROALIN CROCHIN CROCIEUX DENNERAY DERREU DOUCELLE DUNEAU FAY FOLLET FORGE FOURGAS GANDELIN GAUBERT GUE LAUNAY HALOPPE JUPILLES L ABBAYE BRUYERE BUISSONNIERE BULASSIERE CHAUSSEE CHESNAIE FERRIERE FOSSE FOURRERIE GUIERCHE LAIGNE LANDE LIBAUDIERE MOTTE L ANTONNIERE PELOUSE GOUPILLIERE RIVIERE ROCHE ROCHELLE RONCE L AUNAY VAIRIE L ESSARD L ETANG L ILE L ISLE MALTERRE MONTCHENOU NEUFONTAINE NOUANS PASSAY PERAY PIVOT PONTILLEUX PRELAMBERT PREVAL RADRAY RANCE RAY REQUEIL RIFROGER ROCHARD ROSAY ROUILLON DEGAND DEJEAN DEMAMERT DEPAUL DEPERE DEVINCENT ILES NERONS OUCHES PINS ROUCHES RUAUDIERES TESSE THIENVAL THOUARS TRAVERSE TRINNE TROGNE VAUBOUILLE VAUX VENEVELLE VENIS VERT VEZIN D IVERNY D ORGERE D OURNE D OUTILLE BAN BOELLE BREIL BURON MOULIN CRUCHET GASSEAU HAZARD PERRAY PONCEAU RANCHER RAY DUPERE VAL VIVIER MOULINE MOULINERIE HUBERT MOULINIERE JALAIS CHANGE CHENAY GAUTIER MALEFFRE MONTINAULT MONTREUIL ROUVEAU VILLETTE FRENE VAUX PICHON LAURIERE SEUL LISAY LOUP MAIN FERME MEANT MORAND MOYEN NEUF PELERIN ROGER MOULINS MOULINJACQUES MOULINS LOCQUE MOULINS NEUFS TALBOT VIEUX VIEUX LOIR MOUREUIL MOUROUSELIERE MOUSSARDIERE MOUSSAYE MOUSSEAUX MOUSSERIE MOUSSETTERIE MOUSSONNIERE MOUTIER MOUTONNIERE MOYRE MUGUET MULIN MULOCHERIE MULONNERIE MULOTIERE MULOTTERIE MULOTTIERE MURAS MUREAUX MURET MURIERS MURS MUSARDIERE MUSERIE MUSSES MUSSE VILAIN MUSSIERES MUSSOTERIES MUSTIERE MUTTE MUZARDIERE MUZERIE MYOSOTIS NAIS NANTRIES NATA LINE ROEZE NATIONALE NAUDERIE NAUDERIES NAUDUME NAULIERE NAURALLE NAVERIE NEAU NELERIES NELLES NEMERIE NERONS NERRERIES NERRIES NERVILLES NESLE NETRIE NEUFONTAINE NEUVE NEUVES SAULNIERES NEUVILLETTE NEVOUERIE NEW HOUSE NEYE NEYERE NIAUFLE NICOLASIERE NICORBIN NIDS NIELLES NIGEOTIERE NINANDIERE NIVAUDIERE NIVELIERE NIVETTERIES NOBLIERE NOCELIERE NOCHERIES NOE NOE BISEUL NOE COCHAUX NOE TORE NOE CHARME NOE NOE FALLOT NOE GUILBERT NOELLERIE NOE RONDE NOES NOES BLANCHES NOES GOURDET NOES NOIRES NOE YVON NOGUE NOGUERIE NOIERIE NOIRAIE NOIRAIS NOIRAS NOIRIE NOIROTTES NOISERAIE NOISETIERS NOISETTE NONNERIE NORMANDERIE NORMANDIERE NORRIE NOTELLERIES NOTRE DAME NOTRE DAME GROIES NOTRE DAME FRENE NOUAPHETIERE NOUE NOUE D EN HAUT NOUETTE NOUETTE NOUETTES NOUIS NOULIOT NOULLIERE NOUVEAU PARC NOUVELLE FRANCE NOUVELLE ROCHE NOUZILLET NOUZILLIERE NOYAU NOYER NOYER GAILLARD NOYERS NOYER SEC NUILLE OASIS OBUS OBUS BIGNON ODERIE OEILLET OEILLETS OFFICELIERE OFFICIERE OFFLERIE OGERIE OIERIE OIGNE OIGNONNET OIRE OISELIERE OISELIERES OISELLERIE OISELLIERE OISILLONS OISONNERIES OISONNIERE OLIBERT OLIVERIE OLIVERIES OLIVET OLIVIERS OMBRASIERE ONCHETS ONDEE ONDINS ONGLAIS ONGLEE ONGRAIS ONGRAS OPINIONS ORAISIERE ORANGERIE ORASIERE ORATOIRE ORBEAU ORCHIDEES OREE OREE ORESIERE ORGERIE ORGERIE CAP VENTS ORIDIERE ORIERES ORLIARLIERE ORME ORMEAU ORMEAU BOIVIN ORMEAU BRULE ORMEAUX ORMES ORNE ORQUET ORSERIE ORTHIAU ORTOISE OSIER OSIERS OUCHE OUCHE AUX ANES OUCHE PEAU OUCHELLERIE OUCHERIE OUCHES OUCTERIES OULIERE OURIERE OURNE OUSERIE OUTILLE OUVRARDIERE OUZERIE PAGAUDIERE PAGEOTERIE PAGEOTTERIE PAGERE PAGERIE PAILLARDIERE PAILLE PAILLERIE PAIN BENIT PAINERIE PAIRE PAIRES MAILLES PAIRINES PAISANTERIE PAIX PAJOTERIE PAJOTIERE PALAINE PALAIS PALEINE PALENNE PALENNE 4 VENTS PALICOT PALIS PALISSE PALLU PALLUAU PALLUAUX PALMIERS PALOMBIERE PALONERIE PALONNIERE PALUAU PAMBOURG PAMBOURG NOISETIERS PANARDIERE PANCHERIE PANCHEVRERIE PANLOIRES PANORAMA PANSAIS PANTIGNY PAPETAGE PAPILLON PAPILLONNIERE PAPINIERE PAPINIERES PAQUERETTES PAQUERIE PAQUERIES PARADIS PARADIS PERDU PARAGERES PARAGERIE PARC AUX BOEUFS PARC AUX PLANCHES PARC BESNIER PARC BRULE PARC PARC FEUGES PARC TOUCHE PARC D EN BAS PARC AROMES PARC VILLIERS PARC D ICI PARC DUPARC DOUET PARC PARC PONCEAU PARCE PARCELLE PARC ERBRON PARC MAIGNE PARC MULOT PARC POILE PARC POISSON PARC POTTIER PARC ROUTE COULONGE PARCS AUBERTS PARCS MIRSON PARC SEC PAREGERE PARENCE PARENTIERE PARFAIT MORTIER PARFOND PARILLE PARILLERS PARISERIE PARISIERE PARISIERES PARIS VENEVELLES PAROTTE PARRERIE PASQUERIE PASSAGE BOUGLE PASSAGE CENT DIX MARCHES PASSAGE CHESNAIE PASSAGE CHEVAL BLANC PASSAGE CLOUTIERS PASSAGE COLLEGE PASSAGE CONTREAU PASSAGE CHESNAIE PASSAGE GLACIERE PASSAGE NOUVELLE CHASSE PASSAGE L OIE PASSAGE ARCADES PASSAGE PRUNELLES PASSAGE COMMERCE PASSAGE PUITS PASSAGE GARILLE PASSAGE PASSAGE LAVANDIERES PASSAGE LEO DELIBES PASSAGE MANGEARD PASSAGE PALIERS PASSAGE MONDE PASSAGE POSTE PASSAGEMARTIN PASSAGE SANS MARECHAL PASSARDIERE PASSEAU PASSE BRAISE PASSEDOITIERE PASSETEMPS PASSEVITE PASSOIR PASSONNIERE PASTIERE PASTORERIE PASTOURELLE PATAUDIERE PATIENCE PATIS PATIS AU CHAT PATIS AUX BOEUFS PATIS AUX CHEVAUX BREMARDINS PATIS BLANCHET PATIS RAMETTE PATIS SERPE PATIS BERGERES PATIS BROSSES PATIS CHEVERUES PATIS VIGNES PATIS ROUGET PATIS FROID PATIS GODEAU PATIS GUE BELLE AVETTE PATIS LIVET PATIS PONTE PATISSEAU PATISSERIE PATIS VERT PATOUILLARD PATOUILLERE PATOUILLERIE PATRASIERE PATRIERE PATTE NOIRE PATURAIL PATURAS PATURAS CARDUN PATURAS COLLEGES PATURE PATUREAUX PATURERIE PATURES PATURIERE PATURIERES PATY PATY GIBAULT PATY MARSAULE PAU PAUMERIE PAUMONS PAUVERDIERE PAUVERDIERE LANDES PAUVERDIERE VIGNES PAVE PAVENNERIE PAVICERIE PAVILLON PAVILLON D AUTEUIL PAVILLON BORDE PAVILLON CHAISE PAVILLON MALPAIRE PAVILLON MORTRIE PAVILLON ETANGS PAVILLON ROSES PAVILLON PLAT PAVILLON FLEURI PAVILLON L ARCHE PAVILLON PAVILLON BROSSILLOT PAVILLON PONTEAU PAVILLONNIERE PAVIOT PAVOISERIE PAYERE PAYSANTERIE PECHARDIERE PECHAUDIERE PECHERIE PECONNERIE PEDEFFRE 6 MIOTTIERE PEIGNE PEIGNELLERIE PEIGNERIE PEINIERE PEINNIERE PELARDIER PELE PELERINIERE PELINIERES PELL AR MOR PELLETERIE PELLETERIES PELLONNIERES PELLUETTE PELOIERIES PELOIN PELOISERIE PELOISIERE PELOISIERES PELOTERIE PELOUARDERIE PELOUAS PELOUSE PELOUSE COURTEE PELOUSE L AUNAY PELOUSES PELTERIE PELUENE PENARD PENARDIERES PENCHEVRERIE PENCHIEN PENDRIE PENITRES PENNERIE PENSERIE PENTIERE PENTIERES PEOTIERE PEPIE PEPINIERE PERAUDERIE PERAY PERCE PERCE NEIGE PERCHE PERCHERAY PERDREAU PERDREAUX PERDRIELLES PERDRIERE PERDRILLERE PERDRILLIERE PERDRITE PERDRIX PERGOLA PERIGAULT PEROUSE PEROUSERIE PERQUOI PERRAS PERRAUDERIES PERRAUDIERE PERRAUDIERES PERRAY PERRAY NEUF PERRAYS PERRE PERREE PERREES PERREIN PERRICHE PERRICHES PERRIE PERRIER PERRIERE PERRIERES PERRIERES MAIGNAN PERRIGNE PERRIGNE AU GEAU PERRIGNE CRY PERRIN PERRINE PERRINIERE PERRINIERES PERROCHEL PERROCHELLE PERROCHERIE PERRONNIERE PERROT PERROTERIE PERROTIERE PERROUX PERRUCHE PERRUCHER PERRUCHES PERRUCHET PERTU PERTUY PERVANCHE PERVEIGNERIE PERVENCHES PESCHESEUL PESLE PESSARDIERE PESSERIE PESSIERE PETIERE AITRE ALENCON AUBE AUBEPIN AULNAI AULNAIE AULNAY AUNAY AUNEAU AUTREVILLE BAIGNEUX BARDON BARGE BAS LANDES BAURAY BEAUCHENE BEAUGE BEAULIEU BEAUMONT BEAUREPAIRE BEAUVERGER BEL AIR BELIN BENARDIER BENER BESLON BIARD BIGNON BOIS BRUYERE MADERE ROUZIERS BOISGARD GONTHIER LOUDON PARIS BOISQUETIN BOISDENIS BOISVIER BORDAGE BORDIGNE BOSSE BOULAY BOULAYE BOUQUET BOUQUETEAU BOURDAS BOURENARD BOURGNEUF BOURNAIS BOUSSE BOUT BRAY BREHUSSE BREIL BRETEAU BRIAU BRIVES BROSSAY BROUILLARD BRULE BRULON BRUSSON BUFFARD BUISSON CARDUN CARREFOUR CASSEREAU CERVIGNY CHAMBRON BLANC CROIX FAILLY HUARDIERE LONG PENARD CHAMPROUX CHANTOISEAU CHAPELET CHAPITRE CHARDONNERET CHARME CHATELIER CHATON CHAUVON CHEMILLE CHENAY CHENE CHENE ARTUIS CHENE SERVINIERE CHENEVERT CHENEVROLLE CHERAIS CHERBON CHERRE CHESNAY CHEVIGNE CHEVREAU CHOQUETS CLAIRAY CLAIRET CLOSEAU CLOTEAU CLUANNE COIN CONILLE CONTENSOR COQ CORBEAU CORLEVE CORMIER COTEAU COUDEREAU COUDRAI COUDRAY COULEARD COURBOULAY COURCELLE COURCHALON COURPARENT COURTEILLE COURTOIN COURVARIN COURVEILLON COUVENT COZE CRANNES DAUVERS DEHALLAIS DEZE DIDIER DIEUZE AUBERDIERE AUBRIERE AURELIQUE BALEINE BARATTIERE BARBEE BARRE BAZOLIERE BEAUCE BEAUMERIE BEAUSSONNIERE BECANNE BERMONDIERE BEROIZE BEUCHERIE BEUCHETIERE BEURAILLERIE BEUREZIERE BIGOTTERIE BIGOTTIERE BILLARDIERE BINOCHE BIONNIERE BIZIERE BLAIRIE BLANCHARDIERE BLANCHETIERE BLINIERE BOCTIERE BOELLE BOISSIERE BONDE BORDE BORDE BOTTERIE BOUCHERIE BOUGUELIERE BOUHARDIERE BOULAIE BOULARDIERE BOULAS BOUSSARDIERE BOUSSE BRANCHERIE BRANLE BRARIE BRAUDIERE BREACHERE BRETONNIERE BRIERE BROCHERE BROSSE BRUMALLE BRUYERE BUCHETIERE BUFFARDIERE BURAISIERE BURANDIERE BUSSONNIERE BUTTE BUTTIERE CADOIRE CADOLIERE CANCERIE CANNETIERE CAVE CELERINE CEVERIE CHAINE CHALOUZIERE CHAMPAGNE CHAMPRONNIERE CHAPUISIERE CHARBONNIERE CHARPENTRIE CHARTREUSE CHAUSSEE CHAUVINERIE CHENAIE CHENAYE CHEROUVRIE CHEVALLERIE CHEVERIE CHICOTIERE CHOISIERE CHOPERIE CHRISTOPHLERE CLAIE CLAIRIERE CLEMENCIERE COCHINIERE COGNERIE COGRIERE COIRE COLASIERE COLLINIERE CONILLERIE CORBIERE CORBINIERE CORBONNIERE CORVEE ECOTAY COTIERE COUDRAIE COUDRAYE COULEE COULEE RANSOU COULONNIERE COUPELLIERE COURBE COUTURE CROIX CROIX ALINE CROIX BAUDIERE CROIXJOUIN CROUPE DAVIERE DEILLERIE DERVERIE DURANDIERE ECLECHE ECLUSE ENFERMERIE ENTE EUCHE FAUVELIERE FAVRIE FERME FERRIERE FOLIE FONTAINE FORET FORGE FORTINIERE FOSSE FOUASTERIE FOUCAUDIERE FOUCHERIE FOULIERE FOUQUELLERIE FOURNERIE FRANCHAISE FRENAIE FROGERIE FROMAGERIE FUIE FUTAIE GACHETIERE GAGNERIE GALBRUNIERE GALETIERE GALLERIE GARENNE GARLAUDIERE GASNERIE GATE GATINE GAUDIERE GAUDINE GAUDINIERE GAUTRAIE GENESIERE GENITIERE GERIE GERMENIERE GILARDIERE GIRARDERIE GIRAUDIERE GODBERDIERE GODINIERE GOUGERIE GOUJONNIERE GOUMARDIERE GOUPILLERE GOURDINIERE GOURIE GOURMETIERE GRANGE GRASSIERE GRASSINIERE GRENOUILLERE GRENOUILLERIE GROIE GUERRIERE GUIBERDIERE GUILLAUDIERE GUITTONNIERE GUYONNIERE HERBAUDIERE HERISSERIE HERTAUDIERE HORTUSIERE HOUDINIERE HOUSSAIE HOUSSAY HOUSSAYE HUBERDIERE HUCHERIE HUPPERIE HURAUDIERE HUTIERE JAMBIERE JANVRIE JARDINIERE JARIAS JARRETIERE JAUNAS JEAUNIERE JEUILLERE JEVELLERIE JOLIVERIE JOUANNETERIE JOURNEE JUQUELIERE LAIRE LANDE LANDELIERE LEVRIE LINANDIERE LIZAMBARDIERE LOMBARDIERE LONGERE LORENDIERE LOUVETIERE LOUVRAIS MABILIERE MACONNIERE MAILLARDIERE PETITE MAISON MAISONNETTE MAISON NEUVE MALADERIE MALFRAIRIE MALPOUGERE MANCELLERIE MARCHAIS CHERE MARE MARE GATE SEL MARTINIERE MAUPORCHERIE MERCERIE MERELLE MESANGERIE MESSAGERIEDENIS METAIRIE MEURDIERE MIROLETTE MOINERIE MOISIERE MONGENDRIERE MONTAGNE MONTANGERIE MORADERIE MORICIERE MORINIERE MORIONNIERE MOTINTERIE MOTTE MOTTE CERISE MOTTE SAYE MOUCHETIERE MOULIERE MOUSSE MULONNIERE MUSARDIERE NANTRIE NEMERIE NOE NOIRIE NONNERIE NOUSILLIERE OISELLERIE ORIERE OUCHE OUSERIE OUVRARDIERE PAGAUDIERE PANCHEVRIE PAPILLONNIERE PAQUELLERIE PARAGERE DO DINE PARENTIERE PASQUERIE PATURIERE PECHARDIERE PECHERIE PELOUSE PERCHE PERRICHE PERRIERE PERSILLIERE PESNIERE PIE PETITE PILLIERE EPINAY EPINEAU PLAINE PLANCHE PLANCHETTE POCHERIE POIRE TAPEE POISSONNIERE PORCHERE PORCHERIE PORTIERE POTELIERE POTERIE POULAINERIE PRAIRIE PREE PRETERIE PRISE PROUTIERE PROVIDENCE QUERLIERE QUINTE RAGE RAINIERE RANGEE RATERIE RATONNERIE REINIERE REMBEUDIERE RENAICHERE RESERVE RICHARDIERE RIGOUILLERE RIVIERE ROBERDIERE ROBINIERE ROCHE ROCHELLE ROCHELLERIE ROCHERIE ROGERIE RONCE ROUILLARDIERE ROUSSELLIERE ROUSSIERE ROUZIERE RUCHE PETITEPETITES AEREES PETITES ALLEES SANSONNIERE SAPINIERE PETITES AUGUISIERES SAULAIE SAUVAGERE SAVINAIE SAVINAIS PETITES BARRES PETITES BLOTTERIES PETITES BLOTTIERES PETITES BORDERIES PETITES BORDES PETITES BOUERES PETITES BOURDONNIERES PETITES BOURSELIERES PETITES BOUTONNIERES PETITES BRAUDIERES PETITES BRETONNIERES PETITES BROSSES PETITES BRUYERES PETITES BUTTES PETITES CANLOIRES PETITES CHAIGNONNERIES PETITES CHAINES PETITES CHALONGES PETITES CHAUDEFOUASSES PETITES CHEPARDIERES PETITES CHEVRIES PETITES COULONNIERES PETITES CREPINIERES PETITES DREUSERIES PETITES EPINETTES PETITES EPINIERES SERGEOLIERE PETITES FILIERES PETITES FINARDIERES PETITES FONTAINES PETITES FORGES PETITES FRAIRIES PETITES GALESNIERES PETITES GANNERIES PETITES GILBERDIERES PETITES GIMBERDIERES PETITES GODERIES PETITES GONACHERES PETITES GOUSSIERES PETITES GRISELLES PETITES GROIES PETITES GROUAS PETITES GUILARDIERES PETITES GUIMERIES PETITES HAIES PETITES HAIES AUX BICHES PETITES HAYES PETITES HELINIERES PETITES HERTAUDIERES PETITES HOULERIES PETITES ILES PETITES JOLLIERES PETITES LAMES PETITES LANDES PETITES LORIOTIERES PETITES LOUPES PETITES MAISONS PETITES MAISONS ROUGES PETITES MARES PETITES MOLLIERES PETITES MONTREES PETITES MORCINES PETITES MOULINES ESNAY PETITES NOES PETITES PAPINIERES PETITES PEZERIES PETITES PICAUDIERES PETITES PICORIERES PETITES PIERRIERES PETITES PITARDIERES PETITES PLACES PETITES PLAUDERIES PETITES POUSSIERES PETITES PRISES PETITES PROUILLIERES PETITES QUINTES PETITES RIFFAUDIERES PETITES RIGAUDIERES PETITES RIVIERES PETITES ROCHETTES PETITES RONGERES PETITES ROTES PETITES ROTTIERES PETITES ROUSSES PETITES ROYERIES PETITES RUES ESSARD PETITES SOUCHES PETITES THUAUDIERES PETITES TOUCHES PETITES VALLEES PETITES VARENNES PETITES VENELLES PETITES VENTES PETITES VIGNES PETITES YVORIES ETALOIR ETANG TANNERIE TANSOLLERIE TAUPE TEMPLERIE TERCERIE THIBAUDIERE THIBERDIERE TOUCHE TOUCHERONDE TREMBLAIE TROCHE TUILERIE VACHERIE VALLEE VANNERIE VARANNE VASSOLLERIE VEQUERIE VERONNIERE VERRERIE VERRONNERIE VETIERE VICULIERE VIGNE VILLENETTE VINCENDIERE VIOGERE VOLEE ZEZIERE FANEU FAY FERRE FIEF FONTAY FOUILLET FOUR FOURNEAU FRESNE FREU FRILOUX FROU GAIGNE GAILLAC GARANCHER GASSEAU GASTINE GASTINE QUINTA GENETAY GEVRON GOMBAULT GORD GOUPILLON GRAIS GRAVIER GRIPOUCE GROUAS GRUET GUE GUEMANCAIS GUE PERROUX GUIGNIER GUIMONDEAU HABIT HALLIER HALOPPE HERMITAGE HERNIER HETRE HONORE HOUX HUME PETITIERES JANDIER JARDIN JARRIER JONCHERAY JOUBERDIER LAMBERT LAUNAY LAURIER LEARD LETERRAI LIBOIS LIVERNOIS LOIRAY LOIRON LONRAY L OULAIE LUART LUSSEAU MAILLENCOURT MALESSARD MALIDOR MALLEVILLE MANONE MANS MARTIGNE MAULNY MELINAIS MENY MESNIL METEIL MILON MIMBRE MOIRE MONBESLIN MONBROSSE MONCEAU MONCELAS MONFRESLON MONT MONTARGIS MONTBRAY MONTCHEVRIER MORTEUVRE MORTIER MORTIER PINEAU MORTRAY MORVILLE MOTTAY MULOT NAVRIL NOYER NUISEMENT ORMEAU PAIN PARC PARIS PAS PATIS PAVILLON PENCHIEN PERRAY PERRE PERRICHET PERRIN PEZE PIERRELEZ PIGEONNIER PIN PINEAU PLANCHE PLESSIS POIL TRUIE POIRIER PONTFOUR POYER PRE PRE CARREFOUR PREHAUT PRE POURRI PRESBYTERE PRESSAUSSOUS PRESSOIR PRIEURE PRINGE PRISEAU PUISEAUX QUINSAY RAIDIT RAPEILLARD RENUSSON REUCHON RIALAUME RIBARDEAU RIBAY RIBEMONT RIMBERT RIOUSSE RONCERAI RONCERAY RONCHERAY ROSIER ROUILLON ROUPERROUX ROY RUDEAU RUISSEAU RUSE SABLON SABLONNE PETITANTOINE PETITCHEREAU PETITDYNE PETITHUBERT PETITJEAN PETITLOUIS PETITMARTIN PETITOUEN PETITPERE PETITS AITRES SALVERT PETITS ARIS SAULAY PETITS PETITS FADES PETITS BOULAIES PETITS BOULAS PETITS BOURGS PETITS BOUTTINEAUX PETITS BREUILS PETITS BURONS PETITS CELLIERS PETITS PETITS CHATAIGNIERS MASSUERES PETITS CHENES PETITS CHENNEVRIS PETITS CHENONS PETITS COLLINS PETITS COSNIERS PETITS COURPINS PETITS PETITS COURTILS PETITS DINONS PETITS ECULAYES PETITS ETANGS PETITS FOUGERAIS PETITS GAZONS PETITS HETRES PETITS HOMMELETS PETITS HOUSSAIS PETITS HULAIS PETITS LAURIERS PETITS LOIGNES PETITS PETITS MATELAS PETITS MAUBETS PETITS MOLANDS PETITS MOLLANS PETITS MONCEAUX PETITS MONTAIGUS PETITS MONT MARS PETITS MORTRAIS PETITS MOULINS PETITS NIDS BESLAY SOLEIL PETITS ORMEAUX SORTOIR SOUCHET SOUPER SOURCHE PETITS PARCS PETITS PATIS PETITS PONTS PETITS PRES PETITS ROUZIERS PETITS RUAUX PETITS SABLONS PETITS VAUX PETITS VIVIERS TAILLIS TAMBOUR TENU TERTIFUME TERTRE THEUCELLES THORIGNE TIMON TIRON TOUCHENAY TREMBLAI TREMBLAIS TREMBLAY TRONCHET TROUSSANON TURE VALLIERE VAUCHATON VAUGEOIS VAUGERIN VAUGIRAULT VAULOGER VAUMORIN VAUNAVAL VAUX VERDELLE VERGER VIEIL HETRE VIGNAUX VILETTE VILLEPEINTE VILLETTE VINACLE VIVIER VIVREUX PETOU PEUPLERAIE PEUPLERAIE GLATIGNE PEUPLIER PEUPLIERS PEUPLIERS CHENE PEURAY PEUTIBALE PEUTY PEUVERIE PEZEREUIL PEZERIE PHILIPOTIERE PHILIPPERIE PHILIPPIERE PHILIPPOTERIE PIAILLERIE PIARDERIE PIARDIERE PIATS PICAIGNE PICANETTE PICANGRAIGNE PICANNIERES PICARDERIE PICARDIE PICARDIERE PICAUDIAU PICAUDIERE PICAUDIERES PICHARDIERE PICHEREAU PICHOIERIE PICHONNERIE PICHONNIERE PICHONNIERES PICOISEAU PICOISEAUX PICOTIERE PICOULEAUX PICTERIE PIDORIERE PIE PIECE BASSE PIECE DERRIERE PIECE DOUZE PIECE JOUANZE PIECE CUSSE PIECE L AIRE PIECE L ARCHE PIECE L ECOTAY PIECE L PIECE CHENES PIECE PIECE LANDES PIECE SAPINS PIECE D OIZE PIECE PIECE BOURRAY PIECE CORMIER PIECE MAIL PIECE MILIEU PIECE VERGER PIECE PIECE PUITS PIECE TERTRE PIECE VAU PIECE LUERE PIECE LOUDONNEAUX PIECE DERRIERE PIECE HOUSSINE PIECE GRENOUILLES PIECE LANDES PIECE LAURIER PIECE BESLIN PIECE NOYER PIECES PIED BOULERIE PIED BOEUF PIED GREZ PIED GUICHARD PIEDORS PIEGERIE PIEGERIES PIEGU PIELLERIE PIELLIERE PIE QUI COUETTE PIERRAS PIERRAY PIERRE AIGUE AU LADRE BISE BLANCHE BOULAIS DROITE PIERRELEE LONGUE MAZEL MOLLE OUVERTE PERCEE PLATE PIERRERIES PIERRES PIERRES BISES PIERRES FINES PIERREVOUZE PIERROTERIE PIESSE PIETANNERIE PIETRERIE PIFERDIERE PIFEUDIERE PIGALE PIGALERIE PIGALIERE PIGALLE PIGALLERIE PIGALLIERE PIGE PIGEOLIERE PIGEON BLANC PIGEONNERIE PIGEONNIERE PIGNARDIERE PIGNON TORD PIGNON VERT PIHAULT PIHERIE PILANS PILE PILETIERE PILETTERIE PILIERE PILIER VERT PILLERIE PILLETIERE PILLETIERES PILLETRIE PILLETTE PILLIERE PILMY PILOISIERE PILON PILONNERIE PILONNIERE PILORGERE PILOTIERE PILSOTIERE PILTIERE PIMPARDIERES PIN PINARDIERE PINARDIERES PINAUDIERE PINCE ALOUETTE PINCELOUET PINCE LOUP PINCELOUP PINCE LOUVETTE PINCEPAIN PINCONNIERE PINCONS PIN LANDES PINEAU PINEAU POMMERAIE PINEDE PINELIERE PINELLE PINGAUDERIE PINOTERIE PINS PINS BLANCS PIN SEC PINSON PINSONNIERE PINSONNIERES PINTARDIERE PINTERIE PINTIERE PIOGERIE PIOLAY PION PIONNIERE PIQUERIE PIQUET PIRONNIERE PISSELOUP PISSEROTS PISSOT PITARDIERE PITARDIERES PITELLERIE PITIE PITIERE PITOISIERE PITOUSERIE PITOUSIERE PITRIE PITTERIE PIVANNERIE PIVARD PIVARDERIE PIVARDIERE PIVARDRIE PIVERDIERE PIZIEUX 117 EME REGIMENT INFANTERIE 11 NOVEMBRE 11 NOVEMBRE 1918 8 MAI 8 MAI 1945 ABBE BRIERE ABBE DUBOIS ACACIAS ADRIEN TIRONNEAU ALBERT ETIEMBRE ALBERT LIEBAULT AMBROISE PARE ANCIENNE BASCULE ANCIENNES HALLES ANCIENS AFN ANDRE RICORDEAU ARISTIDE BRIAND ARMES ARSENE BAUSSANT AUBEPINE AUVERGNE AVERTON BAIARDI BASCULE BASSUM BEL AIR BELENOS BLAISE PASCAL BON COIN BORDELIERES BOULANGERIE BOURNEUFS CAILLAUX CAILLE CALIFORNIE CANTINE CARDINAL DUBOIS CARNOT CEDRES CERISIERS FOIRE PLAISIR ROUX CHANOINE LANGLAIS CHANTERIE CHAPELLE CHARENTES PLACE CLAUDE CHAPPE CLAUDE MONET COMMANDANT DEMENOIS COMTES BELIN COUTURES CREMAILLERE CROIX ROUGE CROIX VERTE D ARMES D AUVERGNE BOGOTA BREST BRETAGNE CARACAS FONTAINE LIBERATION LIBERTE LICE LUNE MAISON BRULEE NOUVELLE FRANCE PLEIADE REPUBLIQUE VENDEE L L GASTINES L EPERON L EUROPE L HOTEL LIMA L OASIS LUXEMBOURG MONTEVIDEO PORNIC 3 FRERES BUSSON COMTES MAINE CYTISES FLAMANTS HALLES JACOBINS NOISETIERS PRIMEVERES SABLES SABLONS TILLEULS VIGNES SALMON DOMAINE GUERANGER 11 NOVEMBRE 1918 19 MARS 1962 8 MAI 1945 BELVEDERE BRIOLET CANTAL CARDINAL GRENTE FOIRE CHANOINE PANNETIER CROISIC COLLIERE DUFOUR GENERAL GUE MAULNY GUE GALERNE GUESCLIN HALLAI MARCHE AUX PORCS PATISLAZARE PORT PRE PLACE EGLISEMARTIN ERNEST LANGLAIS ESTOURNELLES CONSTANT EUGENE GARNIER EUROPE FELIX MAIRE FLEURENTERIE FLORALIES FLORENT BEAURY FOCH FONTAINEJULIEN MONTBRUN NICOLAY FRANKLIN ROOSEVELT FROUIL GAMBETTA GAUTIER CHEVREUIL GENERAL GENERAL GEORGE SAND BOUTTIE CLEMENCEAU DESNOS GAUTHIER GEORGE WASHINGTON GERMAIN PILON MONARQUE GUSTAVE LANGEVIN HALLES HAUT QUARTIER HELLE DESJARDINS HENRI COUTARD HENRI IV HENRI JEANNE HENRI PAUMIER HOTEL HOWARD FLOREY ISAAC ROCHE JACQUES MOTHE JACQUES HOUDAYER JACQUES MOREAU JACQUES PELETIER JARDINS JUDAS ARMAND GOUJON GRAFFIN MERMOZ PLACE JOHN KENNEDY JULES FERRY CONFERENCE LOCARNO LAURIERS LAVOIR LAZARE BAIF LECESVE LEDRU ROLLIN LEONARD VINCI LIBERATION LIBERTE LIONEL LECOUTEUX LOUIS ARAGON LOUIS CARS LOUISE MICHEL MADAME MAUCLERC MAIL MAINE MARCEL MORAND MARCHE MARCHE AUX BESTIAUX MARCHE AUX BOEUFS MARCHE AUX PORCS MARECHAL CURIE PAPE CARPANTIER MARIN MERSENNE MELUSINE MICHEL VERDIER MINIMES MOLLIERE MONTAIGNE MONUMENT NADAR NATIONALE NEUVE NICEPHORE NIEPCE ORMEAUX ORMES PASTEUR PATIS BRULAT CEZANNE PERCHE PERRAY MARCHE BELON PLACITRE POSTE RAPHAEL ELIZE RENE BAGLAN RENE LELIEVRE RENE LEVASSEUR REPUBLIQUE PLACERIE RIGOURDAINES ROBERT GARNIER RONSARD PLACES PLACEANTOINE PLACEAUBIN PLACEAVOIE PLACEBENOIT PLACEDENIS PLACEAPOLLINE PLACEBARBE PLACEETIENNE PLACEGERMAIN PLACEGILLES PLACEHILAIRE PLACEJEAN BAPTISTE PLACEJOUIN PLACEJULIEN PLACELOUIS PLACEMARC PLACEMARTIN PLACEMEDARD PLACEMICHEL PLACENICOLAS PLACEPIERRE PLACESULPICE PLACEVINCENT SALLE FETES SALVERT SAULES SINCLAIR LEWIS TERTRE THIERS TILLEUL TILLEULS TISSERANDS TOUR TROENES VERDUN VERGER VICTOIRE VICTOR DUBOIS VICTOR HUGO VIEUX PUITS VIOLETTES VOLTAIRE W MOZART PLACIDRE PLACIERE PLACITRE PLAIES PLAINCHAUDIN PLAINE PLAINE DELIVRANCE PLAINE ORMEAUX PLAINERIE PLAINETEAUX PLAINIERE PLAINNIERE PLAIS PLAISANCE PLAISSE PLAN PLANCHE PLANCHE AUGIS PLANCHE PLANCHE VAUX PLANCHE D HIERRAY PLANCHE SAULE PLANCHELLE PLANCHEMOU PLANCHES PLANCHES MONNAY PLANCHES LAUDELLES PLANCHETTE PLANCHETTES PLANSONNIERE PLANTARD PLANTE PLANTES PLAPINIERES PLATANES PLAT D ETAIN PLATE PLATEAU FLEURI PLATELLERIE PLAT PAYS PLAUDIER PLAUDIERE PLAUDIERES PLECES PLEE PLEIN AIR PLEIN FEU PLEINIERE PLEIN VENT PLESSE PLESSES PLESSIS PLESSIS BUFFAY PLESSIS GENTIERE PLESSIS M AGRADO PLESSIS MALHONTE PLESSIS OMER PLESSIS ROLAND PLEUDIERES PLISSON PLOTIERE PLOTONNERIE PLOUSE PLOUTERIE PLUMARDIERE PN 14 LILAS PN 242 COMMISSAIRE PN 74 GRAVIER POCHARDIERE POCHE POCHUET POGERIE POIDEVINIERE POIGNARDIERE POIL TRUIE POIL TRUYE POILE POILLE POILS FILS POINNETIERE POINT JOUR POINTE BELLEVILLE POINTE CARDUN POINTE CORMIER POINTE FOURGAS POINTE JUDAS POINTE GARENNE POINTE BELLES POINTE LANDES POINTE GUE POINTE FLEURIE POINTE GUIMERIES POINTEILLERE POINTES POIPAILLE POIRE TAPEE POIRIER POIRIER JUILLET POIRIER JOANNET POIRIER LARRON POIRIER ROND POIRIER ROUGE POIRIERS POIRIER VERT POIS POISSARD POISSONNERIE POISSONNIERE POISSONS POITEVINIERE POITONNERIE POITRON POLIERE POLKA POLOGNE POLUCAN POLYANTHAS POMMERAIE POMMERAUX POMMERAY POMMERET POMMEREUIL POMMIER POMMIER ROY POMMIER ROND POMPE PONCEAU PONCEAUX AUX BREBIS AUX PRETRES BEC D OIE BESNIER CHARMOIS BIAIS BOSSE BRAYE COEUR COURTEE D EFFE FOLIE FORGES HOUSSAYE VILLETTE L ESPERANCE L ORNE MONAY PONT CLAIES LOGES VAUGOUST D IVERNY D ORNE MACHEFER PONTEAU PONTENAILLE PONTENCHERI ESNAULT FOURI FOURRY GIRARD PONTHIBAULT PONTIGNY LIMARON MAILLARD MICHON NAPOLEON NEUF NIVEAU PONTOME PONTONNIERE PONTORD PINARD PONTQUOI PONTS ANGEVINS PONTS YSSOIRS TATIN PONTVELLERIE PONTVILLAIN POPELINIERE POPLINIERES PORCHERE PORCHERIE PORCHONNIERE PORIE PORJONNIERE PORTAIL PORT AU LIARD PORT AUMONE PORT L EPAU PORT DENET PORT ROCHES PORTEAU PORTE JOIRE PORTE ORTHIAU PORTES PORTES ROUGES PORT ETROIT PORT GAUTIER PORT GOULARDIERES PORTION PORT PORT PORT JUIGNE PORT PONTHOUIN PORT GAUTHIER PORT GAUTIER PORT NICHE PORTMARIE PORT VALIENNE POSSE POSSET POSTE POSTE AVANCE POTARD POTEAU POTELIERE POTENCE POTERAU POTERIE POTERIES POTERNE CRASSEUX POTERNE CRASSEUX LANDE POTERNE FER POTINE POTINERIE POTINIERE POTINIERES POTIRON POTIRONNERIE POTIRONNIERE POTTERIE POUDRIERES POUILLEAU POUILLERS POUILLOTIERE POULAINERIE POULAISERIE POULARDIERE POULARDIERES POULERIE POULETTERIE POULICHERE POULINIERE POULLERIE POULOIN POUNIERE POUPETTERIE POUPLAIN POUPLINIERES POURRIERE POUSSERIES POUSSINIERE POUSTIERE POUTAIE POUTELLIERES POUVARDIERE POUZAS PRAILLERE PRAIRIE PRAIRIE LIVAUDIERE PRAIRIE GAINS PRAIRIES PRALAIS PRALE PRASE PRATEAU PRE PREAU PRE AU CHEVAL PREAUDIN PREAURAY PREAUX PRE AUX CHENES PRE AUX MOINES PREBANDE PRE BAS PRE BOCAGE PRE CABARET PRECHARTIERE PRE D AMONT PRE D ARCEAU PRE DEVANT PRE HIRE PRE BARBEE PRE BOMBE PRE BORDE PRE CHESNAIE PRE CROIX PRE CURE PRE DENISIERE PRE FONTAINE PRE GARENNE PRE L AIRE PRE MARE PRE MARTIGNE PRE PLANCHE PRE L ARCHE PRE TOUCHE PRE L AUNEAU PRE VALLEE PRE VAUDERIE PRE VIGNE PRE L ETANG PRE L ORMEAU PRE PRELANDON PRE RENE PRE AUMONES PRE BEAUSSIERS PRE BOUCHERES PRE CANNES PRE MENAUDES PRE MORINIERES PRE TEINIERES PRE TOUVOIS PRE DEVEAUX PRE BIGNON PRE PRE BOURG PRE BUIS PRE BUISSON PRE CHAILLOU PRE CHALON PRE DUVERT PRE CHENE PRE COCHER PRE GRAVIER PRE GUE PRE MESLIER PRE PAVILLON PRE TORS PRE TUFFEAU PREE PREELLE PREFECTURE PRE FONTENY PRE GILON PRE GRILLARD PREHAUT PREHENCHERE PRE HERPIN PRE HUON PRELAMBERT PRE LATTE PRE LAURENT PRE BOULAS PRE LONG PRE MADAME PRE MARTIN PREMARTINE PRE MERIAU PREMIGNE PRE MOREAU PREMOU PREND SON DROIT PRE NEUF PREOULIERE PRE PAILLIER PRE POURRI PRE PRUD HOMME PRE RABOT PRE ROND PRES PRESAIE PRENICOLAS MURS PRESBYTERE PRESBYTERE D ATHENAY PRESBYTERE BOURG PRES COLOMBERIE PRES RONCE PRES D EN BAS PRES GUE PRES VAL PRES VAUHAROUARD PRE SEC PRESIDIAL PRES LONGS PRESSAILLERIE PRESSAUSSOUS PRES SERRAUX PRESSIERE PRESSOIR PRESSOIRERIE PRESSOIRS PRESTEAU PRETE RIEN PRETERIE PRETERIES PRETRIE PREVAUTE PREVENCHERE PREVERIE PRE VERT PREVIERE PREVOISIERE PREVOLLES PREVOSTERIE PREVOTERIE PRE YVON PRIEULERIE PRIEULERIE CORMIER PRIEURE PRIEURE MAYANNE MAYANNE PRIMAVERA PRIMEVERES PRIMOURS PRINCETIERE PRINCIERE PRINCIPALE PRINTEMPS PRISE PRISE AUX VERTUS PRISE BLIN PRISE BONNE PRISE DRUGEON PRISE HARDOIN PRISE MICHAUT PRISES PRISE THOMAS PRISONNIERE PROMENADE RIVAUDERIES PROMENADE MAIL PROMENADE MAIL PROMENADE MAIL PROMENADE NEWTON PRON PROTIBUS PROUILLERES PROUTERIE PROUTIERE PROVENDERIE PROVOTIERE PRUNETIERE PRUNIERS PRUNUS PT MEZERAY MINE GASTINES PUCE PUCELLE PUISARD PUISEAUX PUISSAIS PUISSANDIERE PUISSET PUIT FLEURI PUITS PUITS AU COQ PUITS AU LARD PUITS FOLIE PUITS TEILLE PUTEAU PUTOIS QUAI ALPHONSE ADET QUAI AMIRAL LALANDE QUAI L HUISNE QUAI JAURES QUAI LEDRU ROLLIN QUAI MARLAND QUAI NATIONAL QUAI WILSON QUARTIER GROSEILLERS QUARTIER GAULOIS QUARTIER VERRON GOUSTIERE QUATERPIERRE BRULE QUATERRUBILLARD QUATER ROUTE PRECIGNE QUATER8 MAI QUATERALPHONSE LAVALLEE QUATERALPHONSE LEPORCHE QUATERESTOURNELLES CONSTANT QUATERGOUIN QUATERHENRY DELAGENIERE QUATERLISBORDE QUATERPREMARTINE QUATERSABLONS QUATERSIEYES QUATERTRISTAN BERNARD QUATERVICTOR HUGO QUATORZE BOISSELEES QUATRE QUATRE CHENES QUATRE POCHES QUATRE SABOTS QUATRE TABATIERES QUATREUSIERE QUATRE VAUX QUATRE VENTS QUELLERIES QUENARDIERE QUERCERIE QUERIE QUEUE L ETANG QUEUE QUEUERIE QUEURIE QUEZIERE QUICAUDERIE QUIELLERIE QUIENIERE QUILLERIE QUINCAMPOIS QUINCAMPOIX QUINCONCE QUINQUIERE QUINSAY QUINTAINES QUINTARD QUINTES QUOILONNIERE RABELLERIE RABERIE RABIERE RABOTTIERE RABOURES RABOURS RACHAT RACINAIE RACINE RACOISIERES RADERIE RADRAY RAFEAU RAFEU RAFFUT RAFIGEAU RAFOURIERE RAFRAIRIE RAGANNE RAGANNERIE RAGE RAGLACE RAGONNIERE RAGONNIERES RAGOT RAGOTIERE RAGOTTERIE RAGOTTERIES RAGOTTIERE RAGUENEAU RAGUENIERES RAGUIDEAUX RAHART RAIE RAILLERE RAILLERIE RAILLON RAINEBAUDIERES RAINERIE RAINIERE RAINIERES RAIRIE RAM RAMADIERES RAMANGEES RAMBAUDRE RAMBOUILLET RAMEE RAMEES RAMERIES RAMETTE RANCH RANCH PONET RANCHE RANCHER RANCONNERIES RANDONNAYS RANGEE RANGOTTIERE RANSOU RAPILLARD RAPINIERE RASERIES RASNAY RATEAU RATEE RATELIERE RATELLERIE RATELLIERE RATERIE RATIERES RATIERS RATTERIE RAUDERIE RAUDIERE RAVALIERES RAVERIE RAVET RAVOIR RAY RAYNIERE REBELLERIE REBILLARDIERES REBOUFFIERE REBOURCERIE REBOURSIERE RECHAMP RECHANGERIE RECOURBE RECOURCERIE REDARDIERE REDOIRIE REDOIRIE HIERRAY REFUGE REGADELLERIE REGARDIERES REGLANNIERE REGORGERIES REGORGERIES GAI PINSON REGRET REHERIES REINEBAUDIERE REINE BOURGERE REINE BOUVIERE REINIERE REINIERES RELAIS RELANDIERE RELANDIERES REMARS REMBLAI REMELLERIE REMIGNER REMOULIERE RENAICHERE RENAISSANCE RENAIZIERE RENARD RENARDERIE RENARDIERE RENARDIERES RENAUDERIE RENAUDIERE RENAUDIERES RENAUDIN RENAUDIN GRANDINIERE RENAUDINIERE RENAUDINS RENAULT DENIS RENCLOS RENCONTRE RENE SALMON RENEUDIERE RENEUDIERES RENIER RENNES RENOTTIERE RENOUILLERE RENOULIERE RENOULIERES RENOURIE RENTIERES RENUSSON REPOS REPRISES REPUCELLERIE REPUSSEAU RESERVE RESERVOIR AMITIE ANILLE AULMES AULNAY AVALOIRS BARRE BEAULIEU BELLEVUE BELLEVUE VIGNE BENJAMIN DELESSERT PIGALLE VERMEIL BON AIR BRULAY BONIO BOSQUET BOUGEON PLACES BRUNETIERE BRUYERES CAMPING VEGRE CANNETIERE CASTEL CHALET 26 SILLE PLAGE AUVOUR CHATAIGNIER CHATAIGNI FORTIER CHANGRENIERS CHANGRENIERS CHANVRERIE CHARMETTES CHARPENTERIE RESIDENCE CHENE CHENE ROUGE CHENE VERT CLOTEAU COQUELICOTS COTEAU COURTILLE COUTURE CROIX HERAULT BELLEVUE COURTILLE NOMBRAIE L AURAUCARIA VALLEE 4 VENTS ORMEAUX VIGNES JARRIER LAC PRESBYTERE PRIEURE STADE VIEUX BOURG ECU EPAIGNE FEUILLANTINES FIERBOIS F METAIRIE FONTENAY FORTERIE FOYER GAUDINE GENDARMERIE GENDARMERIE GARENNE GERANIUMS PREVOSTERIE GUE GUERINIERE HAUT HAUTES TOUCHES HAUTREUX PERE FILS HENRI BESNARD HENRI PASTEAU HERON HERON H NARGEOTTIERES HLM JANINE PROVOT JARDIN JEANNE D ARC JEULINIERE JONCHERAY BRUYERE L ANSUISIERE COUTURE 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ROUTE BASSE DECALAIS ROUTE BASSE DEGERVAIS ROUTE BASSE LANDE ROUTE BAUGE L OSERAIE ROUTE BEAUCE ROUTE BEAUFAY ROUTE BEAUFAY ROSIERS ROUTE BEAUFEU ROUTE BEAUMONT ROUTE BEAUMONT SARTHE ROUTE BEAUVAIS ROUTE BELLEME ROUTE BELLOU TRICHARD PILLETIERE ROUTE BESNES ROUTE BETHETE VAU ROUTE BEUCHER ROUTE BLANCS ROUTE BOESSE SEC ROUTE ROUTE VAUX ROUTE MARTIN ROUTE NEUF ROUTE BONNES DAMES ROUTE BONNETABLE ROUTE BOUER ROUTE BOULOIRE ROUTE BOURDASERIE ROUTE BOURG ROI VALLEES ROUTE BOURRAY ROUTE BOUSSE ROUTE BOUTINIERES ROUTE BRAINS GEE ROUTE BREIL ROUTE BREIL MERIZE ROUTE BRENAILLE ROUTE BRETTE PINS ROUTE BREUILLES ROUTE BRIVES PONTENCHERI ROUTE BROSSE ROUTE BRULON ROUTE BRUYERE ROUTE CALIFORNIE ROUTE CAMP VACHE NOIRE ROUTE CARTES ROUTE CELLE BELLE ETOILE ROUTE CERANS FOULLETOURTE ROUTE CERANS FOURNEAU ROUTE CERIZE ROUTE CHADENIERE ROUTE CHALLES ROUTE CHALLES BORDAGE LOIGNES ROUTE CHAMPAGNE ROUTE CHAMPDORCET ROUTE CHANGE ROUTE CHAPUISIERES ROUTE CHARDONNEUX ROUTE CHARTRE LOIR ROUTE LOIR ROUTE L 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MAMERS BRIERES ROUTE MAQUERE ROUTE MAREIL ROUTE MAROLETTE ROUTE MAYET BOURG ROUTE MONHOUDOU PONTAUME ROUTE MONTFELE ROUTE MONTFORT ROUTE MONTREUIL TESSERIE ROUTE MULSANNE ROUTE NOGENT ROUTE OIZE ROUTE OIZE SERVINIERE ROUTE PARCE VALLEES ROUTE PARENCE BORDEAU ROUTE PARIGNE L EVEQUE ROUTE PARIS ROUTE PINCE ROUTE PIRMIL GREFFIER ROUTE PONTVALLAIN CHATELOUP ROUTE PRECIGNE ROUTE PREMARTINE ROUTE ROUILLON ROUTE RUAUDIN ROUTE 4 ROUTES MAUPERTUIS ROUTE SABLE ROUTE DEAIGNAN ROUTE DEAUBIN ROUTE DEAUBIN AUX TERRASSE ROUTE DEBRICE ROUTE DEGEORGES ROUTE DEMAIXENT ROUTE DEMARTIN ROUTE SAVIGNE CHENE VERT ROUTE BARRES ROUTE BROSSILLOTS SABLONNIERE ROUTE SCEAUX ROUTE FONDUS ROUTE FONTAINES ROUTE GALOPIERES ROUTE HAIES ROUTE LANDES ROUTE LOGES ROUTE ROUTE MOLLANS ROUTE SPAY ROUTE PLANCHES ROUTE PLANTES ROUTE RENAUDES ROUTE TORILLERES BAS VOLLET ROUTE SURE ROUTE TELOCHE ROUTE THELIGNY ROUTE THOIGNE PIGEOLIERE ROUTE TORCE BEAULIEU ROUTE TOURS ROUTE TRANGE ROUTE VARENNE ROUTE VAUFARGIS ROUTE VAUX ROUTE 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FOURNEAU ROUTE FRESNAY SARTHE ROUTE GENNETEIL ROUTE GESNES ROUTE LUCE ROUTE GRANDS ROUTE GUE ROUTE GUE BUFFARD ROUTE GUECELARD ROUTE GUE GUE MEZIERES ROUTE GUIERCHE ROUTE GUILMAUDIERE ROUTE HABIT ROUTE HALLES ROUTE HANTENAISE ROUTE HARAS ROUTE HAUGAISE ROUTEILLERE ROUTE ISAAC ROUTE JASNIERES ROUTE JOUE EN CHARNIE ROUTE JOUE L ABBE ROUTE JOUE L ABBE DONAIRE ROUTE JOUE L ABBE FORGES ROUTE JOUE L ABBE MAHONIAS ROUTE BELLE CROIX FOUJASSIERE ROUTE BOSSE ROUTE LAC ROUTE CHARTRE ROUTE CHARTRE LOIR ROUTE FLECHE ROUTE GUIERCHE ROUTE LAIGNE ROUTE LAILLE ROUTE LAILLE SABLE ROUTE LAINERIE ROUTE LAIRES ROUTE LANDE ROUTE LANDES ROUTE LANDES BOULEAUX ROUTE LANDES COLOMBES ROUTE SUZE GRANDES FORGES ROUTE SUZE SARTHE ROUTE SUZE TOUCHES ROUTE LAVAL ROUTE LAVARDIN ROUTE LAVARDIN LILAS ROUTE LAVARE ROUTE LAVOIR ROUTE LUDE ROUTE LUDE BEAUSEJOUR ROUTE LUDE BRUERE ROUTE LUDE LAURIER ROUTE LUDE MONT LABEUR ROUTE MANS ROUTE MANS JOURANNIERE ROUTE MANS MIMOSAS ROUTE MANS SOUCHE ROUTE MANS TOURSHUBERT ROUTE MANS 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ROUTE MONTFELE ROUTE MONTFORT ROUTE MONTFORT GESNOIS ROUTE MONTFORT ROTROU ROUTE MONTFREIN ROUTE MONTJALU ROUTE MONTMIRAIL ROUTE MONTMIRAIL TERTRES ROUTE MONTREUIL ROUTE MORANNE CHAUSSEE ROUTE MORANNES ROUTE ROUTE MOULINS ROUTE NATIONALE ROUTE NATIONALE 138 ROUTE NATIONALE 23 ROUTE NATIONALE 30 ROUTE NEUVE ROUTE NEUVILLALAIS ROUTE NICOUX MUSSETIERE ROUTE NOGENT ROUTE NOGENT BERNARD ROUTE NOGENT LOIR ROUTE NOIRIE ROUTE NOIRIE CEDRES ROUTE NORMANDERIE AUGERIES ROUTE NORMANDERIE CROSLOUP ROUTE NOTRE DAME PE ROUTE NOYEN SARTHE ROUTE NOYEN SARTHE FORET ROUTE OIZE ROUTE OR ROUTE ORMEAU ROUTE OUVRIE ROUTE PARC ROUTE PARCE SARTHE ROUTE PARENCE ROUTE PARENCE BEAULIEU ROUTE PARENCE TROIS NOYERS ROUTE PARENNES ROUTE PARIGNE ROUTE PARIGNE HERRAUDIERE ROUTE PARIGNE POLIN ROUTE PARIGNE POLIN GRU ROUTE PARIGNE L EVEQUE ROUTE PARIGNE L EVEQUE HERAUDIERE ROUTE PARIS ROUTE PARIS LANDES ROUTE PAVILLON ROUTE PAYS HAUT ROUTE PERREES ROUTE PERSEIGNE ROUTE CHARMOIE ROUTE POIRIER ROUTE TERTRE ROUTE PIERY 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TOUCHERONDE TOUCHE RONDE TOUCHES TOUCHETTE TOUCHETTES TOUILLE TOULIE TOUQUES TOUR ANDROMEDE TOURAUDIERE TOUR BERENICE TOUR CASSIOPEE TOUR CURES TOUR D ORDRE TOURELLES TOURERIE TOURETTES TOUR PINS LANDE TERTRE TOURNADIERE TOURNAY TOURNEBRIDE TOURNE BRIDE TOURNERIE TOURNERIES TOURNES TOURNESAC TOURNE VESTE TOURS TOUSSENT TOUTERI TOUT JOLY TOUVOIE TOUVOIS TOUZELIERE TOUZELIERES TOUZERIE TOYRIERE TRACONNIERE TRAINE TRAINELIERE TRAIT HERSENT TRANCHE FLEURIE TRANCHE POURRIE TR ANDROMEDE TRANQUILITE TRANQUILLITE TRANSONNIERE TRAPPE TRASSERIE TRAVECHERIE TRAVERSERIE TRAVERSIERE TR BERENICE TR CASSIOPEE TREBISERIE TREBUCHET TREBUCHOT TREFINS TREILLE TREIMEILLIERE TREIZE VENTS TRELE TREMAILLERES TREMAILLIERE TREMBINIERE TREMBLAI TREMBLAIE TREMBLAIES TREMBLAIS TREMBLAY TREMBLAYE TREMBLE TREMELLERIE TREMELLIERE TREMOUILLES TREMOUSSERIE TRENAULAY TRENETIERE TRENTES TRENTINIERE TREPELLERIE TREPELLIERE TREPIGNE TREPOINTE TRESOR TRETEAUX TRETILLE TREZARDIERE TRIBERDIERE TRIBOTTRIE TRICHARDERIE TRICORNE TRICOTTERIE TRIE TRIFOLLIERE TRIGANDIERES TRIGOLIERE TRILLERIES TRILLONNIERE TRIMAUDIERES TRINCOTTIERE TRIPERIE TRIPIERES TRIPONNIERE TRIQUETTERIE TROCHARDIERE TROCHERIES TROCHONNIERE TROENES TROGNE TROGNE CHARRETIERE TROIS BORNES TROIS CHANDELLES TROIS CHEMINEES TROIS TROIS CHENES TROIS COINS TROIS CURES TROIS FACELLES TROIS FONTAINES TROIS JOURS TROIS TROIS CROIX TROISNAIS TROIS NOYERS TROIS PIERRES CLOSAGES TROIS PIGEONS TROIS POIRIERS TROIS QUARTIERS TROISSES TROIS SOEURS TROMPE SOURIS TROMPILLE TRONC TRONCERAY TRONCHAIS TRONCHET TRONCHONNIERE TRONCHONNIERES TRONCHOT TRONQUENEAUX TROPARIE TROPHEES TROTERIE TROTINIERE TROTTE SOURIS TROUESSE TROUESSIERE TROUSSANON TROUSSARDIERE TROUSSE LOUP TROUSSERIES TROUSSES CAILLERES TROUTY TROUVERON TRUBERDIERES TRUDELLE TRUGALE TRUITIERE TRUNETIERES TRUTES TUAUDIERES TUAUX ROUGES TUDERIE TUE BOEUF TUELIEVRE TUE LIEVRE TUE LOUPS TUFFAUX TUFFE TUFFEAU TUFFELIERE TUFFES TUFFETIERE TUFFIERE TUFFIERES TUFIERE TUILASSERIE TUILE TUILE CREUSE TUILERIE TUILERIE PEROU TUILERIE FONTAINES TUILERIES TULIEVRE TULLERIE TURBINIERE TURE TURELLERIE TURET TURPIN TURPINIERE TURPINIERES TUSSE TUTELLERIE TUVEILLE TUYAU ROND TUYAU ROUGE UFTIERE ULLIS UNION VACHERIE VACHERIE L ISLEAU VAGOTIERE VAILLANDERIES VAIRIE VALAINS VALAISE VALAMELON VALAS VALAUBRON VALAUBUN VALAUDUN VAL CURES VAL LOIR VAL VAL PILON VAL PRES VAL D IGNIERES VAL D OYRE VALENCELIN VALERIANE VALE SAULE VALET VALET DEJEAN VALETRO VALETTE VAL FLEURI VALIERE VALIFER VALINIERES VALLA VALLAS VALLEE AUX POULAINS VALLEE AUX THERMAUX VALLEE BOURDAINE VALLEE FLOTTE VALLEE RIVIERE VALLEE LOIRAY VALLEE VALLEE CORMIERS VALLEE FOSSES VALLEE PIERRES VALLEE POTS VALLEE TAILLE PIED VALLEE VAUBOUIN VALLEE VAUX VALLEE NEUF VALLEE FRESNE VALLEE HOUX VALLEE LOIR VALLEE PUITS VALLEE SAULE VALLEE VAU VALLEE VEAU VALLEE FEU VALLEE JOUANET VALLEE MICHON VALLEES VALLEEBLAISE VALLEES ROSES VALLEE THUAU VAL LUCARO VAL MISERE VALOUTIN VAL POTERIE VAL ROSES PASSAY VALTERIE VALTIERE VANDOEUVRE 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VAUVELLE VAUX VAUX ETATS VAUX GODIN VAUX GUETIERS VAUX VAUX HUON VAUX JOUIN VAUX JOUSSE VAUX LOGE VAUX THIERRY VAUX VALLIERS VEAU VEAUBENARD VEAU FERRE VEAUGEONNERIE VEAUJARRY VEAUJOURS VEAU MARECHAL VEAUROC VEAUPERE VEAUVERT VEAUX VEAUX FOIN VECQUERES VECQUERIE VECRIE VEDIERES VEGRE VEGRERIE VEGREVILLE VEGRONS VEIDIERE VEILLERE VELALINES VELINIERE VELOBERT VELUERE VENDEMIAIRE VENDOEUVRE VENERAIS VENOTTERIE VENTE VENTE CARREFOUR VENTES VEQUERE VEQUERIE VEQUERIERS VERDEAU VERDELLE VERDERIE VERDET VERDILLON VERDOCHERE VERDURON VEREILLES VERGER VERGER BROSSE VERGER HEULIERE VERGER L ILE VERGERIE VERGER MA GOULE VERGERS VERGER VILLA MILLE ROSES VERGOTTIERE VERNAUDET VERNELLES VERNETS VERNEUIL VERNIETTE VERNIETTE GRIMAUDIERE VERONNERIE VERONNIERE VERRERIE VERRIE VERRIERE VERRIERES VERRON VERRON ANGEVINIERE VERRON BARONNIERE VERRON CHENE VERRON COUDRAIE VERRON GAUDICHERE VERRON GOUSTIERE VERRON HAUT ROTTES VERRONIERE VERRON MARINERIE VERRON MOLLANS VERRONNIERE VERRONNIERES VERRON PERRET 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VALLEEDE GROTTEDE GUERINIEREDE HAIE MARSAULTDE HARDONNIEREDE HARELLEDE HAYEDE HERMERIEDE HETRAIEDE JUSTICEDE LIBERATIONDE LIZONNIEREDE LONDEDE MADELEINEDE MAIRIEDE 103DE MAISON BLANCHEDE MALADRERIEDE MAREDE MARE AUBEDE MARE AU COQDE MARE AU VIVIERDE MARE AUX CHEVAUXDE MARE BOHUEDE MARE CLOSEDE MARE COMMUNEDE MARE PRESDE MARE DODEEDE MARE GEORGIEDE MARE HERMIERDE MARE HUEDE MARE LAINEDE MARE NEUVEDE MARE PLATEDE MARE ROUGEDE MARE ROUXDE MARE SANGSUEDE MARE SENSUELLEDE MARIETTEDE MARNIEREDE MECANIQUEDE MESANGEREDE MESSEDE MORIEREDE MOTTEDE MOTTE FEODALEDE NATIONDE L ANGEDE PERELLEDE PERTUISIEREDE CROIXDE GARENNEDE PLAINEDE PLESSEDE POLKADE POMMERAIEDE PORTEDE PORTE BRULEEDE PORTE MARAISDE PORTE ROUGEDE PUCELLIEREDE PUTHENAYEDE RANGEE BOIVINDE L ARBRE AU LOUPDE L ARCHEDE REDOUTEDE REINE BLANCHEDE RETRAITEDE RIVIEREDE ROCHEDE RONCEDE RONDEDE ROQUETTEDE ROUSSELINEDE RUELLEDE SABLONNIEREDE SEIGNEURIEDE TERRIEREDE TOURDE TOURANGUERIEDE TUILERIEDE L AUMONEDE VALETTEDE VALLEEDE VENTEDE VICOMTERIEDE VIERGEDE VIGNEDE L EGLANTINEDE L EGLISEDE L EMPEREURDE L ENFERDE L ENFER BOURGDE L EPINEDE L EPINE BLANCHEDE L EPINETTEDE L EPINGLEDE L EPTEDE L ESCALIERDE L HABITDE L HERMITAGEDE LIEUREYDE LIGNEROLLESDE L OREE BOISDE L ORIENNEDE LOUVERSEYDE LUCIVELDE LUHEREDE MALOUVEDE MANTESDE MANTHELONDE MARBEUFDE MAREMONTDEMOISELLEDE MONDIMAREDE MONTAIGUDE NEPTUNED ENTREDE PANILLEUSEDE PARISDEPARTEMENTAL 101DEPARTEMENTAL 137DEPARTEMENTAL 138DEPARTEMENTAL 14DEPARTEMENTAL 140DEPARTEMENTAL 144DEPARTEMENTAL 149DEPARTEMENTAL 22DEPARTEMENTAL 32DEPARTEMENTAL 33DEPARTEMENTAL 35DEPARTEMENTAL 42DEPARTEMENTAL 45DEPARTEMENTAL 47DEPARTEMENTAL 50DEPARTEMENTAL 53DEPARTEMENTAL 567DEPARTEMENTAL 623DEPARTEMENTAL 706DEPARTEMENTAL 819DEPARTEMENTAL 83DEPARTEMENTAL 839DEPARTEMENTAL 841DEPARTEMENTAL 88DEPARTEMENTAL 89DEPARTEMENTAL 90DEPARTEMENTAL 94DEPARTEMENTAL 98DE PEROUZETTEDE PIERRELAYEDE REANVILLEDE ROUVILLEDES 10 ECLUSESDES 3 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MAUPASSANGARE GUY MAUPASSANT GUY MAUPASSANT HIRONDELLES IRIS ITON J VARENDE VARENDE JAURES HANOY ROSTAND HANOY JONQUILLES JONQUILLES LAVANDE LAVANDES PARVIS BEGONIAS BLEUETS DALHIAS GLAIEULS IRIS QUATRE VENTS PT HANOY LUPINS CURIE 32 HANOY CURIE HANOY MESANGES MOLIERE MOLIEREIMMEUBLE OEILLETS ELAURD HANOY ELUARD HANOY ELUART HANOY PINSONS QUATRE VENTS HANOY ROMARIN ROUGE GORGE IMMEUBLEAMAND SAUGE SAUGES VARENNES AMIMOSAS IMM VARENDE BATIMENT GA 2 VILLAGES 8 MAI 8 MAI 1945 ABREUVOIR ACACIAS ALBERT BROUET ALBERT CAMUS ALOUETTES ALPHONSE ALLAIS AMOUREUX ANCIENNE FORGE ANCIEN RELAIS ANGLE ANTOINE LAURENT LAVOISIER ARCADE ARISTIDE BRIAND AUBEPINES AUMONE AUVILLIERS AVETTES BAC BAHIN BARETTERIE BASSIN BAS VERRIERES BEAUJOLEY BEAU TERTRE BELLE ILE BELLEVUE BELVEDERE BENGALIS BERNARD TILLON BERQUERIE BERSON BERTHAULT BESACHES BLEUETS BOIS SALLE MARNES HEBERT BON AIR BONIFACE BONNEVILLE BONS BOSC CARME BOUAFLES BOULINGRIN BOURCY BOURG BOUT D AUNIAU BOUVREUIL BREDEL BREVOIE BRIARE BRIQUETERIES BRIQUETTERIE BROUET BRUGES BRULES BRUYERE BRUYERES BUART BUISSONNIERE BUQUET BUTE BUTTE CABLE CADET ROUSSEL CAILLOU BLANC CAILLOU CALVAIRE CAMELIAS CANADIENS CANAL CARREE CARRIERE CATHERINE DESTIVELLE CAVEE CEDRE CEDRES CERISIERS CHAISERIE CHAMBELLAN GALLAY CHANTERELLES CHARIOTS IX CHARON AVENY CHARRON CHEMINOTS IMPASSEVERT CHENE BRUYERES CHEVAL BLANC CHRISTIANE LEFEVRE CIDRERAIE CLE CLOS CHEVALLIER MOTTE COCHEREL COLIBRIS CONTREE CONVENANT COQ D OR COQUELICOTS CORNEREL COTE COTE MARGOT COTE TORCHON COTY BECQUET D ENFER SOUVERAINE CROIX CROIX CORNET CROIX PAGEOT CURE CYPRES D ABONNEL D ANDOUIN D ANGREVILLE DAUPHINE BOURY CATILLON CHERBOURG COCHEREL DECRETOT DREUX FEULARDE GLATIGNY JEUFOSSE BAUGE BERGERIE L ABREUVOIR BRIQUETERIE BROSSE CASCADE CHANTERELLE CHARTREUSE CLAIRIERE CLE COMMUNE COQUETIERE COTE MAROT COTE ROUGE BUHOTTE CROIX DALLE FABRIQUE FAMILLEUSE FEDERATION FERME FIERE FONTAINE FONTAINE CLAIRE FONTAINE MALU FONTELAYE FORET FORGE FRICHE GARENCE GARENNE MASURE MARE GRENOUILLERE HAIE HAUTE EQUERRE HAUTE FOLIE HAUTE FUTAIE LAGUNE CORMIER LAMPE LICORNE MACONNERIE MARE MARE ABRAHAM MARE CLOSE MARE EN MARE IMBOURG MARE PLATE MARE SEULE MARETTE MAROTTE MECANIQUE NOE PAIX PLAINE PLANQUEMARE PLANTE POMMERAIE DELAPORTE PORTE POSTE QUIDELIERE RAVINE REMISE REPUBLIQUE RISLE RIVIERE RUELLE RUETTE SABLONNIERE SAMARITAINE SENTE JUREE SIEUME TRASSINIERE VALLEE VARENDE VENELLERIE VIEILLE ROUTE VIGETTE VOIE FERREE L ECAUBERT L L L EPINE L EST L HOTEL LILLEMARE L ORMAIE L OUEST L OURSE MADRIE D EMANVILLE DEMOISELLE NUISEMENT ACACIAS DEMICHEL AIRS ALOUETTES ARGILIERES BACHINETS BELLEVAIS BERCEAUX BERCHETTES BLEUETS BOQUETS BORDINS BOSQUERETS BOULEAUX BOUQUETS BOUVREUILS BOVES BRULINS BRUYERES CEDRES CERISIERS CHENES CLOS MARYS COQUELICOTS CORDELIERS COSTIS COUTUMES CRIERES DAMES ECOLES ECUREUILS EPINES SEROUX ESSARTS FONDS FORIERES FORRIERES FOULONNIERS FOURS JACQUES FRICHES MAUBERT GLYCINES GRAVIERS GROUETTES GUIBARDIERES HAIES HAYES MELINES IRIS JARDINS LARRIS LAVANDES LILAS LONDELLES LONGCHAMPS MALIS MARAYEURS MARES MARGUERITES MARRONNIERS MASURES MESANGES MORILLES MURIERS MURMURES MYOSOTIS NOES OISEAUX ORMES PAQUERETTES PERRAIS PERVENCHES PETITS DESERTS PEUPLIERS PINS PINSONS POIRIERS POMMIERS PRES RAINETTES RANCONNETS REINETTES ROITELETS ROSES ROSIERS RUELLES SAULES SOURCES THUYAS TILLEULS TOURTERELLES TROENES TUILERIES TULIPES TUYAS VALVALINS VENTES VENTES DUPUIS VERBEAUX VIGNES VIOLETTES D ETREMARE DIME D OMARE DONNETERIE DOUAIRES D OUMARE 14 JUILLET BALIGAN BEC BECHET BOCAGE BOCQUET BOIS FERRIERES MORIN RICHE ROUGE BOULOIR BOURG BOUT VENT BUISSON BUISSON RAS BUQUET CALVAIRE CENDRIER D ENFER POULAIN DU D EAU DUDE MESSE DUVERT CLOCHETON DU LAUBEL L ECU THOREL COMMANDANT COUSTEAU COUDRAY CRUCIFIX DOMAINE DOUE FER CHEVAL FISC FREVENT GRENIER SEL GREVARIN GUE HAMEL HARAS HETRE HEURTEVENT HOUX JARDIN CAFOT JAUNET LAVOIR LOTISSEMENT ANSELME MANOIR MESNIL MOULIN POTEL NOYER PARC PATRIMOINE DUPEL PERE ALEXANDRE PLESSIS POINT JOUR POLISSOIRE PRE MOTTE PRESBYTERE PRESLAY PRIEURE PUITS DUQUESNE ROUTOIR SAPIN SAULE SILO STADE THEATRE ROMAIN VAL VALLON FLEURI VAUCROCQ VERGER VIEUXIMPASSE VIEUX MANOIR VIEUX VIGNOBLE VILLAGE VIVIER ECUREUILS EDOUARD HERRIOT ENFER EPINE MADAME ERIC DHAINAULT ESCALIERES ESSART ETOUPEE EUGENE DELACROIX EURE FAYEL FERME FERNAND FOUQUET FERNAND PIQUET FEURERIE FEVERIE FILATURES FLAUBERT FLEURIE FLEURS FONTAINE FONTAINE GUERARD FONTAINE ROSETTE FONTAINES FONTENAYE FONTENELLE FORET FORGE FOSSES FOUR PAIN FOURNIL FRENETTES FRERES BROGLIE FRICHOT FRUCHARD GAUTHIER GAY LUSSAC GENETS GLYCINES GODON GOSSE AULNAIE HAMEL GRANGE GRAVIERS GRENICHEUX GRIOTTES GRIVES GUE ANGLES CONQUERANT GUSTAVE FLAUBERT GUY MAUPASSANT GUYNEMER HAMEL HELENE BOUCHER HERLUIN HEROUARD HETRAIE HIETTES HOUX HOUX TRANCHEVILLIERS INDUSTRIE IRIS JACQUES MONOD JANVIERS JARDINS FLEURIS JEROME BOSSUYT JOLIOT CURIE JOSAPHA JOUVEAUX JULES BAUDRAN TIZON LAGUNE HAYE ARTUS RAVINE LARIGOT LAUNAY CAIGNARD LAVOIR LAVOISIER GUIDON MANOIR FLOTS LEVERRIER VILLAGE LIBELLULES LIBERATION LONGE LORAILLER LOTISSEMENT DEVOS LOUISE GIVON LOUISE MICHEL MACONS MAI 1945 MAILLIS MAITRE MAMELLE MANOIR MARE MARE BONNE MARE MARE JOUAS MARE MICHAUX MARE THIERRY CLAUDE LESEIGNEUR CURIE MARINIERE MARNIERE MARRONNIERS MARTEL MARTHE MASSACRE MATHILDE MAUDUCAGE MENAGER MERGEANT MEROVEE MESANGES MESLIN MESNIL GOSSE MEURDRAC MICHELS MIGNON MINE D OR MINERETTES MOINEAUX MOISSONS MONT BLANC MONT CROIX MONTIER MONT JOLI MONT ROSE MONTS MONTS CAUVICOURT MORLAC MOTELLE MOULANT MOULIN BRULE NEUF MURAILLES MURET MURIERS NEUVILLE NOE NONAINS NOTRE DAME LOURDES NOUETTES NOYERS AULNAY OISEAUX ONFROY VIEILLES OREE ORION OURASI PAILLARDS PAPIN PARC PATTEYVILLE PAUGE PAVANE PAVILLON PELCAT PERDRIX PERELLES ILE PETITEIMPASSE RUELLE LILLETOT PETITS PETITS PRES PETITS SABLONS PEUPLIERS PICASSO PICOTIN BROSSOLETTE COUBERTIN LAYE PINARD PINS PINSON PIVOINES PLAINE PLAINE SUZAY PLANCHETTES PLAN CURE PLESSIS POMMIERS PORTE PORTE POSTE POTERIES POULE PRASQUEZ PRES PRE SAILLANT PRESSOIRS GRES PREVOSTE PRINCIPALE PUISEAU PUITS PUITS BANAL QUATRE COINS QUATRE TOURS QUESNAY QUINCAMPOIX RAVIN RAVINE RENE CASSIN RIVAL ROBINETS ROHAUX ROITELETS ROLLETS ROMACON ROSES ROSIERS ROSSIGNOLS ROUGEMONTS ROUGES GORGES ROULE ROUSSELINS ROUSSIERES ROUTE ROUEN IMPASSEIMPASSEGRANDE RUISSEAU IMPASSEANTONIN IMPASSEBLAISE IMPASSEBRICE IMPASSEANNE IMPASSEGENEVIEVE IMPASSEGERMAIN IMPASSEHILDEVERT IMPASSEHUBERT IMPASSELAUD IMPASSEMARC IMPASSEMICHEL IMPASSENICOLAS IMPASSEOUEN IMPASSEPAIX IMPASSETHOMAS IMPASSEVINCENT SANGLIER SANSONNETS SAPINS SARCELLES SEIGNEURIE SERGENT TURQUET SERINS SERTONS SIMON SOLEIL ROUGE SOURBELLE SQUARE TERRIERE TEURTRE TILLEULS TISSERANDS TORRENT TOSNY TOURELLE TOURNEFEUILLE TRACINIERE TROENES TULIPES TURBOT VAL AULNAY VALENCINE VALLEE VALLEE BETAN VALLEE LEROY VALLEE MARTIN TIZON VALRIS VARENNE VENELLEPIERRE VERGERS VICTOR HUGO VICTOR LERAY VIEILLES ESTREES VIEUX PUITS VIGNES VIGNETTE VOIE YREL IMP BOULEAUX IMP FEDERATION IMP JARDINS FLEURIS 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RICHERIE LARIS ROCAILLE ROCHE ROCHELLE ROQUE ROSELIERE ROSERAIE ROSIERE ROSSINIERE ROTELLIERE ROUGE MARE ROUSSIERE L ARQUERIE LALA SABOTTERIE VALDORGERE SAGANNERIE LAANNE SAUGERE SAULIERE SAUNERIE SAUSSAYE SAUVAGERIE SAUVAGINE SAVALLERIE SAVINERIE SEBIRERIE SEIGLETERIE SELLE SEMANNERIE SEMELAIGNE SENSERIE SERGENTERIE SIMONNIERE THENNEY SORELLERIE MONT HATIVET SOUDERIE SOUDIERE SOURCE SULLYNIERE BOSC SURENNIERE TAILLERIE TANNEBRUNE TANQUELLERIE TETE D OR TETE D OR MARCOUVILLE THEBAIDE THIBOUTIERE TILLAYE TIRONNERIE TOMBERIE TOUQUE TOURELLE TOUZERIE TRABOUILLERE TRANCHARDIERE TROQUERIE TROTTERIE TROUDIERE TROUVERIE TUILERIE L AUBRIERE L AUFRAGERE LAUJEUX L AUMONE LAUNAY LAUNEL VACHERIE VALLEE VANNETIERE VARENNE VARINNERIE VAUQUELIERE VAUQUELINIERE VAVASSORERIE VEDIERE BOSC ROBERT VERRERIE VIEILLE CHAISE DIEU VIERGE VIGNE VIGNETTE VILLAINERIE VILLE DIEU VILLETTE VIRETTERIE VISSEULE VITROULLERE VOIE VOLARDIERE VORILLONNERIE VOYE RACINE L EAU VIVE BAR BAS BOSCHERVILLE BAS CAUMONT BAS SIMONS BAS VILLAGE BAUDRY BEAU CORNET BEAUDRIEUX BEAUDROUET BEAUGROULT BEAUJOUAS BEAU PIN BEAU POTIER BEAUREGARD BEAURICARD BEAU SOLEIL BECHEREL I BECHEREL II LEBECOURT BEHUE BEL AIR 4 LANDE ASSELINE BELOU BEUHELIN BLOND PELCAT BOCAGE BODART BOHAN BARIL BERCHER BIZET CHEV 36 BOULAY CORDIEU DAVID HEURTELOUP FORGE PILE LOUVINE MEZIERES CHATELETS CHESNOTS VALLEE GRAVIERS VAUX BUC LE LONDE 1 FERM HAULLE LONDE FERM HAULLE DUVAL GAUTIER GIROULT GOUSSEAUX HAMEE HEROULT MARE ITOT HUARD L ABBE LOUVET MAILLARD MAREUIL MIDY NOE PANTHOU RIMBERT BOISSET TAILLEFER BON BARIL BOSC BOSC ALLARD BOSC AUBE BOSC BERANGER BOSC BINET BOSC PRES BOSC DROUET BOSC VAL BOSC FERET BOSC HAMEL BOSCHET BOSC JOUEN BOSC RICARD BOS GUERET BOULAY BOULAY MORIN BOURAT BOURDONNE BOURG (LE BOURG) BOURG EST BOURG L BOURGOIN BOUT AU GRAIN BOUT AU MACON BOUT D AMONT BOUT D AVAL BOUT BAS BOUT L ORME BOUT HAIES BOUT RUES BOUT BOUT PRE BOUT LOUVET BOYON BOYON FLEURIERE BREUIL BROUILLARD BRULE BUISSON BUISSON CANTEPIE BUISSON GARAMBOURG BUISSON ISABELLE BUISSON MOREL BUISSON RABOT BUISSON SAGOULT BUQUET BUS BUTERAY BUTTE CAILLOUET CALVAIRE CAMP ALBERT CANDON CANSOREL CARBONNET CARREFOUR CASTEL CASTEL CASTEL CATELIER CATELIER NORD CATELIER SUD CATILLON CHABLE CHALET L ECHALIER COUPY BLANCHET CANU COTE MOTTEUX PIGEON D ASILE L ECHARDIERE CHASTEL LE 1 2 3 CLAIRVAL BUISSON LE BOURG THEIL CHENARD CHEV 36 CHENE CHENE AUX CROIX CHENE L ABBE CHENE LAPORTE CHENE MILLIARD CHENE PENCHE CHENE PLAT CHENE VARIN CHEREZ CHESNAY CHESNAY HAGUEST CHOUQUET CILOUVET LE COLOMBIER COQ COQUEREL CORBUSIER CORMIER CORNET CORNET D AMBENAY COTENTIN COUDRAY COUPE GORGE COUPE GORGE GROS CHARME COURANT COURT MONT ROUGE LECQUERAYE CRETIL CROULOIR CULERON CULLORON L ECUREIL L ECUREUIL CYGNE DECRET (LE DESERT) DOMAINE DOULT CABLET DOUX MESNIL FAILLY FAUX POTERNE FAY FAYE FEC FEUGRE FIEF FIEF CADOT FIEF VALLEES FIEF GRUEL FIT FOND MARES FROC DUHAMEL FROC PINEL GACEL GALET GENETAY GENEY LEGERS L GODBAN GORD GOUFFRE GOULET BRAUCHER CHESNAY LE FRAY 10 ANGLES HANOY LARGE MESNIL PARC GRANDAUBIN GRAVERON GRAVIER GROS BREUIL GROS BUISSON GROS CHARME LEGUEBERT GUILORICHE HAMEE HAMEE OUEST HAMEL HAMELET HANOY HAUCARD HAUT HAUT BREAUT TRISAY HAUT BRIOT HAUT CROISY BOULAIE MARION HAUT COTE HAUT PARC HAUT GODARD HAUT MOUSSEAUX HAZEY HETRAY HEUDEQUIN HOM HOMO HOUX GAILLARD JARDIN BOETTE CATELON JARDIN BOUETTE CATELON JARDIN BOUETTE 5 CATELON JARDIN THIERRY JONCHERET JONQUAY JULIEN LAUNEL LESME LIEU AUX ECALIERS LIEU AUX PARCS LIEU AUX PLAIDS LIEU COUPEUR LIEU GRANGE LIEU GUERARD LIEU HOMO LIEU MALLEUX LIEU MARQUANT LIEU MILLAIS LONG LONG LONG BRUN MARE ASSE LONG LONG PERRIER LOUVRE MAILLET MALIS MALLEUX MANOIR MANOIR MENILLET LE MARIAGE MAROLLET MAUBUISSON MAUPAS MAU PERTUIS MAY MEGREMESNIL MELBUC MENILLET MESNIL MESNIL EST MESNIL LUCAS MILAN MILLET MONT MONT BRECHE MONT CROCQ MONTENAY MONT GALANT MONT GIGNARD MONT LYS MONT PICOT MONT ROTI MONT ROTY MONT VACHER MONT VALLET MOUCHEL LE VENT MOULINEAU MOULINET MOUSSEL NOUILLON NOUVEAU MONDE NUISEMENT PARADIS PARIOT PARQUET PAVILLON PAVIOT PELISSIER PERREY PERRON (LE BOISNEY) CAMPE LE CHESNAY LE HAMEL HANOY MALHEUR MESNIL NOEL NOYER POIRIER PRE VALLON VILLAGE L EPINAY L EPINE L EPINETTE L EPIVENT PLAYS PLESSIS POIRIER GUILLOT POMMIER AU SEIGNEUR POTTIER PRE CHAUSSEE PRE ANGLES PRE QUETEIL PRESBYTERE PRESBYTERE L PRESBYTERE VILLAGE PRES PRESSOIR PRIEURE PRUNIER BUISSON HOUDIER PUISNIER PUITS GREMONT QUEMIN QUERET QUESNAY QUESNEY REBRAC REMOULEUR RENOULE RENOULET L ERMITAGE ROBARD ROMACON ROSAY ROUGE ROYAUME ROYAUME BAS VILLAGE RUET 9 CORNETS ACACIAS ANCIENS D AFN ANGLES ARDILLIERES ARDILLIERS ARGILLIERES ARONS SARRAZIN SAUSSAY AUTHIEUX AUVRAYS SAVOUREY BACHONNIERES BAQUETS BARABAS BARBEYS BARGUES BAS MONTS CATS BAS PRES BASSES COUTURES BASSES LANDES BASSES TERRES BAUDAIS BAUDOINS BEAUDOIN BEAUTIERS BEGHINS BESNARDS BLEUETS BLONDS PELCATS MAUNY VAUX BONS BOSQUETS BOULAIS BOULETS BOUVREUILS BRIQUETERIES BROSSES BROUDIERES BRUERES BRUYERES BRUYERES COMMUNES BRUYERES MEMONT BRUYERES GIBET BRUYERES GOSSES BRUYERES PINCHELOUP BRUYERESLAURENT BUCAILLES BUISSONS BULLETINS CABARETS CABLES CABOTS CAILLOIS CANONS CANTELAIRES LIEU GUERARD CAROUGES CARRIERES CASTINES CATELETS CATELLIERS CAUCHIES CAUVINS CEDRES BLEUS CERTAUX CHALETS D AZUR VILLAGE CHANTERENNES CHARAMONTS CHARDONNERETS CHARMILLES CHATELETS CHENETS BRUYERES GOSSES CHEROTTES CHESNETS CHEVALLIERS CHOUQUETS CINQ CLAIES CLAIRIERES CLEMENTS CLOSERAIES COFFRES COLVERTS LAMPERIERE COMTES NORD CONNARDS COQUELICOTS COQUETS COSTIS 2 COTES COTTAGES COUDES COUDRES COUTURES CRIQUETS CRITONS CROIX CROTTES CYCLAMENS DESERTS DESSOUS TRIANEL DONNETS DOUAIRES DUTHUILTS DUTHUITS ECLUSES ECOULOIRS SENTENIER EPINAY EPINAYS ERMITAGES ERREUX ESSARTS ESSERTES FAULQUES FAULX FAUVERIES FERREYS FIEFS FLEURIETTES FORGES FORGETTES FORIERES FORIERES SUD FOSSEAUX FOSSES FRETILS FRICHES FRICHES GAUMONT FRILEUSES FRIQUETTES FROIDS VENTS GARDE MONTS GASTINES GATINES GENETAIS GENETS GOBELINS GRANDS BOTTEREAUX GRANDS BUISSONS GRANDS PRES GRANGES GRAVIERS GRAVIERS PELOUSE GREAUMES GREAUMES RONDEMARE GREMONDS GRES GRIS HAIES HAIES ROBINES HAMELETS HAULES HAUTES CRIERES HAUTES CRYERES HAUTES LANDES HAUTES PORTES CAER HAUTS BOISES HAUTS RISLE HAUTS LUCAS HAUTS VENTS HAYES HERBAGES HETRES HONGUEMARETTES HOTES HOULES HOULETTES ISLES JARDINS JARDINS GASNY JARDINS HAUT JARDINS MESNIL JONQUERETS JONQUILLES JONQUILLES NOYER JUMEAUX LABBES LANDES LANDRIERS LEGERS LOGES LONDES LONGS LONGS LONGS SILLONS LONGUEMARES LONGUES PIECES MOTTEU MAISONS BLANCHES MAISONS ROUGES MANGEANTS MARCHARDIERES MARDELLES MARES MARES FLEURIES MARETTES MARGOTTES MARIAUX MARMITTES ST D ASNIERES MARNETTES MARNETTES BOSNORMAND MARNIERES MARNIERES FONDUES MARRONNIERS MARTINS MARTIUS MASSINS MAUNIS MERISIERS MESANGES MESNILS MEURGERS 10 CHESNAIE MEURGERS 16 CHESNAIE MEURGERS 18 CHESNAIE MEURGERS 19 CHESNAIE MEURGERS 21 CHESNAIE MEURGERS 7 CHESNAIE MILLAIS MILLE RIEUX MINERAIS MINIERES MONNIERS MONTAGNES MONTS MONTS AUX RATS BRUYERE MONTS CATS MONTS CAUX MONTS GARDETS MONTS HUGLETS MORILLES MOULES MYOSOTIS NAUX BOUDET NEUF ACRES NEUF FOULANTS NEUF MOULINS NOES NOES GRANDS BOTTEREAUX NOLLANS NORETS ORGERIES ORMES SOUCHET SOUILLARD SOUILLET PAIRELLES PAQUERETTES PELCATS L ESPERANCE PERELLES PERRAIS PERRELLES PERRETS PERRUCHES PETITES BROSSES PETITES BRUYERES PETITS PETITS BOTTEREAUX PETITS FOSSES PIEDS HUMIDES PINTEREAUX PIVENTS PLANCHES PLANETS PLANTES POMMIERS POMMIERS 10 POTERIES POTTIERS PRES PRES GARENNE PRES DENIS PRES HAMEL PRES FLEURIS QUATRE HOUX QUATRE MAISONS QUATRE VENTS QUATRE VENTS HAUTE FOLIE QUATRE VOUGES RAIES CROCHUES REIGNIERES REINIERES RENARDIERES RENARDS RIQUETS ROBILLARDS ROCHERS ROITELETS RONCIERS ROQUES ROTOIRS ROUSSELS ROUSSETTES RUELLES RUES RUFAUX SABLIERES SABLONS SAMSONS SANGSUES SEGLAS SELLIERS SENECHAUX SENETS SIAULES SIMONS SOUCHES SOURCES TASSES TERRES PREAUX THOUQUES TILLEULS TREZ TROIS CORNETS TROIS MOULINS TROIS PIERRES TROTTIERS TUILERIES VALLEES VALLES VALLETS VALLOTS VARENNES VAUX MEREY VEAUX VENTES VIEILLES MAISONS VIEUX VIGNETTES VIVES TERRES VIVIERS VORETS ZIEBLES TAC L ETELON TERTRE THIEULAY MESNIL GUILBERT THUIT TILLEUL LAMBERT TREMBLAY TRIAGE JONCHERETS TRIANEL TROU NORMAND 3 TUILERIE TUILET VAL VALLOT VANCOME VAUROUX L EVECHE L EVEQUERIE VERDAL VERGER VERT BOOS VIEUX VILLAGE VIEUX POMMIER 4 VIEUX PRESSOIR VIEVRE VILLAGE VILLAGE AUX BINET VILLAGE AUX LUCAS VILLAGE L VILLAGE L VILLAGE EST VILLAGE SAMSONS VILLAGE VALLETS VILLAGE ROSAIE VILLARD VOISNIET LEVRARDIERE L HERBERIE L HERMERAIE L HERMITAGE L HORTENSERAIEALAINCOURTAMBERVILLEAMECOURTANGERVILLE RIVIEREARSAULTAUTREBOISBAVIGNYBEAUMARCHAISBEAUMONT VILLEBEAUVAISBEL AIRBELLEVUEBELLOUBERNIENCOURTBLAQUEMAREBOELBOIS SELLESBOIS GAHOTBOOSBOSTONBOUCHEVILLEBOUHOURSBUCAILLEJUSTBUISSONMARGUERITEBULLECABEAUMONTCALLIGNYCAMPAGNEGERMAINCANNETOTCARBECCARENTONNE CHAMPAGNE CHAMPEAUX CHARNELLESCHARRIERE L CHAUSSEECHINCUNCILOVETCOQUERELCORBEAUMONTCOTE FORGESCOUR PITACHECOURTEILLESCRAPOTELCROIX AU LOUPDOMNESQUEDU 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CHEMINETTELA CHEVALLERIELA CHIAISL ACIERLA COCHONNIERELA COIPELLIERELA COLINIERELA COLOMBIERELA COMOMBERIELA CONARDERIELA CONARDIERELA CONTERIELA COQUERIELA CORNIERELA COTELA COTE MAROTTELA COUDRELA COUDRETTELA COURLA COURANTERIE NORDLA MARTINLA COURTOISERIELA VITROUILLA COUTERIELA CRETINIERELA CROIX AUX CHIENSLA CROIX BERANGERLA CROIX BLANCHELA CROIX CRESPINLA CROIX FRANQUEVILLELA CROIX PIERRELA CROIX BREUILLA CROIX ROGERLA CROQUETIE