Neste trabalho utilizamos um modelo espaço-temporal proposto em Rojas (2004) para medir a interação social da criminalidade na região metropolitana de São Paulo. Realizamos simulações de Monte Carlo para testar a capacidade de estimação do modelo em diferentes cenários. Observamos que a estimação melhora com o aumento de observações ao longo do tempo. Já os resultados empíricos indicam que a região metropolitana de São Paulo é um hot spot no estado, pois é encontrado um maior grau de interação social no índice de homicídio em relação aos índices de roubo e furto. / In this paper we employ a spatio-temporal model proposed in Rojas (2004) to evaluate the social interaction in crime in São Paulo metropolitan area. We carry out Monte Carlo simulations to test the model estimation capability in different scenarios. We notice that the estimation gets better as the number of observations in time raises. The results point out that São Paulo metropolitan area is a hot spot in the state since we found out a greater social interaction for the homicide index, compared to robbery and thievery.

Neste trabalho utilizamos um modelo espaço-temporal proposto em Rojas (2004) para medir a interação social da criminalidade na região metropolitana de São Paulo. Realizamos simulações de Monte Carlo para testar a capacidade de estimação do modelo em diferentes cenários. Observamos que a estimação melhora com o aumento de observações ao longo do tempo. Já os resultados empíricos indicam que a região metropolitana de São Paulo é um hot spot no estado, pois é encontrado um maior grau de interação social no índice de homicídio em relação aos índices de roubo e furto. / In this paper we employ a spatio-temporal model proposed in Rojas (2004) to evaluate the social interaction in crime in São Paulo metropolitan area. We carry out Monte Carlo simulations to test the model estimation capability in different scenarios. We notice that the estimation gets better as the number of observations in time raises. The results point out that São Paulo metropolitan area is a hot spot in the state since we found out a greater social interaction for the homicide index, compared to robbery and thievery.

Neste trabalho utilizamos um modelo espaço-temporal proposto em Rojas (2004) para medir a interação social da criminalidade na região metropolitana de São Paulo. Realizamos simulações de Monte Carlo para testar a capacidade de estimação do modelo em diferentes cenários. Observamos que a estimação melhora com o aumento de observações ao longo do tempo. Já os resultados empíricos indicam que a região metropolitana de São Paulo é um hot spot no estado, pois é encontrado um maior grau de interação social no índice de homicídio em relação aos índices de roubo e furto. / In this paper we employ a spatio-temporal model proposed in Rojas (2004) to evaluate the social interaction in crime in São Paulo metropolitan area. We carry out Monte Carlo simulations to test the model estimation capability in different scenarios. We notice that the estimation gets better as the number of observations in time raises. The results point out that São Paulo metropolitan area is a hot spot in the state since we found out a greater social interaction for the homicide index, compared to robbery and thievery.

Les travaux présentés dans ce mémoire de thèse représentent une contribution au problème de diagnostic des Systèmes à Evénements Discrets (SEDs). L'objectif de ce travail est dans un premier temps une proposition d'une démarche de diagnostic en exploitant l'aspect temporel caractérisant l'occurrence des événements. Pour cela, le système est modélisé par des graphes temporels appartenant au formalisme des automates temporisés. L'approche est conçue selon une architecture décentralisée afin d'éviter toute explosion combinatoire dans la construction des modèles. Elle a permis la détection et localisation des défauts abruptes survenant sur les équipements notamment en combinant des conditions d'autorisation d'événements et des fonctions de non-occurrence d'événements.Dans un second temps, les défauts graduels issus du process sont considérés. Pour cela, les contraintes temporelles exprimant les dates d'occurrence des événements dans les Templates et les Chroniques sont modélisées par des distributions de probabilités (DPs). Celles-ci sont utilisées afin de caractériser un fonctionnement normal, dégradé ou défaillant de chaque sous-système avec un certain degré de certitude. Cette identification du fonctionnement est représentée par la valeur d'un indicateur de dégradation. / The work presented in this thesis represents a contribution to the problem of diagnosis in discrete event systems (DES). The objective of our work consists in a proposition for a diagnostic approach by exploiting the temporal aspect which characterizing the occurrence of events. For this, the system is modeled by temporal graphs belonging to the timed automata formwork. The approach is designed according to the decentralized architecture to avoid any combinatorial explosion in the construction of the models. It has allowed the detection and isolation of abrupt faults occurring on equipment by combining the enablement conditions of events and the Boolean functions for the non-occurrence of events.Secondly, gradual faults coming from the process its self are considerate. For this, time constraints expressing the dates of occurrence of events in the Templates and Chronicles are modeled by probability distributions (PDs). These are used to characterize normal, degraded or failed functioning of each subsystem with a degree of certainty. Identification of this functioning mode is represented by the value of a degradation indicator.

This dissertation is concerned with effects dispersal and forest structure on forest insect population dynamics, and with identifying generating processes by comparing observed patterns to model predictions. In chapter 2, we investigated effects of changing forest landscape patterns on integro-difference models of host-parasitoid population dynamics. We demonstrated that removing habitat can increase herbivore density when herbivores don't disperse far, and parasitoids disperse further, due to differences in dispersal success between trophic levels. This is a novel potential explanation for why forest fragmentation increases the duration of forest tent caterpillar outbreaks. To better understand spatial model behaviour, we proposed a new local variation of the dispersal success approximation. The approximation successfully predicts effects of habitat loss and fragmentation on realistically complex landscapes, except when outbreak cycle amplitude is very large. Local dispersal success is useful in part because parameters can be estimated from widely available habitat data. In chapter 3, we investigated how well a discretized integro-difference model of mountain pine beetle population dynamics predicted the occurrence of new infestations in British Columbia. We found that a model with a large dispersal kernel, and high emigration from new, low severity infestations yielded the best predictions. However, we do not believe this to be convincing evidence that many beetles disperse from new, low severity infestations. Rather, we argued that differences in habitat quality, detection errors, and Moran effects can all confound dispersal patterns, making it difficult to infer dispersal parameters from observed infestation patterns. Nonetheless, predicting infestation risk is useful, and large kernels improve predictions. In chapter 4, we used generalized linear mixed models to characterize spatial and temporal variation in the propensity of jack pine trees to produce pollen cones, and account for confounding effects on the relationship between pollen cone production and previous defoliation by jack pine budworm. We found effects of stand age, and synchronous variation in pollen cone production among years. Accounting for background patterns in pollen cone production clarified that pollen cone production declines in with previous defoliation, as expected.

This dissertation is concerned with effects dispersal and forest structure on forest insect population dynamics, and with identifying generating processes by comparing observed patterns to model predictions. In chapter 2, we investigated effects of changing forest landscape patterns on integro-difference models of host-parasitoid population dynamics. We demonstrated that removing habitat can increase herbivore density when herbivores don't disperse far, and parasitoids disperse further, due to differences in dispersal success between trophic levels. This is a novel potential explanation for why forest fragmentation increases the duration of forest tent caterpillar outbreaks. To better understand spatial model behaviour, we proposed a new local variation of the dispersal success approximation. The approximation successfully predicts effects of habitat loss and fragmentation on realistically complex landscapes, except when outbreak cycle amplitude is very large. Local dispersal success is useful in part because parameters can be estimated from widely available habitat data. In chapter 3, we investigated how well a discretized integro-difference model of mountain pine beetle population dynamics predicted the occurrence of new infestations in British Columbia. We found that a model with a large dispersal kernel, and high emigration from new, low severity infestations yielded the best predictions. However, we do not believe this to be convincing evidence that many beetles disperse from new, low severity infestations. Rather, we argued that differences in habitat quality, detection errors, and Moran effects can all confound dispersal patterns, making it difficult to infer dispersal parameters from observed infestation patterns. Nonetheless, predicting infestation risk is useful, and large kernels improve predictions. In chapter 4, we used generalized linear mixed models to characterize spatial and temporal variation in the propensity of jack pine trees to produce pollen cones, and account for confounding effects on the relationship between pollen cone production and previous defoliation by jack pine budworm. We found effects of stand age, and synchronous variation in pollen cone production among years. Accounting for background patterns in pollen cone production clarified that pollen cone production declines in with previous defoliation, as expected.

Cette thèse s’attache à développer des outils et modèles adaptés a l’étude de certains risques spatiaux et en réseaux. Elle est divisée en cinq chapitres. Le premier consiste en une introduction générale, contenant l’état de l’art au sein duquel s’inscrivent les différents travaux, ainsi que les principaux résultats obtenus. Le Chapitre 2 propose un nouveau générateur de précipitations multi-site. Il est important de disposer de modèles capables de produire des séries de précipitations statistiquement réalistes. Alors que les modèles précédemment introduits dans la littérature concernent essentiellement les précipitations journalières, nous développons un modèle horaire. Il n’implique qu’une seule équation et introduit ainsi une dépendance entre occurrence et intensité, processus souvent considérés comme indépendants dans la littérature. Il comporte un facteur commun prenant en compte les conditions atmosphériques grande échelle et un terme de contagion auto-regressif multivarié, représentant la propagation locale des pluies. Malgré sa relative simplicité, ce modèle reproduit très bien les intensités, les durées de sècheresse ainsi que la dépendance spatiale dans le cas de la Bretagne Nord. Dans le Chapitre 3, nous proposons une méthode d’estimation des processus maxstables, basée sur des techniques de vraisemblance simulée. Les processus max-stables sont très adaptés à la modélisation statistique des extrêmes spatiaux mais leur estimation s’avère délicate. En effet, la densité multivariée n’a pas de forme explicite et les méthodes d’estimation standards liées à la vraisemblance ne peuvent donc pas être appliquées. Sous des hypothèses adéquates, notre estimateur est efficace quand le nombre d’observations temporelles et le nombre de simulations tendent vers l’infini. Cette approche par simulation peut être utilisée pour de nombreuses classes de processus max-stables et peut fournir de meilleurs résultats que les méthodes actuelles utilisant la vraisemblance composite, notamment dans le cas où seules quelques observations temporelles sont disponibles et où la dépendance spatiale est importante / This thesis aims at developing tools and models that are relevant for the study of some spatial risks and risks in networks. The thesis is divided into five chapters. The first one is a general introduction containing the state of the art related to each study as well as the main results. Chapter 2 develops a new multi-site precipitation generator. It is crucial to dispose of models able to produce statistically realistic precipitation series. Whereas previously introduced models in the literature deal with daily precipitation, we develop a hourly model. The latter involves only one equation and thus introduces dependence between occurrence and intensity; the aforementioned literature assumes that these processes are independent. Our model contains a common factor taking large scale atmospheric conditions into account and a multivariate autoregressive contagion term accounting for local propagation of rainfall. Despite its relative simplicity, this model shows an impressive ability to reproduce real intensities, lengths of dry periods as well as the spatial dependence structure. In Chapter 3, we propose an estimation method for max-stable processes, based on simulated likelihood techniques. Max-stable processes are ideally suited for the statistical modeling of spatial extremes but their inference is difficult. Indeed the multivariate density function is not available and thus standard likelihood-based estimation methods cannot be applied. Under appropriate assumptions, our estimator is efficient as both the temporal dimension and the number of simulation draws tend towards infinity. This approach by simulation can be used for many classes of max-stable processes and can provide better results than composite-based methods, especially in the case where only a few temporal observations are available and the spatial dependence is high

This biomedical engineering thesis explores the opportunities and challenges of 2D+t contrast echocardiography for left ventricle functional analysis, both clinically and within a computer vision atlas-based deformable template model framework. A database was created for the experiments in this thesis, with 21 studies of contrast Dobutamine Stress Echo, in all 4 principal planes. The database includes clinical variables, human expert hand-traced myocardial contours and visual scoring. First the problem is studied from a clinical perspective. Quantification of endocardial global and local function using standard measures shows expected values and agreement with human expert visual scoring, but the results are less reliable for myocardial thickening. Next, the problem of segmenting the endocardium with a computer is posed in a standard landmark and atlas-based deformable template model framework. The underlying assumption is that these models can emulate human experts in terms of integrating previous knowledge about the anatomy and physiology with three sources of information from the image: texture, geometry and kinetics. Probabilistic atlases of contrast echocardiography are computed, while noting from histograms at selected anatomical locations that modelling texture with just mean intensity values may be too naive. Intensity analysis together with the clinical results above suggest that lack of external boundary definition may preclude this imaging technique for appropriate measuring of myocardial thickening, while endocardial boundary definition is appropriate for evaluation of wall motion. Geometry is presented in a Principal Component Analysis (PCA) context, highlighting issues about Gaussianity, the correlation and covariance matrices with respect to physiology, and analysing different measures of dimensionality. A popular extension of deformable models ---Active Appearance Models (AAMs)--- is then studied in depth. Contrary to common wisdom, it is contended that using a PCA texture space instead of a fixed atlas is detrimental to segmentation, and that PCA models are not convenient for texture modelling. To integrate kinetics, a novel spatio-temporal model of cardiac contours is proposed. The new explicit model does not require frame interpolation, and it is compared to previous implicit models in terms of approximation error when the shape vector changes from frame to frame or remains constant throughout the cardiac cycle. Finally, the 2D+t atlas-based deformable model segmentation problem is formulated and solved with a gradient descent approach. Experiments using the similarity transformation suggest that segmentation of the whole cardiac volume outperforms segmentation of individual frames. A relatively new approach ---the inverse compositional algorithm--- is shown to decrease running times of the classic Lucas-Kanade algorithm by a factor of 20 to 25, to values that are within real-time processing reach.