Les espèces démersales représentent 50% des captures des pêcheries françaises du golfe du Lion, dont la plupart sont pleinement exploitées, voir surexploitées depuis plusieurs décennies. Cette thèse évalue la pertinence d'aires marines protégées (AMPs) comme outil de gestion et de conservation de ces populations. Jusqu'à présent de telles zones ont été uniquement mises en place le long des côtes pour protéger des espèces très peu mobiles. Le problème est plus complexe pour les espèces vivant en haute mer car leur habitat est plus large et plus diffus. Pour ce faire, la distribution spatiale de 12 espèces démersales exploitées clés ont été étudiées de 1994 à 2010, à l'aide d'observations scientifiques et d'outils statistiques ad hoc. Une approche géostatistique a permis de détecter des structures d'auto-corrélation spatiale pour l'ensemble des espèces, et de produire des cartes de distributions annuelles de chaque espèce. Ces distributions sont apparues très stables sur les dix-sept années, mis à part un phénomène d'expansion/ rétraction avec le niveau d'abondance totale sur la région. Par ailleurs une approche par modèle linéaire généralisé a révélé des associations importantes de ces espèces à un habitat stable dans le temps. Ces résultats sont en accord avec la théorie du bassin de MacCall selon laquelle l'association d'une espèce à un habitat est densité-dépendant, et l'augmentation de la densité d'individus dans une zone serait à l'origine de la colonisation d'habitats sub-optimaux. Protéger l'habitat optimal d'une espèce permettrait alors de constituer un habitat «source», si la zone est judicieusement choisie ; en effet le report de l'effort de pêche à l'extérieur de l'AMP peut au contraire rendre cette mesure inefficace voir délétère. Par ailleurs les populations adultes occupaient généralement des zones plus concentrées et incluses dans l'aire de répartition des juvéniles. Ces zones communes d'habitats essentiels (reproduction et nourriceries) peuvent être potentiellement intéressantes à protéger dans un cadre monospécifique. L'hétérogénéité observée des répartitions d'une espèce à l'autre implique l'instauration de zones très clairsemées, et rend la gestion difficile dans une cadre pluri-spécifique. Une zone de taille raisonnable a tout de même été identifiée, représentant 20% de la population de chaque espèce et représentative de la diversité des habitats de cette région. / Demersal species represent 50% of French fisheries catches in theGulf of Lions, most of which are fully exploited, or overfished for decades. This thesis evaluates the relevance of marine protected areas (MPAs) as a tool for conservation and management of these populations. So far these areas have been implemented only along the coast to protect the very few mobile species. The problem is more complex for deep sea species because their habitat is broader and more diffuse. To do this, the spatial distribution of 12 key demersal species exploited were studied from 1994 to 2010, with scientific observations and ad hoc statistical tools. A geostatistical approach allowed to detect spatial autocorrelation structures for all species, and produce maps of annual distributions of each species. These distributions appeared very stable over 17 years, apart from a phenomenon of expansion/ contraction with the level of total abundance in the region. In addition, a generalized linear model approach revealed significant associations of these species to a temporally stable habitat. These results are consistent with MacCall basin theory, according which habitat suitability is a density-dependent thus the increase of individuals in an area make them colonize sub-optimal habitats. An optimal habitat under protection could thus be "source" habitat, if the area is carefully chosen. Indeed reporting the fishing effort outside the MPA can instead make this measure ineffective or deleterious. The adult population were generally in more concentrated areas and included in the spatial range of juveniles. These common areas of essential habitat (breeding and nursery) may be potentially interesting to protect a single species . However, the heterogeneity of distributions of a species to another involves the introduction of very sparse areas, making the management difficult. However an area of ​​reasonable size has been identified, covering 20% ​​of the population of each species and representative of the diversity of bottom habitats in the region.

O objetivo desta pesquisa é mostrar como aproveitar novas bases de dados disponíveis e o avanço de métodos computacionais para extrair informações estatísticas sobre a localização espacial de firmas. Para isso, propomos uma aplicação de métodos de estatística espacial para modelar o padrão de localização de novas empresas de serviços no município de São Paulo. Neste trabalho, assumimos que a localização espacial dessas firmas foi gerada através de um processo pontual bidimensional e assim aplicamos dois modelos distintos: um baseado em intensidade não estocástica baseada no processo de Poisson, e um modelo de intensidade estocástica baseado processo de Cox log Gaussiano (Log Gaussian Cox Process - LGCP). A principal base de dados utilizada é base georeferenciada baseada no Cadastro Central de Empresas construída pelo Centro de Estudos da Metrópole (CEM), contendo observações de empresas na região metropolitana de São Paulo, para o ano base de 2000. Utilizamos como variáveis explicativas de localização informações advindas de sistemas de informações geográficas (SIG), o Censo demográfico e imagens de satélite do National Oceanic and Atmospheric Administration (NOAA). Os resultados encontrados mostram a importância dessa metodologia no processo de construção de modelos de localização espacial, combinando distintas fontes de dados e introduzindo novas perspectivas sobre o estudo empírico de economia urbana. / The objective of this research is to show how to take advantage of new available databases and computational methods to extract statistical information about the spatial location of firms. In this sense, we propose an application of spatial statistics methods to model the location patterns of new services firms in the city of São Paulo. In this paper, we assume that the spatial location of these firms was generated through a two-dimensional point process and thus we applied two distinct models: one based on non-stochastic intensity based on the Poisson process, and a stochastic intensity model based on the Log Gaussian Cox process (LGCP). The main input used is a georeferenced database based on the Central Business Register made by the Center for Metropolis Studies (CEM), containing data of firms in the metropolitan region of São Paulo, for the base year 2000. We use as explanatory variables information from geographic information systems (GIS), demographic census and satellite imagery from National Oceanic and Atmospheric Administration (NOAA). The results show the usefulness of these models the construction of spatial location models, combining different data sources and introducing new perspectives on the empirical study of urban economics.

A acessibilidade está relacionada com a maneira como a disponibilidade de transportes e os usos do solo afetam os indivíduos na realização de viagens para o desenvolvimento de suas atividades habituais. Freqüentemente se assume que os moradores de baixa renda da periferia são os mais afetados pela falta de acesso aos meios de transporte. A questão subjacente a esta afirmação, no entanto, permanece sem uma resposta definitiva: o nível de renda, por si só, seria um indicativo do nível de acessibilidade? O objetivo deste estudo é explorar a união de ferramentas de estatística espacial e SIG (Sistema de Informações Geográficas) com um propósito específico, que é o de analisar as relações entre aspectos da distribuição espacial de características da população (como a renda, por exemplo) de uma cidade média brasileira e os diversos níveis de acessibilidade por diferentes modos de transporte nela observados, buscando possíveis respostas para esta pergunta. Quando se utiliza procedimentos de visualização e classificação de dados espaciais comuns em SIG, nem sempre as informações são diretamente perceptíveis. Logo, deve-se utilizar ferramentas que ampliem as possibilidades de compreensão e análise dos dados. Inicialmente, as ferramentas selecionadas para uso neste trabalho são apresentadas e discutidas quanto à sua aplicação e utilização na análise proposta. Para tal foram utilizados dados coletados em uma pesquisa origem–destino (O-D) realizada na cidade de Bauru - SP, agrupados por setores censitários e adicionados ao SIG, aplicando técnicas de estatística espacial utilizadas para entidades do tipo área. Os resultados obtidos são apresentados na forma de mapas e de índices que medem a associação espacial global e local entre estas zonas. Uma das conclusões interessantes da aplicação foi a identificação de regiões da cidade com dinâmica particular, que contrariam o padrão global observado nas demais partes da área urbana. Pôde-se constatar ainda particularidades a respeito do uso de cada modo de transportes. O modo automóvel como motorista, por exemplo, possui agrupamento espacial bem definido no nível de renda alta tanto nas regiões de periferia, como nas de transição e central. Já o modo ônibus é predominantemente utilizado nas zonas de renda baixa das regiões de periferia e transição, enquanto que os modos não motorizados possuem uma dinâmica bem diversificada em toda a área urbana. Estes e outros resultados do estudo de caso deixam claro que as análises de estatística espacial em ambiente SIG criam uma ferramenta para ampliar a análise convencional de acessibilidade em transportes / Transportation accessibility is directly related to the level of transportation supply and land uses and the way they affect individuals in their trip desires for accomplishing regular-basis activities. It is often assumed that low-income segments of the population living at the periphery of the cities are those affected the most by poor conditions of transportation accessibility. There is a subjacent question behind this statement, however, which is: can the income level or the location of an individual alone explain his/her accessibility level? In order to look for answers to this question, the aim of this study is to analyze, making use of spatial statistics tools in a GIS (Geographic Information System) environment, the relationships between accessibility and income and their geographical distributions in a medium-sized Brazilian city. The application of the most commonly used GIS resources, such as visualization and spatial data classification tools, not always assures a full comprehension of the phenomenon under analysis. As a consequence, many problems require tools that enhance the possibilities of observation and analysis. As tools with this characteristic have been used in this work, they were initially introduced. Thereafter, the possibilities of use of these tools in the problem analyzed were also discussed. Data of an origin-destination (O-D) survey carried out in the city of Bauru, located in the state of São Paulo, which brings information about four different transportation modes, were used in this study. Such data, grouped following the census tracts, were carefully examined in a Geographic Information System in order to look for spatial patterns of accessibility that are not visible in the traditional approaches. The results of the analysis are presented in maps and as indices that are able to capture glabal and local spatial association patterns in areas. One of the interesting outcomes of the application was the identification of regions with particular dynamics, which go against the pattern found in the overall urban area. Particularities regarding each particular transportation mode have also been noticed. The zones where the automobile is most used (by drivers, not by passengers) are spatially clustered, regardless if the zone is at the periphery, transition zone or central area of the city. The bus trips are predominantly carried out in low-income areas of the periphery and transition rings, while the non-motorized modes (walk and bicycle) have shown a very diversified dynamics in the entire urban area. This and other results of the case study clearly indicate that spatial statistics analyses in a GIS environment create a powerful tool to extend conventional transportation accessibility analysis

A modelagem clássica da demanda por transportes ignora um importante aspecto normalmente presente na estrutura das variáveis de interesse: a autocorrelação espacial. Pesquisas recentes reconhecem e incluem tal característica à estimativa da demanda, mas há limitações referentes aos elementos básicos de tratamento utilizados nas abordagens. No intuito de superar alguns problemas e restrições associados aos estudos anteriores, o presente trabalho utiliza a dependência espacial entre as observações de viagens no intuito de gerar estimativas do volume de Embarques e Desembarques por ponto de parada, bem como Carregamento nos trechos, ao longo de uma linha de transporte público. Dados de uma pesquisa sobe/desce, realizada em oito linhas de ônibus da cidade de São Paulo, foram disponibilizados pela SPTrans, sendo que, selecionou-se a linha 856R-10 para compor uma análise do desempenho da Geoestatística, ferramental adequado para a estimativa de valores de variáveis em locais não amostrados, sobretudo em bancos de dados espacialmente dependentes. O processo de estimação foi realizado por meio da Krigagem Ordinária com distâncias euclidianas, técnica de interpolação geoestatística que, para sua aplicação, exige apenas o valor da variável em pontos do espaço e suas respectivas coordenadas geográficas. Posteriormente, tal método foi comparado às estimativas obtidas por meio da utilização de distâncias em rede ao longo da linha de ônibus. Os resultados obtidos, a partir da validação cruzada e métricas de aderência, apontaram um ajuste considerável dos valores observados e estimados para ambos os tipos de distância. Dessa forma, a investigação proposta confirmou, com sucesso, a viabilidade de aplicação da Geoestatística às variáveis de demanda por transporte público ao longo de uma linha de ônibus. Além disso, uma vez que a pesquisa sobe/desce exige recursos elevados para sua realização, os resultados destacam o potencial dos interpoladores geoestatísticos em eliminar a necessidade de execução do referido levantamento em todo o percurso da linha de ônibus, já que a Geoestatística garante estimativas aproximadas aos valores da pesquisa completa. / The classical modeling of travel demand overlooks an important aspect normally found in the variables of interest: spatial autocorrelation. Recent researches recognize and include this characteristic in travel demand forecasting, but there are limitations regarding the basic elements of treatment used in the approaches. In order to overcome some of the problems and constraints associated with previous researches, the present study relies on the spatial dependence between the observations of trips in order to generate estimates of the Boarding and Alighting passenger volume per bus stop and Loading in the stretches, along a public transport line, that would not be sampled for the survey occasion. Data from a passenger Boarding and Alighting survey carried out on eight bus lines in the city of São Paulo were made available by SPTrans, and line 856R-10 was selected to compose an analysis of the performance of Geostatistics, a tool that deals with the problem of scarce data and estimation in non-sampled places in the forecast of variables of public transport demand, especially in spatially dependent databases. The estimation process was performed by means of Ordinary Kriging with Euclidian distances, a geostatistical interpolation technique that, for its application, only requires the value of the variable in points of the space and their respective geographical coordinates. Subsequently, this method was compared to the estimates obtained through the use of network distances along the bus line. The results obtained, from cross-validation and measures of goodness-of-fit, indicated a considerable adjustment of the observed values to the estimated ones for both types of distance. Thus, the proposed research successfully confirmed the feasibility of applying Geostatistics to the variables of public transport demand along a bus transit line. Moreover, since the boarding/alighting survey requires high resources for its realization, the results highlight the potential of the geostatistical interpolators in eliminating the need to carry out the survey in the whole course of the bus line, since Geostatistics provides estimates similar to the values of the complete survey.

Graphical models use Markov properties to establish associations among dependent variables. To estimate spatial correlation and other parameters in graphical models, the conditional independences and joint probability distribution of the graph need to be specified. We can rely on Gaussian multivariate models to derive the joint distribution when all the nodes of the graph are assumed to be normally distributed. However, when some of the nodes are discrete, the Gaussian model no longer affords an appropriate joint distribution function. We develop methods specifying the joint distribution of a chain graph with both discrete and continuous components, with spatial dependencies assumed among all variables on the graph. We propose a new group of chain graphs known as the generalized tree networks. Constructing the chain graph as a generalized tree network, we partition its joint distributions according to the maximal cliques. Copula models help us to model correlation among discrete variables in the cliques. We examine the method by analyzing datasets with simulated Gaussian and Bernoulli Markov random fields, as well as with a real dataset involving household income and election results. Estimates from the graphical models are compared with those from spatial random effects models and multivariate regression models. / Graduation date: 2013

In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.

In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.

With increasing levels of motor vehicle ownership, automobile crashes have become a serious public issue in the U.S. and around the world. Knowing when, where, and how traffic accidents happen is critical in order to ensure road safety and to plan for adequate road infrastructure. There is a rich body of literature pertaining to time-related fatal crashes, most of which focuses on non-spatial factors such as a driver’s visibility at night, drinking and drug use, and road conditions. These studies provide a theoretical basis for understanding the causes of crashes from a non-spatial perspective, and a number of traffic laws and policies consequently have been enacted to minimize the impacts of non-spatial factors. Over the past few years, advances in Geographic Information Systems (GIS) have greatly enhanced our ability to analyze traffic accidents from a spatial perspective. This study aims to fill a void in traffic safety studies by comparing and analyzing the differences in the spatial distribution of fatal crashes based on temporal factors, specifically in three periods: 1) day and night; 2) A.M. rush hours and P.M. rush hours; and 3) weekdays and weekends. With the Nashville Metropolitan Area as the study area, the research utilized a number of spatial point-pattern analysis (SPPA) methods, including planar KDE, planar global auto K function, network global cross K functions, and network local cross K functions. All fatal crashes in the Nashville area were found to be clustered and generally follow the patterns of average daily traffic flow. All time-based subtypes of fatal crashes also were found to be concentrated within the central urban area of Nashville, mostly along major roads, and especially near major road intersections and highway interchanges. No notable spatial differences were detected among the subtypes of fatal crashes when applying network global cross K function. However, with the help of the network local cross K function, some localized spatial differences were identified. Some specific locations of hotspots of nighttime and P.M. rush hour fatal crashes were found not to be at the same locations as those at of daytime and A.M. rush hour fatal crashes, respectively. The approach adopted in this study not only provides a new way to analyze spatial distribution of spatial point events such as fatal crashes, but it also can be applied readily to real-world applications. A good understanding of where these spatial differences are should help various agencies practice effective measures and policies in order to improve road conditions, reduce traffic accidents, and ensure road safety.

Mild cognitive impairment (MCI) is a transition phase between normal aging and Alzheimer’s disease. Individuals with MCI show impairment in cognition as well as corresponding damage to areas of their brain. Performance on tasks such as discriminating objects with ambiguous features has been associated with damage to the perirhinal cortex, while scenes with structural (spatial) elements have been associated with damage to the hippocampus. In addition, attention is regarded as one of the first non-memory domains to decline in MCI. A relatively new MRI technique called diffusion tensor imaging (DTI) is sensitive to white matter microstructural integrity and has been associated with changes due to cognitive decline. 18 MCI (14 amnesic, 4 vascular) and 12 healthy matched controls were assessed in feature ambiguity, attention and structural learning to assess associated deficits in MCI. Associations with white matter microstructural integrity were then investigated. The MCI groups were discovered to perform worse than controls on the test of structural learning. In addition, altered attention networks were found in MCI and were associated with white matter microstructural integrity. No significant differences were found for feature ambiguity. These findings suggest there may be specific damage to the hippocampus while the perirhinal cortex may be preserved in MCI. Furthermore, dysfunction in attention was found to be associated with white matter microstructural integrity. These experimental tests may be useful in assessing dysfunction in MCI and identifying degeneration in white matter microstructural integrity. Further studies with larger sample sizes are needed to validate these findings.

檢測某些地區是否有較高的疾病發生率，亦即群集(Cluster)現象，是近年來空間統計(Spatial Statistics)在流行病學的主要應用之一，常見的偵測方法包括SaTScan (Kulldorff, 1995)及Spatial Scan Statistic (Li et al., 2011)。這些方法多半大都採用一次性偵測，也就是比較疑似群集之內外相對風險(Relative Risk)，如此確實可提高計算效率，同時檢視所有疑似群集。然而，一次性偵測會受到群集外其他發生率較高群集的影響，對於相對風險較小群集的偵測能力過於保守(Zhang et al., 2010)。 本文以多重群集偵測為研究目標，以逐次分析的方式修正SaTScan等群集偵測方法，逐一篩選出發生率較高的顯著群集，並探討逐次分析在使用上的時機及限制。除了透過電腦模擬，測試逐次群集分析的改進效果，我們也分析臺灣地區的癌症死亡率，比較偵測結果的差異。研究發現，逐次群集偵測確實能提高相對風險較小群集的偵測能力，像是在相對風險不大於1.6的群集時尤其有效，但若相對風險大於1.6時，SaTScan的偵測能力不受多重群集的影響。 / Cluster detection, one of the major research topics in spatial statistics, has been applied to identify areas with higher incidence rates and is very popular in many fields such as epidemiology. Many famous cluster detection methods are proposed, such as SaTScan (Kulldorff, 1995) and Spatial Scan Statistic (Li et al., 2011). Most of these methods adapt the idea for comparing the relative risk inside and outside the suspected clusters. Although these methods are efficient computationally, clusters with smaller relative risk are not easy to be detected (Zhang et al, 2010). The goal of this study is to apply the idea of sequential search into SaTScan, in order to improve the power of detecting clusters with smaller relative risk, and to explore the limitation of sequential method. The computer simulation and empirical study (Taiwan cancer mortality data) are used to evaluate the sequential SaTScan. We found that the Sequential method can improve the power of cluster detection, especially effective for the cases where the clusters with relative risk not greater than 1.6. However, the sequential method also suffers from identifying false clusters.