Modeling pavement deterioration and predicting the pavement performance is crucial for optimum pavement network management. Currently only a few models exist that incorporate the structural capacity of the pavements into deterioration modeling. This thesis develops pavement deterioration models that take into account, along with the age of the pavement, the pavement structural condition expressed in terms of the Modified Structural Index (MSI). The research found MSI to be a significant input parameter that affects the rate of deterioration of a pavement section by using the Akaike Information Criterion (AIC). The AIC method suggests that a model that includes the MSI is at least 10^21 times more likely to be closer to the true model than a model that does not include the MSI. The developed models display the average deterioration of pavement sections for specific ages and MSI values. Virginia Department of Transportation (VDOT) annually collects pavement condition data on road sections with various lengths. Due to the nature of data collection practices, many biased measurements or influential outliers exist in this data. Upon the investigation of data quality and characteristics, the models were built based on filtered and cleansed data. Following the regression models, an empirical Bayesian approach was employed to reduce the variance between observed and predicted conditions and to deliver a more accurate prediction model. / Master of Science

Time series analysis has been explored by the researchers in many areas such, as statistical research, engineering applications, medical analysis, and finance study. To represent the data more efficiently, the mining process is supported by time series segmentation. Time series segmentation algorithm looks for the change points between two different patterns and develops a suitable model, depending on the data observed in such segment. Based on the issue of limited computing and storage capability, it is necessary to consider an adaptive and incremental online segmentation method. In this study, we propose an Online Empirical Bayesian Kernel Segmentation (OBKS), which combines Online Multivariate Kernel Density Estimation (OMKDE) and Online Empirical Bayesian Segmentation (OBS) algorithm. This innovative method considers Online Multivariate Kernel density as a predictive distribution derived by Online Empirical Bayesian segmentation instead of using posterior predictive distribution as a predictive distribution. The benefit of Online Multivariate Kernel Density Estimation is that it does not require the assumption of a pre-defined prior function, which makes the OMKDE more adaptive and adjustable than the posterior predictive distribution. Human Activity Recognition (HAR) by smartphones with embedded sensors is a modern time series application applied in many areas, such as therapeutic applications and sensors of cars. The important procedures related to the HAR problem include classification, clustering, feature extraction, dimension reduction, and segmentation. Segmentation as the first step of HAR analysis attempts to represent the time interval more effectively and efficiently. The traditional segmentation method of HAR is to partition the time series into short and fixed length segments. However, these segments might not be long enough to capture the sufficient information for the entire activity time interval. In this research, we segment the observations of a whole activity as a whole interval using the Online Empirical Bayesian Kernel Segmentation algorithm as the first step. The smartphone with built-in accelerometer generates observations of these activities. Based on the segmenting result, we introduce a two-layer random forest classification method. The first layer is used to identify the main group; the second layer is designed to analyze the subgroup from each core group. We evaluate the performance of our method based on six activities: sitting, standing, lying, walking, walking\_upstairs, and walking\_downstairs on 30 volunteers. If we want to create a machine that can detect walking\_upstairs and walking\_downstairs automatically, it requires more information and more detail that can generate more complicated features, since these two activities are very similar. Continuously, considering the real-time Activity Recognition application on the smartphones by the embedded accelerometers, the first layer classifies the activities as static and dynamic activities, the second layer classifies each main group into the sub-classes, depending on the first layer result. For the data collected, we get an overall accuracy of 91.4\% based on the six activities and an overall accuracy of 100\% based only on the dynamic activity (walking, walking\_upstairs, walking\_downstairs) and the static activity (sitting, standing, lying).

Native palms, such as the Sabal palmetto, play an important role in maintaining the ecological balance in Florida. As a side-effect of modern globalization, new phytopathogens like Texas Phoenix Palm Decline have been introduced into forest systems that threaten native palms. This presents new challenges for forestry managers and geographers. Advances in remote sensing has assisted the practice of forestry by providing spatial metrics regarding the type, quantity, location, and the state of heath for trees for many years. This study provides spatial details regarding the general palm decline in Florida by taking advantage of the new developments in deep learning constructs coupled with high resolution WorldView-2 multispectral/temporal satellite imagery and LiDAR point cloud data. A novel approach using TensorFlow deep learning classification, multiband spatial statistics and indices, data reduction, and step-wise refinement masking yielded a significant improvement over Random Forest classification in a comparison analysis. The results from the TensorFlow deep learning were then used to develop an Empirical Bayesian Kriging continuous raster as an informative map regarding palm decline zones using Normalized Difference Vegetation Index Change. The significance from this research showed a large portion of the study area exhibiting palm decline and provides a new methodology for deploying TensorFlow learning for multispectral satellite imagery.

No description available.

The implementation and application of Geographic Information Systems (GIS) and spatial analyses have become standard practice in many archaeological projects. In this study, we demonstrate how GIS can play a crucial role in the study of taphonomy, i.e., understanding the processes that underpinned the creation of archaeological deposits, in this case the distribution of artifacts across an archeological site. The Stelida Naxos Archeological Project (SNAP) is focused on the exploration of a Paleolithic-Mesolithic stone tool quarry site located on the island of Naxos, Greece. An extensive pedestrian survey was conducted during the 2013 and 2014 archeological field seasons. An abundance of lithic material was collected across the surface, with some diagnostic pieces dating to more than 250 Kya. Spatial statistical analysis (Empirical Bayesian Kriging) was conducted on the survey data to generate predictive distribution maps for the site. This study then determined the contextual integrity of the surface artifact distributions through a study of geomorphic processes. A digital surface model (DSM) of the site was produced using Unmanned Aerial Vehicle (UAV) aerial photography and Structure from Motion (SfM) terrain modeling. The DSM employed to develop a Revised Universal Soil Loss Equation (RUSLE) model and hydrological flow models. The model results provide important insights into the site geomorphological processes and allow categorization of the diagnostic surface material locations based on their contextual integrity. The GIS analysis demonstrates that the surface artifact distribution has been significantly altered by post-depositional geomorphic processes, resulting in an overall low contextual integrity of surface artifacts. Conversely, the study identified a few areas with high contextual integrity, loci that represent prime locations for excavation. The results from this study will not only be used to inform and guide further development of the archeological project (as well as representing significant new data in its own right), but also contributes to current debates in survey archaeology, and in mapping and prospection more generally. This project demonstrates the benefit of using spatial analysis as a tool for planning of pedestrian surveys and for predictive mapping of artifact distributions prior to archaeological excavations. / Thesis / Master of Science (MSc)

Made available in DSpace on 2014-12-17T14:23:30Z (GMT). No. of bitstreams: 1 JosivanRJ_DISSERT.pdf: 3858170 bytes, checksum: cf220eba177815a3f2e7efdc0fc51b69 (MD5) Previous issue date: 2013-08-15 / One of the greatest challenges of demography, nowadays, is to obtain estimates of mortality, in a consistent manner, mainly in small areas. The lack of this information, hinders public health actions and leads to impairment of quality of classification of deaths, generating concern on the part of demographers and epidemiologists in obtaining reliable statistics of mortality in the country. In this context, the objective of this work is to obtain estimates of deaths adjustment factors for correction of adult mortality, by States, meso-regions and age groups in the northeastern region, in 2010. The proposal is based on two lines of observation: a demographic one and a statistical one, considering also two areas of coverage in the States of the Northeast region, the meso-regions, as larger areas and counties, as small areas. The methodological principle is to use the General Equation and Balancing demographic method or General Growth Balance to correct the observed deaths, in larger areas (meso-regions) of the states, since they are less prone to breakage of methodological assumptions. In the sequence, it will be applied the statistical empirical Bayesian estimator method, considering as sum of deaths in the meso-regions, the death value corrected by the demographic method, and as reference of observation of smaller area, the observed deaths in small areas (counties). As results of this combination, a smoothing effect on the degree of coverage of deaths is obtained, due to the association with the empirical Bayesian Estimator, and the possibility of evaluating the degree of coverage of deaths by age groups at counties, meso-regions and states levels, with the advantage of estimete adjustment factors, according to the desired level of aggregation. The results grouped by State, point to a significant improvement of the degree of coverage of deaths, according to the combination of the methods with values above 80%. Alagoas (0.88), Bahia (0.90), Cear? (0.90), Maranh?o (0.84), Para?ba (0.88), Pernambuco (0.93), Piau? (0.85), Rio Grande do Norte (0.89) and Sergipe (0.92). Advances in the control of the registry information in the health system, linked to improvements in socioeconomic conditions and urbanization of the counties, in the last decade, provided a better quality of information registry of deaths in small areas / Um dos grandes desafios atuais da demografia ? obter estimativas de mortalidade, de maneira consistente, principalmente em pequenas ?reas. A car?ncia destas informa??es, dificulta a??es de sa?de p?blica e leva ao comprometimento da qualidade de classifica??o de ?bitos, gerando preocupa??o por parte dos dem?grafos e epidemiologistas na obten??o de estat?sticas confi?veis da mortalidade no Pa?s. Neste contexto, o objetivo deste trabalho ? obter estimativas de fatores de ajuste de ?bitos para corre??o da mortalidade adulta, por estados, mesorregi?es e grupos et?rios na regi?o nordeste, em 2010. A proposta est? pautada sobre duas linhas de observa??o: uma demogr?fica e outra estat?stica, considerando tamb?m duas ?reas de abrang?ncia nos estados da regi?o Nordeste, as mesorregi?es como ?reas maiores e os munic?pios como pequenas ?reas. O principio metodol?gico ? usar o m?todo demogr?fico Equa??o Geral e Balanceamento ou General Growth Balance, para corrigir os ?bitos observados, nas ?reas maiores (mesorregi?es) dos estados, por estas serem regi?es menos prop?cias a quebra dos pressupostos metodol?gicos. Em seguida, ser? aplicado o m?todo estat?stico estimador bayesiano emp?rico, considerando como soma dos ?bitos nas mesorregi?es, o valor de ?bito corrigido pelo m?todo demogr?fico e como refer?ncia de observa??o de ?rea menor os ?bitos observados nas pequenas ?reas (munic?pios). Como resultados desta combina??o, um efeito de suaviza??o do grau de cobertura dos ?bitos ? obtido, fruto da associa??o com o estimador bayesiano emp?rico e a possibilidade de avaliar o grau de cobertura de ?bitos por grupos et?rios em n?vel de munic?pios, mesorregi?es e estado, com a vantagem de estimar fatores de ajuste, conforme o n?vel de agrega??o desejado. Os resultados agrupados por estado, apontam para uma melhora significante do grau de cobertura de ?bitos, segundo a combina??o dos m?todos com valores acima de 80%. Alagoas (0,88), Bahia (0,90), Cear? (0,90), Maranh?o (0,84), Para?ba (0,88), Pernambuco (0,93), Piau? (0,85) , Rio Grande do Norte (0,89) e Sergipe (0,92). Os avan?os no controle do registro das informa??es no sistema de sa?de, associado ?s melhorias nas condi??es socioecon?micas e de urbaniza??o dos munic?pios, na ?ltima d?cada, proporcionaram uma melhor qualidade do registro das informa??es de ?bitos nas pequenas ?reas

A hanseníase, doença crônica estigmatizante com potencial de causar danos neurológicos, resulta da infecção pelo Mycobacterium leprae. Análises epidemiológicas atuais têm utilizado ferramentas clínicas e de análise espacial para o mapeamento dos principais focos de ocorrência de doenças e de áreas de alto risco. Analisar os municípios maranhenses quanto à distribuição dos casos de hanseníase torna-se uma ferramenta a mais na prevenção e controle da Hanseníase no estado por inúmeros fatores: comporta-se como área hiperendêmica de hanseníase; apresenta fluxo migratório intenso com outras cidades de forma interestadual; e tem grandes contrastes sociais marcados por pouca, ou nenhuma, infraestrutura básica em algumas áreas dos vários municípios deste. Objetivos: Analisar a distribuição espaço-temporal da hanseníase para o estado do Maranhão, no período de 2001 a 2013. Identificar a ocorrência de agrupamentos espaços-temporais de provável alta transmissão (risco) e verificar se há associação dessa distribuição de taxas de detecção de risco relativo (RR) da doença com as variáveis do contexto geográfico como socioeconômicas e ambientais. Metodologia: A fonte de coleta dos dados clínicos e epidemiológicos foi o Sistema de Informação Nacional de Agravos Notificáveis do Ministério da Saúde e dos dados demográficos, ambientais e bases cartográficas digitais do Instituto Brasileiro de Geografia e Estatística. Foi adotada uma abordagem ecológica sobre tendências dos padrões espaçostemporais de transmissibilidade, com utilização dos métodos: varredura espacial (scan), para a identificação dos agregados (clusters) de risco, considerando o modelo de distribuição de probabilidade Discreto de Poisson; Estimador Bayesiano Empírico para a suavização local de taxas, a partir de informações de municípios vizinhos tendo como estratégia de construção o critério da contiguidade; regressão múltipla espacial considerando uma modelagem com distribuição de Poisson no contexto Bayesiano, levando em conta a dependência espacial, com o propósito de avaliar a relação entre a ocorrência da variável dependente com as variáveis demográficas, socioeconômicas e ambientais. Resultados: A taxa média de detecção foi de 6,73 casos por 10.000 hab., com 53.826 casos notificados no período. O estudo revelou que a distribuição dos casos de sexo masculino (57,75%) apresentou maior proporção em relação ao feminino (42,25%), havendo predominância da doença na faixa etária >15 anos (89,87%). A alta ocorrência na classificação operacional multibacilar (60,10%) é um forte indicativo decorrente do longo período de incubação da doença somado ao não diagnóstico precoce. A análise da distribuição dos agregados espaciais identificou 14 (7 de risco alto e 7 de risco baixo) e 6 (3 de risco alto e 3 de risco baixo) agrupamentos espaciais, considerando-se 10% e 50% da população em risco, respectivamente, em áreas com taxas de detecção alta e que possuem baixa qualidade de vida. O estimador Bayesiano empírico local possibilitou gerar índices corrigidos e com menores instabilidades. A análise de regressão múltipla espacial mostrou que as variáveis índice Gini, bioma predominante cerrado/caatinga e percentual de população urbana tiveram associação positiva e significativa para explicar o risco relativo (RR) no estado do Maranhão. Conclusões: O estudo mostrou que existem aglomerados com elevado risco para transmissão da hanseníase no estado do Maranhão. A associação entre o risco relativo da hanseníase e o percentual de população urbana indica que a hipótese que associa o M. leprae e a população que vive em condições de acentuada desigualdade socioeconômica ainda é forte. Essa hiperendemicidade pode demonstrar que o crescimento da população urbana é um preditor de incidência da hanseníase, face à urbanização descontrolada e ao fluxo de migrantes advindos de diferentes espaços rurais. Foi possível identificar áreas prioritárias para implementação de programas eficazes de controle de hanseníase no estado do Maranhão. / Leprosy, a chronic stigmatizing disease with the potential to cause neurological damage resulting from infection by Mycobacterium leprae. Current epidemiological studies have used clinical and spatial analysis for mapping of the main occurrence of disease outbreaks and high-risk areas. Analyze the municipalities of Maranhão state regarding the distribution of leprosy cases becomes another tool in the prevention and control of leprosy in the state by numerous factors like behaves as hyper-endemic area of leprosy; It presents intense migration to other interstate cities; and has great social contrasts marked by little or no basic infrastructure in some areas of several municipalities.. Objectives: To analyze the spatiotemporal distribution of leprosy in the Maranhão state, from 2001 to 2013. To identify the spatiotemporal clusters occurrence of probable high transmission (risk) and check for association of this distribution of relative risk (RR) detection rates of the disease with the variables of geographic context as socioeconomic and environmental. Methodology: Clinical and epidemiological data was obtained from the Ministry of Healths Disease Reporting System and demographic data, environmental and digital cartographic bases were obtained from the Brazilian Geography and Statistics Institute. An ecological approach to trends transmissibility of spatiotemporal patterns, using the methods: spatial scan to identification the clusters of risk, considering the Discrete Poisson probability distribution model; empirical Bayesian method was applied for local rate flattening, using data from municipalities having as building strategy the criterion of contiguity; ecological regression modeling with considering a Poisson distribution in the Bayesian context, taking into account the spatial dependence, in order to evaluate the relationship between the occurrence of the dependent variable with demographic, socioeconomic and environmental variables. Results: The mean detection rate was 6.73 cases per 10,000 inhabitants, with 53,826 reported cases. The study revealed that the distribution of male cases (57.75%) showed a predominance over female (42.25%), with predominance of the disease in the age group upper than 15 years (89.87%). The high occurrence in operational classification multibacillary (60.10%) is a strong indication due to the long incubation period of the disease added to no early diagnosis. The analysis of the distribution of spatial clusters identified 14 (7 high risk and 7 low risk) and 6 (3 high risk and 3 low risk) spatial clusters, considering 10% and 50% of the population at risk in areas with high detection rates and which have low quality of life. Local empirical Bayes estimator allowed to generate fixed and minor instabilities indexes. The best results of modeling to spatial multiple regression analysis for the relative risk (RR) presented for the variables Gini index, cerrado/caatinga biome and percentage of urban population. Conclusions: The study showed that there are clusters at high risk for transmission of leprosy in the Maranhao state. The association between the relative risk of leprosy and the percentage of urban population indicates that the hypothesis that associates M. leprae and the population living in severe socioeconomic inequality is still strong. This hyperendemicity can demonstrate that the growth of the urban population is a predictor incidence of leprosy due to uncontrolled urbanization and the influx of migrants coming from different rural areas.It was possible to identify priority areas for implementation of effective leprosy control programs in the Maranhão state.

L'objectif de cette thèse est de proposer une méthodologie statistique permettant de formuler une règle de classement capable de surmonter les difficultés qui se présentent dans le traitement des données lorsque la distribution a priori de la variable réponse est déséquilibrée. Notre proposition est construite autour d'un ensemble particulier de règles d'association appelées "class association rules". Dans le chapitre II, nous avons exposé les bases théoriques qui sous-tendent la méthode. Nous avons utilisé les indicateurs de performance usuels existant dans la littérature pour évaluer un classifieur. A chaque règle "class association rule" est associée un classifieur faible engendré par l'antécédent de la règle que nous appelons profils. L'idée de la méthode est alors de combiner un nombre réduit de classifieurs faibles pour constituer une règle de classement performante. Dans le chapitre III, nous avons développé les différentes étapes de la procédure d'apprentissage statistique lorsque les observations sont indépendantes et identiquement distribuées. On distingue trois grandes étapes: (1) une étape de génération d'un ensemble initial de profils, (2) une étape d'élagage de profils redondants et (3) une étape de sélection d'un ensemble optimal de profils. Pour la première étape, nous avons utilisé l'algorithme "apriori" reconnu comme l'un des algorithmes de base pour l'exploration des règles d'association. Pour la deuxième étape, nous avons proposé un test stochastique. Et pour la dernière étape un test asymptotique est effectué sur le rapport des valeurs prédictives positives des classifieurs lorsque les profils générateurs respectifs sont emboîtés. Il en résulte un ensemble réduit et optimal de profils dont la combinaison produit une règle de classement performante. Dans le chapitre IV, nous avons proposé une extension de la méthode d'apprentissage statistique lorsque les observations ne sont pas identiquement distribuées. Il s'agit précisément d'adapter la procédure de sélection de l'ensemble optimal lorsque les données ne sont pas identiquement distribuées. L'idée générale consiste à faire une estimation bayésienne de toutes les valeurs prédictives positives des classifieurs faibles. Par la suite, à l'aide du facteur de Bayes, on effectue un test d'hypothèse sur le rapport des valeurs prédictives positives lorsque les profils sont emboîtés. Dans le chapitre V, nous avons appliqué la méthodologie mise en place dans les chapitres précédents aux données du projet QUARITE concernant la mortalité maternelle au Sénégal et au Mali. / The aim of this thesis is to design a supervised statistical learning methodology that can overcome the weakness of standard methods when the prior distribution of the response variable is unbalanced. The proposed methodology is built using class association rules. Chapter II deals with theorical basis of statistical learning method by relating various classifiers performance metrics with class association rules. Since the classifier corresponding to a class association rules is a weak classifer, we propose to select a small number of such weak classifiers and to combine them in the aim to build an efficient classifier. In Chapter III, we develop the different steps of the statistical learning method when observations are independent and identically distributed. There are three main steps: In the first step, an initial set of patterns correlated with the target class is generated using "apriori" algorithm. In the second step, we propose a hypothesis test to prune redondant patterns. In the third step, an hypothesis test is performed based on the ratio of the positive predictive values of the classifiers when respective generating patterns are nested. This results in a reduced and optimal set of patterns whose combination provides an efficient classifier. In Chapter IV, we extend the classification method that we proposed in Chapter III in order to handle the case where observations are not identically distributed. The aim being here to adapt the procedure for selecting the optimal set of patterns when data are grouped data. In this setting we compute the estimation of the positive predictive values as the mean of the posterior distribution of the target class probability by using empirical Bayes method. Thereafter, using Bayes factor, a hypothesis test based on the ratio of the positive predictive values is carried out when patterns are nested. Chapter V is devoted to the application of the proposed methodology to process a real world dataset. We studied the QUARITE project dataset on maternal mortality in Senegal and Mali in order to provide a decision making tree that health care professionals can refer to when managing patients delivering in their health facilities.

A hanseníase, doença crônica estigmatizante com potencial de causar danos neurológicos, resulta da infecção pelo Mycobacterium leprae. Análises epidemiológicas atuais têm utilizado ferramentas clínicas e de análise espacial para o mapeamento dos principais focos de ocorrência de doenças e de áreas de alto risco. Analisar os municípios maranhenses quanto à distribuição dos casos de hanseníase torna-se uma ferramenta a mais na prevenção e controle da Hanseníase no estado por inúmeros fatores: comporta-se como área hiperendêmica de hanseníase; apresenta fluxo migratório intenso com outras cidades de forma interestadual; e tem grandes contrastes sociais marcados por pouca, ou nenhuma, infraestrutura básica em algumas áreas dos vários municípios deste. Objetivos: Analisar a distribuição espaço-temporal da hanseníase para o estado do Maranhão, no período de 2001 a 2013. Identificar a ocorrência de agrupamentos espaços-temporais de provável alta transmissão (risco) e verificar se há associação dessa distribuição de taxas de detecção de risco relativo (RR) da doença com as variáveis do contexto geográfico como socioeconômicas e ambientais. Metodologia: A fonte de coleta dos dados clínicos e epidemiológicos foi o Sistema de Informação Nacional de Agravos Notificáveis do Ministério da Saúde e dos dados demográficos, ambientais e bases cartográficas digitais do Instituto Brasileiro de Geografia e Estatística. Foi adotada uma abordagem ecológica sobre tendências dos padrões espaçostemporais de transmissibilidade, com utilização dos métodos: varredura espacial (scan), para a identificação dos agregados (clusters) de risco, considerando o modelo de distribuição de probabilidade Discreto de Poisson; Estimador Bayesiano Empírico para a suavização local de taxas, a partir de informações de municípios vizinhos tendo como estratégia de construção o critério da contiguidade; regressão múltipla espacial considerando uma modelagem com distribuição de Poisson no contexto Bayesiano, levando em conta a dependência espacial, com o propósito de avaliar a relação entre a ocorrência da variável dependente com as variáveis demográficas, socioeconômicas e ambientais. Resultados: A taxa média de detecção foi de 6,73 casos por 10.000 hab., com 53.826 casos notificados no período. O estudo revelou que a distribuição dos casos de sexo masculino (57,75%) apresentou maior proporção em relação ao feminino (42,25%), havendo predominância da doença na faixa etária >15 anos (89,87%). A alta ocorrência na classificação operacional multibacilar (60,10%) é um forte indicativo decorrente do longo período de incubação da doença somado ao não diagnóstico precoce. A análise da distribuição dos agregados espaciais identificou 14 (7 de risco alto e 7 de risco baixo) e 6 (3 de risco alto e 3 de risco baixo) agrupamentos espaciais, considerando-se 10% e 50% da população em risco, respectivamente, em áreas com taxas de detecção alta e que possuem baixa qualidade de vida. O estimador Bayesiano empírico local possibilitou gerar índices corrigidos e com menores instabilidades. A análise de regressão múltipla espacial mostrou que as variáveis índice Gini, bioma predominante cerrado/caatinga e percentual de população urbana tiveram associação positiva e significativa para explicar o risco relativo (RR) no estado do Maranhão. Conclusões: O estudo mostrou que existem aglomerados com elevado risco para transmissão da hanseníase no estado do Maranhão. A associação entre o risco relativo da hanseníase e o percentual de população urbana indica que a hipótese que associa o M. leprae e a população que vive em condições de acentuada desigualdade socioeconômica ainda é forte. Essa hiperendemicidade pode demonstrar que o crescimento da população urbana é um preditor de incidência da hanseníase, face à urbanização descontrolada e ao fluxo de migrantes advindos de diferentes espaços rurais. Foi possível identificar áreas prioritárias para implementação de programas eficazes de controle de hanseníase no estado do Maranhão. / Leprosy, a chronic stigmatizing disease with the potential to cause neurological damage resulting from infection by Mycobacterium leprae. Current epidemiological studies have used clinical and spatial analysis for mapping of the main occurrence of disease outbreaks and high-risk areas. Analyze the municipalities of Maranhão state regarding the distribution of leprosy cases becomes another tool in the prevention and control of leprosy in the state by numerous factors like behaves as hyper-endemic area of leprosy; It presents intense migration to other interstate cities; and has great social contrasts marked by little or no basic infrastructure in some areas of several municipalities.. Objectives: To analyze the spatiotemporal distribution of leprosy in the Maranhão state, from 2001 to 2013. To identify the spatiotemporal clusters occurrence of probable high transmission (risk) and check for association of this distribution of relative risk (RR) detection rates of the disease with the variables of geographic context as socioeconomic and environmental. Methodology: Clinical and epidemiological data was obtained from the Ministry of Healths Disease Reporting System and demographic data, environmental and digital cartographic bases were obtained from the Brazilian Geography and Statistics Institute. An ecological approach to trends transmissibility of spatiotemporal patterns, using the methods: spatial scan to identification the clusters of risk, considering the Discrete Poisson probability distribution model; empirical Bayesian method was applied for local rate flattening, using data from municipalities having as building strategy the criterion of contiguity; ecological regression modeling with considering a Poisson distribution in the Bayesian context, taking into account the spatial dependence, in order to evaluate the relationship between the occurrence of the dependent variable with demographic, socioeconomic and environmental variables. Results: The mean detection rate was 6.73 cases per 10,000 inhabitants, with 53,826 reported cases. The study revealed that the distribution of male cases (57.75%) showed a predominance over female (42.25%), with predominance of the disease in the age group upper than 15 years (89.87%). The high occurrence in operational classification multibacillary (60.10%) is a strong indication due to the long incubation period of the disease added to no early diagnosis. The analysis of the distribution of spatial clusters identified 14 (7 high risk and 7 low risk) and 6 (3 high risk and 3 low risk) spatial clusters, considering 10% and 50% of the population at risk in areas with high detection rates and which have low quality of life. Local empirical Bayes estimator allowed to generate fixed and minor instabilities indexes. The best results of modeling to spatial multiple regression analysis for the relative risk (RR) presented for the variables Gini index, cerrado/caatinga biome and percentage of urban population. Conclusions: The study showed that there are clusters at high risk for transmission of leprosy in the Maranhao state. The association between the relative risk of leprosy and the percentage of urban population indicates that the hypothesis that associates M. leprae and the population living in severe socioeconomic inequality is still strong. This hyperendemicity can demonstrate that the growth of the urban population is a predictor incidence of leprosy due to uncontrolled urbanization and the influx of migrants coming from different rural areas.It was possible to identify priority areas for implementation of effective leprosy control programs in the Maranhão state.