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Entre as técnicas de análise exploratória de dados espaciais encontram-se os indicadores de associação espacial, que mensuram o grau de dependência espacial dos dados analisados e são aplicáveis apenas a dados quantitativos. Outro procedimento disponível é a geoestatística, a qual se baseia no variograma, descrevendo quantitativa e qualitativamente a estrutura espacial de determinada variável. Neste trabalho, utilizam-se conceitos do variograma para desenvolver um indicador de associação espacial global (SIVAR-G) e um indicador de associação espacial local (SIVAR-L). São utilizados dois bancos de dados: dados binários de escolha modal de uma Cidade Fictícia e dados de média de viagens por modo automóvel por domicílio para um recorte da região central da cidade de São Paulo (Pesquisa de Mobilidade 2012). Em ambos os casos, o indicador global, para diferentes vizinhanças, foi calculado com base em valores padronizados, provenientes do variograma experimental e teórico. Em seguida, aplicou-se um teste de hipótese baseado em pseudo-significância para avaliar a significância do indicador proposto previamente. Por fim, os resultados do indicador proposto foram comparados ao índice de Moran, calculado com os mesmos parâmetros. Para o indicador local, foi elaborado um procedimento similar, porém os cálculos foram feitos pontualmente. Cada observação do banco de dados teve um variograma experimental calculado e um variograma teórico modelado para uma análise omniderecional. Um teste de hipótese similar ao do indicador global foi desenvolvido e aplicado. Assim obtiveram-se indicadores de associação espacial local ponto a ponto. Conclui-se que o indicador SIVAR-G possui desempenho satisfatório na estimação de associação espacial para dados contínuos e binários, mostrando-se sensível a anisotropia dos dados. O indicador SIVAR-L é capaz de identificar \"bolsões\" de associação espacial. É aplicável a dados contínuos e binários. Os indicadores propostos permitem a modelagem de variogramas teóricos globais e locais, fornecendo uma maior riqueza de detalhes da estrutura espacial dos dados. Os indicadores SIVAR baseiam-se na dissimilaridade espacial, enquanto o índice de Moran e LISA baseiam-se na similaridade espacial. / Among the exploratory spatial data analysis tools, there are the indicators of spatial association, which measure the degree of spatial dependence of the analyzed data and can be applied to quantitative data. Other procedure available is the geoestatistics, which is based on the variogram, describing quantitatively and qualitatively the spatial structure of a variable. The aim of this thesis is to use the concept of the variogram to develop a global indicator of spatial association (SIVAR-G) and a local indicator of spatial association (SIVAR-L). Two data bases were used: binary data of travel mode choice of a hypothetical city and mean of automobile trips by household to a region of São Paulo\'s center (Mobility Survey, 2012). In both cases, the global indicator, for different neighborhoods, was calculated based on standardized values, derived from the experimental and theoretical variogram. Then, a pseudo-significance test was applied to evaluate the significance of the previously proposed indicator. The results of the proposed indicator were compared to Moran\'s I, calculated with the same parameters. For the local indicator, it was made a similar procedure, however the calculation was punctual. For each observation of the database, it was calculated experimental variogram and adjusted a theoretical variogram for a omnidirectional analysis. A hypothesis test similar to the one applied in the global indicator was developed and applied. Therefore, it was obtained local indicators point by point. It was concluded that the indicator SIVAR-G has a satisfactory performance for spatial association of binary and continuous data, with sensibility for anisotropy cases. The SIVAR-L indicator is able to identify spatial association pockets and outliers. The local indicator is suitable for continuous and binary data. The developed indicators allow the modeling of theoretical global and local variograms, providing more details of the spatial structure of the data. The SIVAR indicators are based on spatial dissimilarity, while the Moran and LISA index are based on spatial similarity.

Entre as técnicas de análise exploratória de dados espaciais encontram-se os indicadores de associação espacial, que mensuram o grau de dependência espacial dos dados analisados e são aplicáveis apenas a dados quantitativos. Outro procedimento disponível é a geoestatística, a qual se baseia no variograma, descrevendo quantitativa e qualitativamente a estrutura espacial de determinada variável. Neste trabalho, utilizam-se conceitos do variograma para desenvolver um indicador de associação espacial global (SIVAR-G) e um indicador de associação espacial local (SIVAR-L). São utilizados dois bancos de dados: dados binários de escolha modal de uma Cidade Fictícia e dados de média de viagens por modo automóvel por domicílio para um recorte da região central da cidade de São Paulo (Pesquisa de Mobilidade 2012). Em ambos os casos, o indicador global, para diferentes vizinhanças, foi calculado com base em valores padronizados, provenientes do variograma experimental e teórico. Em seguida, aplicou-se um teste de hipótese baseado em pseudo-significância para avaliar a significância do indicador proposto previamente. Por fim, os resultados do indicador proposto foram comparados ao índice de Moran, calculado com os mesmos parâmetros. Para o indicador local, foi elaborado um procedimento similar, porém os cálculos foram feitos pontualmente. Cada observação do banco de dados teve um variograma experimental calculado e um variograma teórico modelado para uma análise omniderecional. Um teste de hipótese similar ao do indicador global foi desenvolvido e aplicado. Assim obtiveram-se indicadores de associação espacial local ponto a ponto. Conclui-se que o indicador SIVAR-G possui desempenho satisfatório na estimação de associação espacial para dados contínuos e binários, mostrando-se sensível a anisotropia dos dados. O indicador SIVAR-L é capaz de identificar \"bolsões\" de associação espacial. É aplicável a dados contínuos e binários. Os indicadores propostos permitem a modelagem de variogramas teóricos globais e locais, fornecendo uma maior riqueza de detalhes da estrutura espacial dos dados. Os indicadores SIVAR baseiam-se na dissimilaridade espacial, enquanto o índice de Moran e LISA baseiam-se na similaridade espacial. / Among the exploratory spatial data analysis tools, there are the indicators of spatial association, which measure the degree of spatial dependence of the analyzed data and can be applied to quantitative data. Other procedure available is the geoestatistics, which is based on the variogram, describing quantitatively and qualitatively the spatial structure of a variable. The aim of this thesis is to use the concept of the variogram to develop a global indicator of spatial association (SIVAR-G) and a local indicator of spatial association (SIVAR-L). Two data bases were used: binary data of travel mode choice of a hypothetical city and mean of automobile trips by household to a region of São Paulo\'s center (Mobility Survey, 2012). In both cases, the global indicator, for different neighborhoods, was calculated based on standardized values, derived from the experimental and theoretical variogram. Then, a pseudo-significance test was applied to evaluate the significance of the previously proposed indicator. The results of the proposed indicator were compared to Moran\'s I, calculated with the same parameters. For the local indicator, it was made a similar procedure, however the calculation was punctual. For each observation of the database, it was calculated experimental variogram and adjusted a theoretical variogram for a omnidirectional analysis. A hypothesis test similar to the one applied in the global indicator was developed and applied. Therefore, it was obtained local indicators point by point. It was concluded that the indicator SIVAR-G has a satisfactory performance for spatial association of binary and continuous data, with sensibility for anisotropy cases. The SIVAR-L indicator is able to identify spatial association pockets and outliers. The local indicator is suitable for continuous and binary data. The developed indicators allow the modeling of theoretical global and local variograms, providing more details of the spatial structure of the data. The SIVAR indicators are based on spatial dissimilarity, while the Moran and LISA index are based on spatial similarity.

In the world today and in Turkey, poverty and its alleviation has become an important issue. As a result, detailed studies for the identification of poverty need to be done. In the recent years, the spatial aspect of the multidimensional character of poverty is gaining significance. For this purpose, in this thesis, spatial aspects of poverty are tried to be analysed using Geographic Information Systems (GIS) in the case of Ke&ccedil / i&ouml / ren District in Ankara. Firstly, a digital map of the spatial distribution of the urban poor living in Ke&ccedil / i&ouml / ren is made and linked to the database to analyse the spatial distribution. The poverty database used in this study is based on the data collected by the Social Assistance and Solidarity Foundation in the district. It includes state of poverty and some sociodemographic characteristics of the households who applied for social assistance. The analyses with respect to the methodology of this study aims at finding the common characteristics of poor settlements and the areas/households which are the poorest of the poor in Ke&ccedil / i&ouml / ren. The maps obtained as a result of spatial data analysis indicate the dense living areas of the poor, clusters of poor households, neighbourhood level poverty analysis and poor areas within neighbourhoods. There are also additional analyses which compare the characteristics of the geographical distribution of the poor with other aspects, such as land values, roads and building conditions. Such a study can be helpful to re-allocate the poverty alleviation efforts more efficiently by determining priority areas.

Natural hazards are the reality of today&amp / #8217 / s world, which considerably affect people&amp / #8217 / s living conditions. As they cannot be prevented, the basic precautions should be taken before the occurrence to protect people. At this point, the preparedness for any threat is really important, which does decrease destructive effects of the hazard for communities and shorten recovery interventions. In terms of preparedness, identification of vulnerable people in the community gives an important contribution for better planning in disaster management. In this respect, this thesis aims to develop a methodology in order to define vulnerable groups in terms of their social conditions for any possible hazard, with Geographic Information Systems (GIS) technology. Moreover, the thesis aims to find out an interrelation between hazards and vulnerability, to build awareness about identification of socially vulnerable groups in the pre- and post-disaster planning. A case study area is selected in earthquake-prone Pendik, Istanbul, in order to find the contribution of the assessment. A study is carried out to describe social vulnerability levels in the study area using GIS. Criterion standardization, weighting and combining are accomplished by multi criteria evaluation methods. These calculations are supported with five explorative spatial data analyses to understand global trends and spatial interactions of the study data. The objectivity of the assessment and the complicated structure of the study data are also discussed. The main outcomes of the methodology and its applications in the case study area show that, the southeast part of Pendik is socially vulnerable to any possible hazard.

The location where crime events occur is an important factor in the analysis of crime. It is not only important where a crime incident takes place but also the characteristics of those places and the environment in which incident occurs. Thus, examination of spatial data such as street networks, parcel information, school locations, commercial and residential zoning, is imperative for effective crime analysis, crime prevention and police activities. Using variables, relating to five incident types as burglary, auto, pick pocket, usurp and murder, which are recorded by the police in 2003 and including the information of where and when crime incident occurs, this thesis attempts to analyze the high-incident areas and the relationship between these incidents and land uses for two important police precincts of &Ccedil / ankaya district of Ankara / Centre of &Ccedil / ankaya Police Station Zone and Bah&ccedil / elievler Police Station Zone. The aim of the study is to improve a methodology for the determination of the effects of land use variables on the distribution of crime incidents. The study applies some methods related to the spatial data analysis which can be also integrated with Geographic Information Systems. In the study, in order to identify the current pattern of the incidents, kernel estimation method is found as the best method while Nearest Neighbor Hierarchical Clustering method is preferred to analyze what land uses are prone to incidents. In order to determine the properties of the relationships between land use and incidents correlation calculations are performed. At the end, the potential crime incident areas are determined by referring these relationships with GIS functions.

Submitted by TIAGO BARRETO DE ANDRADE COSTA null (tiagobac@yahoo.com.br) on 2017-07-13T22:57:26Z No. of bitstreams: 1 TESE_TIAGO COSTA_VERSÃO FINAL.pdf: 23380596 bytes, checksum: 619a0746dd076316d81d596da3513a6c (MD5) / Approved for entry into archive by Monique Sasaki (sayumi_sasaki@hotmail.com) on 2017-07-14T18:38:21Z (GMT) No. of bitstreams: 1 costa_tba_dr_prud.pdf: 23380596 bytes, checksum: 619a0746dd076316d81d596da3513a6c (MD5) / Made available in DSpace on 2017-07-14T18:38:21Z (GMT). No. of bitstreams: 1 costa_tba_dr_prud.pdf: 23380596 bytes, checksum: 619a0746dd076316d81d596da3513a6c (MD5) Previous issue date: 2017-07-06 / A mortalidade por agressão no Brasil desde pelo menos o início de 1980 vem apresentando um recrudescimento linear, em paralelo ao contexto de produção das periferias urbanas. A desagregação desse fenômeno em escalas de maior detalhe, entretanto, permite-nos perceber que esse crescimento linear é na verdade o resultado de diversas dinâmicas locais, várias delas com características epidêmicas. Isto é, crescimentos abruptos fora do padrão histórico de dada localidade, portanto, crescimentos não-lineares. De acordo com a análise proposta no presente trabalho, identificamos Belém/PA enquanto um desses contextos epidêmicos de agressões letais. Tais expressões de violência naquela cidade vêm crescendo fora de sua dinâmica costumeira desde o início dos anos 2000 e a expressão geográfica desse fenômeno tem sido a difusão das mortes pelo espaço intraurbano. Dessa forma, propomos enquanto tese que tal propagação espacial da problemática, sendo uma típica expressão de difusão sobre o espaço geográfico, não se dá de forma homogênea em todas as direções a partir de um foco. Antes, segue por caminhos que oferecem condições mais favoráveis à epidemia (corredores de difusão) e evita aqueles mais desfavoráveis (barreiras à difusão). Enfim, no presente trabalho argumentamos no sentido de demonstrar essa dinâmica espaço-temporal peculiar do crescimento dos homicídios em Belém nos últimos anos, a partir de uma abordagem dos conceitos de epidemia e difusão espacial calcada em análise de dados espaciais. Ficou demonstrado, com base nessa análise, que os homicídios nessa metrópole brasileira têm alta correlação espacial, o que aponta para a necessidade dos fundamentos teóricos e metodológicos da Geografia na análise dessa problemática no contexto brasileiro. / Since at least 1980 the homicide mortality on Brazil has increased linearly at the same time of the urban slums has being shaped. However, in a larger zoom into the fenomeno, we can to see this linear growth is a result of several local dynamics, some of which are epidemic. That is, abrupt growth outside the historical pattern of a given locality, therefore, nonlinear growth. According to the analysis proposed in the present study, we identified Belém/PA as one of those epidemic contexts of lethal violence. Such expressions of violence in that city have been growing out of their usual dynamics since the early 2000s and the geographic expression of this phenomenon has been the spread of deaths through intraurban space. In this way, we propose as thesis that such spread of the problematic, as a typical expression of the diffusion on the geographical space, does not take place homogeneously in all directions from a focus. Indeed, it follows paths that offer more favorable conditions to the epidemic (diffusion channels) and avoid the more unfavorable ones (barriers to diffusion). Therefore, using a spatial data analysis approach, in the present work we argue to demonstrating this peculiar space-time dynamics of the present-day increase in the Belém’s homicides events. We do this from an approach of the concepts of epidemic and spatial diffusion based on spatial data analysis. It was demonstrated that the homicides in this Brazilian metropolis have a high spatial correlation, which points to the need of the theoretical and methodological foundations of Geography in the analysis of this problem in the Brazilian context.

Made available in DSpace on 2017-07-10T19:24:09Z (GMT). No. of bitstreams: 1 tese__fabiana.pdf: 3413093 bytes, checksum: 69eef866f8ca47e7714ae83768804879 (MD5) Previous issue date: 2016-02-02 / Understanding the spatial distribution knowledge regarding georeferenced data has been essencial to various areas including agriculture. Thus, several trials have been carried out. However, most of these studies assume that the underlying stochastic process is Gaussian. When the data associated with this process do not present normality, data transformations are applied. And though the use of these transformations has presented satisfactory results, it is important to consider models which take into account the characteristics of such phenomenon. It may be more appropriate than using a normal model. So, this trial aimed at proposing a spatial model based on the Birnbaum-Saunders distribution (BS). This distribution has been shown effective to model data that take positive values and whose behavior presents positive asymmetry and unimodality. Thefore, this trial has proposed a methodology that includes the formulation of the spatial Birnbaum-Saunders model , estimation of its parameters using maximum likelihood (ML), and application of diagnostic techniques which can detect the sensitivity of the model to atypical data and evaluate the proposed model through a simulation study and studies using real data sets of agricultural engineering. These data were obtadined in a 167.35-ha commercial area for grain production, in Cascavel city, to validate the studied model. In the study with simulated data and large samples, estimation parameters and diagnostic analysis showed a good performance. According to the study with real data, calculations of AIC (Akaike s information criterion) and BIC (Bayesian information criterion) indexes, Bayes factor as well as Q-Q plots constrution have shown that the proposed model is appropriate to fit the obtained data. Influential cases were detected, and their removal from data set caused a considerable change in contour maps. It is therefore concluided that Birnbaum-Saunders spatial model is adequate to carry out studies with spatially correlated data. Is is also an alternative model to the normal model when the data set present positive asymmetrical distribution / O conhecimento da distribuição espacial de dados georrefenciados é de interesse de diversas áreas do conhecimento, incluindo a área agrícola. Neste sentido, diversos trabalhos já foram realizados; no entanto, a maioria destes trabalhos assumem que o processo estocástico subjacente é gaussiano. Quando os dados associados com este processo não apresentam normalidade, transformações de dados são usadas. E ainda que o uso dessas transformações tenha apresentado resultados satisfatórios, considerar modelos que levem em conta as características do fenômeno pode ser mais adequado do que a utilização do modelo normal. O objetivo deste trabalho é propor um modelo espacial baseado na distribuição Birnbaum-Saunders (BS). Esta distribuição tem se mostrado eficiente para modelar conjuntos de dados formados por valores estritamente positivos e cujo comportamento apresenta assimetria positiva e unimodalidade. A metodologia proposta neste trabalho inclui a formulação do modelo espacial Birnbaum-Saunders, a estimação de seus parâmetros utilizando o método de máxima verossimilhança (ML), a aplicação de técnicas de diagnóstico que permitem detectar a sensibilidade do modelo a dados atípicos, a avaliação do modelo proposto por um estudo de simulação e aplicação da metodologia desenvolvida em análise de dados reais da área agrícola. Os dados utilizados para validação do modelo estudado foram obtidos em uma área comercial de produção de grãos de 167,35 ha de Cascavel. No estudo com dados simulados, para amostras grandes, a estimação dos parâmetros e a análise de diagnóstico apresentaram boa performance. No estudo com dados reais, os cálculos dos índices AIC, BIC e fator Bayes bem como a construção de Q-Q plots mostraram que o modelo proposto é adequado para ajustar os dados. Casos influentes foram detectados e suas retiradas do conjunto de dados causaram uma mudança considerável nos mapas de contorno. Conclui-se portanto, que o modelo espacial Birnbaum-Saunders é adequado para realização de estudos com dados espacialmente correlacionados, e é um modelo alternativo ao modelo normal quando o conjunto de dados apresenta distribuição assimétrica positiva

Made available in DSpace on 2017-05-12T14:47:25Z (GMT). No. of bitstreams: 1 tese__fabiana.pdf: 3413093 bytes, checksum: 69eef866f8ca47e7714ae83768804879 (MD5) Previous issue date: 2016-02-02 / Understanding the spatial distribution knowledge regarding georeferenced data has been essencial to various areas including agriculture. Thus, several trials have been carried out. However, most of these studies assume that the underlying stochastic process is Gaussian. When the data associated with this process do not present normality, data transformations are applied. And though the use of these transformations has presented satisfactory results, it is important to consider models which take into account the characteristics of such phenomenon. It may be more appropriate than using a normal model. So, this trial aimed at proposing a spatial model based on the Birnbaum-Saunders distribution (BS). This distribution has been shown effective to model data that take positive values and whose behavior presents positive asymmetry and unimodality. Thefore, this trial has proposed a methodology that includes the formulation of the spatial Birnbaum-Saunders model , estimation of its parameters using maximum likelihood (ML), and application of diagnostic techniques which can detect the sensitivity of the model to atypical data and evaluate the proposed model through a simulation study and studies using real data sets of agricultural engineering. These data were obtadined in a 167.35-ha commercial area for grain production, in Cascavel city, to validate the studied model. In the study with simulated data and large samples, estimation parameters and diagnostic analysis showed a good performance. According to the study with real data, calculations of AIC (Akaike s information criterion) and BIC (Bayesian information criterion) indexes, Bayes factor as well as Q-Q plots constrution have shown that the proposed model is appropriate to fit the obtained data. Influential cases were detected, and their removal from data set caused a considerable change in contour maps. It is therefore concluided that Birnbaum-Saunders spatial model is adequate to carry out studies with spatially correlated data. Is is also an alternative model to the normal model when the data set present positive asymmetrical distribution / O conhecimento da distribuição espacial de dados georrefenciados é de interesse de diversas áreas do conhecimento, incluindo a área agrícola. Neste sentido, diversos trabalhos já foram realizados; no entanto, a maioria destes trabalhos assumem que o processo estocástico subjacente é gaussiano. Quando os dados associados com este processo não apresentam normalidade, transformações de dados são usadas. E ainda que o uso dessas transformações tenha apresentado resultados satisfatórios, considerar modelos que levem em conta as características do fenômeno pode ser mais adequado do que a utilização do modelo normal. O objetivo deste trabalho é propor um modelo espacial baseado na distribuição Birnbaum-Saunders (BS). Esta distribuição tem se mostrado eficiente para modelar conjuntos de dados formados por valores estritamente positivos e cujo comportamento apresenta assimetria positiva e unimodalidade. A metodologia proposta neste trabalho inclui a formulação do modelo espacial Birnbaum-Saunders, a estimação de seus parâmetros utilizando o método de máxima verossimilhança (ML), a aplicação de técnicas de diagnóstico que permitem detectar a sensibilidade do modelo a dados atípicos, a avaliação do modelo proposto por um estudo de simulação e aplicação da metodologia desenvolvida em análise de dados reais da área agrícola. Os dados utilizados para validação do modelo estudado foram obtidos em uma área comercial de produção de grãos de 167,35 ha de Cascavel. No estudo com dados simulados, para amostras grandes, a estimação dos parâmetros e a análise de diagnóstico apresentaram boa performance. No estudo com dados reais, os cálculos dos índices AIC, BIC e fator Bayes bem como a construção de Q-Q plots mostraram que o modelo proposto é adequado para ajustar os dados. Casos influentes foram detectados e suas retiradas do conjunto de dados causaram uma mudança considerável nos mapas de contorno. Conclui-se portanto, que o modelo espacial Birnbaum-Saunders é adequado para realização de estudos com dados espacialmente correlacionados, e é um modelo alternativo ao modelo normal quando o conjunto de dados apresenta distribuição assimétrica positiva

Cette thèse défend l’intérêt d’appliquer une démarche d’analyse exploratoire de données spatiales pour examiner un phénomène complexe irréductible, dans un contexte limité en données : le paludisme-infection à Dakar. Chaque partie du système pathogène du paludisme est nécessaire mais non suffisante au fonctionnement du système. Il n’y a paludisme-infection que lorsque les trois composantes sont en contact : le parasite, le vecteur et l’hôte humain. La recherche des lieux où ces contacts peuvent s’opérer facilement est donc primordiale dans la lutte contre le paludisme et l’amélioration des programmes visant à la diminution voire l’élimination de la maladie. L’analyse exploratoire, encore très peu appliquée dans les pays dits du Sud, se définit ainsi comme une démarche de recherche mais aussi comme un moyen d’apporter des réponses aux besoins sanitaires. Elle pousse à l’observation, sous différents angles, des déterminants sociaux qui sont impliqués dans la réalisation du phénomène, tout comme à l’examen des interactions existantes entre eux. Nous avons récolté des informations quantitatives variées, en lien direct et indirect avec l’étude du paludisme. Interprétation d’images satellites, données censitaires, résultats d’enquêtes sociales et sanitaires ont été intégrées dans un système d’information géographique pour décrire la ville et ses habitants. Le croisement de ces sources a permis d’étudier les faces spatiales du risque épidémique palustre. Le recours à des analyses statistiques et géostatistiques, bivariées et multivariées, a permis de souligner que le risque d’infection des populations dépendait fortement d’une distance, que l’on a qualifié de sociale. / This thesis applies an Exploratory Spatial Data Analysis (ESDA) approach to study a complex phenomenon in a data scarce environment: malaria infection in Dakar. Each component of the malaria pathogenic system is necessary but not sufficient to result in an infection when acting in isolation. For malaria infection to occur, three components need to interact: the parasite, the vector, and the human host. The identification of areas where these three components can easily interact is therefore essential in the fight against malaria and the improvement of programs for the prevention and control or elimination of the disease. ESDA, still rarely applied in developing countries, is thus defined as a research approach but also as a way to provide answers to global health challenges. It leads to observation, from different angles, on the social and spatial determinants of malaria infection, as well as the examination of existing interactions between its three components. Several streams of quantitative information were collected, both directly and indirectly related to the study of malaria. More specifically, multi-temporal satellite imagery, census data, and results from social and health surveys have been integrated into a Geographic Information System (GIS) to describe the city and its inhabitants. Combining these datasets has enabled to study the spatial variability of the risk of malaria infection.