A cultura do milho é uma das atividades agrícolas mais importantes para a economia no Brasil e a utilização de modelos estatísticos pode auxiliar a tomada de decisão neste setor produtivo. O presente estudo visou identificar áreas com correlação e autocorrelação espacial para a produtividade de milho e suas variáveis preditoras (temperatura média, precipitação pluvial, radiação solar, potencialidade agrícola do solo e altitude), e também, verificar o modelo de regressão espacial mais adequado para a explicação da cultura. O estudo foi realizado utilizando dados de municípios do estado do Paraná referente a safras de verão dos anos agrícolas de 2011/2012, 2012/2013 e 2013/2014. Os softwares utilizados para a análise estatística e geração dos mapas temáticos foram o ArcMap 9.3 e GeoDa 1.6.7. A identificação da dependência espacial entre as variáveis foi realizada por meio do índice de Moran Global (Univariado e Bivariado) e o índice local de associação espacial (LISA), concluindo-se que para todos os anos e critérios de vizinhança utilizados, houve autocorrelação espacial significativa ao nível de 1% para todas as variáveis. Verificouse ainda que a temperatura média, precipitação e altitude, estão correlacionadas significativamente (P-value<5%) com a produtividade do milho em todos os anos e critérios estudados. As variáveis: radiação solar e potencialidade agrícola do solo não apresentaram correlação significativa para alguns dos anos (2012/2013) e matrizes de vizinhança (contingência queen e vizinho mais próximo). Para determinar o modelo de regressão mais apropriado para a estimativa da produtividade de milho, foi adotado o diagnóstico estatístico do modelo de regressão OLS - Ordinary Least Square, que verifica se é necessário aplicar algum modelo de regressão espacial para explicação dos dados. Para todos anos agrícolas foi recomendado a utilização do modelo de regressão espacial SAR – Spatial Lag Model, sendo que apenas para o ano agrícola 2013/2014 pode ser recomendado o modelo Spatial Error Model (CAR). A regressão espacial (SAR e CAR) adotada para a estimativa da produtividade de milho em diferentes anos, obteve melhores resultados quando comparada com os resultados da regressão que não incorpora a autocorrelação espacial dos dados (OLS). O coeficiente de determinação R², os critérios de informação bayesiano (BIC) e o máximo valor do logaritmo da função verossimilhança (Log-likelihood), apresentou melhora significativa na estimação da produtividade do milho quando utilizado SAR e CAR. / Corn cultivation is one of the most important agricultural activities for the Brazilian economy and the use of statistical models can help decision making in this productive sector. The present study aimed to identify areas with correlation and spatial autocorrelation for maize productivity and its predictive variables (mean temperature, rainfall, solar radiation, soil potential and altitude), and to verify the spatial regression model most appropriate for The explanation of culture. The study was carried out using data from municipalities in the state of Paraná referring to summer crops of the 2011/2012, 2012/2013 and 2013/2014 agricultural years. The software used for the statistical analysis and generation of thematic maps were ArcMap 9.3 and GeoDa 1.6.7. The identification of spatial dependence among variables was performed using the Moran Global Index (Univariate and Bivariate) and the local spatial association index (LISA). It was concluded that for all the years and neighborhood criteria used, there was spatial autocorrelation Significant at the 1% level for all variables. It was also verified that the average temperature, precipitation and altitude, are correlated significantly (P-value <5%) with corn yield in all years and criteria studied. The variables: solar radiation and soil agricultural potential did not present significant correlation for some of the years (2012/2013) and neighborhood matrices (queen contingency and nearest neighbor). To determine the most appropriate regression model for estimating maize productivity, the OLS - Ordinary Least Square regression model was used to verify if it is necessary to apply some spatial regression model to explain the data. For all agricultural years it was recommended to use the spatial regression model Spatial Lag Model (SLM), and only for the agricultural year 2013/2014 can the Spatial Error Model (SEM) be recommended. The spatial regression (SLM and SEM) adopted for estimating maize productivity in different years yielded better results when compared with regression results that did not incorporate spatial data autocorrelation (OLS). The coefficient of determination R², the Bayesian information criteria (BIC) and the maximum log-likelihood value, showed a significant improvement in corn productivity estimation when using SLM and SEM.

Orientador: Tirza Aidar / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Filosofia e Ciencias Humanas / Made available in DSpace on 2018-08-12T21:57:32Z (GMT). No. of bitstreams: 1 Maia_PauloBorlina_D.pdf: 17064044 bytes, checksum: c5a7f76cc959d1abdcec9a6bfb9aa950 (MD5) Previous issue date: 2009 / Resumo: Este trabalho teve como objetivo analisar a mortalidade dos acidentes de trânsito no Município de São Paulo para o período de 2003 a 2005, avaliando a distribuição espacial a partir do local de ocorrência dos eventos e as possíveis relações destes acidentes com o local de residência das vítimas. Análises espaciais foram conduzidas por meio da vinculação de dois bancos de dados oficiais, referentes aos registros das Declarações de Óbito da Fundação Seade e Boletins de Ocorrência, da Secretaria de Segurança Pública do Estado de São Paulo. Os resultados indicam padrões espaciais específicos e diferenciados segundo o tipo do acidente e as características das vítimas, apontando para a enorme potencialidade das informações georreferenciadas geradas e das técnicas de análise utilizadas para a construção do conhecimento, orientação de políticas e planejamento urbano. / Abstract: This study aimed to examine the mortality of transport accidents in the city of Sao Paulo for the period from 2003 to 2005, evaluating the spatial distribution from the site of occurrence of the events and the possible relationship of these accidents with the place of residence of the victims. Spatial analyses were conducted through the linking of two data banks related to the records of official statements of deaths the Seade Foundation and the Bulletins of Occurrence of the Secretary of Public Security of the State of Sao Paulo. The results indicate specific and spatial patterns which vary with the type and characteristics of the accident victims, pointing to the enormous potential of the georeferenciate information and techniques of analysis used to build the knowledge, directions for public policies and urban planning. / Doutorado / Doutor em Demografia

Orientador: Maurício Aguiar Serra / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Economia / Made available in DSpace on 2018-08-20T09:09:02Z (GMT). No. of bitstreams: 1 Carvalho_AndreCutrim_D.pdf: 4474029 bytes, checksum: c2a4434f05f9639325475df7d98b3350 (MD5) Previous issue date: 2012 / Resumo: A presente Tese de Doutorado se propõe a investigar e discutir teoricamente os principais fatores responsáveis pela dinâmica do desmatamento florestal na Amazônia paraense, numa perspectiva de abordagem realizada por intermédio da Nova Economia Institucional. A hipótese básica desta Tese é de que a expansão da fronteira agropecuária é o elemento condutor do fenômeno do desmatamento florestal na Amazônia paraense, sendo que esse avanço da fronteira agropecuária carrega, também, outros fatores que estão ligadas diretamente ao avanço do progresso econômico capitalista, tais como: crescimento econômico (PIB), construção de estradas, incremento do rebanho bovino, estrutura fundiária concentrada, especulação sobre o preço da terra, crédito rural, Instituições, mecanismos de governança, entre outros. No entanto, a principal atividade causadora de desmatamento florestal na Amazônia paraense é a pecuária de corte em regime extensivo. Nesse contexto, a econometria espacial será uma ferramenta de extrema importância para mensurar, a partir dos resultados obtidos no modelo econométrico espacial, os efeitos que o desmatamento florestal vem provocando na Amazônia paraense, bem como a especificidade de cada um, traçando um panorama histórico-institucionalista para sustentar os argumentos empíricos. O período escolhido para a análise econométrica compreende os anos de 2000 e 2008, devido à elevada taxa de desmatamento florestal na Amazônia paraense / Abstract: This PhD thesis proposes to investigate theoretically and discuss the main factors responsible for the dynamics of deforestation in the Amazon forest in Pará, in the interests of approach through the New Institutional Economics. The basic hypothesis of this thesis is that the expansion of the agricultural frontier is the conductive element of the phenomenon of deforestation in the Amazon forest in Pará, and this advancing agricultural frontier bears are also other factors that are directly linked to the advance of capitalist economic progress, such as: cresimento economic (GDP), construction of roads, increase of cattle, concentrated land ownership, speculation about the price of land, rural credit institutions, governance mechanisms, among others. However, the main activity causing deforestation in the Amazon forest in Pará is the beef cattle in extensive farming.. In this context, spatial econometrics is an extremely important tool to measure, from the results obtained in the spatial econometric model, the effects of deforestation in the Amazon forest has led to Para, and the specificity of each one, drawing a historical-institutionalist arguments to support the empirical. The period chosen for the econometric analysis covers the years 2000 and 2008 due to the high rate of deforestation in the Amazon forest in Pará / Doutorado / Desenvolvimento Economico, Espaço e Meio Ambiente / Doutor em Desenvolvimento Economico

M.A. (Geography) / Please refer to full text to view abstract

Analytical problems caused by over-fitting, confounding and non-independence in the data is a major challenge for variable selection. As more variables are tested against a certain data set, there is a greater risk that some will explain the data merely by chance, but will fail to explain new data. The main aim of this study is to employ a systematic and practicable variable selection process for the spatial analysis and mapping of historical malaria risk in Botswana using data collected from the MARA (Mapping Malaria Risk in Africa) project and environmental and climatic datasets from various sources. Details of how a spatial database is compiled for a statistical analysis to proceed is provided. The automation of the entire process is also explored. The final bayesian spatial model derived from the non-spatial variable selection procedure using Markov Chain Monte Carlo simulation was fitted to the data. Winter temperature had the greatest effect of malaria prevalence in Botswana. Summer rainfall, maximum temperature of the warmest month, annual range of temperature, altitude and distance to closest water source were also significantly associated with malaria prevalence in the final spatial model after accounting for spatial correlation. Using this spatial model malaria prevalence at unobserved locations was predicted, producing a smooth risk map covering Botswana. The automation of both compiling the spatial database and the variable selection procedure proved challenging and could only be achieved in parts of the process. The non-spatial selection procedure proved practical and was able to identify stable explanatory variables and provide an objective means for selecting one variable over another, however ultimately it was not entirely successful due to the fact that a unique set of spatial variables could not be selected.

One of the key concerns in spatial analysis and modelling is to study and analyse similarities or dissimilarities between places over geographical space. However, ”global“ spatial models may fail to identify spatial variations of relationships (spatial heterogeneity) by assuming spatial stationarity of relationships. In many real-life situations spatial variation in relationships possibly exists and the assumption of global stationarity might be highly unrealistic leading to ignorance of a large amount of spatial information. In contrast, local spatial models emphasise differences or dissimilarity over space and focus on identifying spatial variations in relationships. These models allow the parameters of models to vary locally and can provide more useful information on the processes generating the data in different parts of the study area. In this study, a framework for localising spatial interaction models, based on geographically weighted (GW) techniques, has been developed. This framework can help in detecting, visualising and analysing spatial heterogeneity in spatial interaction systems. In order to apply the GW concept to spatial interaction models, we investigate several approaches differing mainly in the way calibration points (flows) are defined and spatial separation (distance) between flows is calculated. As a result, a series of localised geographically weighted spatial interaction (GWSI) models are developed. Using custom-built algorithms and computer code, we apply the GWSI models to a journey-to-work dataset in Switzerland for validation and comparison with the related global models. The results of the model calibrations are visualised using a series of conventional and flow maps along with some matrix visualisations. The comparison of the results indicates that in most cases local GWSI models exhibit an improvement over the global models both in providing more useful local information and also in model performance and goodness-of-fit.

Species Distribution Modelling (SDM) plays a key role in a number of biological applications: assessment of temporal trends in distribution, environmental impact assessment and spatial conservation planning. From a statistical perspective, this thesis develops two methods for increasing the accuracy and reliability of maps of density surfaces and provides a solution to the problem of how to collate multiple density maps of the same region, obtained from differing sources. From a biological perspective, these statistical methods are used to analyse two marine mammal datasets to produce accurate maps for use in spatial conservation planning and temporal trend assessment. The first new method, Complex Region Spatial Smoother [CReSS; Scott-Hayward et al., 2013], improves smoothing in areas where the real distance an animal must travel (`as the animal swims') between two points may be greater than the straight line distance between them, a problem that occurs in complex domains with coastline or islands. CReSS uses estimates of the geodesic distance between points, model averaging and local radial smoothing. Simulation is used to compare its performance with other traditional and recently-developed smoothing techniques: Thin Plate Splines (TPS, Harder and Desmarais [1972]), Geodesic Low rank TPS (GLTPS; Wang and Ranalli [2007]) and the Soap lm smoother (SOAP; Wood et al. [2008]). GLTPS cannot be used in areas with islands and SOAP can be very hard to parametrise. CReSS outperforms all of the other methods on a range of simulations, based on their fit to the underlying function as measured by mean squared error, particularly for sparse data sets. Smoothing functions need to be flexible when they are used to model density surfaces that are highly heterogeneous, in order to avoid biases due to under- or over-fitting. This issue was addressed using an adaptation of a Spatially Adaptive Local Smoothing Algorithm (SALSA, Walker et al. [2010]) in combination with the CReSS method (CReSS-SALSA2D). Unlike traditional methods, such as Generalised Additive Modelling, the adaptive knot selection approach used in SALSA2D naturally accommodates local changes in the smoothness of the density surface that is being modelled. At the time of writing, there are no other methods available to deal with this issue in topographically complex regions. Simulation results show that CReSS-SALSA2D performs better than CReSS (based on MSE scores), except at very high noise levels where there is an issue with over-fitting. There is an increasing need for a facility to combine multiple density surface maps of individual species in order to make best use of meta-databases, to maintain existing maps, and to extend their geographical coverage. This thesis develops a framework and methods for combining species distribution maps as new information becomes available. The methods use Bayes Theorem to combine density surfaces, taking account of the levels of precision associated with the different sets of estimates, and kernel smoothing to alleviate artefacts that may be created where pairs of surfaces join. The methods were used as part of an algorithm (the Dynamic Cetacean Abundance Predictor) designed for BAE Systems to aid in risk mitigation for naval exercises. Two case studies show the capabilities of CReSS and CReSS-SALSA2D when applied to real ecological data. In the first case study, CReSS was used in a Generalised Estimating Equation framework to identify a candidate Marine Protected Area for the Southern Resident Killer Whale population to the south of San Juan Island, off the Pacific coast of the United States. In the second case study, changes in the spatial and temporal distribution of harbour porpoise and minke whale in north-western European waters over a period of 17 years (1994-2010) were modelled. CReSS and CReSS-SALSA2D performed well in a large, topographically complex study area. Based on simulation results, maps produced using these methods are more accurate than if a traditional GAM-based method is used. The resulting maps identified particularly high densities of both harbour porpoise and minke whale in an area off the west coast of Scotland in 2010, that might be a candidate for inclusion into the Scottish network of Nature Conservation Marine Protected Areas.

Les mouvements collectifs observés dans la nature, tels les nuées d'étourneaux ou les bancs de poissons, peuvent être décrits dans le cadre d'un nouveau type de matière condensée fondamentalement hors d'équilibre : la matière active polaire. Elle est constituée de particules, dites polaires, capables d'utiliser de l'énergie qu'on leur injecte pour se mouvoir dans une direction propre. Nous avons réalisé un système bidimensionnel de disques polaires soumis à une vibration homogène qui n'interagissent que par contact. Ces disques se comportent comme des marcheurs aléatoires, dont les trajectoires présentent une longueur de persistance grande devant leur taille et contrôlée par les fluctuations angulaires de la polarité des disques. Les modes d'alignement résultant du couplage entre la persistance du mouvement et l'interaction de coeur dur entre les particules sont complexes. En particulier, nous observons que seules 10% des collisions se traduisent par un alignement effectif de type ferromagnétique. Pourtant, nous observons l'émergence de mouvements collectifs spontanés au sein du système caractérisés par des fluctuations géantes de densité. Ces résultats montrent la robustesse de l'ordre polaire observé dans les modèles théoriques et numériques de la matière active polaire 2D sur substrat.

On étudie la dynamique et la rhéologie d'une suspension de vésicules (un modèle pour les globules rouges) dans la limite de faibles nombres de Reynolds en utilisant des simulations numériques, basées sur les méthodes des intégrales au bord et du champ de phase. L'attention est mise sur le lien entre la dynamique microscopique des particules et le comportement d'ensemble de la suspension (c. à d. la rhéologie). On analyse une suspension diluée de vésicules dans un écoulement de cisaillement linéaire et on décrit en détail l'influence des paramètres qui en gouvernent la dynamique. On explique le comportement complexe des grandeurs rhéologiques (viscosité effective et différence des contraintes normales) et on détail le rôle de la membrane de la vésicule. On examine l'influence de la courbure des lignes d'écoulement sur la dynamique des vésicules, et on reporte une migration non-négligeable dans la direction de concavité. L'interprétation donnée reste valable pour la plupart des fluides complexes, comme les émulsions et les suspensions de polymères. De plus, on a investigué le comportement d'une suspension de vésicules dans un apparat de Taylor-Couette microscopique, et une transition vers des états ordonnés à été mise en évidence à des fractions volumiques très faibles. On étudie aussi le comportement d'ensembles de vésicules dans un écoulement parabolique, une situation qui imite les globules rouges dans les capillaires sanguins. Les vésicules, soumises au seules forces hydrodynamiques, forment des agrégats de taille finie, un fait qui pourrait être d'importance physiologique.

Les systèmes complexes propres à la biologie moléculaire sont des sujets d'investigations privilégiés pour la physique statistique hors équilibre. En particulier la dynamique des systèmes d'adhérents qui a déjà été l'objet de description théorique. Ces descriptions sont restreintes au comportement des plaques d'adhérence focales matures, dont la durée caractéristique est la dizaine de minutes et où beaucoup d'acteurs moléculaires différents interviennent, notamment le cortex d'actine. Cependant, la question des mécanismes moléculaires précoces, précédant la mise en place de ces structures, reste entière et ouverte. L'objectif de cette thèse est de proposer un modèle de transduction mécano-chimique bidirectionnelle — de l'intérieur de la cellule vers l'extérieur et inversement — en se basant sur le caractère allostérique de l'interaction entre les intégrines (sensibles aux propriétés des matrices extracellulalires) et un partenaire cytoplasmique activable, la taline. Ce travail se divise en trois parties : i) une modélisation du bord cellulaire qui repose sur le calcul du potentiel chimique du partenaire activable et de son cycle d'activation, ii) la résolution numérique et analytique des équations précédemment définies et iii) une évolution du précédent modèle où les intégrines sont laissés libres de diffuser et qui vont dans ce cas là se regrouper dans les zones de fortes contraintes.