Made available in DSpace on 2017-07-10T19:24:40Z (GMT). No. of bitstreams: 1 Gustavo Henrique Dalposso.pdf: 751881 bytes, checksum: d4ec13dacd0e510c7549e67525afd909 (MD5) Previous issue date: 2010-01-13 / The methods provided by the spatial statistics are of great importance for studies involving data related to agriculture, for they allow one to know the space variability of the study and identify regions that have similar characteristics, which allows completely localized treatment, maximizing productivity and minimizing the impacts of excessive input application. One of the branches of spatial statistics is geostatistics, which uses a set of regionalized variables to model the structure of spatial dependence, allowing the preparation of thematic maps. Currently, geostatistical studies do not end with the preparation of maps, but also estimates monitored the attribute in non-sampled locations. It is necessary to investigate the quality of these maps, investigating influential points and using measurements to compare maps and area estimations. Another form of research is known as spatial statistics of areas where the objects of analysis are polygons representing blocks, neighborhoods, cities, states and others. This type of analysis seeks to identify spatial autocorrelation in global and local levels, and the usual form of reporting is through thematic maps. In this work we used geostatistics to investigate the productivity of wheat in an agricultural area of 13.7 hectares in the municipality of Salto do Lontra PR. Out of the 50 samples, two were identified as influential, and thus, we chose to build two thematic maps and to compare them using metrics derived from the matrix of errors. The results showed that the maps are different and the removal of influential points was essential to improve the quality of thematic map, since the difference between the estimated yield and actual yield was only 40 Kilos. In order to display the resources provided by the spatial statistics of areas we compared to the vegetation rates NDVI and GVI's of soybean yield from 36 cities in Western Paraná in the agricultural year of 2004/2005. The results showed regions with similar characteristics and that soybeans grow at different times in the region. / As metodologias fornecidas pela estatística espacial são de grande importância para estudos envolvendo dados relacionados à agricultura, pois permitem conhecer a variabilidade espacial dos atributos estudados e identificar regiões que apresentam características semelhantes, o que permite realizar tratamentos localizados, maximizando as produtividades e minimizando os impactos causados pela aplicação de insumos em excesso. Um dos ramos da estatística espacial é a geoestatística, que utiliza um conjunto de variáveis regionalizadas para modelar a estrutura de dependência espacial, possibilitando a elaboração de mapas temáticos. Atualmente os estudos geoestatísticos não terminam com a elaboração dos mapas, pois além de estimar o atributo monitorado em locais não amostrados se faz necessário investigar a qualidade destes mapas, investigando pontos influentes e utilizando medidas que permitam comparar mapas e realizar estimações de áreas. Outra forma de investigação é conhecida como estatística espacial de áreas, em que os objetos de análise são polígonos que representam talhões, bairros, municípios, estados entre outros. Neste tipo de análise, procura-se identificar autocorrelações espaciais em nível global e local, e a forma usual de apresentação dos resultados é feita utilizando mapas temáticos. Neste trabalho utilizou-se a geoestatística para investigar a produtividade de trigo em uma área agrícola de 13,7 hectares no município de Salto do Lontra Pr. Das 50 amostras coletadas, identificou-se duas como influentes e, com isso, optou-se por construir dois mapas temáticos e compará-los utilizando métricas derivadas da matriz dos erros. Os resultados mostraram que os mapas são diferentes e a retirada dos pontos influentes foi de fundamental importância para melhorar a qualidade do mapa temático, visto que a diferença entre a produtividade estimada e a produtividade real foi de apenas 40 quilos. Para apresentar os recursos fornecidos pela estatística espacial de áreas comparou-se os índices de vegetação NDVI e GVI da produtividade de soja de 36 municípios da região Oeste do Paraná no ano agrícola 2004/2005. Os resultados permitiram identificar regiões com características semelhantes e que a soja é cultivada em períodos distintos na região.

Made available in DSpace on 2017-05-12T14:48:03Z (GMT). No. of bitstreams: 1 Gustavo Henrique Dalposso.pdf: 751881 bytes, checksum: d4ec13dacd0e510c7549e67525afd909 (MD5) Previous issue date: 2010-01-13 / The methods provided by the spatial statistics are of great importance for studies involving data related to agriculture, for they allow one to know the space variability of the study and identify regions that have similar characteristics, which allows completely localized treatment, maximizing productivity and minimizing the impacts of excessive input application. One of the branches of spatial statistics is geostatistics, which uses a set of regionalized variables to model the structure of spatial dependence, allowing the preparation of thematic maps. Currently, geostatistical studies do not end with the preparation of maps, but also estimates monitored the attribute in non-sampled locations. It is necessary to investigate the quality of these maps, investigating influential points and using measurements to compare maps and area estimations. Another form of research is known as spatial statistics of areas where the objects of analysis are polygons representing blocks, neighborhoods, cities, states and others. This type of analysis seeks to identify spatial autocorrelation in global and local levels, and the usual form of reporting is through thematic maps. In this work we used geostatistics to investigate the productivity of wheat in an agricultural area of 13.7 hectares in the municipality of Salto do Lontra PR. Out of the 50 samples, two were identified as influential, and thus, we chose to build two thematic maps and to compare them using metrics derived from the matrix of errors. The results showed that the maps are different and the removal of influential points was essential to improve the quality of thematic map, since the difference between the estimated yield and actual yield was only 40 Kilos. In order to display the resources provided by the spatial statistics of areas we compared to the vegetation rates NDVI and GVI's of soybean yield from 36 cities in Western Paraná in the agricultural year of 2004/2005. The results showed regions with similar characteristics and that soybeans grow at different times in the region. / As metodologias fornecidas pela estatística espacial são de grande importância para estudos envolvendo dados relacionados à agricultura, pois permitem conhecer a variabilidade espacial dos atributos estudados e identificar regiões que apresentam características semelhantes, o que permite realizar tratamentos localizados, maximizando as produtividades e minimizando os impactos causados pela aplicação de insumos em excesso. Um dos ramos da estatística espacial é a geoestatística, que utiliza um conjunto de variáveis regionalizadas para modelar a estrutura de dependência espacial, possibilitando a elaboração de mapas temáticos. Atualmente os estudos geoestatísticos não terminam com a elaboração dos mapas, pois além de estimar o atributo monitorado em locais não amostrados se faz necessário investigar a qualidade destes mapas, investigando pontos influentes e utilizando medidas que permitam comparar mapas e realizar estimações de áreas. Outra forma de investigação é conhecida como estatística espacial de áreas, em que os objetos de análise são polígonos que representam talhões, bairros, municípios, estados entre outros. Neste tipo de análise, procura-se identificar autocorrelações espaciais em nível global e local, e a forma usual de apresentação dos resultados é feita utilizando mapas temáticos. Neste trabalho utilizou-se a geoestatística para investigar a produtividade de trigo em uma área agrícola de 13,7 hectares no município de Salto do Lontra Pr. Das 50 amostras coletadas, identificou-se duas como influentes e, com isso, optou-se por construir dois mapas temáticos e compará-los utilizando métricas derivadas da matriz dos erros. Os resultados mostraram que os mapas são diferentes e a retirada dos pontos influentes foi de fundamental importância para melhorar a qualidade do mapa temático, visto que a diferença entre a produtividade estimada e a produtividade real foi de apenas 40 quilos. Para apresentar os recursos fornecidos pela estatística espacial de áreas comparou-se os índices de vegetação NDVI e GVI da produtividade de soja de 36 municípios da região Oeste do Paraná no ano agrícola 2004/2005. Os resultados permitiram identificar regiões com características semelhantes e que a soja é cultivada em períodos distintos na região.

Traditional latent class analysis (LCA) considers entropy R2 as the only measure of effect size. However, entropy may not always be reliable, a low boundary is not agreed upon, and good separation is limited to values of greater than .80. As applications of LCA grow in popularity, it is imperative to use additional sources to quantify LCA classification accuracy. Greater classification accuracy helps to ensure that the profile of the latent classes reflect the profile of the true underlying subgroups. This Monte Carlo study compared the quantification of classification accuracy and confidence intervals of three effect sizes, entropy R2, I-index, and Cohen’s d. Study conditions included total sample size, number of dichotomous indicators, latent class membership probabilities (γ), conditional item-response probabilities (ρ), variance ratio, sample size ratio, and distribution types for a 2-class model. Overall, entropy R2 and I-index showed the best accuracy and standard error, along with the smallest confidence interval widths. Results showed that I-index only performed well for a few cases.

Submitted by (ana.araujo@ufrpe.br) on 2016-05-20T12:26:11Z No. of bitstreams: 1 Ana Clara Paixao Campos.pdf: 1515481 bytes, checksum: 29c30eb35f6e7da1f6e63d0971def668 (MD5) / Made available in DSpace on 2016-05-20T12:26:11Z (GMT). No. of bitstreams: 1 Ana Clara Paixao Campos.pdf: 1515481 bytes, checksum: 29c30eb35f6e7da1f6e63d0971def668 (MD5) Previous issue date: 2013-02-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Knowing the spatial pattern of patients with genetic disease with physical disabilities in the treatment of Pernambuco AACD is of great importance because it makes it possible to guide the basic care assistance to these individuals and provide solid basis for planning public health policies. However, there are few references in the literature using the tools of spatial analysis in the context of disability. This dissertation was structured in the form of two papers. In the first article the global Moran's I index was used to characterize the spatial pattern of the rate of patients with genetic disease with physical disabilities in the AACD treatment of Pernambuco and the results were compared with those obtained with the randomization test. In both approaches we found the existence of spatial pattern for the rate of patients with genetic disease with physical disabilities in the treatment of Pernambuco AACD when we took into account the rates of four municipalities closest to each location. In the second article we evaluated the performance of the global Moran's I index and the Mantel test with Spearman correlation, both using randomization to assess the statistical significance, regarding the ability to detect spatial pattern for the rate of patients with diseases genetic with physical disabilities in the AACD treatment of Pernambuco. The results showed that the global Moran's I index proved to be a more satisfactory method for detecting the spatial pattern, since it uses the information in its calculations of the neighborhood, and provide greater control of the rejection rates of the null hypothesis under study. / Conhecer o padrão espacial dos portadores de doença genética com deficiência física em tratamento na AACD de Pernambuco é de grande importância, pois torna possível orientar a assistência aos cuidados básicos desses indivíduos e fornecer base sólida para o planejamento de políticas públicas de saúde. Entretanto, existem poucas referências na literatura utilizando o instrumental da análise espacial no contexto da deficiência física. A presente dissertação foi estruturada na forma de dois artigos científicos. No primeiro artigo o Índice I global de Moran foi utilizado para caracterizar o padrão espacial da taxa de pacientes portadores de doença genética com deficiência física em tratamento na AACD de Pernambuco e os resultados encontrados foram comparados com os obtidos através do teste de aleatorização. Em ambas as metodologias constatou-se a existência de padrão espacial agregado para a taxa de pacientes portadores de doença genética com deficiência física em tratamento na AACD de Pernambuco quando se levou em consideração as taxas dos 4 municípios mais próximos de cada localidade. No segundo artigo foi avaliado o desempenho do índice I global de Moran e do teste de Mantel com correlação de Spearman, ambos utilizando aleatorização para avaliar a significância da estatística, no que tange a capacidade de detectar padrão espacial para a taxa de pacientes portadores de doenças genéticas com deficiência física em tratamento na AACD de Pernambuco. Os resultados indicaram que o índice I global de Moran mostrou-se uma metodologia mais satisfatória para detectar do padrão espacial, uma vez que utiliza em seus cálculos as informações da vizinhança, além de proporcionar maior controle das taxas de rejeição da hipótese nula em estudo.

Abstract: Road traffic accidents claim more than 1,500 lives each year in Canada and affect society adversely, so transport authorities must reduce their impact. This is a major concern in Quebec, where the traffic-accident risks increase year by year proportionally to provincial population growth. In reality, the occurrence of traffic crashes is rarely random in space-time; they tend to cluster in specific areas such as intersections, ramps, and work zones. Moreover, weather stands out as an environmental risk factor that affects the crash rate. Therefore, traffic-safety engineers need to accurately identify the location and time of traffic accidents. The occurrence of such accidents actually is determined by some important factors, including traffic volume, weather conditions, and geometric design. This study aimed at identifying hotspot locations based on a historical crash data set and spatiotemporal patterns of traffic accidents with a view to improving road safety. This thesis proposes two new methods for identifying hotspot locations on a road network. The first method could be used to identify and rank hotspot locations in cases in which the value of traffic volume is available, while the second method is useful in cases in which the value of traffic volume is not. These methods were examined with three years of traffic-accident data (2011–2013) in Sherbrooke. The first method proposes a two-step integrated approach for identifying traffic-accident hotspots on a road network. The first step included a spatial-analysis method called network kernel-density estimation. The second step involved a network-screening method using the critical crash rate, which is described in the Highway Safety Manual. Once the traffic-accident density had been estimated using the network kernel-density estimation method, the selected potential hotspot locations were then tested with the critical-crash-rate method. The second method offers an integrated approach to analyzing spatial and temporal (spatiotemporal) patterns of traffic accidents and organizes them according to their level of significance. The spatiotemporal seasonal patterns of traffic accidents were analyzed using the kernel-density estimation; it was then applied as the attribute for a significance test using the local Moran’s I index value. The results of the first method demonstrated that over 90% of hotspot locations in Sherbrooke were located at intersections and in a downtown area with significant conflicts between road users. It also showed that signalized intersections were more dangerous than unsignalized ones; over half (58%) of the hotspot locations were located at four-leg signalized intersections. The results of the second method show that crash patterns varied according to season and during certain time periods. Total seasonal patterns revealed denser trends and patterns during the summer, fall, and winter, then a steady trend and pattern during the spring. Our findings also illustrated that crash patterns that applied accident severity were denser than the results that only involved the observed crash counts. The results clearly show that the proposed methods could assist transport authorities in quickly identifying the most hazardous sites in a road network, prioritizing hotspot locations in a decreasing order more efficiently, and assessing the relationship between traffic accidents and seasons. / Les accidents de la route sont responsables de plus de 1500 décès par année au Canada et ont des effets néfastes sur la société. Aux yeux des autorités en transport, il devient impératif d’en réduire les impacts. Il s’agit d’une préoccupation majeure au Québec depuis que les risques d’accidents augmentent chaque année au rythme de la population. En réalité, les accidents routiers se produisent rarement de façon aléatoire dans l’espace-temps. Ils surviennent généralement à des endroits spécifiques notamment aux intersections, dans les bretelles d’accès, sur les chantiers routiers, etc. De plus, les conditions climatiques associées aux saisons constituent l’un des facteurs environnementaux à risque affectant les taux d’accidents. Par conséquent, il devient impératif pour les ingénieurs en sécurité routière de localiser ces accidents de façon plus précise dans le temps (moment) et dans l’espace (endroit). Cependant, les accidents routiers sont influencés par d’importants facteurs comme le volume de circulation, les conditions climatiques, la géométrie de la route, etc. Le but de cette étude consiste donc à identifier les points chauds au moyen d’un historique des données d’accidents et de leurs répartitions spatiotemporelles en vue d’améliorer la sécurité routière. Cette thèse propose deux nouvelles méthodes permettant d’identifier les points chauds à l’intérieur d’un réseau routier. La première méthode peut être utilisée afin d’identifier et de prioriser les points chauds dans les cas où les données sur le volume de circulation sont disponibles alors que la deuxième méthode est utile dans les cas où ces informations sont absentes. Ces méthodes ont été conçues en utilisant des données d’accidents sur trois ans (2011-2013) survenus à Sherbrooke. La première méthode propose une approche intégrée en deux étapes afin d’identifier les points chauds au sein du réseau routier. La première étape s’appuie sur une méthode d’analyse spatiale connue sous le nom d’estimation par noyau. La deuxième étape repose sur une méthode de balayage du réseau routier en utilisant les taux critiques d’accidents, une démarche éprouvée et décrite dans le manuel de sécurité routière. Lorsque la densité des accidents routiers a été calculée au moyen de l’estimation par noyau, les points chauds potentiels sont ensuite testés à l’aide des taux critiques. La seconde méthode propose une approche intégrée destinée à analyser les distributions spatiales et temporelles des accidents et à les classer selon leur niveau de signification. La répartition des accidents selon les saisons a été analysée à l’aide de l’estimation par noyau, puis ces valeurs ont été assignées comme attributs dans le test de signification de Moran. Les résultats de la première méthode démontrent que plus de 90 % des points chauds à Sherbrooke sont concentrés aux intersections et au centre-ville où les conflits entre les usagers de la route sont élevés. Ils révèlent aussi que les intersections contrôlées sont plus à risque par comparaison aux intersections non contrôlées et que plus de la moitié des points chauds (58 %) sont situés aux intersections à quatre branches (en croix). Les résultats de la deuxième méthode montrent que les distributions d’accidents varient selon les saisons et à certains moments de l’année. Les répartitions saisonnières montrent des tendances à la densification durant l’été, l’automne et l’hiver alors que les distributions sont plus dispersées au cours du printemps. Nos observations indiquent aussi que les répartitions ayant considéré la sévérité des accidents sont plus denses que les résultats ayant recours au simple cumul des accidents. Les résultats démontrent clairement que les méthodes proposées peuvent: premièrement, aider les autorités en transport en identifiant rapidement les sites les plus à risque à l’intérieur du réseau routier; deuxièmement, prioriser les points chauds en ordre décroissant plus efficacement et de manière significative; troisièmement, estimer l’interrelation entre les accidents routiers et les saisons.