Técnicas de análise de dados distribuídos em áreas

• Ano de defesa: 2015
• Autor(a) principal: Bertolla, Jane Maiara [UNESP]
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Biometria; Bioestatistica; Análise espacial (Estatística); Biometry
• Link de acesso: http://hdl.handle.net/11449/126401; http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/08-07-2015/000839869.pdf

The goal of this piece is to study spacial analysis techniques to understand the patterns associated to data over areas, test if the observed pattern is random or if the event distributes itself by agglomeration, get smoother maps than the observed maps and look for better estimates of adjacent structures. A database concerning of 1656 positive dengue occurrences in the city of Rio Claro-SP during the first semester of 2011 was used. With this database, using the software Terra View 4.2.2, it were constructed the kernel estimates for two kind of estimate functions: Normal Kernel and Quartic Kernel. For the Normal Kernel Function estimate it were used the following influence radius: 100m, 150m, 200m and 500m. On the other hand, for the Quartic Kernel Function estimate it were used the following influence radius: 250m, 375m, 500m and 625m. Yet using the same software, maps were constructed for a visual exploratory analysis with three different criterions: equal intervals, quintiles intervals and standard deviation intervals. In the sequence, the respective smoothing maps were constructed using the Spacial Moving Averages. Depending on the used criterion (quintiles, equal intervals or standard deviation) it was observed differences between the dengue occurrences, considering the original data or the ones transformed by the moving average. It was observed that the quartic kernel behavior is similar to the normal's kernel, but with different influence radius. This result corroborates Bailey and Gatrell's observations that the adjustment function is not of considerable importance, considering that the control can be made through the influence radius for the estimate in each point. Through the random permutation test it was verified that there is special dependence between the observed values, where the stats equals 0,389828 and the p-value equals 0,01. Kawamoto (2012) applied the kernel estimate for the same database, but considering the ...