Modelagem de dados climáticos e socioeconômicos em municípios do estado de Pernambuco utilizando análise de componentes principais (ACP).

• Autor(a) principal: Silva, Vicente Natanael Lima
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UNICAP
• Texto Completo: http://tede2.unicap.br:8080/handle/tede/993
• topic: Principal component analysis; Dissertations; Climatology; Data modeling; Análise de componentes principais; Dissertações; Climatologia; Modelagem de dados; CIENCIAS BIOLOGICAS::BIOLOGIA GERAL#; #-1634559385931244697#; #600

In the State of Pernambuco, as well as throughout the Northeast region of Brazil, the expressive interaction between climate elements and human activities is evident. Numerous scientific studies have already demonstrated a significant correlation between climate behavior with social, economic, cultural, etc. This work served as a case study of the application of the multivariate statistical technique of Principal Components Analysis (PCA) in the making of socioeconomic diagnoses, where the elements of the climate were used as independent variables on the socioeconomic responses (Gross Domestic Product and Municipal Development Index) Of some municipalities that presented significant development in the State of Pernambuco - Brazil, between 1999 and 2013. Even considering the climatic, socioeconomic and essential dependence of water for the economic development of the municipalities studied, the PCA showed that the socioeconomic indexes of the municipalities located in the Sertão (Petrolina and Arcoverde) will present a higher correlation with the indices of temperature and Insulation, in the Agreste and Zona da Mata (Garanhuns and Surubim) evaporation and temperatures, in the Litoral (Recife) precipitation and humidity. The PCA was also effective in allowing the removal or disposal of variables that presented low variability or were redundant because they were correlated with those of greater importance for the first two main components. Understanding the behavior of climate elements and their consequences on human activities is of fundamental importance in helping public policies to mitigate the adverse effects of environmental change.