Agrupamento dos municípios do Sul/Sudoeste de Minas Gerais em relação ao envelhecimento populacional

• Ano de defesa: 2017
• Autor(a) principal: Souza, Larissa Gonçalves
• Orientador(a): Ramos, Patrícia De Siqueira
• Banca de defesa: Ferreira, Leandro, Ferreira, Eric Batista
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Alfenas
• Programa de Pós-Graduação: Programa de Pós-Graduação em Estatística Aplicada e Biometria
• Departamento: Instituto de Ciências Exatas
• País: Brasil
• Palavras-chave em Português: Análise Multivariada; Classificação; Transição demográfica
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
• Link de acesso: https://repositorio.unifal-mg.edu.br/handle/123456789/904

In recent decades, Brazil has experienced changes in its demographic regime that lead to population aging. This phenomenon doesn’t occur equally for all regions of the country. In this sense, the objective of this work is to group the cities of the South/Southwest mesoregion of Minas Gerais in relation to the population aging. Specifically, to identify the groups of cities that are more and less advanced in the process of population aging, based on the k-means non-hierarchical method. The selected variables for the study were: life expectancy at birth, total fertility rate, infant mortality, mortality up to 5 years of age, dependency ratio, probability of survival up to 40 years, probability of survival up to 60 years and aging rate. These data come from the IBGE Demographic Census of 2010, through the Atlas of Human Development in Brazil. The R language was used to implement the analyses through the RStudio program. The k-means method proposed a division of cities into four clusters. The G1 is the least aged and is composed by 17 cities; the cluster G2, formed by 46 cities, is considered the most aged one. In the G3 e G4 clusters are 36 e 47 cities, respectively, and are characterized by intermediate positions between the first two (G3 being less aged than G4). Therefore, approximately 64% of the cities were classified in the more aged clusters (G2 and G4), which correspond to 93 of the 146 cities.