Análise espacial da amazônia legal maranhense na região de influência da Matopiba: desmatamento e indicadores socioeconômicos nos anos de 2013 e 2023

• Ano de defesa: 2024
• Autor(a) principal: BARRADA, Dacélia Brito
• Orientador(a): RIBEIRO, Eliene Cristina Barros
• Banca de defesa: RIBEIRO, Eliene Cristina Barros , MELO, Aline Alvares , CÉSAR, Aldara da Silva
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM DESENVOLVIMENTO SOCIOECONOMICO/CCSO
• Departamento: DEPARTAMENTO DE CIENCIAS CONTÁBEIS E ADMINISTRAÇÃO/CCSO
• País: Brasil
• Palavras-chave em Português: desenvolvimento; desmatamento; indicadores socioeconômicos; Amazônia Legal; região MATOPIBA; AEDE;
• Palavras-chave em Inglês: development; deforestation; socioeconomic indicators; Legal Amazon; MATOPIBA Region; AEDE.
• Área do conhecimento CNPq: Crescimento e Desenvolvimento Econômico
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/5547

This study aims to analyze the socioeconomic impacts of deforestation in the Legal Amazon of Maranhão in the MATOPIBA region, identifying spatial dependence and cluster formation. The time frame was 2013 and 2023, and 99 municipalities were selected. The methodology used was Exploratory Analysis of Spatial Data (AEDE). The following software were used in the analysis: Tableau 2014.1 for data processing and graphing; QGIS 3.34 for data analysis and map making; and GeoDa 1.22 for spatial analysis and statistics. The variables selected for research at the municipal level were Deforestation, GDP per capita, IDEB, and Formal employment. Moran's I statistic was calculated to identify the existence of spatial autocorrelation of the variables, in which spatial autocorrelation was found for the variables Deforestation, GDP per capita, and IDEB. However, no spatial autocorrelation was found for the Formal Employment variable. For the three variables with spatial autocorrelation, the Univariate Local Moran Index (LISA) was calculated, which showed the formation of significant clusters in the three variables in the years 2013 and 2023. It was possible that deforestation in one municipality could affect nearby municipalities, just as a low GDP per capita in one municipality could affect nearby municipalities, and an increase in the IDEB score could lead to an increase in the score in a nearby municipality.