Spatial modeling of income inequality and population aging in Brazil
| Autor(a) principal: | |
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| Data de Publicação: | 2021 |
| Outros Autores: | , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | por |
| Título da fonte: | Research, Society and Development |
| Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/16433 |
Resumo: | Objective: To analyze the spatial dynamics of income inequality and its relationship with population aging in Brazil. Method: A population-based ecological study using spatial analysis techniques and data from the last two Brazilian Demographic Census. Spatial modeling presented the Global Moran Index (I) and Local Spatial Association Index (LISA) through spatial autocorrelation, and the correlation between income inequality, life expectancy, and aging rate. Results: We observed significant spatial autocorrelation of income inequality (I=0.284, I=0.462; p=0.001), life expectancy (I=0.560, I=0.352; p=0.001), and aging rate (I=0.663, I=0.646; p=0.001). Predominant clusters were found in the North, Northeast, and Southern regions of the country. Clusters from the North and Northeast regions were associated with higher inequalities and lower indicators of aging. There was an inverse spatial correlation between income inequality, life expectancy, and aging rate. Conclusion: Population aging in Brazil presents a non-random distribution revealing spatial correlations with income inequality. Given the social and economic disparities across Brazilian territory, spatial analysis proved to be a significant contribution to the formulation of public policies that respect locoregional peculiarities. |
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Spatial modeling of income inequality and population aging in BrazilModelación espacial de la desigualdad de ingresos y el envejecimiento de la población en BrasilModelagem espacial da desigualdade de renda e envelhecimento populacional no BrasilInequalitySocial determinants of healthAgingGeographic information systemsSpatial analysis.DesigualdadDeterminantes sociales de la saludEnvejecimientoSistemas de información geográficaAnálisis espacial.DesigualdadeDeterminantes sociais da saúdeEnvelhecimentoSistemas de informação geográficaAnálise espacial.Objective: To analyze the spatial dynamics of income inequality and its relationship with population aging in Brazil. Method: A population-based ecological study using spatial analysis techniques and data from the last two Brazilian Demographic Census. Spatial modeling presented the Global Moran Index (I) and Local Spatial Association Index (LISA) through spatial autocorrelation, and the correlation between income inequality, life expectancy, and aging rate. Results: We observed significant spatial autocorrelation of income inequality (I=0.284, I=0.462; p=0.001), life expectancy (I=0.560, I=0.352; p=0.001), and aging rate (I=0.663, I=0.646; p=0.001). Predominant clusters were found in the North, Northeast, and Southern regions of the country. Clusters from the North and Northeast regions were associated with higher inequalities and lower indicators of aging. There was an inverse spatial correlation between income inequality, life expectancy, and aging rate. Conclusion: Population aging in Brazil presents a non-random distribution revealing spatial correlations with income inequality. Given the social and economic disparities across Brazilian territory, spatial analysis proved to be a significant contribution to the formulation of public policies that respect locoregional peculiarities.Objetivo: Analizar la dinámica espacial de la desigualdad de ingresos y su relación con el envejecimiento de la población en Brasil. Método: Estudio ecológico de base poblacional utilizando técnicas de análisis espacial y datos de los dos últimos Censos Demográficos Brasileños. El modelado espacial presentó el Índice de Moran Global (I) y el Índice de Asociación Espacial Local (LISA) a través de la autocorrelación espacial y la correlación entre la desigualdad de ingresos, la esperanza de vida y la tasa de envejecimiento. Resultados: Observamos una autocorrelación espacial significativa de la desigualdad de ingresos (I=0,284, I=0,462; p=0,001), la esperanza de vida (I=0,560, I=0,352; p=0,001) y la tasa de envejecimiento (I=0,693, I=0,646; p=0,001). Los conglomerados predominantes se encuentran en las regiones Norte, Nordeste y Sur del país. Los conglomerados de las regiones Norte y Nordeste se asociaron con una mayor desigualdad y menores indicadores de envejecimiento. Hubo una correlación espacial inversa entre la desigualdad de ingresos, la esperanza de vida y la tasa de envejecimiento. Conclusión: El envejecimiento de la población en Brasil tiene una distribución no aleatoria que revela correlaciones espaciales con la desigualdad de ingresos. Dadas las disparidades sociales y económicas existentes en el territorio brasileño, el análisis espacial resultó ser una contribución significativa a la formulación de políticas públicas que respeten las peculiaridades locorregionales.Objetivo: Analisar a dinâmica espacial da desigualdade de renda e sua relação com o envelhecimento populacional no Brasil. Método: Estudo ecológico de base populacional utilizando técnicas de análise espacial e dados dois últimos Censos Demográficos Brasileiros. A modelagem espacial apresentou o Índice de Moran Global (I) e o Índice de Associação Espacial Local (LISA) através da autocorrelação espacial e correlação entre a desigualdade de renda, expectativa de vida e taxa de envelhecimento. Resultados: Observamos autocorrelação espacial significativa da desigualdade de renda (I=0,284; I=0,462; p=0,001), expectativa de vida (I=0,560; I=0,352; p=0,001) e taxa de envelhecimento (I=0,663; I=0,646; p=0,001). Os conglomerados predominantes foram encontrados nas regiões Norte, Nordeste e Sul do país. Os clusters das regiões Norte e Nordeste estiveram associados a maiores desigualdades e menores indicadores de envelhecimento. Houve uma correlação espacial inversa entre desigualdade de renda, expectativa de vida e taxa de envelhecimento. Conclusão: O envelhecimento populacional no Brasil apresenta uma distribuição não aleatória revelando correlações espaciais com a desigualdade de renda. Dadas as disparidades sociais e econômicas existentes no território brasileiro, a análise espacial mostrou-se um contributo significativo para a formulação de políticas públicas que respeitem as peculiaridades locorregionais.Research, Society and Development2021-06-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1643310.33448/rsd-v10i7.16433Research, Society and Development; Vol. 10 No. 7; e8010716433Research, Society and Development; Vol. 10 Núm. 7; e8010716433Research, Society and Development; v. 10 n. 7; e80107164332525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/16433/14559Copyright (c) 2021 Andrezza Marques Duque; Marcus Valerius da Silva Peixoto; Shirley Verônica Melo Almeida Lima; Allan Dantas dos Santos; Caique Jordan Nunes Ribeiro; Julia Guimarães Reis da Costa; Marco Aurélio de Oliveira Góes; Karina Conceição Gomes Machado de Araújo; Marco Antônio Prado Nuneshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess Duque, Andrezza Marques Peixoto, Marcus Valerius da Silva Lima, Shirley Verônica Melo AlmeidaSantos, Allan Dantas dosRibeiro, Caique Jordan NunesCosta, Julia Guimarães Reis daGóes, Marco Aurélio de OliveiraAraújo, Karina Conceição Gomes Machado deNunes, Marco Antônio Prado2021-07-18T21:07:03Zoai:ojs.pkp.sfu.ca:article/16433Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:36:59.087326Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
| dc.title.none.fl_str_mv |
Spatial modeling of income inequality and population aging in Brazil Modelación espacial de la desigualdad de ingresos y el envejecimiento de la población en Brasil Modelagem espacial da desigualdade de renda e envelhecimento populacional no Brasil |
| title |
Spatial modeling of income inequality and population aging in Brazil |
| spellingShingle |
Spatial modeling of income inequality and population aging in Brazil Duque, Andrezza Marques Inequality Social determinants of health Aging Geographic information systems Spatial analysis. Desigualdad Determinantes sociales de la salud Envejecimiento Sistemas de información geográfica Análisis espacial. Desigualdade Determinantes sociais da saúde Envelhecimento Sistemas de informação geográfica Análise espacial. |
| title_short |
Spatial modeling of income inequality and population aging in Brazil |
| title_full |
Spatial modeling of income inequality and population aging in Brazil |
| title_fullStr |
Spatial modeling of income inequality and population aging in Brazil |
| title_full_unstemmed |
Spatial modeling of income inequality and population aging in Brazil |
| title_sort |
Spatial modeling of income inequality and population aging in Brazil |
| author |
Duque, Andrezza Marques |
| author_facet |
Duque, Andrezza Marques Peixoto, Marcus Valerius da Silva Lima, Shirley Verônica Melo Almeida Santos, Allan Dantas dos Ribeiro, Caique Jordan Nunes Costa, Julia Guimarães Reis da Góes, Marco Aurélio de Oliveira Araújo, Karina Conceição Gomes Machado de Nunes, Marco Antônio Prado |
| author_role |
author |
| author2 |
Peixoto, Marcus Valerius da Silva Lima, Shirley Verônica Melo Almeida Santos, Allan Dantas dos Ribeiro, Caique Jordan Nunes Costa, Julia Guimarães Reis da Góes, Marco Aurélio de Oliveira Araújo, Karina Conceição Gomes Machado de Nunes, Marco Antônio Prado |
| author2_role |
author author author author author author author author |
| dc.contributor.author.fl_str_mv |
Duque, Andrezza Marques Peixoto, Marcus Valerius da Silva Lima, Shirley Verônica Melo Almeida Santos, Allan Dantas dos Ribeiro, Caique Jordan Nunes Costa, Julia Guimarães Reis da Góes, Marco Aurélio de Oliveira Araújo, Karina Conceição Gomes Machado de Nunes, Marco Antônio Prado |
| dc.subject.por.fl_str_mv |
Inequality Social determinants of health Aging Geographic information systems Spatial analysis. Desigualdad Determinantes sociales de la salud Envejecimiento Sistemas de información geográfica Análisis espacial. Desigualdade Determinantes sociais da saúde Envelhecimento Sistemas de informação geográfica Análise espacial. |
| topic |
Inequality Social determinants of health Aging Geographic information systems Spatial analysis. Desigualdad Determinantes sociales de la salud Envejecimiento Sistemas de información geográfica Análisis espacial. Desigualdade Determinantes sociais da saúde Envelhecimento Sistemas de informação geográfica Análise espacial. |
| description |
Objective: To analyze the spatial dynamics of income inequality and its relationship with population aging in Brazil. Method: A population-based ecological study using spatial analysis techniques and data from the last two Brazilian Demographic Census. Spatial modeling presented the Global Moran Index (I) and Local Spatial Association Index (LISA) through spatial autocorrelation, and the correlation between income inequality, life expectancy, and aging rate. Results: We observed significant spatial autocorrelation of income inequality (I=0.284, I=0.462; p=0.001), life expectancy (I=0.560, I=0.352; p=0.001), and aging rate (I=0.663, I=0.646; p=0.001). Predominant clusters were found in the North, Northeast, and Southern regions of the country. Clusters from the North and Northeast regions were associated with higher inequalities and lower indicators of aging. There was an inverse spatial correlation between income inequality, life expectancy, and aging rate. Conclusion: Population aging in Brazil presents a non-random distribution revealing spatial correlations with income inequality. Given the social and economic disparities across Brazilian territory, spatial analysis proved to be a significant contribution to the formulation of public policies that respect locoregional peculiarities. |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021-06-14 |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
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https://rsdjournal.org/index.php/rsd/article/view/16433 10.33448/rsd-v10i7.16433 |
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https://rsdjournal.org/index.php/rsd/article/view/16433 |
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10.33448/rsd-v10i7.16433 |
| dc.language.iso.fl_str_mv |
por |
| language |
por |
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https://rsdjournal.org/index.php/rsd/article/view/16433/14559 |
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https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
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https://creativecommons.org/licenses/by/4.0 |
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openAccess |
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application/pdf |
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Research, Society and Development |
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Research, Society and Development |
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Research, Society and Development; Vol. 10 No. 7; e8010716433 Research, Society and Development; Vol. 10 Núm. 7; e8010716433 Research, Society and Development; v. 10 n. 7; e8010716433 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
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Universidade Federal de Itajubá (UNIFEI) |
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UNIFEI |
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UNIFEI |
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Research, Society and Development |
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Research, Society and Development |
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Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
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rsd.articles@gmail.com |
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1797052750663516160 |