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Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China

BACKGROUND: China is one of the world’s fastest-aging countries. Population aging and social-economic development show close relations. This study aims to illustrate the spatial-temporal distribution and movement of gravity centers of population aging and social-economic factors and thier spatial in...

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Autores principales: Man, Wang, Wang, Shaobin, Yang, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140474/
https://www.ncbi.nlm.nih.gov/pubmed/34020620
http://dx.doi.org/10.1186/s12889-021-11032-z
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author Man, Wang
Wang, Shaobin
Yang, Hao
author_facet Man, Wang
Wang, Shaobin
Yang, Hao
author_sort Man, Wang
collection PubMed
description BACKGROUND: China is one of the world’s fastest-aging countries. Population aging and social-economic development show close relations. This study aims to illustrate the spatial-temporal distribution and movement of gravity centers of population aging and social-economic factors and thier spatial interaction across the provinces in China. METHODS: Factors of elderly population rate (EPR), elderly dependency ratio (EDR), per capita gross regional product (GRP(pc)), and urban population rate (UPR) were collected. Distribution patterns were detected by using global spatial autocorrelation, Kernel density estimation, and coefficient of variation. Further, Arc GIS software was used to find the gravity centers and their movement trends yearly from 2002 to 2018. The spatial interaction between the variables was investigated based on bivariate spatial autocorrelation analysis. RESULTS: The results showed a larger variety of global spatial autocorrelation indexed by Moran’s I and stable trends of dispersion degree without obvious convergence in EPR and EDR. Furthermore, the gravity centers of the proportion of EPR and EDR moved northeastward. In contrast, the economic and urbanization factors showed a southwestward movement, which exhibited an reverse trend compared to population aging indicators. Moreover, the movement rates of EPR and EDR (15.12 and 18.75 km/year, respectively) were higher than that of GRP(pc) (13.79 km/year) and UPR (6.89 km/year) annually during the study period. Further, the bivariate spatial autocorrelation variation is in line with the movement trends of gravity centers which showed a polarization trend of population aging and social-economic factors that the difference between southwest and northeast directions and exhibited a tendency to expand in China. CONCLUSIONS: In sum, our findings revealed the difference in spatio-temporal distribution and variation between population aging and social-economic factors in China. It further indicates that the opposite movements of gravity centers and the change of the BiLISA in space which may result in the increase of the economic burden of the elderly care in northern China. Hence, future development policy should focus on the social-economic growth and distribution of old-aged supporting resources, especially in northern China. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-11032-z.
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spelling pubmed-81404742021-05-25 Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China Man, Wang Wang, Shaobin Yang, Hao BMC Public Health Research BACKGROUND: China is one of the world’s fastest-aging countries. Population aging and social-economic development show close relations. This study aims to illustrate the spatial-temporal distribution and movement of gravity centers of population aging and social-economic factors and thier spatial interaction across the provinces in China. METHODS: Factors of elderly population rate (EPR), elderly dependency ratio (EDR), per capita gross regional product (GRP(pc)), and urban population rate (UPR) were collected. Distribution patterns were detected by using global spatial autocorrelation, Kernel density estimation, and coefficient of variation. Further, Arc GIS software was used to find the gravity centers and their movement trends yearly from 2002 to 2018. The spatial interaction between the variables was investigated based on bivariate spatial autocorrelation analysis. RESULTS: The results showed a larger variety of global spatial autocorrelation indexed by Moran’s I and stable trends of dispersion degree without obvious convergence in EPR and EDR. Furthermore, the gravity centers of the proportion of EPR and EDR moved northeastward. In contrast, the economic and urbanization factors showed a southwestward movement, which exhibited an reverse trend compared to population aging indicators. Moreover, the movement rates of EPR and EDR (15.12 and 18.75 km/year, respectively) were higher than that of GRP(pc) (13.79 km/year) and UPR (6.89 km/year) annually during the study period. Further, the bivariate spatial autocorrelation variation is in line with the movement trends of gravity centers which showed a polarization trend of population aging and social-economic factors that the difference between southwest and northeast directions and exhibited a tendency to expand in China. CONCLUSIONS: In sum, our findings revealed the difference in spatio-temporal distribution and variation between population aging and social-economic factors in China. It further indicates that the opposite movements of gravity centers and the change of the BiLISA in space which may result in the increase of the economic burden of the elderly care in northern China. Hence, future development policy should focus on the social-economic growth and distribution of old-aged supporting resources, especially in northern China. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-11032-z. BioMed Central 2021-05-22 /pmc/articles/PMC8140474/ /pubmed/34020620 http://dx.doi.org/10.1186/s12889-021-11032-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Man, Wang
Wang, Shaobin
Yang, Hao
Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China
title Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China
title_full Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China
title_fullStr Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China
title_full_unstemmed Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China
title_short Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China
title_sort exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140474/
https://www.ncbi.nlm.nih.gov/pubmed/34020620
http://dx.doi.org/10.1186/s12889-021-11032-z
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