Spatial–temporal evolution and associated factors of older adult care institutions in Shanghai
BackgroundShanghai is one of the first Chinese cities to tackle the challenges presented by an aging population. In response, the city has been actively seeking solutions for older adult care within a metropolitan context. This study is based on data from 1,272 older adult care institutions in Shang...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1598394/full |
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| author | Xingxing Yin Jinghang Cui Yifan Wu Mingxuan Cui Kun Li Haoxiang Guo |
| author_facet | Xingxing Yin Jinghang Cui Yifan Wu Mingxuan Cui Kun Li Haoxiang Guo |
| author_sort | Xingxing Yin |
| collection | DOAJ |
| description | BackgroundShanghai is one of the first Chinese cities to tackle the challenges presented by an aging population. In response, the city has been actively seeking solutions for older adult care within a metropolitan context. This study is based on data from 1,272 older adult care institutions in Shanghai. It uses spatial analysis methods to visually display the spatial evolution of older adult care institutions in the city and analyzes the influencing factors of their spatial distribution patterns using a geographic detector approach.MethodsThe research methodology used in the study includes Kernel Density Estimation for visualizing the spatial distribution of older adult care institutions in Shanghai, the Rand Index for measuring the match between older adult care institutions and the older adult population, and Geographically Weighted Regression for addressing spatial heterogeneity by providing local estimates for regression coefficients at different geographical locations.ResultsThe number of older adult care institutions in Shanghai has seen significant growth over the past 20 years, increasing fourfold due to the rise in both public and private facilities. Older adult care institutions in Shanghai exhibit clear spatial clustering features, evolving from a central cluster to a pattern of one major center surrounded by multiple secondary centers. The distribution of older adult care institutions and the aging population shows a positive trend, with the matching degree constantly increasing. The mean deviation index (M) decreased from 0.015 in 2005 to 0.011 in 2020. However, resources for older adult care institutions in central urban areas remain relatively scarce, particularly in Xuhui, Jing’an, and Putuo districts. Factors influencing the distribution of older adult care institutions show spatial heterogeneity, with varying correlations with residential, recreational, commercial, transportation-related, and healthcare facilities.ConclusionThe findings suggest that Shanghai has made progress in expanding older adult care resources to meet the needs of its aging population. However, there are still disparities in the distribution of these facilities, particularly in central urban areas. Understanding the spatial patterns and factors influencing the location of older adult care institutions can guide future planning efforts to ensure equitable access to care for the older adults in Shanghai. |
| format | Article |
| id | doaj-art-e64bac3f0ca44a23abd3344daeb55b04 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj-art-e64bac3f0ca44a23abd3344daeb55b042025-08-20T03:29:18ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-07-011310.3389/fpubh.2025.15983941598394Spatial–temporal evolution and associated factors of older adult care institutions in ShanghaiXingxing Yin0Jinghang Cui1Yifan Wu2Mingxuan Cui3Kun Li4Haoxiang Guo5School of Social Development, East China Normal University, Shanghai, ChinaCenter for Applied Science in Health and Aging, Western Kentucky University, Bowling Green, KY, United StatesDepartment of Economics, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Cognitive Studies, Vanderbilt University, Nashville, TN, United StatesShanxi Provincial Water Conservancy Development Center, Taiyuan, Shanxi, ChinaDepartment of Computer Science, University of Rochester, Rochester, NY, United StatesBackgroundShanghai is one of the first Chinese cities to tackle the challenges presented by an aging population. In response, the city has been actively seeking solutions for older adult care within a metropolitan context. This study is based on data from 1,272 older adult care institutions in Shanghai. It uses spatial analysis methods to visually display the spatial evolution of older adult care institutions in the city and analyzes the influencing factors of their spatial distribution patterns using a geographic detector approach.MethodsThe research methodology used in the study includes Kernel Density Estimation for visualizing the spatial distribution of older adult care institutions in Shanghai, the Rand Index for measuring the match between older adult care institutions and the older adult population, and Geographically Weighted Regression for addressing spatial heterogeneity by providing local estimates for regression coefficients at different geographical locations.ResultsThe number of older adult care institutions in Shanghai has seen significant growth over the past 20 years, increasing fourfold due to the rise in both public and private facilities. Older adult care institutions in Shanghai exhibit clear spatial clustering features, evolving from a central cluster to a pattern of one major center surrounded by multiple secondary centers. The distribution of older adult care institutions and the aging population shows a positive trend, with the matching degree constantly increasing. The mean deviation index (M) decreased from 0.015 in 2005 to 0.011 in 2020. However, resources for older adult care institutions in central urban areas remain relatively scarce, particularly in Xuhui, Jing’an, and Putuo districts. Factors influencing the distribution of older adult care institutions show spatial heterogeneity, with varying correlations with residential, recreational, commercial, transportation-related, and healthcare facilities.ConclusionThe findings suggest that Shanghai has made progress in expanding older adult care resources to meet the needs of its aging population. However, there are still disparities in the distribution of these facilities, particularly in central urban areas. Understanding the spatial patterns and factors influencing the location of older adult care institutions can guide future planning efforts to ensure equitable access to care for the older adults in Shanghai.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1598394/fullolder adult care institutionsspatial distributiongeographically weighted regression (GWR)Shanghai urbanizationaging population |
| spellingShingle | Xingxing Yin Jinghang Cui Yifan Wu Mingxuan Cui Kun Li Haoxiang Guo Spatial–temporal evolution and associated factors of older adult care institutions in Shanghai Frontiers in Public Health older adult care institutions spatial distribution geographically weighted regression (GWR) Shanghai urbanization aging population |
| title | Spatial–temporal evolution and associated factors of older adult care institutions in Shanghai |
| title_full | Spatial–temporal evolution and associated factors of older adult care institutions in Shanghai |
| title_fullStr | Spatial–temporal evolution and associated factors of older adult care institutions in Shanghai |
| title_full_unstemmed | Spatial–temporal evolution and associated factors of older adult care institutions in Shanghai |
| title_short | Spatial–temporal evolution and associated factors of older adult care institutions in Shanghai |
| title_sort | spatial temporal evolution and associated factors of older adult care institutions in shanghai |
| topic | older adult care institutions spatial distribution geographically weighted regression (GWR) Shanghai urbanization aging population |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1598394/full |
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