More Urban Elderly Care Facilities Should Be Placed in Densely Populated Areas for an Aging Wuhan of China

Zhenwei Wang, Xiaochun Wang, Zijin Dong, Lisan Li, Wangjun Li and Shicheng Li ()
Additional contact information
Zhenwei Wang: School of Public Administration, Hubei University, Wuhan 430062, China
Xiaochun Wang: School of Public Administration, Hubei University, Wuhan 430062, China
Zijin Dong: School of Public Administration, Hubei University, Wuhan 430062, China
Lisan Li: School of Public Administration, Hubei University, Wuhan 430062, China
Wangjun Li: School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Shicheng Li: School of Public Administration, China University of Geosciences, Wuhan 430074, China

Land, 2023, vol. 12, issue 1, 1-13

Abstract: Global aging is getting worse, especially in China, a country with a large population. It is urgently needed to plan the site of new urban elderly care facilities for an aging society. Based on point of interest data and machine learning algorithms, we established a site selection model of urban elderly care facilities for Wuhan in China and selected potential optimal sites for new urban elderly care facilities. We found that 2059 of the 31,390 grids with a resolution of 500 m × 500 m of Wuhan are priority layout grids for new urban elderly care facilities. A total of 635 priority grids were further selected based on the agglomeration degree of the aging population in each street. They are mainly distributed in the areas with a concentrated aging population within the Second Ring Road around the urban centers. Additionally, some outer suburban streets with a relatively high aging degree also require immediate facility construction. The point of interest data and machine learning algorithms to select the location of urban elderly care facilities can optimize their overall configuration and avoid the subjectivity of site selection to some degree, provide empirical support for how to achieve a good configuration of “population–facilities” in space, and continuously improve the science of the spatial allocation of elderly care facilities.

Keywords: urban elderly care facilities; planning; point of interest; machine learning; Wuhan (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2073-445X/12/1/220/pdf (application/pdf)
https://www.mdpi.com/2073-445X/12/1/220/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:1:p:220-:d:1031346

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().