Understanding evolution process of community-embedded elderly care facilities with big data: A spatiotemporal analytical framework. Issue 3 (September 2022)

• Record Type: Journal Article
• Authors: Liu, Zhuo; Zhao, Hanqi; Li, Sichun; Chen, Yanyan; Yao, Enjian
• Is Part Of: Transactions in urban data, science, and technology. Volume 1:Issue 3/4(2022)
• Page Start: 142
• Page End: 163
• Publication Date: 2022-09
• Subjects: Elderly-care facility planning -- spatial evolution -- standard deviation ellipse -- 2SFCA -- multi-source data; Urbanization -- Periodicals; Urbanization -- China -- Periodicals; City planning -- Periodicals; City planning -- Technological innovations -- Periodicals; City planning -- Data processing -- Periodicals; Urban ecology (Sociology) -- Periodicals
• DOI: 10.1177/27541231221136406
• Languages: English
• ISSNs: 2754-1231

Abstract

The severe population ageing has rapidly increased the demand for urban elderly care services in most countries. As a novel urban elderly care mode, community-embedded elderly care facilities integrate various functions and allow older urban adults to enjoy comprehensive care services in a familiar environment at an acceptable cost. Therefore, it is widely recognised as an effective way to resolve the contradiction between the increasing demand and limited supply capacity of elderly care services in large cities. However, spatial analysis of elderly care facilities in previous studies were focused on static characters, ignoring the evolution process. The traditional static analysis methods might be one-sided for the spatial analysis of community-embedded elderly care facilities, considering their highly dynamic development. This study considers Beijing as a case study and establishes a novel spatiotemporal analytical framework, augmented by big data, to analyse the spatial distribution of the local community-embedded elderly care facilities (elderly stations) from a dynamic view. The multi-source data regarding elderly stations, the elderly population and basic geographic information of Beijing were extracted and integrated into the analysis. On this basis, the proposed framework was applied to examine the distribution tendency, evolution trend and accessibility of elderly stations from 2017 to 2020. The results reveal a significant cluster development characteristic of elderly stations. Although the density of elderly stations in the downtown area is much higher than that in the urban periphery, the elderly stations might still be unable to satisfy the enormous elderly care demand in Xicheng and Dongcheng districts. Moreover, the imbalance between the urban centre and peripheries and the spatial mismatch between the elderly stations and population were identified. The research output could support the planning practice of elderly stations for relevant departments.