Study on Spatial Distribution Equilibrium of Elderly Care Facilities in Downtown Shanghai

Abstract

With the growing challenge of aging populations around the world, the study of the care services for older adults is an essential initiative to accommodate the particular needs of the disadvantaged communities and promote social equity. Based on open-source data and the geographic information system (GIS), this paper quantifies and visualizes the imbalance in the spatial distribution of elderly care facilities in 14,578 neighborhoods in downtown (seven districts) Shanghai, China. Eight types of elderly care facilities were obtained from Shanghai elderly care service platform, divided into two categories according to their service scale. With the introduction of the improved Gaussian 2-step floating catchment area method, the accessibility of two category facilities was calculated. Through the global autocorrelation analysis, it is found that the accessibility of elderly care facilities has the characteristics of spatial agglomeration. Local autocorrelation analysis indicates the cold and hot spots in the accessibility agglomeration state of the two types of facilities, by which we summarized the characteristics of their spatial heterogeneity. It is found that for Category−Ⅰ, there is a large range of hot spots in Huangpu District. For Category−Ⅱ, the hot-spot and cold-spot areas show staggered distribution, and the two categories of hot spot distribution show a negative correlation. We conclude that the two categories are not evenly distributed in the urban area, which will lead to the low efficiency of resource allocation of elderly care facilities and have a negative impact on social fairness. This research offers a systematic method to study urban access to care services for older adults as well as a new perspective on improving social fairness.

Influences of Spatial Accessibility and Service Capacity on the Utilization of Elderly-Care Facilities: A Case Study of the Main Urban Area of Chongqing

“…Somewhat surprisingly, accessibility by driving and bus-riding was not significantly associated with the utilization of ECFs at the global level. While previous research has focused on improving accessibility methods [ 63 ] and fairness assessments [ 45 ] for ECFs in driving mode, our findings demonstrate that these conclusions may not be fully applicable in practice since this mode does not contribute to utilization.…”

“…Given the increasing inequality of ECFs, existing studies fail to provide evidence regarding the impact of accessibility and service capacity on the utilization of ECFs. Contrary to previous studies based on POI [ 45 ], the data in this study were sourced from the Chongqing government’s latest statistical data on ECFs in 2020, ensuring complete and scientific findings. The findings from this study suggest that accessibility by rail transit and walking, and service capacity, can affect the utilization of ECFs.…”

“…The traditional two-step floating catchment area method (2SFCA) treats supply and demand point interactions by dichotomy [ 45 ]. However, this method has two problems: the unreasonable setting of the catchment area, and the homogeneity of weight in the search domain.…”

Spatial Distribution of Pension Institutions in Shanghai Based on the Perspective of Wisdom Grade

“…Second, based on the perspective of spatial accessibility distribution, relevant scholars mainly use different spatial analysis methods to calculate the spatial accessibility of pension institutions and put forward corresponding suggestions [4]. Huang et al used an improved Gaussian 2-step floating catchment area search method [5]. Yin and Liu adopted the average nearest neighbor index, kernel density analysis, mean ratio, and improved two-step mobile search method [6].…”

Understanding evolution process of community-embedded elderly care facilities with big data: A spatiotemporal analytical framework

“…Besides, recent studies have focused on the accessibility of elderly care facilities. Much of the related research adopted accessibility-based approaches to evaluate the spatial matching of elderly care facilities and older adults (Cheng and Huang, 2020; Wu et al, 2017; Xu et al, 2017), recognised as the primary factor for analysing spatial equity and social fairness (Huang et al, 2022; Luo et al, 2018; Zeng et al, 2017). Collectively, these studies outlined a critical role for the spatial analysis of elderly care facilities as planning support; however, these studies remain narrow, focusing only on the static sectional distribution, barely providing sufficient information about the spatial evolution process and developing trend of newly emerged facilities, community-embedded elderly care facilities for instance.…”