Spatial–Social Synergy: GIS-Genetic Algorithm Optimization With Social Things-Enabled Cooperative Learning for Elderly Care Resource Allocation in Shanghai

There is very little research on the small scale of the residential areas, and most studies have not taken into account the community embedded resources. This article employs different scales of subdistricts and residential areas and includes the embedded elderly care beds in the communities. There was an obvious difference between the spatial layout and regional characteristics of elderly care resources and the elderly population in downtown Shanghai in 2020. Based on the perspective of constructing the 15-minute community pension life circle, the spatial accessibility of residential areas to elderly care facilities was significantly different in downtown Shanghai in 2020. This study presents a novel approach to elderly care resource allocation in Shanghai by integrating social things-enabled cooperative learning with spatial optimization methods. Our methodology focuses on optimizing the location and scale through hybrid spatial–social synergy analysis, aiming to reduce spatial inequality in the allocation of elderly care resources and to approach the planning target values in downtown Shanghai. These insights might contribute to enhancing the planning layout and the service system of elderly care resources in Shanghai.