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

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 informati