Population ageing is intensifying pressure on elderly-care provision in megacity suburbs, but spatially explicit evidence on who benefits and where gaps persist remains limited. Using Daxing District, Beijing, as a case study, under the 15-min community living circle framework, we integrate cleaned elderly-care facility POIs from the municipal government portal (209 points), census-calibrated age-stratified WorldPop 100 m grids, and an OpenStreetMap road network to evaluate walking-based supply–demand matching. Kernel density estimation (KDE) characterizes facility agglomeration; the Gaussian Two-Step Floating Catchment Area (Ga2SFCA) method (1 km threshold) measures accessibility for two cohorts (60–80 and 80+); and global Moran’s I with bivariate LISA identifies spatial coupling between accessibility and elderly population density. The results indicate the following: (1) pronounced spatial imbalance—facilities are concentrated in the northwest and east but remain sparse in central and southern areas, while elderly population density follows a center–periphery gradient, peaking at 12,000 persons/km2 in core areas (e.g., Jiugong and Huangcun); (2) clear accessibility stratification—overall accessibility is low and spatially clustered, yet the 80+ cohort (13.6% of the elderly population) exhibits markedly higher accessibility than the 60–80 cohort; and (3) differentiated coupling types—global bivariate Moran’s I = 0.773143 (p < 0.01), with LISA dominated by low-demand–low-accessibility (LL) areas and additional high-demand–low-accessibility (HL) shortage zones and low-demand–high-accessibility (LH) potential redundancy zones, while HH areas are scarce. These diagnostics support zone-specific gap filling to mitigate spatial inequities and age–structural mismatches.
Deng et al. (2026) studied this question.