Deep Learning-Driven Geospatial Modeling of Elderly Care Accessibility: Disparities Across the Urban-Rural Continuum in Central China

With the intensification of aging, the imbalance between the supply and demand of elderly care services has become increasingly prominent. Taking Changsha as a case study, this research constructs an accessibility evaluation system based on the 15-min life circle theory, utilizing multi-source data. Spatial weighting characteristics of elderly care facility locations were analyzed through machine learning algorithms, and service coverage disparities between urban districts and suburban towns were assessed under 5-, 10-, and 15-min walking thresholds. Street view semantic segmentation technology was employed to extract street environmental elements in central urban areas, and a multiple regression model was established to elucidate the impact mechanisms of the built environment on walking accessibility. Key findings include: (1) Significant urban-rural service disparities exist, with 91.4% of urban core facilities offering seven service categories within 15-min walking catchments compared to 26.86% in township areas, demonstrating suburban infrastructure’s heavy reliance on administrative resource allocation. (2) Street environmental factors exhibit significant correlations with walking accessibility scores. At the 15-min walking threshold, building space ratio and transportation infrastructure coverage positively influenced walking convenience, while sky view ratio showed a negative correlation. (3) A random forest-based location prediction framework identified multiple service gaps in existing facilities. Suburban service deficiencies (e.g., 59.8% medical facility coverage within walkable catchments) emerge as critical equity barriers, prompting recommendations for integrated “micro-clinic + smart pharmacy” networks and prioritized mixed-use zoning in new urban planning. This research advances a data-driven framework for reconciling urbanization-aging conflicts, offering practical insights for developing nations in creating age-friendly urban environments.