Decoding Multi-Scale Environmental Configurations for Older Adults’ Walkability with Explainable Machine Learning

The rapid growth of the aging population, alongside functional decline and more older adults living independently, has increased demand for age-friendly infrastructure and walkable communities. This study proposes a quantitative framework to assess how multi-scale built environments influence older adults’ walkability, addressing the scarcity of scalable and interpretable models in age-friendly urban research. By combining the cumulative opportunity method, street-scene semantic segmentation, XGBoost, and GeoSHapley-based spatial effect analysis, the study finds that (1) significant spatial disparities in walkability exist in Xiamen’s central urban area. Over half of the communities (54.46%) failed to meet the minimum threshold (20 points) within the 15 min community life circle (15-min CLC), indicating inadequate infrastructure. The primary issue is low coverage of older adults’ welfare facilities (only 16.26% of communities are within a 15 min walk). Despite renovations in Jinhu Community, walkability remains low, highlighting persistent disparities. (2) Communities with abundant green space are predominantly newly developed areas (64.06%). However, these areas provide fewer facilities on average (2.3) than older communities (5.7), resulting in a “green space–service mismatch”, where visually appealing environments lack essential services. (3) Human perception variables such as safety, traffic flow, and closure positively influence walkability, while visual complexity, heat risk, exposure, and greenness have negative effects. (4) There is a clear supply and demand mismatch. Central districts combine high walkability with substantial older adults’ service demand. Newly built residential areas in the periphery and north have low density and insufficient pedestrian facilities. They fail to meet daily accessibility needs, revealing delays in age-friendly development. This framework, integrating nonlinear modeling and spatial analysis, reveals spatial non-stationarity and optimal thresholds in how the built environment influences walkability. Beyond methodological contributions, this study offers guidance for planners and policymakers to optimize infrastructure allocation, promote equitable, age-friendly cities, and enhance the health and wellbeing of older residents.