Scenario-based assessment and prediction of community public service facilities’ relation to resident activity patterns

Resident engagement in community activities promotes neighborhood cohesion and individual well-being. However, the ways in which alternative spatial configurations of community public service facilities relate to diverse everyday activity patterns are poorly understood in neighborhood design research. Guided by adaptive community development trajectories, this study identifies seven representative development themes and integrates machine learning, scenario analysis, and Monte Carlo simulation to investigate how changes in community public service facility configurations influence diverse resident activities in Harbin under different planning orientations. Results indicate that the number of community activity centers and health centers, kindergarten accessibility, and community management quality are the primary factors associated with activity frequency across four categories: survival, self-development, health-oriented activities, and caregiving. The polycentric development scenario (Scenario C) is linked to survival and self-development activities, while the resilient green infrastructure scenario (Scenario G) and healthcare and the compact development scenario (Scenario B) show the strongest positive associations with health-oriented activities and caregiving, respectively. This study supports the design of community public service facilities across multi-scenario development pathways through quantitative prediction.