Uncovering Static–Dynamic Interaction Patterns Between Commercial and Residential Spaces Within Beijing’s Sixth Ring Road Using POI and Human Mobility Trajectory Data

Commercial and residential spaces constitute two fundamental components of urban spatial structure, and their coupling relationship critically shapes urban functional organization and planning interventions. However, integrated analyses of static facility layouts and observed human mobility remain limited. This study develops a static–dynamic framework that integrates facility co-distribution with observed travel connectivity. It further distinguishes catering, shopping, living, financial and insurance, accommodation, and vehicle-related services for category-specific comparison. Kernel density estimation (KDE), average nearest neighbor (ANN) analysis, spatial autocorrelation, Spearman’s rank correlations, median-based classification, and social network analysis characterize static–dynamic relationships. Data comprise Amap commercial points of interest (POIs), Anjuke residential POIs, and a single weekday of Didi origin–destination (OD) records. After removing records with invalid positioning or missing fields, 10:00–16:00 trips within the study area were matched to POI buffers. Both commercial and residential POIs exhibit clustering, with commercial POIs more strongly clustered. Residential POIs follow a core–periphery gradient with a stronger presence in western Beijing. Residential intensity is positively associated with overall commercial intensity in neighboring subdistricts (bivariate Moran’s I = 0.645). Category-specific associations range from 0.282 for vehicle-related services to 0.642 for living services, remaining supported after false-discovery-rate correction (q ≤ 0.0002). Residential–commercial facility intensity and commercial facility–inbound mobility intensity show strong rank correspondence (Spearman’s ρ = 0.869 and 0.883, respectively). Median-based classification identifies 170 subdistricts with concordant high–high or low–low facility–mobility patterns and 20 with contrasting high–low or low–high patterns. Catering, shopping, and living services form more continuous networks, whereas financial and insurance, accommodation, and vehicle-related services exhibit sparser or more spatially peripheral network patterns. Distinguishing facility co-distribution from observed travel connectivity provides service-specific evidence for commercial facility allocation, community life-circle planning, and peripheral cluster development.