Resident Behavior-Driven Zonation and Optimization of Commercial Service Facilities at the Community Scale

Precise assessment of commercial service facilities (CSFs) is a vital pillar for megacity governance. However, existing evaluations rely on static population and 2D metrics, overlooking behavioral heterogeneity and 3D spatial supply at the micro scale. This study constructs a “3D Supply–Group Demand–Matching” framework at the community level. On the supply side, a Building Coupling Entropy (BCE) model integrates 3D volume and morphology to characterize service capacity. On the demand side, a dynamic behavioral model measures multi-group needs. Mismatch patterns are identified using the Entropy-modified Spatial Disparity Ratio (ESDR). Using Guangzhou as a case, the results reveal three paradigms: (1) Core districts exhibit rigid path dependency, where first-tier sub-districts rose from 48 to 51, and elderly service shortages in old areas plummeted by nearly 80% via micro-regeneration; (2) Growth poles show spatial fragmentation, with core labor demand spilling over but infrastructure lagging, creating a fast production–slow urbanism mismatch; (3) Far-suburban areas reduced extreme-shortage sub-districts from 38 to 34, identifying resource islands besieged by residential demand. Overall, the framework elucidates the shape–flow mismatch mechanism and provides a transferable basis for precision zonation governance, supporting a shift from static quantity-based allocation to dynamic quality-oriented provision in high-density megacities.