Travel demand-driven identification and optimization of urban service facility spatial imbalances: A case study of Harbin, China

Accelerated urbanization presents significant challenges for matching the supply and demand of urban service facilities. This study proposes a travel-demand-oriented analytical framework to optimize facility allocation and mitigate regional supply-demand imbalances. Adaptive kernel density estimation and local hotspot detection techniques were employed to identify urban travel hotspots, forming demand-driven fundamental analytical units. Bivariate Moran's I and coupling coordination degree models were then integrated to conduct a granular analysis of supply-demand matching within these hotspots, identifying imbalanced areas and establishing optimization priorities. An empirical study utilizing taxi trajectory data and catering facility data in Harbin, China, demonstrates that spatial zoning schemes at different scales significantly influence supply-demand analysis conclusions. The proposed demand-oriented approach circumvents conventional spatial partitioning, inherently mitigating the Modifiable Areal Unit Problem. This method precisely identified 52 high-demand-low-supply areas experiencing critical shortages and 118 low-demand-high-supply areas at risk of resource waste in Harbin. Tailored optimization strategies were formulated: prioritized facility expansion in high-demand-low-supply areas, and business model transformation or demand stimulation to reduce redundancy in low-demand-high-supply areas. This methodological framework provides urban planners with a scientifically rigorous basis for resource allocation, facility siting, and spatial functional adjustments, offering an effective pathway to alleviate urban service facility supply-demand disparities.