How Urban Morphology Is Associated with Simulated Drone Logistics Network Costs: Location Simulation Evidence from 101 Chinese Cities

Low-altitude logistics is increasingly considered a promising solution for urban last-mile delivery, yet how urban morphology is associated with the simulated cost of drone logistics networks across cities remains unclear. This study examines model-based relationships between urban spatial form and the cost performance of drone logistics networks under unified simulation assumptions. A multi-tier facility location model is developed and applied to 101 Chinese cities, with simulated annealing used to obtain cost-minimizing configurations of drone take-off and landing facilities. An XGBoost model with SHAP analysis is employed to interpret nonlinear associations and interaction patterns between urban morphology indicators and simulated network cost, while K-means clustering is used to identify representative morphology–cost patterns. The results show that built-up area and landscape shape index are the most influential predictors in the adopted modeling setting, both exhibiting threshold-like sensitivity ranges. Simulated network costs increase more rapidly when built-up area exceeds approximately 1000 km2 and when landscape shape index falls within 5–15, with a notable interaction between them. Three morphology–cost types are further identified, reflecting systematic differences in simulated network organization. These findings provide simulation-derived evidence for morphology-sensitive planning of low-altitude logistics infrastructure, while actual deployment decisions still require calibration with local demand, operational, regulatory, and airspace conditions.