Impacts of the Built Environment in Typical Medical-Circle Catchments on Residents’ Activities: A Gradient Boosting Decision Tree Framework with Visual SHAP Interpretation

Urban emergency medical services (EMSs) depend on time-critical accessibility, spatial demand distribution, and resilient transport networks. This study examines how built-environment characteristics shape spatiotemporal population intensity (as a proxy for latent EMS demand) within Shenzhen’s 10 min ambulance-accessible Emergency Medical Circle (EMC), using high-resolution Baidu Huiyan mobile-device data. Human activity intensity was quantified in 200 × 200 m grids and modeled against 20 built-environment indicators using a Gradient Boosting Decision Tree (LightGBM), with SHAP employed for interpretable attribution. By analyzing the distribution density and variance of SHAP dependence patterns, pronounced diurnal shifts in dominant drivers were identified. Medical facility density anchors nocturnal demand, road network permeability dominates pre-dawn mobility, land-use entropy and functional diversity peak during the midday period, while transit hubs and mixed-use amenities consolidate evening activity. The results further reveal critical non-linear thresholds—such as medical facility density (~1.5–2.5 km−2) and building density (~45,000–60,000 m2 km−2)—beyond which marginal contributions diminish or become negative, indicating that proximity alone does not guarantee effective emergency coverage. These findings provide quantitative, time-sensitive guidance for EMC planning, highlighting the need for balanced facility dispersion, network prioritization, and demand-aware spatial design. By integrating high-resolution population dynamics with visually interpretable machine learning, this study advances a human-centered and operationally grounded framework for resilient emergency medical systems.