Assessing Urban Ventilation Resistance and Surface Warming Using Multi-Source Data: A Case Study of Kaifeng City

Changes in urban form strongly affect surface thermal conditions, yet long-term quantitative assessments of this relationship, particularly the role of ventilation resistance, remain limited. To address this gap, this study integrates XGBoost, SHapley Additive explanations (SHAP), and multi-scale geographically weighted regression (MGWR) to examine how six morphological, ecological, and human-activity factors influence land surface temperature (LST) in Kaifeng City. The results indicate three main findings. First, LST increased significantly from 1986 to 2024, while interannual variability declined, indicating a gradual reduction in regional thermal fluctuations. Second, NTL was consistently the dominant indicator across the five representative years, while BF and NTL together captured the effects of urban expansion and intensified human activity. Third, FAD coefficients were more spatially heterogeneous in urban fringe areas than in the urban core. In 2020, the dispersion of FAD coefficients in fringe areas was 2.74 times greater than that in the central area, indicating stronger spatial differentiation in ventilation-related morphological constraints during urban expansion. Although FAD made only a modest contribution to overall predictive accuracy, it provided supplementary diagnostic information not captured by conventional density indicators and showed nonlinear, directional, and spatially heterogeneous responses. Compared with previous studies that mainly examined short-term or single-dimensional relationships between urban morphology and LST, this study integrates building densification, ventilation-related morphological resistance, ecological conditions, and human activity intensity into a long-term LST-driver framework, providing evidence to support heat-risk management during urban regeneration and outward expansion.