Beyond Land Surface Temperature: Explainable Spatial Machine Learning Reveals Urban Morphology Effects on Human-Centric Heat Stress
By employing a novel geographically weighted XGBoost and GAM workflow in Singapore, this study demonstrates that land surface temperature (LST) fails to adequately capture human heat stress (UTCI) because it overlooks critical physiological drivers like shading and sky view factor, highlighting the need for more human-centric metrics in climate-adaptive urban planning.