Spatial Heterogeneity and Nonlinear Threshold Effects of Urban Morphology on Land Surface Temperature Across Different Altitudes and Topographic Contexts

Understanding how urban morphology affects land surface temperature (LST) across altitudinal and topographic contexts is vital for urban heat island mitigation. However, spatial heterogeneity, nonlinearities, and threshold effects remain understudied. Using an interpretable geographically weighted machine learning (IGWML) framework incorporating Shapley additive explanations (SHAP), we examined these effects at the block scale in Beijing, Chongqing, Lanzhou, and Lhasa. Mean summer daytime LST across the study blocks was highest in Beijing (43.87 °C) and lowest in Lhasa (37.84 °C). Hot and cold spots were clustered in Beijing and Lhasa but dispersed in Chongqing and Lanzhou. Hotspots occurred in commercial cores, transport hubs, and dense residential areas, whereas cold spots coincided with large blue-green spaces and rivers. The normalized difference built-up index (NDBI) was the strongest predictor in all cities. In Beijing and Chongqing, LST was primarily influenced by elevation and mean building height (BH); in Lanzhou and Lhasa, it was more sensitive to the Shannon diversity index. Nonlinear analyses identified exploratory reference points for changes in SHAP contribution sign. Interactions were also important: BH and the topographic wetness index showed negative SHAP interaction values in Beijing, whereas building density and NDBI showed positive SHAP interaction values in Chongqing. These findings provide quantitative support for heat-adaptation strategies tailored to different altitudinal and topographic contexts.