Revealing the impact of 2D/3D urban morphology on spatial heterogeneity of diurnal and nocturnal UHI through X-GeoAI driven analytics

Urban morphology plays a pivotal role in shaping urban heat islands (UHI), especially in high-density cities, by influencing land surface temperature (LST) and air temperature. While previous studies have explored the relationships between urban morphology and temperature, they often fail to simultaneously capture the nonlinear interactions of these relationships and spatial heterogeneity arising from regional variations in urban form and environmental conditions. Furthermore, the varying influence of urban morphology on diurnal and nocturnal UHI remains insufficiently understood. This study bridges this gap by applying Geographically Weighted Machine Learning (GWML) and Graph Neural Network (GNN) models to investigate the non-stationary relationships between UHI and its influencing factors. Using high-resolution diurnal and nocturnal LST data from Landsat and ECOSTRESS, combined with 3D building morphology metrics (e.g., Sky View Factor), road network attributes, socio-demographic characteristics, and landscape indices, we systematically analyse the spatial variations in these associations. The analysis includes calculating Moran’s I to detect spatial patterns and comparing the predictive performance of GWML and GNN against Geographically Weighted Regression (GWR). SHapley Additive exPlanations (SHAP) enhance interpretability of explainable GeoAI (X-GeoAI) models, revealing localized impacts of key influencing factors. Our findings demonstrate significant spatial variations in the effects of 2/3D urban morphology on UHI across diurnal and nocturnal cycles. These insights provide a robust foundation for targeted UHI mitigation strategies and adaptive urban planning. This work highlights the potential of advanced GeoAI methods in urban climate research and offers actionable pathways for enhancing climate resilience in high-density cities.