Exploring the influence of block environmental characteristics on land surface temperature and its spatial heterogeneity for a high-density city

A fundamental understanding of the spatial change trends and driving mechanisms of land surface temperature (LST) under urbanization is a prerequisite for the development of effective strategies to mitigate the urban heat island effect. In this study, the built-up blocks of Shenzhen, a high-density city in China, were selected as the unit of analysis. Multi-source datasets were utilized to calculate a total of 44 environmental characteristic indicators, covering four categories. In order to comprehensively analyze the influence of each environmental feature indicator on LST and spatial heterogeneity, MLR, XGBoost and MGWR models were constructed. Furthermore, the nonlinear relationship between the variables was investigated using the SHAP method. The results demonstrated that the predictive efficacy of the MGWR and XGBoost models was markedly superior to that of the MLR model. The percentage cover of forest, the average elevation, NDVI, the frontal area index and the standard deviation of building height were identified as the primary determinants of the LST. These factors account for >52 % to the explanation of the LST distribution. The effects of the majority of landscape pattern, building form and street view indicators on LST exhibited spatial heterogeneity. Furthermore, the indicators also showed nonlinear patterns and threshold effects on LST. The findings offer valuable insights for enhancing the urban thermal environment, particularly in high-density urban areas.