Analysis of spatial heterogeneity in Xi'an's urban heat island effect using multi-source data fusion.

ABSTRACT: In the context of global climate change, this study aims to investigate the spatial heterogeneity and driving mechanisms of the urban heat island (UHI) effect within Xi'an's second ring road area. We constructed a novel multi-source data fusion framework that integrates high-resolution remote sensing imagery, detailed building spatial data, and semantic indicators from street view imagery. Based on this framework, we extracted seven key environmental features and land surface temperature (LST) data. We employed Multi-scale Geographically Weighted Regression (MGWR) and machine learning models, including Random Forest, XGBoost, and Gradient Boosted Regression, to analyze both nonlinear interactions and spatially localized variations influencing UHI intensity. The results indicate that buildi

SUBMITTER: Meng Y 

PROVIDER: S-EPMC12533849 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature