GeoShapley-Based Explainable GeoAI for Sustainable Community Satisfaction Assessment: Evidence from Chengdu, China

Understanding the spatial drivers of community satisfaction is crucial for achieving inclusive and sustainable urban development. However, traditional spatial regression models often assume linearity and fail to capture complex, spatially heterogeneous relationships. This study integrates a GeoShapley-based explainable GeoAI framework with the XGBoost algorithm to identify and quantify spatially varying factors influencing community satisfaction in Chengdu, China. By incorporating geographic coordinates as explicit spatial features, the GeoShapley method decomposes model outputs into intrinsic spatial effects and feature-specific interaction effects, enabling the interpretation of how and where each factor matters. Results show significant spatial clustering (Moran’s I = 0.60, p < 0.01) and a distinct south–north gradient in satisfaction. Built environment indicators—including building coverage ratio (BCR), walkability index (WI), and distance to green space (DGS)—exhibit nonlinear relationships and clear thresholds (e.g., BCR > 0.15, DGS > 590 m). Social vitality (Weibo check-ins) emerges as a key local differentiator, while education and healthcare accessibility remain spatially uniform. These findings reveal a dual structure of public service homogenization and spatial-quality heterogeneity, highlighting the need for place-specific, precision-oriented community renewal. The proposed GeoXAI framework provides a transferable pathway for integrating explainable AI into spatial sustainability research and urban governance.