Nonlinear relationship and spatial heterogeneity between built environment and residents' sentiments: A comprehensive framework integrating multimodal data with AI​

The impact of built environment on residents’ sentiments is a critical concern. This study integrates multiple AI models, including Large Language Model (LLM), Pyramid Scene Parsing Network (PSPNet), eXtreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), Geographically Weighted Regression (GWR), and automatic clustering models, to establish an environment-emotion framework for analyzing the nonlinear relationships and spatial heterogeneity between the built environment and residents' sentiments. LLMs are used to analyze social media data, revealing the spatial distribution characteristics of residents' sentiments. Multimodal data are combined with PSPNet models and spatial econometric models to measure the characteristics of the built environment. The nonlinear relationships and spatial heterogeneity between the built environment and residents' sentiments are uncovered through XGBoost, SHAP and GWR models. Automatic clustering method is employed to select typical cases to examine how spatial heterogeneity influences the nonlinear and interaction effects. The findings reveal that the relationships between built environment and residents’ sentiments exhibited complex nonlinear patterns, with threshold effects observed for specific indicators. Inter-element interactions demonstrated context-dependent synergies or antagonisms. And the influence of built environment on residents’ sentiments varied significantly across spatial contexts. Moreover, identical built environment exerted divergent effects on residents’ sentiments due to spatial heterogeneity in nonlinear relationships. This study constructs a comprehensive framework integrating multimodal data with AI and offers actionable insights for urban livability enhancement. The findings contribute to an understanding of how built environment might be effectively optimized to improve residents’ sentiments in urban areas, which deepens the action mechanism and implementation pathways through which AI technology empowers sustainable development planning.