Decoding Urban Emotions: Exploring the Association between Urban Built Environment and Residents' Emotional Health Using Interpretable Machine Learning Models

With the advancement of urbanization and the improvement of living standards, residents' demands for living environments and quality of life are increasingly high. The built environment not only shapes the daily living space of residents but also significantly impacts their emotional well-being. This study, from a grid-based micro perspective, integrates geospatial big data and social media data from Weibo (Chinese Twitter), employing an interpretable machine learning model that combines XGBoost and SHAP to conduct an in-depth analysis of the complex relationship between the built environment and residents' emotional health in Nanning City. The study finds that, first, residents' emotional health exhibits distinct temporal and spatial distribution characteristics; second, transportation stations and green spaces are the two environmental variables that most significantly affect residents' emotional health; third, there is a nonlinear relationship and threshold effect between built environmental elements and residents' emotional health, indicating that the impact of built environment elements on emotional health tends to stabilize or reverse after reaching certain thresholds; fourth, there are interactive effects among different built environmental elements, suggesting that certain combinations of environmental elements may have a more pronounced impact on residents' emotional health. These findings also highlight the importance of considering multidimensional environmental characteristics and their interactions in urban planning to enhance residents' emotional health and achieve sustainable urban development. The innovation of this study lies in the combination of Weibo big data and geospatial big data, using an interpretable machine learning model to precisely capture the distribution characteristics of residents' emotions at a small-scale spatial level and explore the complex relationship with the 5D built environment. This provides a basis for optimizing the layout of urban built environment elements from the perspective of residents' emotions and holds significant theoretical and practical significance for urban planning and health management.