The Impact of Built Environment on Urban Vitality—A Multi-Scale Geographically Weighted Regression Analysis in the Case of Shenyang, China

Abstract

Urban vitality acts as a key driver of sustainable urban development, while the built environment serves as its physical foundation. However, spatial heterogeneity in urban landscapes leads to imbalanced impacts of economic, social, and environmental factors on vitality. Therefore, it is essential to investigate the underlying principles governing vitality impacts imposed by diverse components of the built environment at the spatial level. This study synthesized multi-source remote sensing data alongside geospatial datasets aiming to quantify vitality and built environment indicators across Shenyang, China. We applied Ordinary Least Squares (OLS) regression for collinearity diagnosis and Multi-scale Geographically Weighted Regression (MGWR) to model spatial heterogeneity impacts at the planning-unit level. The regression factor analysis yielded three primary conclusions: (1) Functional Mixture Degree, Bus Stop Density, and Subway Station Density demonstrated a statistically significant positive correlation with urban vitality. (2) FAR (Floor Area Ratio), Vegetation Coverage, Commercial Facility Density, and Road Density exhibited differentiated effects in core areas versus peripheral areas. (3) Public Facility Density and Bus Stop Density showed a negative correlation trend with vitality levels in Industrial Functional Zones. We propose a geospatial analysis framework that leverages remote sensing to decode spatially heterogeneous built environment–vitality linkages. This approach supports precision urban renewal planning by identifying location-specific interventions. Geospatial big data and MGWR offer replicable tools for analyzing urban sustainability. Future work should integrate real-time sensor data to track vitality dynamics.