Explaining Urban Vitality Through Interpretable Machine Learning: A Big Data Approach Using Street View Images and Environmental Factors

Urban vitality (UV) is a critical indicator for measuring the level of sustainable urban development, closely associated with environmental factors such as population density, economic activity, and spatial utilization efficiency. However, traditional methods face significant limitations in capturing the heterogeneity and nonlinear relationships between urban vitality and its influencing factors. This study suggests an interpretable machine learning framework to address the aforementioned issues. It combines a gradient boosting decision tree (GBDT) model with the SHapley Additive exPlanation (SHAP) framework to examine the urban vitality distribution characteristics and factors that influence them in Beijing’s fifth ring road. The main findings include the following: Urban vitality within Beijing’s fifth ring road exhibits significant spatial clustering and positive correlations, with clear spatial heterogeneity. The plot ratio (PR) exerts a notable positive influence on urban vitality, while green space accessibility (DG) demonstrates the strongest negative impact. The building density (BD), in contrast, shows a strong negative correlation with urban vitality. Variables such as the NDVI, average housing price (AHP), and road network density (RND) contribute significantly to urban vitality, reflecting the combined effects of vegetation coverage, economic conditions, and transportation layout. The findings provide a quantitative analytical tool for urban planning, facilitating resource optimization, improving urban vitality, and supporting scientific and rational decision-making.