Measurement of urban vitality and the influence mechanism of the built environment on it based on multi-source data: A case study of Yantai City

Under a human-centered approach, accurately identifying the spatial patterns of urban vitality and revealing the mechanisms through which the built environment affects it can scientifically guide the organic cultivation of urban vitality. In light of this, the main urban area of Yantai City is taken as a case study, utilizing multi-source geographic big data to conduct both theoretical and empirical research. An index system for the urban built environment is established based on four dimensions: human perception, functional, accessibility, and building form. Advanced methods, including Deep Fully Convolutional Neural Networks (SegNet), Random Forest Regression (RFR), and Spatial Lag Regression (SLR), are employed to explore the impact of the built environment on urban vitality. The research findings indicate that: (1) Urban vitality presents a composite spatial structure that embodies both "multi-center" and "clustered" characteristics, exhibiting two primary types of local spatial autocorrelation: "high-high" clustering and "low-low" clustering. (2) The disparities in urban vitality reflect an imbalance in the distribution of functional, accessibility, building form, and human perception, with functional playing a more critical role in nighttime and daytime urban vitality than other dimensions. (3) The effects of the built environment on daytime and nighttime urban vitality show varying degrees of heterogeneity regarding significance and direction. Factors such as BPOI(Commercial Points of Interest), integration, accessibility, and vibrancy have a substantial positive impact on vitality clustering, while human perception becomes increasingly important for enhancing nighttime vitality. These results provide refined technical support for urban micro-renewal, enhancing the relevance and effectiveness of response strategies.