Spatial Analysis of the Impact of Urban Spatial Structure on Urban Vitality in Yantai, China Using Multiscale Geographically Weighted Regression.

Understanding how urban spatial structure influences urban vitality is essential for developing human-centered urban planning strategies. However, the spatial heterogeneity of these relationships and the relative contributions of different urban spatial structure elements to daytime and nighttime vitality remain insufficiently understood. This study aimed to quantify urban vitality and investigate how urban spatial structure affects its spatiotemporal variation in the main urban area of Yantai, China. Multisource spatial datasets, including Baidu heatmap data, points of interest, street-view imagery, road networks, building information, and land-use data, were integrated to characterize urban vitality and urban spatial structure. Kernel density estimation, spatial autocorrelation analysis, and multiscale geographically weighted regression (MGWR) were employed to evaluate the spatial distribution of urban vitality and the scale-dependent effects of urban spatial structure variables. The results demonstrated that urban vitality exhibits a pronounced spatial polarization characterized by a composite "center-periphery" and "one core with multiple points" pattern, together with significant positive spatial autocorrelation. The influence of urban spatial structure varied considerably across space and between daytime and nighttime. Points of interest diversity and proximity were the dominant drivers of daytime vitality, whereas residential function and points of interest diversity exerted the greatest influence on nighttime vitality. Walkability consistently showed a positive association with urban vitality during both periods, highlighting the importance of human-centered street environments. These findings demonstrate the value of integrating multisource spatial data with MGWR for evaluating urban vitality and provide a practical framework to support evidence-based urban planning and spatial optimization.