Abstract
Urban vitality reflects the dynamic interaction between residents' activities and the urban environment, playing a pivotal role in fostering social and economic development. It enhances urban quality, competitiveness, and residents' well-being. Despite extensive scholarly attention, previous research has often overlooked the integration of big data and the spatial heterogeneity inherent in urban environments. This study addresses these gaps by using Yinchuan as a case study, employing multi-source big data to measure urban vitality, and incorporating 2D/3D variables to comprehensively characterize the built environment. Furthermore, a Geographically Weighted Regression (GWR) model was applied to uncover the spatially heterogeneous relationships between these variables and urban vitality. The findings reveal that built environment factors significantly influence urban vitality, particularly in central districts such as Xingqing, Jinfeng, and Xixia district. These effects demonstrate positive spatial autocorrelation and clustering patterns, with notable spatial heterogeneity across different regions. Based on these insights, the study proposes targeted, multidimensional strategies for the comprehensive enhancement of urban vitality. This research not only provides theoretical support for Yinchuan's urban development but also offers valuable practical guidance for other cities confronting similar urban challenges.