Elaborating Spatiotemporal Associations Between the Built Environment and Urban Vibrancy: A Case of Guangzhou City, China

This study applies multi-source datasets (i.e., Baidu Heat Map data, points of interest (POIs) data, and floor area and land use data) and geographically and temporally weighted regression (GTWR) models to elaborate the spatiotemporal relationships between the built environment and urban vibrancy on both weekdays and weekends, using Guangzhou City as a case. First, we verified the spatially and temporally nonstationary nature of the built environment correlates, which have been largely ignored in previous studies based on local regression techniques. The spatially and temporally heterogeneous effects of the built environment on urban vibrancy are then presented and visualized, based on the GTWR results. We found that the elasticity of location (i.e., distance), land use mix (i.e., diversity), building intensity and numbers of POIs with various functions (i.e., density) are different across time (2-h intervals within a day) and space (grids), due to people’s everyday lifestyle, time-space constraints, and geographical context (e.g., spatial structure). The findings highlight the importance of a better understanding of the local geography on the spatiotemporal relationships for urban planners and local governments so as to put forward decision-making support for fostering and maintaining urban vibrancy.