Toward effective urban regeneration post-COVID-19: Urban vitality assessment to evaluate people preferences and place settings integrating LBSNs and POI

Abstract

COVID-19 brought tremendous disruption to daily life, accelerated urban digital transformation, and prompted sustainable improvement in socio-spatial development. The emergence of analytic technologies stimulates possibilities for integrating socio-spatial relationships, and new urban research methods and assessment systems are required, which are rarely discussed in recent studies. To bridge the research gap, this study proposes a measurement framework that combines Location Based Social Networking Services (LBSNs) and POI data to test the effectiveness of urban regeneration projects. Specifically, the study quantifies urban vitality to evaluate people’s preferences and place settings integrating by objectively utilizing LBSNs with social media (Weibo check-in and Dianping data) and POI data. This study focuses on 153 regeneration projects in Suzhou, China, from 2019 to 2021 and categorizes urban regeneration projects into four types: (1) community, (2) commercial, (3) public space, and (4) industry. Further, a binary logistic regression model was conducted to analyze the effects of different regeneration measures on urban spatial vitality. The research findings show that combining LBSNs data with POI data can effectively assess the transformation in urban vitality with spatiotemporal analysis. The results indicate that commercial and community regeneration projects significantly impacted urban vitality, and cultural-related renovation measures are critical in improving urban vitality. The systematic urban regeneration measurement framework could promote the regeneration efficiency and rationalization of decision-making, providing a basis for understanding complex socio-spatial relationships and ensuring sustainable urban developmeng sustainable urban development significantly.