Abstract

With the changes in people's spatial cognitions, preferences and behavior patterns as a response to the COVID-19 pandemic crisis, the way transit-oriented development (TOD) boosts economic vitality has enormously altered. Hence, this study employs machine learning methods to explore the effects of TOD on economic vitality under COVID-19 and recalibrate existing TOD planning models and design principles. Based on multi-source data of Hong Kong, it measures economic vitality of MTR station areas with life service reviews and depicts built environment therein from three dimensions including node, tie, and place. It discovers that (1) the outbreak of COVID-19 impaired the economic vitality effects of TOD; (2) the global relative importance of MTR station centrality and ground space index declined during the outbreak and bounced back afterwards, meanwhile, that of street centrality, street betweenness, street detour ratio, and green space coverage increased and that of bus density, MTR station betweenness, and average building height decreased; (3) the economic vitality effects of TOD were nonlinear, and the threshold values and effective ranges of built environment variables remained constant across the time; (4) the economic vitality effects of TOD were moderated by the pandemic. This study enlightens urban policymakers and practitioners with nuanced criteria for pandemic-adaptive TOD planning and design strategies.

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