Investigating relationships between built environment and urban resilience: A case study of Singapore

The built environment is a critical component in shaping urban areas and their resilience. While existing studies frequently discuss how different built environment elements can enhance a city's capacity to respond quickly and effectively to external shocks, limited research has validated these effects through actual disaster event outcomes. This study aims to empirically investigate the relationship between the built environment and urban resilience, focusing on the context of the COVID-19 pandemic in Singapore. Using urban vitality, approximated by public transit passenger data, as an indicator of the resilience process, we quantified urban resilience through three metrics: robustness, recovery degree, and total performance loss, derived from temporal changes in urban vitality. Multiple linear regression (MLR), spatial lag model (SLM), and geographically weighted regression (GWR) were applied to examine how built environment factors relate to these metrics. The results across models indicate that higher residential density, more diverse land use, and greater distance to CBD are positively associated with resilience, whereas greater transit service is associated with lower resilience. Moreover, GWR explains the highest variations in all resilience metrics compared to MLR and SLM. By mapping spatially varying associations, the findings offer insights to support localized and data-driven planning for building resilient urban environments capable of withstanding and adapting to future shocks.