Leveraging context-adjusted nighttime light data for socioeconomic explanations of global urban resilience

Urban resilience, crucial for achieving sustainable development goals, entails the adaptation and recovery of urban systems from external shocks, such as the recent global pandemic. To investigate the response of urban systems to COVID-19, this study employs context-adjusted nighttime light data to model global urban resilience by combining an evolving urban resilience metric (EURm) with the shape similarity of resilience curves. Random effect analysis and counterfactual explanations are then implemented to explore the socioeconomic impacts on urban resilience during the pandemic, providing strategic insights for improvement. Our results delineate five diverse urban resilience patterns, each characterized by distinct phases of downturn, minimum, and recovery, with examples from Malaysia, Japan, the United States, China, and South Sudan. We also find notable correlations between socioeconomic factors and urban resilience, highlighting that stringent measures may reduce resilience, whereas proactive health and containment strategies could bolster resilience. Meanwhile, economic stress reflected by inflation adversely affects resilience. Furthermore, we delve into strategic socioeconomic modifications to enhance urban resilience using counterfactual explanations, underlining the importance of customized interpretation for varying countries. Overall, this study advances our understanding of urban resilience during global crises, guiding context-specific resilience strategies in urban planning and policy-making.