The COVID-19 pandemic and the associated restrictions have raised the awareness of building pandemic-resilient cities. Prior studies often evaluated the resilience of one type of urban system while lacking a comparison across various urban subsystems. This study fills this gap by measuring and comparing the adaptive resilience to the pandemic of various urban subsystems in Chinese cities. We propose a novel outcome measurement of the pandemic's socioeconomic impacts on cities, i.e., the citizens' complaints data, and use its temporal changes to measure cities' adaptive resilience to the pandemic. We find a wide range of urban subsystems were severely shocked by the pandemic, including the urban economy, construction-and-housing sector, welfare system, and education system. Different urban subsystems exhibit divergent degrees of adaptive resilience to the pandemic. Using cluster analysis, we also identify three types of cities with different patterns of adaptive resilience: cities whose general economies were the least resilient, cities whose construction-and-housing system was the least resilient, and cities that were mostly affected by restriction measures. Our findings contribute to the understanding of the pandemic's socioeconomic costs and help identify the divergent resilience of different urban subsystems so as to develop targeted policy interventions to improve cities' resilience to the pandemic.
Keywords: COVID-19; Socioeconomic disruptions; Text mining; Topic modeling; Urban resilience.
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