Evidence from Chinese Cities on the Effect of the COVID-19 Pandemic on the Resilience of Intercity Population Flow Networks

Abstract

The rapidly spreading coronavirus disease 2019 (COVID-19) pandemic significantly affected population flows between cities and triggered a chain reaction within global urban networks. Using 366 Chinese cities as a case study, this study developed a dynamic and comprehensive framework to assess the resilience of the intercity population flow network. Employing Autonavi migration spatiotemporal big data, the evolutionary characteristics of the intercity population flow network at different scales were revealed. Subsequently, the resilience of the intercity population flow network was quantitatively measured under the influence of two waves of the pandemic. Furthermore, its dynamic response mechanisms were investigated through a series of gray information attack simulations. Results indicated that as the pandemic persisted, network resilience gradually strengthened, particularly in the dimensions of connectivity and adaptability. During the COVID-19 pandemic, China’s intercity population flow network primarily managed and mitigated risks through interrupting connections. Moreover, it was found that following the outbreak of the pandemic, short-distance (<200 km) intercity population movement demonstrated stronger resilience. Additionally, a compensation phenomenon between nodes was observed in the network under the influence of special and unexpected events: as the centrality of epicenter cities weakened, certain neighboring or sister cities prioritized assuming the role of replacement. These findings provide important insights into understanding and guiding the region’s sustainable development.