Spatiotemporal patterns of human mobility during the COVID-19 pandemic in China - Jingjing Liu; Lei Xu; Nengcheng Chen; Zeqiang Chen (Geo-spatial Information Science)
The outbreak of the COVID-19 pandemic has significantly reshaped population mobility, exerting a sustained impact on the patterns and dynamics of population mobility in China over the next three years. To comprehend the changes in population mobility patterns during the early stages of the COVID-19 outbreak, as well as standard epidemic prevention and control measures, we conducted an analysis using data from Baidu Huiyan’s migration scale index. This data was used to examine the characteristics of population movement in China during the Spring Festival and National Day from 2020 to 2022. We employed the Louvain algorithm and SVD decomposition to examine the spatiotemporal patterns of population movement. In addition, we calculated the response speed of urban population arrival flow to the pandemic using the Pearson correlation coefficient. Furthermore, we analyzed the factors influencing this correlation and response speed using random forest eigenvalues. The findings suggest that daily commuting and holiday travel patterns were not significantly altered by the pandemic. Over the past three years, there has been a trend in population mobility toward quicker responses to the pandemic, influenced primarily by economic, policy, medical conditions, and population density. Areas with higher population density and greater structural complexity exhibit increased sensitivity of population mobility to the severity of the pandemic. Examining population movement patterns and influencing factors against the backdrop of the COVID-19 pandemic can offer valuable insights for devising more targeted and effective prevention and control measures. Ultimately, this endeavor contributes to enhancing health-related urban resilience and sustainability.