Changes in spatiotemporal pattern and network characteristics in population migration of China’s cities before and after COVID-19

Abstract

AbstractPopulation mobility is a key component in promoting the re-agglomeration and dissemination of social and economic factors. Based on Spring Festival data from 2019 to 2023 on Baidu Migration Big Data, this paper analyses the spatiotemporal patterns and structural characteristics of population mobility in cities across China through spatiotemporal statistics and social network analysis and investigates the evolution patterns of Chinese population migration behavior under the influence of COVID-19 epidemic during Spring Festival. The results of the study show that: (1) There are significant temporal and spatial differences in the impact of COVID-19 on population migration, with much stronger shocks on the cities of middle migration scale; (2) Population migration in Chinese cities is robust, and the impact of COVID-19 on population movement and community evolution is mainly manifested in short-term effects, with essentially no residual effects; (3) Between 2020 and 2023, a total of 119 cities experience a transfer of communities (32.25%), of which 69 cities transfer once, 20 cities transfer twice, and 30 cities transfer three times. In addition, it is found that the closeness of urban links based on population movements remains subject to geospatial effects, and the boundaries of “communities” coincide very closely with provincial borders. The results of this study have important theoretical and practical implications for a deeper understanding of the long-term impact of major public health events on changes in the geographical characteristics of population distribution and the structure of population mobility networks.