Spatio-temporal evolution of population mobility differentiation patterns in a pandemic context: based on a network perspective

Abstract

The COVID-19 has caused adverse effects in various aspects, and its impact on population mobility cannot be ignored. In this study, we obtain mobility data for the Beijing–Tianjin–Hebei region during the Chunyun period of 2019–2021 and divide it into four stages. By developing directional weighted networks, the mobility patterns for pre- and post-epidemic are examined. Firstly, the migration scale declines significantly post-epidemic. The fourth stage migration scale exceeds 65% of 2019 in 2021 indicating the situation improves. Secondly, the structural characteristics are compared. The average degree values, clustering coefficients, and path lengths in the fourth stage of 2021 are higher than the values in 2020, showing the recovery of migration, but also efficiency degradation. Thirdly, community detection results show that three communities can be highlighted in the network, which followed Regional Development Pattern. Finally, the findings of Modified Alter-based Centrality and Alter-based Power models show that Beijing (27.78, 17.74) and Tianjin (14.92, 4.38) are typical cities, Tangshan (3.74, 0.03) and Langfang (3.40, 2.00) are gateway cities and others are general cities. A reasonable population distribution has not been formed. The study provides fresh perspectives for applying spatio-temporal data in health emergencies, and the results provide theoretical support for policy formulation of socio-economic recovery.