Guo ZIJIN, China University of Geosciences (Wuhan), China, China
Yao YAO, China University of Geosciences (Wuhan), China, China
Guan QINGFENG, China University of Geosciences (Wuhan), China, China
The social distancing measures taken by the government were effective in controlling the spread of the coronavirus disease 2019 (COVID-19), but has also changed the structure of urban resident behaviour. Understanding the changes of the intra-city population mobility network can help institutions, governments, and individuals navigate the pandemic. The study mainly applies social network analysis (SNA) methods to quantify and visualize urban residents’ mobility. Firstly, this study constructs the complex network, and combined with the changes of network degree, network density, clustering coefficient to explore the impact of COVID-19 on residents' behavior. Meanwhile, this study proposes a concept of "social resistance" and "social resilience" to describe the stability characteristics of mobility structure. Finally, the degree centrality and closeness centrality is calculated under different periods, thus exploring the evolutionary patterns of urban resident mobility. The main results in thirty most-populated Metropolitan Statistical Areas (MSAs) are as follows: (i) In all cities, there is a consistency in the trends of residential mobility networks. The city's residential mobility network first sparse and then dense, but still below pre-outbreak levels; (ii) There are differences in stability characteristics between cities and different evolutionary patterns of residential mobility networks. The main evolutionary pattern in the spatial distribution of active residential areas is "shrink-expand", but there are also "expand-shrink" and "continuous shrink " evolutionary patterns. The results of this study will contribute to the formulation of urban sustainable development management policies.
Mots clés : COVID-19|mobility network|social network analysis (SNA)|resistance|resilience
A104891GZ