Mobility in China, 2020: a tale of four phases

Author:

Tan Suoyi1ORCID,Lai Shengjie2ORCID,Fang Fan1,Cao Ziqiang1,Sai Bin1,Song Bing1,Dai Bitao1,Guo Shuhui1,Liu Chuchu1,Cai Mengsi1,Wang Tong1,Wang Mengning1,Li Jiaxu1,Chen Saran3,Qin Shuo4,Floyd Jessica R2,Cao Zhidong5,Tan Jing6,Sun Xin6,Zhou Tao7,Zhang Wei8,Tatem Andrew J2,Holme Petter9,Chen Xiaohong1011,Lu Xin1ORCID

Affiliation:

1. College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

2. WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK

3. School of Mathematics and Big Data, Foshan University, Foshan 510000, China

4. State Key Laboratory on Blind Signal Processing, Chengdu 610041, China

5. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

6. Chinese Evidence-Based Medicine Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, China

7. Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611713, China

8. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610047, China

9. Tokyo Tech World Hub Research Initiative, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo 226-8503, Japan

10. School of Business, Central South University, Changsha 410083, China

11. Institute of Big Data and Internet Innovations, Hunan University of Technology and Business, Changsha 410205, China

Abstract

Abstract 2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others.

Funder

National Natural Science Foundation of China

Bill and Melinda Gates Foundation

NIH

FCD

Wellcome Trust

JSPS

Department of Science and Technology of Hainan Province

Publisher

Oxford University Press (OUP)

Subject

Multidisciplinary

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