(Peer-Reviewed) Mobility in China, 2020: a tale of four phases
Suo-yi Tan 谭索怡 ¹, Shengjie Lai ², Fan Fang ¹, Ziqiang Cao ¹, Bin Sai ¹, Bing Song ¹, Bitao Dai ¹, Shuhui Guo ¹, Chuchu Liu ¹, Mengsi Cai ¹, Tong Wang ¹, Mengning Wang ¹, Jiaxu Li ¹, Saran Chen ³, Shuo Qin ⁴, Jessica R Floyd ², Zhidong Cao ⁵, Jing Tan ⁶, Xin Sun ⁶, Tao Zhou ⁷, Wei Zhang ⁸, Andrew J Tatem ², Petter Holme ⁹, Xiaohong Chen 陈晓红 ¹⁰ ¹¹, Xin Lu 吕欣 ¹
¹ College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; 国防科技大学 系统工程学院
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; 佛山科学技术学院 数学与大数据学院
⁴ State Key Laboratory on Blind Signal Processing, Chengdu 610041, China; 盲信号处理国家重点实验室
⁵ State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; 中国科学院 自动化研究所 复杂系统管理与控制国家重点实验室
⁶ Chinese Evidence-based Medicine Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, China; 四川大学 华西医院 国家老年疾病临床医学研究中心 中国循证医学中心
⁷ Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611713, China; 电子科技大学 大数据研究中心
⁸ West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610047, China; 四川大学 华西医院 生物医学大数据中心
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; 中南大学 商学院
¹¹ Institute of Big Data and Internet Innovations, Hunan University of Technology and Business, Changsha 410205, China 湖南工商大学 大数据与互联网创新研究院
National Science Review, 2021-08-16

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 lockdowns, and then to the recovery periods.

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 net flows 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.
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