Spatiotemporal patterns and influencing factors of human migration networks in China during COVID-19

Abstract

The social lockdowns and strict control measures initiated to combat the COVID-19 pandemic have had an impact on human migration. In this study, big data was used to analyze spatial patterns of population migration in 369 Chinese cities during the COVID-19 outbreak and to identify determinants of population migration. We found that the overall migration intensity decreased by 39.87% compared to the same period in 2019 prior to the COVID-19 outbreak. COVID-19 severely affected human migration. The public holidays and weekends have impacted human migration from the perspective of time scale. The spatial pattern of China's population distribution presents a diamond structure that is dense in the east and sparse in the west, which is bounded by the Hu line and the cities such as Beijing, Shanghai, Guangzhou and Chengdu as nodes to connect. There is a strong consistency between the population distribution center and the level of urban development. The urban human migration network is centered on provincial capitals or municipalities at the regional scale, showing a prominent "center-periphery" structure. COVID-19 dispersed the forces of human migration in time and changed the direction of human migration in space. But it did not change the pattern of national migration. The most critical factors influencing mass migration are income levels and traditional culture. This study reveals the impacts of major public health emergencies on conventional migration patterns and provides a scientific theoretical reference for COVID-19 prevention and control.

Keywords

Human migration / COVID-19, Influencing factors / Baidu big data

Declaration of Competing Interest

The authors declare that no known financial interests or personal relationships influenced the research reported in this paper.

Acknowledgments

This work was sponsored by Natural Science Foundation of Henan (Grant No. 202300410076).

References

[1] Brown, E.H., 1965. Complications of smallpox vaccination. Postgrad. Med. J. 41 (480), 634.

[2] Castells, M., 2015. Space of flows, space of places:Materials for a theory of urbanism in the information age. In: LeGates R.T., Stout F. (The City Reader,Eds.), 6th Edition. Routledge, London and New York, pp. 263-274.

[3] Chan, W.K.V., Hsu, C., 2015. When human networks collide: The degree distributions of hyper-networks. IIE Trans. 47 (9), 929-942.

[4] Chin, C.S., Sorenson, J., Harris, J.B., Robins, W.P., Charles, R.C., Jean-Charles, R.R., Paxinos, E.E., 2011. The origin of the Haitian cholera outbreak strain. N. Engl. J. Med. 364 (1), 33-42.

[5] Cui, C., Wu, X., Liu, L., Zhang, W., 2020. The spatial-temporal dynamics of daily intercity mobility in the Yangtze River Delta: An analysis using big data. Habitat Int. 106, 102174.

[6] Development Research Centre of the State Council, 2019. Migrant People, Changing City. China Development Press, Beijing.

[7] Dörig, R.E., Marcil, A., Chopra, A., Richardson, C.D., 1993. The human CD 46 molecule is a receptor for measles virus (Edmonston strain). Cell 75 (2), 295-305.

[8] Feldmann, H., Klenk, H.D., 1996. Marburg and ebola viruses. Adv. Virus Res. 47, 1-52.

[9] Ghinai, I., McPherson, T.D., Hunter, J.C., Kirking, H.L., Christiansen, D., Joshi, K., Fricchione, M.J., 2020. First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the USA. Lancet 395, 1137-1144.

[10] Herlihy, D., 1997. The Black Death and the Transformation of the West. Harvard University Press, Cambridge.

[11] Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Cheng, Z., 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395 (10223), 497-506.

[12] Huang, Q., 2020. Inspirations of the Wuhan lockdown on global urban publich health under the outbreak of COVID-19 pandemic. Sustain. Dev. 10, 381.

[13] Jia, J.S., Lu, X., Yuan, Y., Xu, G., Jia, J., Christakis, N.A., 2020. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 582, 389-394.

[14] Jiang, X., Wang, S., 2017. Research on China’s urban population mobility network: Based on Baidu migration big data. Chin. J. Popul. Sci. 37 (2), 35-46. (in Chinese)

[15] Ke, W., Yu, Z., Chen, W., Wang, H., Zhao, Z., 2015. Architecture and key issues for human space-time behavior data observation. Geogr. Res. 34 (2), 373-383. (in Chinese)

[16] Li, D., Shao, Z., Yu, W., Zhu, X., Zhou, S., 2020. Public pandemic prevention and control services based on Big Data of spatiotemporal location make cities smarter. Geomatics Inf. Sci. Wuhan Univ. 45 (4), 475-487+556. (in Chinese)

[17] Liu, W., Shi, E., 2016. Spatial pattern of population daily flow among cities based on ICT: A case study of “Baidu Migration ”. Acta Geogr. Sin. 71 (10), 1667-1679. (in Chinese)

[18] Murray, C.J., Rosenfeld, L.C., Lim, S.S., Andrews, K.G., Foreman, K.J., Haring, D., Lopez, A.D., 2012. Global malaria mortality between 1980 and 2010: A systematic analysis. Lancet 379 (9814), 413-431.

[19] Pan, J., Lai, J., 2019. Spatial pattern of population mobility among cities in China: Case study of the National Day plus Mid-Autumn Festival based on Tencent migration data. Cities 94, 55-69.

[20] Rota, P.A., Oberste, M.S., Monroe, S.S., Nix, W.A., Campagnoli, R., Icenogle, J.P., Tong, S., 2003. Characterization of a novel coronavirus associated with severe acute respiratory syndrome. Science 300 (5624), 1394-1399.

[21] Shi, G., 2020. The impact of spring festival migration on coronavirus pandemic. Rev. Ind. Econ. (2), 24-36. (in Chinese)

[22] Sulaymon, I.D., Zhang, Y., Hopke, P.K., Zhang, Y., Hua, J., Mei, X., 2021. COVID-19 pandemic in Wuhan: Ambient air quality and the relationships between criteria air pollutants and meteorological variables before, during, and after lockdown. Atmos. Res. 250, 105362.

[23] Taylor, S.J., Letham, B., 2018. Forecasting at scale. Am. Stat. 72 (1), 37-45.

[24] Tian, H., Liu, Y., Li, Y., Wu, C.H., Chen, B., Kraemer, M.U., Wang, B., 2020. An investigation of transmission control measures during the first 50 days of the COVID-19 pandemic in China. Science 368 (6491), 638-642.

[25] Wei, Y., Song, W., Xiu, C., Zhao, Z., 2018. The rich-club phenomenon of China’s population flow network during the country’s spring festival. App. Geogr. 96, 77-85.

[26] Xu, X., Wen, C., Zhang, G., Sun, H., Liu, B., Wang, X., 2020. The geographical destination distribution and effect of outflow population of Wuhan when the outbreak of the 2019-nCoV Pneumonia. J. Electron. Sci. Technol. 49, 1-6. (in Chinese)

[27] Yang, Z., Gao, W., Zhao, X., Hao, C., Xie, X., 2020. Spatiotemporal patterns of population mobility and its determinants in Chinese cities based on travel big data. Sustainability 12 (10), 4012.

[28] You, S., Wang, H., Zhang, M., Song, H., Xu, X., Lai, Y., 2020. Assessment of monthly economic losses in Wuhan under the lockdown against COVID-19. Humanit. Soc. Sci. Commun. 7 (1), 1-12.

[29] Zhang, S.X., Wang, Y., Rauch, A., Wei, F., 2020. Unprecedented disruption of lives and work: Health, distress and life satisfaction of working adults in China one month into the COVID-19 outbreak. Psychiat. Res. 288, 112958.

[30] Zhao, Z., Wei, Y., Pang, R., Feng, Z., 2017. Alter-based centrality and power of Chinese city network using inter-provincial population flow. Acta Geogr. Sin. 72 (6), 1032-1048. (in Chinese)

[31] Zhou, J., Wang, D., Gao, R., Zhao, B., Song, J., Qi, X., Bai, T., 2013. Biological features of novel avian influenza A (H7N9) virus. Nature 499 (7459), 500-503.