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Examining the population flow network in China and its implications for epidemic control based on Baidu migration data

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  • Sheng Wei

    (Jiangsu Institute of Urban Planning and Design)

  • Lei Wang

    (Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences
    University of Manchester)

Abstract

This paper examines the spatial pattern of the population flow network and its implications for containing epidemic spread in China. The hierarchical and spatial subnetwork structure of national population movement networks is analysed by using Baidu migration data before and during the Chinese Spring Festival. The results show that the population flow was mainly concentrated on the east side of the Hu Huanyong Line, a national east-west division of population density. Some local hot spots of migration were formed in various regions. Although there were a large number of migrants in eastern regions, they tended to concentrate in corresponding provincial capital cities and the population movement subnetworks were affected by provincial administrative divisions. The patterns identified are helpful for the provincial government to formulate population policies on epidemic control. The movement flow from Wuhan (the city where the covid-19 outbreak) to other cities is significantly and positively correlated with the number of confirmed cases in other Chinese cities (about 70% of the population was constituted through innerprovincial movement in Hubei). The results show that the population flow network has great significance for informing the containment of the epidemic spread in the early stage. It suggests the importance for the Chinese government to implement provincial and municipal lockdown measures to contain the epidemic spread. The paper indicates that spatial analysis of population flow network has practical implications for controlling epidemic outbreaks.

Suggested Citation

  • Sheng Wei & Lei Wang, 2020. "Examining the population flow network in China and its implications for epidemic control based on Baidu migration data," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-10, December.
    • Handle: RePEc:pal:palcom:v:7:y:2020:i:1:d:10.1057_s41599-020-00633-5
    DOI: 10.1057/s41599-020-00633-5
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    1. Shanmukhappa, Tanuja & Ho, Ivan Wang-Hei & Tse, Chi Kong, 2018. "Spatial analysis of bus transport networks using network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 295-314.
    2. Paolo Bajardi & Chiara Poletto & Jose J Ramasco & Michele Tizzoni & Vittoria Colizza & Alessandro Vespignani, 2011. "Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    3. Miguel Picornell & Tomás Ruiz & Maxime Lenormand & José Ramasco & Thibaut Dubernet & Enrique Frías-Martínez, 2015. "Exploring the potential of phone call data to characterize the relationship between social network and travel behavior," Transportation, Springer, vol. 42(4), pages 647-668, July.
    4. Sheng Wei & Jiangang Xu & Haitao Ma, 2019. "Exploring public bicycle network structure based on complex network theory and shortest path analysis: the public bicycle system in Yixing, China," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(3), pages 293-307, April.
    5. Sheng Wei & Jinfu Yuan & Yanning Qiu & Xiali Luan & Shanrui Han & Wen Zhou & Chi Xu, 2017. "Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-13, June.
    6. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    7. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    8. Wang, Lei, 2018. "High-speed rail services development and regional accessibility restructuring in megaregions: A case of the Yangtze River Delta, China," Transport Policy, Elsevier, vol. 72(C), pages 34-44.
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    Cited by:

    1. Hongwei Guo & Ji Han & Jian Wang, 2021. "Population mobility, urban centrality and subnetworks in China revealed by social sensing big data," Environment and Planning A, , vol. 53(8), pages 1855-1858, November.
    2. Xufang Mu & Chuanglin Fang & Zhiqi Yang & Xiaomin Guo, 2022. "Impact of the COVID-19 Epidemic on Population Mobility Networks in the Beijing–Tianjin–Hebei Urban Agglomeration from a Resilience Perspective," Land, MDPI, vol. 11(5), pages 1-23, May.
    3. Munirul H. Nabin & Mohammad Tarequl Hasan Chowdhury & Sukanto Bhattacharya, 2021. "It matters to be in good hands: the relationship between good governance and pandemic spread inferred from cross-country COVID-19 data," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    4. Shenzhen Tian & Jialin Jiang & Hang Li & Xueming Li & Jun Yang & Chuanglin Fang, 2023. "Flow space reveals the urban network structure and development mode of cities in Liaoning, China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    5. Xiaodong Zhang & Haoying Han, 2023. "Spatiotemporal Dynamic Characteristics and Causes of China’s Population Aging from 2000 to 2020," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
    6. Yaming Zhang & Xiaoyu Guo & Yanyuan Su & Yaya Hamadou Koura H & Na Wang & Wenjie Song, 2023. "Changes in spatiotemporal pattern and network characteristics in population migration of China’s cities before and after COVID-19," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.

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