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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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    Cited by:

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    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. Zheng Wen & Dongwei Tian & Naiming Wu, 2024. "Modeling and Analyzing the Spatiotemporal Travel Patterns of Bike Sharing: A Case Study of Citi Bike in New York," Sustainability, MDPI, vol. 17(1), pages 1-21, December.
    6. 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.
    7. Zheng, Wei & Wei, Sheng, 2024. "A ‘node-place-network-city’ framework to examine HSR station area development dynamics: Station typologies and development strategies," Journal of Transport Geography, Elsevier, vol. 120(C).
    8. Ma, Qiwei & Liu, Anqi & Chen, Yuzhou & Tao, Ran, 2024. "Border effects for domestic travel in China during COVID-19 pandemic," Journal of Transport Geography, Elsevier, vol. 116(C).
    9. 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.
    10. 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.
    11. Kaixin Zhu & Zhifeng Cheng & Jianghao Wang, 2024. "Measuring Chinese mobility behaviour during COVID-19 using geotagged social media data," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.

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