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Using mobile phone big data to discover the spatial patterns of rural migrant workers’ return to work in China’s three urban agglomerations in the post-COVID-19 era

Author

Listed:
  • Kai Liu
  • Pengjun Zhao
  • Dan Wan
  • Xiaodong Hai
  • Zhangyuan He
  • Qiyang Liu
  • Yonghui Qu
  • Xue Zhang
  • Kaixi Li
  • Ling Yu

Abstract

Knowing how workers return to work is a key policymaking issue for economic recovery in the post-COVID-19 era. This paper uses country-wide time-series mobile phone big data (comparing monthly and annual figures), obtained between February 2019 and October 2019 and between February 2020 and October 2020, to discover the spatial patterns of rural migrant workers’ (RMWs’) return to work in China’s three urban agglomerations (UAs): the Beijing–Tianjin–Hebei Region, the Yangtze River Delta and the Pearl River Delta. Spatial patterns of RMWs’ return to work and how these patterns vary with location, city level and human attribute were investigated using the fine-scale social sensing related to post-pandemic human mobility. The results confirmed the multidimensional spatiotemporal differentiations, interaction effects between variable pairs and effects of the actual situation on the changing patterns of RMWs’ return to work. The spatial patterns of RMWs’ return to work in China’s major three UAs can be regarded as a comprehensive and complex interaction result accompanying the nationwide population redistribution, which was affected by various hidden factors. Our findings provide crucial implications and suggestions for data-informed policy decisions for a harmonious society in the post-COVID-19 era.

Suggested Citation

  • Kai Liu & Pengjun Zhao & Dan Wan & Xiaodong Hai & Zhangyuan He & Qiyang Liu & Yonghui Qu & Xue Zhang & Kaixi Li & Ling Yu, 2023. "Using mobile phone big data to discover the spatial patterns of rural migrant workers’ return to work in China’s three urban agglomerations in the post-COVID-19 era," Environment and Planning B, , vol. 50(4), pages 878-894, May.
    • Handle: RePEc:sae:envirb:v:50:y:2023:i:4:p:878-894
    DOI: 10.1177/23998083211069375
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    References listed on IDEAS

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    3. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
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