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Workplace segregation of rural migrants in urban China: A case study of Shenzhen using cellphone big data

Author

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  • Xingang Zhou

    (College of Architecture and Urban Planning, Tongji University; Shanghai Institute of Intelligent Science and Technology, Tongji University, China)

  • Zifeng Chen
  • Anthony GO Yeh
  • Yang Yue

Abstract

Studies of socio-spatial segregation in recent decades have shifted the focus from residential areas to people’s workplaces and other activity places. This study attempts to elucidate workplace segregation using cellular network data and to examine urban China with a special focus on the segregation of rural migrants, using residents living in the migrant enclaves of urban villages as a proxy. Furthermore, this study identifies factors that affect the variations of workplace segregation. The study shows that rural migrants who work in manufacturing industries and live in suburban areas suffer from higher workplace segregation from other social groups compared with those who work in service jobs and reside in the central-city areas, indicating that migrant enclaves in central-city areas play a significant role in housing rural migrants. It provides them with considerable access to service jobs and, thus, alleviates workplace segregation. Our results show that the use of big data can effectively capture the dynamics of population composition in activity places and provide a useful perspective for a deeper understanding of socio-spatial segregation.

Suggested Citation

  • Xingang Zhou & Zifeng Chen & Anthony GO Yeh & Yang Yue, 2021. "Workplace segregation of rural migrants in urban China: A case study of Shenzhen using cellphone big data," Environment and Planning B, , vol. 48(1), pages 25-42, January.
    • Handle: RePEc:sae:envirb:v:48:y:2021:i:1:p:25-42
    DOI: 10.1177/2399808319846903
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    References listed on IDEAS

    as
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    Cited by:

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    2. Zhang, Yanji & Wang, Jiejing & Kan, Changcheng, 2022. "Temporal variation in activity-space-based segregation: A case study of Beijing using location-based service data," Journal of Transport Geography, Elsevier, vol. 98(C).

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