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Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China

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  • Xiping Yang

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
    Shaanxi Key Laboratory of Tourism Informatics, Xi’an 710119, China)

  • Zhixiang Fang

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Ling Yin

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Road, Shenzhen 518005, China)

  • Junyi Li

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
    Shaanxi Key Laboratory of Tourism Informatics, Xi’an 710119, China)

  • Yang Zhou

    (College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Shiwei Lu

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Understanding commuting patterns has been a classic research topic in the fields of geography, transportation and urban planning, and it is significant for handling the increasingly serious urban traffic congestion and air pollution and their impacts on the quality of life. Traditional studies have used travel survey data to investigate commuting from the aspects of commuting mode, efficiency and influence factors. Due to the limited sample size of these data, it is difficult to examine the large-scale commuting patterns of urban citizens, especially when exploring the spatial structure of commuting. This study attempts to understand the spatial structure characteristics generated by human commutes to work by using massive mobile phone datasets. A three-step workflow was proposed to accomplish this goal, which includes extracting the home and work locations of phone users, detecting the communities from the commuting network, and identifying the commuting convergence and divergence areas for each community. A case study of Shenzhen, China was implemented to determine the commuting structure. We found that there are thirteen communities detected from the commuting network and that some of the communities are in accordance with urban planning; moreover, spatial polycentric polygons exist in each community. These findings can be referenced by urban planners or policy-makers to optimize the spatial layout of the urban functional zones.

Suggested Citation

  • Xiping Yang & Zhixiang Fang & Ling Yin & Junyi Li & Yang Zhou & Shiwei Lu, 2018. "Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 10(5), pages 1-14, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1435-:d:144730
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    References listed on IDEAS

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    7. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.
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