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Spatial Characteristics and Influencing Factors of Commuting in Central Urban Areas Using Mobile Phone Data: A Case Study of Nanning

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  • Jinfeng Wang

    (School of Geography and Planning, Nanning Normal University, Nanning 530001, China)

  • Guowei Luo

    (School of Geography and Planning, Nanning Normal University, Nanning 530001, China
    Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China)

  • Yanjia Huang

    (Guangxi City Planning and Survey Technology Co., Ltd., Nanning 530001, China)

  • Min Liu

    (Guangxi City Planning and Survey Technology Co., Ltd., Nanning 530001, China)

  • Yi Wei

    (School of Geography and Planning, Nanning Normal University, Nanning 530001, China)

Abstract

Urban commuting characteristics have important implications for both the spatial planning and governance of cities. However, the traditional methods of surveying the characteristics of commuting are very time- and labour-intensive, with the results susceptible to subjective influences. In this work, taking the central city of Nanning as the research object, the commuting space of the population was constructed on the grid-block-subdistrict scale, and the distribution characteristics of the commuting space were systematically analysed. In addition, the influencing factors of the commuting volume were explored by combining multi-source and spatiotemporal data with a geodetector. From our analysis, it was demonstrated that the population density in the central city of Nanning showed a spatial distribution pattern of “decaying distance from the city centre”, with a weak agglomeration effect of large-scale commuters at the grid scale and a larger east-west than north-south commuter scale. At the block scale, large-scale commuters were more concentrated, and the commuting distances were shorter in areas with large commuter populations. At the subdistrict scale, the internal commuting population was also more than the cross-subdistrict commuting population, with more cross-subdistrict commuting flows and an uneven distribution of the flow sizes, with most commuters concentrating on two or three subdistricts for commuting. Various important factors that affect the size of the commuting population should be controlled, including the permanent population, residential distribution, medical facilities, recreational facilities, food services and workplace distribution; the interactions between the permanent population, the residential distribution and the house price factors have the strongest impact values. Our work provides valuable insights for the understanding of commuting patterns in cities and can be used as a scientific basis for urban spatial decision-making.

Suggested Citation

  • Jinfeng Wang & Guowei Luo & Yanjia Huang & Min Liu & Yi Wei, 2023. "Spatial Characteristics and Influencing Factors of Commuting in Central Urban Areas Using Mobile Phone Data: A Case Study of Nanning," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9648-:d:1172500
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    References listed on IDEAS

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    1. Šveda, Martin & Madajová, Michala Sládeková, 2023. "Estimating distance decay of intra-urban trips using mobile phone data: The case of Bratislava, Slovakia," Journal of Transport Geography, Elsevier, vol. 107(C).
    2. Giménez-Nadal, José Ignacio & Molina, José Alberto & Velilla, Jorge, 2022. "Trends in commuting time of European workers: A cross-country analysis," Transport Policy, Elsevier, vol. 116(C), pages 327-342.
    3. Liya Ma & Chunliang Xiu, 2022. "Analyzing the Structure of Residence–Leisure Network in Shenyang City," Land, MDPI, vol. 11(12), pages 1-15, November.
    4. Ying Jing & Junjiao Shu & Rushan Wang & Xiang Zhang, 2021. "Tempo‐spatial variability of urban leisure functional zones: An analysis based on geo‐big data," Growth and Change, Wiley Blackwell, vol. 52(3), pages 1852-1865, September.
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