IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i4p884-d1123050.html
   My bibliography  Save this article

Impact of Administrative Division and Regional Accessibility on Rural Mobility in the Pearl River Delta: Evidence from Cellphone Big Data

Author

Listed:
  • Yi Zhao

    (School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China)

  • Daming Lu

    (School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China)

  • Pu Zhao

    (School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China)

  • Senkai Xie

    (Urban Planning and Transportation Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

  • Wenjia Zhang

    (School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China)

Abstract

Mobility plays a critical role in promoting rural development. However, the current knowledge regarding the factors that influence mobility between rural towns is limited. The objective of this study is to explore the impact of administrative division and regional accessibility on rural mobility to inform development policies and strategies. The administrative division is demarcated by district and city boundaries, and regional accessibility is assessed using various modes of transportation, including cars, high-speed railways (HSRs), and intercity commuter railways (ICRs). A flow-based geographically weighted regression (FGWR) method is employed based on mobile phone signaling data to quantify the associations and identify the local effects of these factors in the Pearl River Delta (PRD). The findings suggest that both administrative division and regional accessibility significantly influence rural mobility. Specifically, the effects of district boundaries on commuting mobility are more pronounced in the central areas along the Pearl River, while the effects of city boundaries on non-commuting mobility between the core area and surrounding regions are more significant. With regard to regional accessibility, cars are the preferred mode of transportation for connections between the core areas of cities along the Pearl River, whereas HSR is favored more for non-commuting trips between the northwest and center regions. This study provides novel empirical insights into the understanding of rural mobility and has significant implications for promoting regional integration.

Suggested Citation

  • Yi Zhao & Daming Lu & Pu Zhao & Senkai Xie & Wenjia Zhang, 2023. "Impact of Administrative Division and Regional Accessibility on Rural Mobility in the Pearl River Delta: Evidence from Cellphone Big Data," Land, MDPI, vol. 12(4), pages 1-16, April.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:884-:d:1123050
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/4/884/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/4/884/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wenjia Zhang & Jean-Claude Thill, 2019. "Mesoscale Structures in World City Networks," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(3), pages 887-908, May.
    2. Wenjia Zhang & Ming Zhang, 2018. "Incorporating land use and pricing policies for reducing car dependence: Analytical framework and empirical evidence," Urban Studies, Urban Studies Journal Limited, vol. 55(13), pages 3012-3033, October.
    3. Jia, Yingnan & Fu, Hua, 2019. "Association between innovative dockless bicycle sharing programs and adopting cycling in commuting and non-commuting trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 12-21.
    4. Millward, Hugh & Spinney, Jamie, 2011. "Time use, travel behavior, and the rural–urban continuum: results from the Halifax STAR project," Journal of Transport Geography, Elsevier, vol. 19(1), pages 51-58.
    5. Weiyang Zhang & Ben Derudder & Jianghao Wang & Wei Shen, 2018. "Regionalization in the Yangtze River Delta, China, from the perspective of inter-city daily mobility," Regional Studies, Taylor & Francis Journals, vol. 52(4), pages 528-541, April.
    6. Wang, Shiguang & Yu, Dexin & Kwan, Mei-Po & Zheng, Lili & Miao, Hongzhi & Li, Yongxing, 2020. "The impacts of road network density on motor vehicle travel: An empirical study of Chinese cities based on network theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 144-156.
    7. Ding, Chuan & Wang, Donggen & Liu, Chao & Zhang, Yi & Yang, Jiawen, 2017. "Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 65-80.
    8. Liu, Kai & Murayama, Yuji & Ichinose, Toshiaki, 2021. "A multi-view of the daily urban rhythms of human mobility in the Tokyo metropolitan area," Journal of Transport Geography, Elsevier, vol. 91(C).
    9. Shen, Qing & Chen, Peng & Pan, Haixiao, 2016. "Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 31-44.
    10. Zhao, Pengjun & Yu, Zhao, 2020. "Investigating mobility in rural areas of China: Features, equity, and factors," Transport Policy, Elsevier, vol. 94(C), pages 66-77.
    11. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    12. Feng, Rundong & Wang, Kaiyong, 2022. "The direct and lag effects of administrative division adjustment on urban expansion patterns in Chinese mega-urban agglomerations," Land Use Policy, Elsevier, vol. 112(C).
    13. Lucas, Karen, 2012. "Transport and social exclusion: Where are we now?," Transport Policy, Elsevier, vol. 20(C), pages 105-113.
    14. Sottile, Eleonora & Tuveri, Giovanni & Piras, Francesco & Meloni, Italo, 2022. "Modelling commuting tours versus non-commuting tours for university students. A panel data analysis from different contexts," Transport Policy, Elsevier, vol. 118(C), pages 56-67.
    15. Asif Raza & Ming Zhong & Muhammad Safdar, 2022. "Evaluating Locational Preference of Urban Activities with the Time-Dependent Accessibility Using Integrated Spatial Economic Models," IJERPH, MDPI, vol. 19(14), pages 1-33, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Xiaoquan & Yin, Chaoying & Zhang, Junyi & Shao, Chunfu & Wang, Shengyou, 2021. "Nonlinear effects of residential and workplace built environment on car dependence," Journal of Transport Geography, Elsevier, vol. 96(C).
    2. Zhao, Pengjun & Yu, Zhao, 2021. "Rural poverty and mobility in China: A national-level survey," Journal of Transport Geography, Elsevier, vol. 93(C).
    3. Naqavi, Fatemeh & Sundberg, Marcus & Västberg, Oskar Blom & Karlström, Anders & Hugosson, Muriel Beser, 2023. "Mobility constraints and accessibility to work: Application to Stockholm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    4. Hui Zheng & Baohong He & Mingwei He & Jinghui Guo, 2022. "Impact of Urban Spatial Transformation on the Mobility of Commuters with Different Transportation Modes in China: Evidence from Kunming 2011–2016," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    5. Mohammad Hesam Hafezi & Lei Liu & Hugh Millward, 2019. "A time-use activity-pattern recognition model for activity-based travel demand modeling," Transportation, Springer, vol. 46(4), pages 1369-1394, August.
    6. Emami, Maryam & Haghshenas, Hossein & Talebian, Ahmadreza & Kermanshahi, Shahab, 2022. "A game theoretic approach to study the impact of transportation policies on the competition between transit and private car in the urban context," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 320-337.
    7. Arefeh Nasri & Lei Zhang, 2019. "How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    8. Jixiang Liu & Longzhu Xiao, 2024. "Socioeconomic differences in effect size: predicting commuting mode choice of migrants and locals using a light gradient boosting approach," Transportation, Springer, vol. 51(1), pages 1-24, February.
    9. Yixue Zhang & Steven Farber & Mischa Young, 2022. "Eliminating barriers to nighttime activity participation: the case of on-demand transit in Belleville, Canada," Transportation, Springer, vol. 49(5), pages 1385-1408, October.
    10. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.
    11. Lan Wu & Xiaorui Yuan & Chaoyin Yin & Ming Yang & Hongjian Ouyang, 2023. "Car Ownership Behavior Model Considering Nonlinear Impacts of Multi-Scale Built Environment Characteristics," Sustainability, MDPI, vol. 15(12), pages 1-14, June.
    12. Chaoying Yin & Xiaoquan Wang & Chunfu Shao & Jianxiao Ma, 2022. "Exploring the Relationship between Built Environment and Commuting Mode Choice: Longitudinal Evidence from China," IJERPH, MDPI, vol. 19(21), pages 1-15, October.
    13. Altieri, Marcelo & Silva, Cecília & Terabe, Shintaro, 2020. "Give public transit a chance: A comparative analysis of competitive travel time in public transit modal share," Journal of Transport Geography, Elsevier, vol. 87(C).
    14. Ding, Chuan & Cao, Xinyu & Wang, Yunpeng, 2018. "Synergistic effects of the built environment and commuting programs on commute mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 104-118.
    15. Ke Wang & Xin Ye & Ram M Pendyala & Yajie Zou, 2017. "On the development of a semi-nonparametric generalized multinomial logit model for travel-related choices," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-19, October.
    16. Zhao, Pengjun & Wan, Jie, 2021. "Land use and travel burden of residents in urban fringe and rural areas: An evaluation of urban-rural integration initiatives in Beijing," Land Use Policy, Elsevier, vol. 103(C).
    17. Zhao, Pengjun & Yu, Zhao, 2020. "Investigating mobility in rural areas of China: Features, equity, and factors," Transport Policy, Elsevier, vol. 94(C), pages 66-77.
    18. Ikezoe, Keigo & Kiriyama, Eriko & Fujimura, Shuzo, 2021. "Analysis of car ownership motivation in Tokyo for sustainable mobility service and urban development," Transport Policy, Elsevier, vol. 114(C), pages 1-14.
    19. Vega-Gonzalo, Maria & Gomez, Juan & Christidis, Panayotis, 2023. "How has COVID-19 changed private car use in European urban areas? An analysis of the effect of socio-economic characteristics and mobility habits," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    20. Bhat, Chandra R. & Mondal, Aupal, 2022. "A New Flexible Generalized Heterogeneous Data Model (GHDM) with an Application to Examine the Effect of High Density Neighborhood Living on Bicycling Frequency," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 244-266.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:884-:d:1123050. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.