IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/cvjnx_v1.html
   My bibliography  Save this paper

Divergent Trajectories of Urban Development in 287 Chinese Cities

Author

Listed:
  • Wang, Shenhao
  • Zhao, Jinhua

Abstract

The urbanization and motorization of Chinese cities follow divergent trajectories. However, how the diversity occurred, particularly within the small and medium cities, is understudied. Using panel data from 287 cities from 2001 to 2014 and a time-series clustering method, this study identified representative trajectories along which Chinese cities were urbanized and motorized. Urbanization was measured by scale, wealth, urban form, and infrastructure; motorization by automobile, taxi, bus numbers, and subway lines. Chinese cities were classified into four clusters: 23 Cluster-1 cities were the large cities with heavy rails; 41 Cluster-2 cities were the low-density wealthy cities with auto-oriented mobility; 134 Cluster-3 cities were the low-density medium-wealth cities with moderate mobility levels; and 89 Cluster-4 cities were the high-density poor cities with lowest mobility levels. Comparing to the traditional three-tier structure, exclusively based on political tiers, the four-cluster structure respects the multi-dimensional nature of cities and reflects the essential diversities among the medium and small cities. While political tiers remain critical, other features including scale, density, infrastructure, and mobility patterns are also important: scale differentiates Cluster-1 from others; low density characterizes Clusters 2 and 3; heavy rail and auto-oriented mobility respectively identify Clusters 1 and 2. We contribute to China’s urban development literature by explicitly examining the temporal dimension, analyzing both urbanization and motorization, and incorporating all the medium and small cities in China. The distinct patterns of Clusters 2, 3, and 4 are evident, and the variation within them were as important as that between them and large cities.

Suggested Citation

  • Wang, Shenhao & Zhao, Jinhua, 2018. "Divergent Trajectories of Urban Development in 287 Chinese Cities," OSF Preprints cvjnx_v1, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:cvjnx_v1
    DOI: 10.31219/osf.io/cvjnx_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/5b58fdc54e7b15000ee52ad3/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/cvjnx_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
    2. Baum-Snow, Nathaniel & Kahn, Matthew E., 2000. "The effects of new public projects to expand urban rail transit," Journal of Public Economics, Elsevier, vol. 77(2), pages 241-263, August.
    3. Joyce Dargay & Dermot Gately & Martin Sommer, 2007. "Vehicle Ownership and Income Growth, Worldwide: 1960-2030," The Energy Journal, , vol. 28(4), pages 143-170, October.
    4. Domenico Piccolo, 1990. "A Distance Measure For Classifying Arima Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(2), pages 153-164, March.
    5. Joyce Dargay & Dermot Gately & Martin Sommer, 2007. "Vehicle Ownership and Income Growth, Worldwide: 1960-2030," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 143-170.
    6. Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth & Börsch-Supan, Axel, 2002. "Hybrid Choice Models: Progress and Challenges," Sonderforschungsbereich 504 Publications 02-29, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    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, Shenhao & Zhao, Jinhua, 2018. "Divergent Trajectories of Urban Development in 287 Chinese Cities," OSF Preprints cvjnx, Center for Open Science.
    2. Kudłak, Robert & Kisiała, Wojciech & Kołsut, Bartłomiej, 2024. "Systemic transformation, political reforms and car ownership in Poland," Journal of Transport Geography, Elsevier, vol. 117(C).
    3. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    4. Malte Welling & Ewa Zawojska & Julian Sagebiel, 2022. "Information, Consequentiality and Credibility in Stated Preference Surveys: A Choice Experiment on Climate Adaptation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 82(1), pages 257-283, May.
    5. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    6. Joan L. Walker & Moshe Ben-Akiva, 2011. "Advances in Discrete Choice: Mixture Models," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 8, Edward Elgar Publishing.
    7. Wang, Qingyi & Wang, Shenhao & Zheng, Yunhan & Lin, Hongzhou & Zhang, Xiaohu & Zhao, Jinhua & Walker, Joan, 2024. "Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    8. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    9. Parmar, Janak & Saiyed, Gulnazbanu & Dave, Sanjaykumar, 2023. "Analysis of taste heterogeneity in commuters’ travel decisions using joint parking– and mode–choice model: A case from urban India," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    10. Muhammad Zudhy Irawan & Prawira Fajarindra Belgiawan & Tri Basuki Joewono & Nurvita I. M. Simanjuntak, 2020. "Do motorcycle-based ride-hailing apps threaten bus ridership? A hybrid choice modeling approach with latent variables," Public Transport, Springer, vol. 12(1), pages 207-231, March.
    11. Glerum, Aurélie & Atasoy, Bilge & Bierlaire, Michel, 2014. "Using semi-open questions to integrate perceptions in choice models," Journal of choice modelling, Elsevier, vol. 10(C), pages 11-33.
    12. Schmid, Basil & Axhausen, Kay W., 2019. "In-store or online shopping of search and experience goods: A hybrid choice approach," Journal of choice modelling, Elsevier, vol. 31(C), pages 156-180.
    13. Muñoz, Begoña & Monzon, Andres & López, Elena, 2016. "Transition to a cyclable city: Latent variables affecting bicycle commuting," Transportation Research Part A: Policy and Practice, Elsevier, vol. 84(C), pages 4-17.
    14. Fernández-Antolín, Anna & Guevara, C. Angelo & de Lapparent, Matthieu & Bierlaire, Michel, 2016. "Correcting for endogeneity due to omitted attitudes: Empirical assessment of a modified MIS method using RP mode choice data," Journal of choice modelling, Elsevier, vol. 20(C), pages 1-15.
    15. Kroesen, Maarten & Chorus, Caspar, 2020. "A new perspective on the role of attitudes in explaining travel behavior: A psychological network model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 82-94.
    16. Marley, A.A.J. & Swait, J., 2017. "Goal-based models for discrete choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 72-88.
    17. Kun Gao & Minhua Shao & Lijun Sun, 2019. "Roles of Psychological Resistance to Change Factors and Heterogeneity in Car Stickiness and Transit Loyalty in Mode Shift Behavior: A Hybrid Choice Approach," Sustainability, MDPI, vol. 11(17), pages 1-20, September.
    18. Barajas, Jesus, 2021. "The Roots of Racialized Travel Behavior," SocArXiv unmkx_v1, Center for Open Science.
    19. Vasquez Lavin, Felipe & Hanemann, W. Michael, 2008. "Taste Indicators and Heterogeneous Revealed Preferences for Congestion in Recreation Demand," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt4rz5z706, Department of Agricultural & Resource Economics, UC Berkeley.
    20. La Paix Puello, Lissy & Olde-Kalter, Marie-José & Geurs, Karst T., 2017. "Measurement of non-random attrition effects on mobility rates using trip diaries data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 51-64.

    More about this item

    Statistics

    Access and download statistics

    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:osf:osfxxx:cvjnx_v1. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.