IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v41y2014i5p813-828.html
   My bibliography  Save this article

Topological Structure of Urban Street Networks from the Perspective of Degree Correlations

Author

Listed:
  • Bin Jiang

    (Department of Technology and Built Environment, Division of Geomatics, University of Gävle, SE-801 76 Gävle, Sweden)

  • Yingying Duan
  • Feng Lu
  • Tinghong Yang
  • Jing Zhao

Abstract

Many complex networks demonstrate a phenomenon of striking degree correlations: that is, a node tends to link to other nodes with similar (or dissimilar) degrees. From the perspective of degree correlations, in this paper we attempt to characterize topological structures of urban street networks. We adopted six urban street networks (three European and three North American), and converted them into network topologies in which nodes and edges represent individual streets and street intersections, respectively, and compared the network topologies with three reference network topologies (biological, technological, and social). The urban street network topologies (with the exception of Manhattan) showed a consistent pattern that distinctly differs from the three reference networks. The topologies of urban street networks lack striking degree correlations in general. Through reshuffling the network topologies towards, for example, maximum or minimum degree correlations while retaining the initial degree distributions, we found that all the surrogate topologies of the urban street networks, as well as the reference ones, tended to deviate from small-world properties. This implies that the initial degree correlations do not have any positive or negative effect on the networks' performance or functions.

Suggested Citation

  • Bin Jiang & Yingying Duan & Feng Lu & Tinghong Yang & Jing Zhao, 2014. "Topological Structure of Urban Street Networks from the Perspective of Degree Correlations," Environment and Planning B, , vol. 41(5), pages 813-828, October.
  • Handle: RePEc:sae:envirb:v:41:y:2014:i:5:p:813-828
    DOI: 10.1068/b39110
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/b39110
    Download Restriction: no

    File URL: https://libkey.io/10.1068/b39110?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Asya Natapov & Daniel Czamanski & Dafna Fisher-Gewirtzman, 2018. "A Network Approach to Link Visibility and Urban Activity Location," Networks and Spatial Economics, Springer, vol. 18(3), pages 555-575, September.

    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:sae:envirb:v:41:y:2014:i:5:p:813-828. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: SAGE Publications (email available below). General contact details of provider: .

    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.