Advertisement

Geo-spatial Information Science

, Volume 14, Issue 4, pp 274–281 | Cite as

A spatially weighted degree model for network vulnerability analysis

  • Neng WanEmail author
  • F. Benjamin Zhan
  • Zhongliang Cai
Article

Abstract

Using degree distribution to assess network vulnerability represents a promising direction of network analysis. However, the traditional degree distribution model is inadequate for analyzing the vulnerability of spatial networks because it does not take into consideration the geographical aspects of spatial networks. This paper proposes a spatially weighted degree model in which both the functional class and the length of network links are considered to be important factors for determining the node degrees of spatial networks. A weight coefficient is used in this new model to account for the contribution of each factor to the node degree. The proposed model is compared with the traditional degree model and an accessibility-based vulnerability model in the vulnerability analysis of a highway network. Experiment results indicate that, although node degrees of spatial networks derived from the traditional degree model follow a random distribution, node degrees determined by the spatially weighted model exhibit a scale-free distribution, which is a common characteristic of robust networks. Compared to the accessibility-based model, the proposed model has similar performance in identifying critical nodes but with higher computational efficiency and better ability to reveal the overall vulnerability of a spatial network.

Keywords

GIS network analysis spatial analysis vulnerability analysis 

CLC number

P208 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Barthélemy M (2003) Crossover from scale-free to spatial networks [J]. Europhysics Letters, 63(6): 915–921CrossRefGoogle Scholar
  2. [2]
    Demšar U, Olga S, Kirsi V (2008) Identifying critical locations in a spatial network with graph theory [J]. Transactions in GIS, 12(1): 61–82CrossRefGoogle Scholar
  3. [3]
    Berdica K (2002) An introduction to road vulnerability: What has been done, is done and should be done? [J]. Transport Policy, 9: 117–127CrossRefGoogle Scholar
  4. [4]
    Holmgren Å (2004) Vulnerability analysis of electrical power delivery networks [D]. Stockholm: Licentiate thesis TRITA-LWR-LIC 2020, KTHGoogle Scholar
  5. [5]
    Jenelius E (2007) Analysis on the vulnerability of road networks [D]. Stockholm: Licentiate thesis TRITA-LWR-LIC 07-002, KTHGoogle Scholar
  6. [6]
    Scott D M, Novak D C, Aultman-Hall L, et al. (2006) Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks [J]. Journal of Transport Geography, 14: 215–227CrossRefGoogle Scholar
  7. [7]
    Albert R, Jeong G, Barabási A L (2000) Error and attack tolerance of complex networks [J]. Nature, 406: 378–382CrossRefGoogle Scholar
  8. [8]
    Newman M E J (2003) The structure and function of complex networks [J]. SIAM Review, (45):167–256.Google Scholar
  9. [9]
    Taylor M A P, Sekhar S V C, D’Este G M (2006) Application of accessibility based methods for vulnerability analysis of strategic road networks [J]. Networks and Spatial Economics, 6(3–4): 267–291CrossRefGoogle Scholar
  10. [10]
    Willis H H (2007) Guiding resource allocations based on terrorism risk [J]. Risk Analysis, 27: 597–606CrossRefGoogle Scholar
  11. [11]
    Dipple K M, Phelan J K, McCabe E R B (2001) Consequences of complexity within biological networks: robustness and health, or vulnerability and disease [J]. Molecular Genetics and Metabolism, 74: 45–50CrossRefGoogle Scholar
  12. [12]
    Bollobos B (2001) Random graphs [M]. Cambridge: Cambridge University PressCrossRefGoogle Scholar
  13. [13]
    Watts D, Strogatz S (1998) Collective dynamics of ’small-world’ networks [J]. Nature, 393(6684): 440–442CrossRefGoogle Scholar
  14. [14]
    Barabási A L, Albert R (1999) Emergence of scaling in random networks [J]. Science, 286: 509–512CrossRefGoogle Scholar
  15. [15]
    Huberman B, Adamic L (1999) Growth dynamics of the World-Wide Web [J]. Nature, 40: 131Google Scholar
  16. [16]
    Barabási A L, Bonabeau E (2003) Scale-free networks [J]. Scientific American, 288(5): 50–59CrossRefGoogle Scholar
  17. [17]
    Crucitti P, Latora V, Porta S (2006) Centrality measures in spatial networks of urban streets [J]. Physical Review E, 73: 036125CrossRefGoogle Scholar
  18. [18]
    The National Highway Planning Network (NHPN) [OL]. http://www.fhwa.dot.gov/planning/nhpn/accessed on May, 20th 2010

Copyright information

© Wuhan University and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.School Texas Center for Geographic Information Science, Department of GeographyTexas State UniversitySan MarcosUSA
  2. 2.School of Resource and Environmental ScienceWuhan UniversityWuhanChina

Personalised recommendations