Automatic Extraction of Hierarchical Urban Networks: A Micro-Spatial Approach

  • Rui Carvalho
  • Michael Batty
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3038)


We present an image processing technique for the identification of ‘axial lines’ [1] from ridges in isovist fields first proposed by Rana [2,3]. These ridges are formed from the maximum diametric lengths of the individual isovists, sometimes called viewsheds, that make up the isovist fields [4]. We discuss current strengths and weaknesses of the method, and show how it can be implemented easily and effectively.


Digital Elevation Model Automatic Extraction Automate Vehicle Steering Axial Line Hough Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Hillier, B., Hanson, J.: The Social Logic of Space. Cambridge University Press, Cambridge (1984)CrossRefGoogle Scholar
  2. 2.
    Benedikt, M.: To take hold of space: isovists and isovist fields. Environ. Plan. B 6, 47–65 (1979)CrossRefGoogle Scholar
  3. 3.
    Rana, S.: Isovist analyst extension for arcview 3.2 (2002)Google Scholar
  4. 4.
    Batty, M., Rana, S.: The automatic definition and generation of axial lines and axial maps. Environ. Plan. B 31 (2004) (forthcoming)Google Scholar
  5. 5.
    Hillier, B., Penn, A., Hanson, J., T, T.G., Xu, J.: Natural movement: or, configuration and attraction in urban pedestrian movement. Environ. Plan. B 20, 29–66 (1993)CrossRefGoogle Scholar
  6. 6.
    Peponis, J., Ross, C., Rashid, M.: The structure of urban space, movement and co-presence: The case of atlanta. Geoforum 28, 341–358 (1997)CrossRefGoogle Scholar
  7. 7.
    Peponis, J., Wineman, J., Bafna, S., Rashid, M., Kim, S.: On the generation of linear representations of spatial configuration. Environ. Plan. B 25, 559–576 (1998)CrossRefGoogle Scholar
  8. 8.
    Ratti, C.: Urban analysis for environmental prediction. Phd thesis, University of Cambridge (2001)Google Scholar
  9. 9.
    Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002)CrossRefzbMATHGoogle Scholar
  10. 10.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Batty, M., Longley, P.: Fractal Cities. Academic Press, San Diego (1994)zbMATHGoogle Scholar
  12. 12.
    Carvalho, R., Penn, A.: Scaling and universality in the micro-structure of urban space. Physica A 332, 539–547 (2004)CrossRefGoogle Scholar
  13. 13.
    Frankhauser, P.: La Fractalité des Structures Urbaines. Anthropos, Paris (1994)Google Scholar
  14. 14.
    Makse, H.A., Havlin, S., Stanley, H.E.: Modelling urban growth patterns. Nature 377, 608–612 (1995)CrossRefGoogle Scholar
  15. 15.
    Makse Jr., H.A., A., J.S., Batty, M., Havlin, S., Stanley, H.E.: Modeling urban growth patterns with correlated percolation. Phys. Rev. E 58, 7054–7062 (1998)CrossRefGoogle Scholar
  16. 16.
    Latora, V., Marchiori, M.: Is the boston subway a small-world network? Physica A 314, 109–113 (2002)zbMATHCrossRefGoogle Scholar
  17. 17.
    Chowell, G., Hyman, J.M., Eubank, S., Castillo-Chavez, C.: Scaling laws for the movement of people between locations in a large city. Phys. Rev. E 68, 66–102 (2003)CrossRefGoogle Scholar
  18. 18.
    Blum, H.: Biological shape and visual science (part 1). J. Theor. Biol. 38, 205–287 (1973)CrossRefGoogle Scholar
  19. 19.
    Blum, H., Nagel, R.N.: Shape description using weighted symmetric features. Pattern Recogn. 10, 167–180 (1978)zbMATHCrossRefGoogle Scholar
  20. 20.
    Illingworth, J., Kittler, J.: A survey of the hough transform. Computer Vision, Graphics, and Image Processing 44, 87–116 (1988)CrossRefGoogle Scholar
  21. 21.
    Batty, M.: Exploring isovist fields: space and shape in architectural and urban morphology. Environ. Plan. B 28, 123–150 (2001)CrossRefGoogle Scholar
  22. 22.
    Turner, A., Doxa, M., O’Sullivan, D., Penn, A.: From isovists to visibility graphs: a methodology for the analysis of architectural space. Environ. Plan. B 28, 103–121 (2001)CrossRefGoogle Scholar
  23. 23.
    Burrough, P.A., McDonnell, R.A.: Principles of Geographical Information Systems. Spatial Information Systems and Geostatistics. Oxford University Press, Oxford (1998)Google Scholar
  24. 24.
    Niblack, C., Gibbons, P., Capson, D.: Generating skeletons and ceterlines from the distance transform. CVGIP: Graphical Models and Image Processing 54, 420–437 (1992)CrossRefGoogle Scholar
  25. 25.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)Google Scholar
  26. 26.
    Mills, K., Fox, G., Heimbach, R.: Implementing an intervisibility analysis model on a parallel computing system. Computers & Geosciences 18, 1047–1054 (1992)CrossRefGoogle Scholar
  27. 27.
    Kamat-Sadekar, V., Ganesan, S.: Complete description of multiple line segments using the hough transform. Image and Vision Computing 16, 597–613 (1998)CrossRefGoogle Scholar
  28. 28.
    Pomerleau, D., Jochem, T.: Rapidly adapting machine vision for automated vehicle steering. IEEE Expert 11, 19–27 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Rui Carvalho
    • 1
  • Michael Batty
    • 2
  1. 1.The Bartlett School of Graduate Studies 
  2. 2.Centre for Advanced Spatial AnalysisUniversity College LondonLondonUK

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