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Modelling Proximal Space in Urban Cellular Automata

  • Ivan Blecic
  • Arnaldo Cecchini
  • Giuseppe A. Trunfio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6782)

Abstract

In the great majority of urban models based on Cellular Automata (CA), the concept of proximity is assumed to reflect two fundamental sources of spatial interaction: (1) accessibility and (2) vicinity in Euclidean sense. While the geographical space defined by the latter clearly has an Euclidean representation, the former, based on the accessibility, does not admit such a regular representation. Very little operational efforts have been undertaken in CA-based urban modelling to investigate and provide a more coherent and cogent treatment of such irregular geometries, which indeed are a fundamental feature of any urban geography. In this paper, we suggest an operational approach – entirely based on cellular automata techniques – to model the complex topology of proximities arising from urban geography, and to entangle such proximity topology with a CA model of spatial interactions.

Keywords

urban cellular automata land-use dynamics proximal space irregular neighbourhood informational signal propagation informational field 

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References

  1. 1.
    Batty, M.: Distance in space syntax. CASA Working Papers (80). Centre for Advanced Spatial Analysis (UCL), London, UK (2004)Google Scholar
  2. 2.
    White, R., Engelen, G.: Cellular automata and fractal urban form: A cellular modeling approach to the evolution of urban land-use patterns. Environment and Planning, 1175–1199 (1993)Google Scholar
  3. 3.
    White, R., Engelen, G., Uljee, I.: The use of constrained cellular automata for high-resolution modelling of urban land use dynamics. Environment and Planning B 24, 323–343 (1997)CrossRefGoogle Scholar
  4. 4.
    White, R., Engelen, G.: High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems 28(24), 383–400 (2000)CrossRefGoogle Scholar
  5. 5.
    Clarke, K., Hoppen, S., Gaydos, L.: A self-modifying cellular automaton model of historical urbanization in the san francisco bay area. Environment and Planning B 24, 247–261 (1997)CrossRefGoogle Scholar
  6. 6.
    Clarke, K.C., Gaydos, L.J.: Loose-coupling a cellular automaton model and GIS: long-term urban growth predictions for San Francisco and Baltimore. International Journal of Geographic Information Science, 699–714 (1998)Google Scholar
  7. 7.
    Project Gigalopolis, NCGIA (2003), http://www.ncgia.ucsb.edu/projects/gig/
  8. 8.
    Benenson, I., Torrens, P.M.: Geosimulation: object-based modeling of urban phenomena. Computers, Environment and Urban Systems 28(1-2), 1–8 (2004)CrossRefGoogle Scholar
  9. 9.
    Torrens, P.M., Benenson, I.: Geographic Automata Systems. International Journal of Geographical Information Science 19(4), 385–412 (2005)CrossRefGoogle Scholar
  10. 10.
    Tobler, W.: Cellular geography, in S. Gale & G. Olsson. In: Philosophy in Geography, pp. 379–386. Reidel, Dordrecht (1979)CrossRefGoogle Scholar
  11. 11.
    Takeyama, M., Couclelis, H.: Map dynamics: integrating cellular automata and GIS through Geo-Algebra. Intern. Journ. of Geogr. Inf. Science 11, 73–91 (1997)CrossRefGoogle Scholar
  12. 12.
    Bandini, S., Mauri, G., Vizzari, G.: Supporting Action-at-a-distance in Situated Cellular Agents. Fundam. Inform 69(3), 251–271 (2006)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ivan Blecic
    • 1
  • Arnaldo Cecchini
    • 1
  • Giuseppe A. Trunfio
    • 1
  1. 1.Department of Architecture and PlanningUniversity of SassariAlgheroItaly

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