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Geosimulation and its Application to Urban Growth Modeling

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Abstract

Automata-based models have enjoyed widespread application to urban simulation in recent years. Cellular automata (CA) and multi-agent systems (MAS) have been particularly popular. However, CA and MAS are often confused. In many instances, CA are paraphrased as agent-based models and simply re-interpreted as MAS. This is interesting from a geographical standpoint, because the two may be distinguished by their spatial attributes. First, they differ in terms of their mobility: CA cannot “move”, but MAS are mobile entities. Second, in terms of interaction, CA transmit information by diffusion over neighborhoods; MAS transmit information by themselves, moving between locations that can be at any distance from an agent’s current position. These different views on the basic geography of the system can have important implications for urban simulations developed using the tools. It may result in different space-time dynamics between model runs and may have important consequences for the use of the models as applied tools. In this chapter, a patently spatial framework for urban simulation with automata Tools is described: Geographic Automata Systems (GAS). The applicability of the GAS approach will be demonstrated with reference to practical implementations, showing how the framework can be used to develop intuitive models of urban dynamics.

Keywords

  • Cellular Automaton
  • Cellular Automaton
  • Urban Growth
  • Urban System
  • Cellular Automaton Model

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.

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References

  • Bahl, R.W. (1968). A land speculation model: the role of the property tax as a constraint to urban sprawl. Journal of Regional Science 8(2):199–208.

    Google Scholar 

  • Batty, M., and Longley, P. (1994). Fractal Cities. London: Academic Press.

    Google Scholar 

  • Batty, M., Longley, P.A. and Fotheringham, A.S. (1989). Urban growth and form: scaling, fractal geometry, and diffusion-limited-aggregation. Environment and Planning A 21:1447–1472.

    Google Scholar 

  • Benenson, I., and Torrens, P.M. (2004a). Geosimulation: Automata-Based Modeling of Urban Phenomena. London: John Wiley & Sons.

    Google Scholar 

  • Benenson, I. (2004b). A minimal prototype for integrating GIS and geographic simulation through Geographic Automata Systems. In GeoDynamics, edited by P. Atkinson, G. Foody, S. Darby and F. Wu. Florida: CRC Press.

    Google Scholar 

  • Benenson, I., Aronovich, S., and Noam, S. (2004). Let’s talk objects. Computers, Environment and Urban Systems, 29(4): 425–471.

    CrossRef  Google Scholar 

  • Benenson, I., and Torrens, P.M. (2004c). Geosimulation: object-based modeling of urban phenomena. Computers, Environment and Urban Systems 28(1/2):1–8.

    CrossRef  Google Scholar 

  • Benenson, I. & Torrens, P. M. (2004d). Special Issue: Geosimulation: object-based modeling of urban phenomena. Computers, Environment and Urban Systems 28:1–8.

    CrossRef  Google Scholar 

  • Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems, Santa Fe Institue Studies in the Sciences of Complexity. New York: Oxford University Press.

    Google Scholar 

  • Calthorpe, P., Fulton, W. and Fishman, R. (2001). The Regional City: Planning for the End of Sprawl. Washington, D.C.: Island Press.

    Google Scholar 

  • Clarke, K.C., and Gaydos, L. (1998). Loose coupling a cellular automaton model and GIS: long-term growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science 12(7):699–714.

    CrossRef  Google Scholar 

  • Dibble, C., and Feldman, P.G. (2004). The GeoGraph 3D Computational Laboratory: Network and Terrain Landscapes for RePast. Journal of Artificial Societies and Social Simulation 7(1).

    Google Scholar 

  • Duany, A., Plater-Zyberk, E. and Speck, J. (2000). Suburban Nation: The Rise of Sprawl and the Decline of the American Dream. New York: North Point Press.

    Google Scholar 

  • Duany, A., Speck, J. and Plater-Zyberk, E. (2001). Smart Growth: New Urbanism in American Communities. New York: McGraw-Hill.

    Google Scholar 

  • Engelen, G., White, R. Uljee, I. and Drazan, P. (1995). Using cellular automata for integrated modeling of socio-environmental systems. Environmental Monitoring and Assessment 30:203–214.

    CrossRef  Google Scholar 

  • Epstein, J.M. (1999). Agent-based computational models and generative social science. Complexity 4(5):41–60.

    CrossRef  Google Scholar 

  • Epstein, J.M., and Axtell, R. (1996). Growing Artificial Societies from the Bottom Up. Washington D.C.: Brookings Institution.

    Google Scholar 

  • Ewing, R. (1997). Is Los Angeles-style sprawl desirable? Journal of the American Planning Association 63(1):107–126.

    Google Scholar 

  • Ferber, J. (1999). Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Harlow (UK): Addison-Wesley.

    Google Scholar 

  • Garreau, J. (1992). Edge City: Life on the New Frontier. New York: Anchor Books/Doubleday.

    Google Scholar 

  • Gimblett, H.R. (ed.) (2002). Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Simulating Social and Ecological Processes, Santa Fe Institute Studies in the Sciences of Complexity. Oxford: Oxford University Press.

    Google Scholar 

  • Gordon, P. and Richardson, H.W. (1997a). Are compact cities a desirable planning goal? Journal of the American Planning Association 63(1):95–106.

    Google Scholar 

  • Gordon, P. (1997b). Where’s the sprawl? Journal of the American Planning Association 63(2):275–278.

    Google Scholar 

  • Johnson, S. (2001). Emergence: The Connected Lives of Ants, Brains, Cities, and Software. London: Allen Lane, The Penguin Press.

    Google Scholar 

  • Katz, P. (1993). The New Urbanism: Toward an Architecture of Community. New York: McGraw-Hill.

    Google Scholar 

  • Kohler, T.A., and Gumerman, G. (2001). Dynamics in Human and Primate Societies. New York.: Oxford University Press.

    Google Scholar 

  • Leonard, A. (1997). Bots: The Origin of a New Species. San Francisco: Hardwired.

    Google Scholar 

  • Luna, F, and Stefansson, B. (eds.) (2000). Economic Simulation in Swarm: Agent-based Modeling and Object Oriented Programming. Dordrecht: Kluwer.

    Google Scholar 

  • Meyer, J.A., and Guillot, A. (1994). From SAB90 to SAB94: four years of animat research. In From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behavior, edited by D. Cliff, P. Husbands, J.-A. Meyer and S. Wilson. Cambridge, MA: The MIT Press, 2–11.

    Google Scholar 

  • O’sullivan, D., and Torrens, P.M. (2000). Cellular models of urban systems. In Theoretical and Practical Issues on Cellular Automata, edited by S. Bandini and T. Worsch. London: Springer-Verlag, 108–117.

    Google Scholar 

  • Pallman, D. (1999). Programming Bots, Spiders, and Intelligent Agents in Microsoft Visual C++, Microsoft Programming Series. Redmond, WA: Microsoft Press.

    Google Scholar 

  • Reynolds, C. (1987). Flocks, herds, and schools: A distributed behavioral model. Computer Graphics 21(4):25–34.

    Google Scholar 

  • Reynolds, C. (1999). Steering behaviors for autonomous characters. Paper read at Game Developers Conference, at San Jose, CA.

    Google Scholar 

  • Semboloni, F., Assfalg, J. Armeni, S., Gianassi, R. and Marsoni, F. (2004). CityDev, an interactive multi-agents urban model on the web. Computers, Environment and Urban Systems 28(1/2):45–64.

    CrossRef  Google Scholar 

  • Torrens, P.M. (2001). New tools for simulating housing choices. Program on Housing and Urban Policy Conference Paper Series. Berkeley, CA: University of California Institute of Business and Economic Research and Fisher Center for Real Estate and Urban Economics. http://urbanpolicy.berkeley.edu/pdf/torrens.pdf.

    Google Scholar 

  • Torrens, P.M. (2002a). Cellular automata and multi-agent systems as planning support tools. In Planning Support Systems in Practice, edited by S. Geertman and J. Stillwell. London: Springer-Verlag, 205–222.

    Google Scholar 

  • Torrens, P.M. (2002b). SprawlSim: modeling sprawling urban growth using automata-based models. In Agent-Based Models of Land-Use/Land-Cover Change, edited by D. C. Parker, T. Berger, S. M. Manson and W. J. McConnell. Louvain-la-Neuve, Belgium: LUCC International Project Office, 69–76.

    Google Scholar 

  • Torrens, P.M. (2003). Automata-based models of urban systems. In Advanced Spatial Analysis, edited by P. A. Longley and M. Batty. Redlands, CA: ESRI Press, 61–81.

    Google Scholar 

  • Torrens, P.M. (2004). Geosimulation approaches to traffic modeling. In P. Stopher, K. Button, K. Haynes & D. Hensher (Eds.). Transport geography and spatial systems. London: Pergamon, pp. 549–565.

    Google Scholar 

  • Torrens, P.M., and Alberti, M. (2000). Measuring sprawl. CASA Working Paper. London: University College London, Centre for Advanced Spatial Analysis. http://www.casa.ucl.ac.uk/measuring-sprawl.pdf.

    Google Scholar 

  • Torrens, P.M., and Benenson, I. (2005). Geographic automata systems. International Journal of Geographical Information Science, 19(4), 385–412.

    CrossRef  Google Scholar 

  • Torrens, P.M., and O’sullivan, D. (2001). Cellular automata and urban simulation: where do we go from here? Environment and Planning B 28(2):163–168.

    Google Scholar 

  • Watts, D.J. (2003). Six Degrees: The Science of a Connected Age. New York: W.W. Norton & Company.

    Google Scholar 

  • White, R., and Engelen, G. (2000). High-resolution integrated modeling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems 24:383–400.

    CrossRef  Google Scholar 

  • Wiener, N. (1961). Cybernetics: or Control and Communication in the Animal and the Machine. Cambridge, MA: MIT Press.

    Google Scholar 

  • Xie, Y. (1994). Analytical models and algorithims for cellular urban dynamics. Ph.D., Department of Geography, University of New York at Buffalo, Buffalo, NY.

    Google Scholar 

  • Yeh, A. Gar-On, and Xia L. (2000). Simulation of compact cities based on the integration of cellular automata and GIS. In Theoretical and Practical Issues on Cellular Automata, edited by S. Bandini and T. Worsch. London: Springer-Verlag, 170–178.

    Google Scholar 

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Torrens, P.M. (2006). Geosimulation and its Application to Urban Growth Modeling. In: Portugali, J. (eds) Complex Artificial Environments. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-29710-3_8

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