Environmental Monitoring and Assessment

, Volume 34, Issue 2, pp 203–214 | Cite as

Using cellular automata for integrated modelling of socio-environmental systems

  • Guy Engelen
  • Roger White
  • Inge Uljee
  • Paul Drazan


Cellular automata provide the key to a dynamic modelling and simulation framework that integrates socio-economic with environmental models, and that operates at both micro and macro geographical scales. An application to the problem of forecasting the effect of climate change on a small island state suggests that such modelling techniques could help planners and policy makers design more effective policies — policies better tuned both to specific local needs and to overall socio-economic and environmental constraints.


Climate Change Environmental Management Policy Maker Modelling Technique Cellular Automaton 
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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  1. Allen, P.M. and Lesser, M.: 1991, “Evolutionary Human Systems: Learning, Ignorance and Subjectivity” in: Saviotti, P. and Metcalfe S. (eds.), Evolutionary of economic and technological change: present status and future prospects, Harwood Academic Publishers, Reading, U.K., 160–171.Google Scholar
  2. Alm, A., Blommestein, E. and Broadus, J.H.: 1993, “Climate Change and Socio-economic impacts”, in: Maul G.A. (ed.), Climatic Change in the Intra-Americas Sea, Edward Arnold, London, 333–349.Google Scholar
  3. Blommestein, E.: 1993, Sustainable Development and Small Island Developing Countries, ECLAC, Trinidad and Tobago.Google Scholar
  4. Brimicombe, A.J.: 1992, “Flood Risk Assessment Using Spatial Decision Support Systems”, Simulation, December 1992, 379–380.Google Scholar
  5. Burrough, P.A.: 1989, Principles of Geographic Information Systems for Land Resources Assessment, Clarendon Press, Oxford.Google Scholar
  6. Couclelis, H.: 1985, “Cellular Worlds: A Framework for Modelling Micro-Macro Dynamics”, Environment and Planning A 17, 585–596.Google Scholar
  7. Couclelis, H.: 1988, “Of Mice and Men: What Rodent Populations Can Teach Us About Complex Spatial Dynamics”, Environment and Planning A 20, 99–109.Google Scholar
  8. Engelen, G. and Allen, P.M.: 1986, “Modelling the spatial distribution of energy demand for the Province of Noord Holland; towards an integrated approach.” Sistemi Urbani 2/3, 241–261.Google Scholar
  9. Engelen, G., White, R. and Uljee, I.: 1993a, “Exploratory Modelling of Socio-Economic Impacts of Climatic Change.” in: Maul, G.A. (ed.), Climate Change in the Intra-Americas Sea, Edward Arnold, London, 306–324.Google Scholar
  10. Engelen, G., White, R., Uljee, I. and Wargnies, S.: 1993b, Vulnerability Assessment of Low-Lying Coastal Areas and Small Islands to Climate Change and Sea Level Rise. Report to United Nations Environment Programme, Caribbean Regional Co-ordinating Unit, Kingston, Jamaica. RIKS publication 905000/9379.Google Scholar
  11. Engelen, G., White, R., Uljee, I., Wargnies, S. and Schutzelaars, A.: 1994 (in press), “Learning Geography By Means of Simulation: A Model-Based Intelligent Tutoring System” in: Chamussy, H., Bradshaw, R. and Antrop, M. (eds.), Intelligent Tutorial Systems in Geography, Springer-Verlag, Berlin.Google Scholar
  12. Frankhauser, P.: 1991, “Aspects fractals des structures urbaines”, L'Espace Geographique, 45–69.Google Scholar
  13. Gardner, M.: 1970, “The fantastic combinations of John Conways new solitaire game Life”, Scientific American, 223, 120–123.Google Scholar
  14. Gutowitz, H.: 1991, Cellular Automata. Theory and Experiment, The MIT Press, Cambridge, Mass.Google Scholar
  15. Kauffman, S.: 1993, The Origins of Order, Oxford University Press.Google Scholar
  16. Langton, C.: 1992, “Life at the Edge of Chaos” in: C. Langton et al., (eds.) Artificial Life II: Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, Santa Fe Institute Studies in the Science of Complexity 10, Redwood City, Addison Wesley, 41–92.Google Scholar
  17. Nicolis, G., Nicolis, C. and Nicolis, J.: 1989, “Chaotic dynamics, Markov partitions, and Zipf's law”, Journal of Statistical Physics 54, 915–924.Google Scholar
  18. Pumain, D., Sanders, L. and Saint-Julien, T.: 1989, Villes et Auto-Organisation, Economica, Paris.Google Scholar
  19. Tobler, W.: 1979, “Cellular Geography”, in: Gale, S. and Olsson, G. (eds.), Philosophy in Geography, 379–386.Google Scholar
  20. White, R.: 1977, “Dynamic central place theory — Results of a simulation approach”, Geographical Analysis 9, 279–386.Google Scholar
  21. White, R. and Engelen, G.: 1993, “Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land Use Patterns”, Environment and Planning A 25(8), 1175–1199.Google Scholar
  22. White, R. and Engelen, G.: 1994, “Cellular Dynamics and GIS: Modelling Spatial Complexity”, Geographical Systems 1(2), 237–253.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Guy Engelen
    • 1
  • Roger White
    • 2
  • Inge Uljee
    • 1
  • Paul Drazan
    • 1
  1. 1.RIKS Research Institute for Knowledge SystemsMaastrichtThe Netherlands
  2. 2.Memorial University of NewfoundlandSt. John'sCanada

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