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A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area

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  • K C Clarke
  • S Hoppen
  • L Gaydos
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    Abstract

    In this paper we describe a cellular automaton (CA) simulation model developed to predict urban growth as part of a project for estimating the regional and broader impact of urbanization on the San Francisco Bay area's climate. The rules of the model are more complex than those of a typical CA and involve the use of multiple data sources, including topography, road networks, and existing settlement distributions, and their modification over time. In addition, the control parameters of the model are allowed to self-modify: that is, the CA adapts itself to the circumstances it generates, in particular, during periods of rapid growth or stagnation. In addition, the model was written to allow the accumulation of probabilistic estimates based on Monte Carlo methods. Calibration of the model has been accomplished by the use of historical maps to compare model predictions of urbanization, based solely upon the distribution in year 1900, with observed data for years 1940, 1954, 1962, 1974, and 1990. The complexity of this model has made calibration a particularly demanding step. Lessons learned about the methods, measures, and strategies developed to calibrate the model may be of use in other environmental modeling contexts. With the calibration complete, the model is being used to generate a set of future scenarios for the San Francisco Bay area along with their probabilities based on the Monte Carlo version of the model. Animated dynamic mapping of the simulations will be used to allow visualization of the impact of future urban growth.

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    Bibliographic Info

    Article provided by Pion Ltd, London in its journal Environment and Planning B: Planning and Design.

    Volume (Year): 24 (1997)
    Issue (Month): 2 (March)
    Pages: 247-261

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    Handle: RePEc:pio:envirb:v:24:y:1997:i:2:p:247-261

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    Web page: http://www.pion.co.uk

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    Cited by:
    1. Noronha Vaz, E. de & Nijkamp, P., 2009. "Historico-Cultural Sustainability and Urban Dynamics - A Geo-Information Science Approach to the Algarve Area," Serie Research Memoranda 0021, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    2. Jose I. Barredo & Carlo Lavalle & Valentina Sagris & Guy Engelen, 2005. "Representing future urban and regional scenarios for flood hazard mitigation," ERSA conference papers ersa05p147, European Regional Science Association.
    3. Michael Iacono & David Levinson & Ahmed El-Geneidy, 2007. "Models of Transportation and Land Use Change: A Guide to the Territory," Working Papers 200805, University of Minnesota: Nexus Research Group.
    4. Geoffrey Caruso & Dominique Peeters & Jean Cavailhes & Mark Rounsevell, 2007. "Spatial configuration in a periurban city. A cellular automata-based microeconomic model," Working Papers 25364, Institut National de la Recherche Agronomique, France.
    5. Apostolos Lagarias, 2013. "Dynamics of Urban Sprawl: Applying a CA-based Model to Explore Future Development Scenarios in Thessaloniki," ERSA conference papers ersa13p304, European Regional Science Association.
    6. Coppola, Pierluigi & Nuzzolo, Agostino, 2011. "Changing accessibility, dwelling price and the spatial distribution of socio-economic activities," Research in Transportation Economics, Elsevier, vol. 31(1), pages 63-71.
    7. Andersson, Claes & Hellervik, Alexander & Lindgren, Kristian, 2005. "A spatial network explanation for a hierarchy of urban power laws," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(1), pages 227-244.
    8. Lizhong Hua & Lina Tang & Shenghui Cui & Kai Yin, 2014. "Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China," Sustainability, MDPI, Open Access Journal, vol. 6(6), pages 3899-3914, June.
    9. Michael Jacobsen & Michael Webster & Kalanithy Vairavamoorthy, 2013. "The Future of Water in African Cities : Why Waste Water?," World Bank Publications, The World Bank, number 11964, August.
    10. Dagmar Haase, 2005. "Derivation of robust predictor variables for modelling urban shrinkage and its effects at different scales," ERSA conference papers ersa05p322, European Regional Science Association.
    11. Bin Zhou & Kara Kockelman, 2008. "Neighborhood impacts on land use change: a multinomial logit model of spatial relationships," The Annals of Regional Science, Springer, vol. 42(2), pages 321-340, June.
    12. Deal, Brian & Schunk, Daniel, 2004. "Spatial dynamic modeling and urban land use transformation: a simulation approach to assessing the costs of urban sprawl," Ecological Economics, Elsevier, vol. 51(1-2), pages 79-95, November.
    13. repec:asg:wpaper:1038 is not listed on IDEAS
    14. Andreas Rienow & Dirk Stenger, 2014. "Geosimulation of urban growth and demographic decline in the Ruhr: a case study for 2025 using the artificial intelligence of cells and agents," Journal of Geographical Systems, Springer, vol. 16(3), pages 311-342, July.

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