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Cellular Automata as the Basis of Integrated Dynamic Regional Modelling

Author

Listed:
  • R White

    (Department of Geography, Memorial University of Newfoundland, St John's, Newfoundland, Canada)

  • G Engelen

    (Research Institute for Knowledge Systems, Postbus 463, 6200 AL Maastricht, The Netherlands)

Abstract

We present an integrated model of regional spatial dynamics consisting of a cellular automaton-based model of land use linked both to a geographic information system (GIS) and to standard nonspatial models of regional economics and demographics, as well as to a simple model of environmental change. The operation of the model is illustrated with an application to the island of St Lucia developed for the purpose of providing insights into the possible socioeconomic consequences for the island of global climate change. On the basis of results from this and other applications of the model, we conclude that cellular automata not only permit a detailed modelling and realistic prediction of land-use patterns, but they also provide a means of introducing the effects of spatially localized environmental factors, as represented in the GIS, into the operation of standard economic and demographic models, which are otherwise unconstrained.

Suggested Citation

  • R White & G Engelen, 1997. "Cellular Automata as the Basis of Integrated Dynamic Regional Modelling," Environment and Planning B, , vol. 24(2), pages 235-246, April.
    • Handle: RePEc:sae:envirb:v:24:y:1997:i:2:p:235-246
    DOI: 10.1068/b240235
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    Citations

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    Cited by:

    1. Yingen Hu & Ye Zhang & Xinli Ke, 2018. "Dynamics of Tradeoffs between Economic Benefits and Ecosystem Services due to Urban Expansion," Sustainability, MDPI, vol. 10(7), pages 1-13, July.
    2. Xinli Ke & Liye Wang & Yanchun Ma & Kunpeng Pu & Ting Zhou & Bangyong Xiao & Jiahe Wang, 2019. "Impacts of Strict Cropland Protection on Water Yield: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 11(1), pages 1-16, January.
    3. Cong Cao & Suzana Dragićević & Songnian Li, 2019. "Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    4. Basse, Reine Maria, 2013. "A constrained cellular automata model to simulate the potential effects of high-speed train stations on land-use dynamics in trans-border regions," Journal of Transport Geography, Elsevier, vol. 32(C), pages 23-37.
    5. Xuesong Gao & Yu Liu & Lun Liu & Qiquan Li & Ouping Deng & Yali Wei & Jing Ling & Min Zeng, 2018. "Is Big Good or Bad?: Testing the Performance of Urban Growth Cellular Automata Simulation at Different Spatial Extents," Sustainability, MDPI, vol. 10(12), pages 1-10, December.
    6. Gong, Jian-zhou & Liu, Yan-sui & Xia, Bei-cheng & Zhao, Guan-wei, 2009. "Urban ecological security assessment and forecasting, based on a cellular automata model: A case study of Guangzhou, China," Ecological Modelling, Elsevier, vol. 220(24), pages 3612-3620.
    7. Lianyan Li & Xiaobin Ren, 2019. "A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network," Sustainability, MDPI, vol. 11(21), pages 1-25, November.
    8. Jo Hendy & Levente Timar & Dominic White, 2018. "Land-use modelling in New Zealand: current practice and future needs," Working Papers 18_16, Motu Economic and Public Policy Research.
    9. Chuanzhun Sun & Chao Sun & Zhenshan Yang & Jikang Zhang & Yu Deng, 2016. "Urban Land Development for Industrial and Commercial Use: A Case Study of Beijing," Sustainability, MDPI, vol. 8(12), pages 1-18, December.
    10. Yassemi, S. & Dragićević, S. & Schmidt, M., 2008. "Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour," Ecological Modelling, Elsevier, vol. 210(1), pages 71-84.

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