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Integrating spatial optimization and cellular automata for evaluating urban change

Author

Listed:
  • Douglas P. Ward
  • Alan T. Murray
  • Stuart R. Phinn

Abstract

Urban growth and change presents numerous challenges for planners and policy makers. Effective and appropriate strategies for managing growth and change must address issues of social, environmental and economic sustainability. Doing so in practical terms is a difficult task given the uncertainty associated with likely growth trends not to mention the uncertainty associated with how social and environmental structures will respond to such change. An optimization based approach is developed for evaluating growth and change based upon spatial restrictions and impact thresholds. The spatial optimization model is integrated with a cellular automata growth simulation process. Application results are presented and discussed with respect to possible growth scenarios in south east Queensland, Australia. Copyright Springer-Verlag Berlin Heidelberg 2003

Suggested Citation

  • Douglas P. Ward & Alan T. Murray & Stuart R. Phinn, 2003. "Integrating spatial optimization and cellular automata for evaluating urban change," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 37(1), pages 131-148, February.
    • Handle: RePEc:spr:anresc:v:37:y:2003:i:1:p:131-148
    DOI: 10.1007/s001680200113
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    Citations

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    Cited by:

    1. Jonathan Corcoran & Alan T. Murray & Robert J. Stimson, 2011. "Spatially Disaggregating Employment Growth Estimates," International Regional Science Review, , vol. 34(2), pages 138-156, April.
    2. Jing Yao & Xiaoxiang Zhang & Alan T. Murray, 2018. "Spatial Optimization for Land-use Allocation," International Regional Science Review, , vol. 41(6), pages 579-600, November.
    3. Jing Yang & Feng Shi & Yizhong Sun & Jie Zhu, 2019. "A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    4. Reza Taromi & Michael DuRoss & Bintong Chen & Ardeshir Faghri & Mingxin Li & Tracy DeLiberty, 2015. "A multiobjective land development optimization model: the case of New Castle County, Delaware," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(3), pages 277-304, April.
    5. Alan T. Murray, 2010. "Quantitative Geography," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 143-163, February.

    More about this item

    Keywords

    JEL classification: C61; Q01; R11;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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