IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v24y1997i3p323-343.html
   My bibliography  Save this article

The Use of Constrained Cellular Automata for High-Resolution Modelling of Urban Land-Use Dynamics

Author

Listed:
  • R White

    (Department of Geography, Memorial University of Newfoundland, St John's, Newfoundland A1B 3X9, Canada)

  • G Engelen
  • I Uljee

Abstract

A cellular automaton is specified to give a spatially detailed representation of the evolution of urban land-use patterns. Cell states represent land uses, and transition rules express the likelihood of a change from one state to another as a function both of existing land use in the 113-cell neighbourhood of the cell and of the inherent suitability of the cell for each possible use. The model is used to simulate the land-use pattern of Cincinnati, Ohio. The simulation results are realistic and sensitivity analysis shows that the predictions of the model are relatively accurate and reproducible, thus suggesting that cellular ast;automata-based models may be useful in a planning context.

Suggested Citation

  • R White & G Engelen & I Uljee, 1997. "The Use of Constrained Cellular Automata for High-Resolution Modelling of Urban Land-Use Dynamics," Environment and Planning B, , vol. 24(3), pages 323-343, June.
    • Handle: RePEc:sae:envirb:v:24:y:1997:i:3:p:323-343
    DOI: 10.1068/b240323
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/b240323
    Download Restriction: no
    File URL: https://libkey.io/10.1068/b240323?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pakawan Chotchaiwong & Saowanee Wijitkosum, 2019. "Predicting Urban Expansion and Urban Land Use Changes in Nakhon Ratchasima City Using a CA-Markov Model under Two Different Scenarios," Land, MDPI, vol. 8(9), pages 1-16, September.
    2. 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.
    3. Jie Liu & Lang Zhang & Qingping Zhang, 2019. "The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China," Sustainability, MDPI, vol. 12(1), pages 1-19, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:24:y:1997:i:3:p:323-343. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.