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Coupling Bayesian Networks with GIS-Based Cellular Automata for Modeling Land Use Change

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Book cover Geographic Information Science (GIScience 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4197))

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Abstract

Complex systems theory and Cellular Automata (CA) are widely used in geospatial modeling. However, existing models have been limited by challenges such as handling of multiple datasets, parameter definition and the calibration procedures in the modeling process. Bayesian network (BN) formalisms provide an alternative method to address the drawbacks of these existing models. This study proposes a hybrid model that integrates BNs, CA and Geographic Information Systems (GIS) to model land use change. The transition rules of the CA model are generated from a graphical formalism where the key land use drivers are represented by nodes and the dependencies between them are expressed by conditional probabilities extracted from historical spatial datasets. The results indicate that the proposed model is able to realistically simulate and forecast spatio-temporal process of land use change. Further, it forms the basis for new synergies in CA model design that can lead to improved model outcomes.

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Author information

Authors and Affiliations

  1. Spatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada

    Verda Kocabas & Suzana Dragicevic

Authors
  1. Verda Kocabas
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  2. Suzana Dragicevic
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Editor information

Editors and Affiliations

  1. Department of Geography, University of California, Santa Barbara, 5713 Ellison Hall, 93106, Santa Barbara, CA, U.S.A.

    Martin Raubal

  2. University of Utah, 84112-9155, Salt Lake City, UT, USA

    Harvey J. Miller

  3. Vienna University of Technology, 1040, Vienna, Austria

    Andrew U. Frank

  4. University of California, 93106-4060, Santa Barbara, CA, USA

    Michael F. Goodchild

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© 2006 Springer-Verlag Berlin Heidelberg

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Kocabas, V., Dragicevic, S. (2006). Coupling Bayesian Networks with GIS-Based Cellular Automata for Modeling Land Use Change. In: Raubal, M., Miller, H.J., Frank, A.U., Goodchild, M.F. (eds) Geographic Information Science. GIScience 2006. Lecture Notes in Computer Science, vol 4197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11863939_15

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  • DOI: https://doi.org/10.1007/11863939_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44526-5

  • Online ISBN: 978-3-540-44528-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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