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

  • Verda Kocabas
  • Suzana Dragicevic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4197)


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.


Bayesian Network Geographic Information System Cellular Automaton Cellular Automaton Transition Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Verda Kocabas
    • 1
  • Suzana Dragicevic
    • 1
  1. 1.Spatial Analysis and Modeling Laboratory, Department of GeographySimon Fraser UniversityBurnabyCanada

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