Journal of Geographical Systems

, Volume 15, Issue 4, pp 403–426 | Cite as

Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model

  • Verda Kocabas
  • Suzana DragicevicEmail author
Original Article


Land-use change models grounded in complexity theory such as agent-based models (ABMs) are increasingly being used to examine evolving urban systems. The objective of this study is to develop a spatial model that simulates land-use change under the influence of human land-use choice behavior. This is achieved by integrating the key physical and social drivers of land-use change using Bayesian networks (BNs) coupled with agent-based modeling. The BNAS model, integrated Bayesian network–based agent system, presented in this study uses geographic information systems, ABMs, BNs, and influence diagram principles to model population change on an irregular spatial structure. The model is parameterized with historical data and then used to simulate 20 years of future population and land-use change for the City of Surrey, British Columbia, Canada. The simulation results identify feasible new urban areas for development around the main transportation corridors. The obtained new development areas and the projected population trajectories with the“what-if” scenario capabilities can provide insights into urban planners for better and more informed land-use policy or decision-making processes.


Agent-based models (ABMs) Bayesian networks (BNs) Cellular automata (CA) Geographic information systems (GIS) Land-use change Population change 

JEL Classification

C11 C63 021 R23 



The authors thank the Natural Sciences and Engineering Research Council (NSERC) and Social Sciences and Humanities Research Council (SSHRC) of Canada for financial support of this study. The Metro Vancouver and the Greater Vancouver Transportation Authority (Translink) provided some of the spatial data. The MATLAB software was made available by the Network Support Group, Faculty of Applied Sciences and Centre for Systems Science at Simon Fraser University. The authors are thankful to the journal Editor and the two anonymous reviewers for their valuable comments.


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Spatial Analysis and Modeling Laboratory, Department of GeographySimon Fraser UniversityBurnabyCanada

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