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

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  • Verda Kocabas
  • Suzana Dragicevic

Abstract

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. Copyright Springer-Verlag 2013

Suggested Citation

  • Verda Kocabas & Suzana Dragicevic, 2013. "Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model," Journal of Geographical Systems, Springer, vol. 15(4), pages 403-426, October.
    • Handle: RePEc:kap:jgeosy:v:15:y:2013:i:4:p:403-426
    DOI: 10.1007/s10109-012-0171-2
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    References listed on IDEAS

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    Cited by:

    1. Yajun Ma & Ping Zhang & Kaixu Zhao & Yong Zhou & Sidong Zhao, 2022. "A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China," Land, MDPI, vol. 11(6), pages 1-31, June.
    2. Gebrekidan, B.H., 2018. "Modeling Farmers Intensi cation Decisions with a Bayesian Belief Network: The case of the Kilombero Floodplain in Tanzania," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277081, International Association of Agricultural Economists.
    3. Michail Tsagris, 2022. "The FEDHC Bayesian Network Learning Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-28, July.
    4. Mohammad Vahidnia & Ali Alesheikh & Seyed Alavipanah, 2015. "A multi-agent architecture for geosimulation of moving agents," Journal of Geographical Systems, Springer, vol. 17(4), pages 353-390, October.
    5. Bernard Collins & Steven Doskey & James Moreland, 2017. "Modeling the Convergence of Collaborative Systems of Systems: A Quantitative Case Study," Systems Engineering, John Wiley & Sons, vol. 20(4), pages 357-378, July.

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    More about this item

    Keywords

    Agent-based models (ABMs); Bayesian networks (BNs); Cellular automata (CA); Geographic information systems (GIS); Land-use change; Population change; C11; C63; 021; R23;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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