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A Review of Urban Residential Choice Models Using Agent-Based Modeling

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  • Qingxu Huang

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 19 Xinjiekouwai Street, Beijng, China 100875)

  • Dawn C Parker

    (Faculty of Environment, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L3G1, Canada)

  • Tatiana Filatova

    (Centre for Studies of Technology and Sustainable Development, Faculty of Management and Governance (MB), University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands)

  • Shipeng Sun

    (Institute on the Environment, University of Minnesota, Twin Cities, 325 Learning and Environmental Sciences, 1954 Buford Avenue, St. Paul, MN 55108, USA)

Abstract

Urban land-use modeling methods have experienced substantial improvements in the last several decades. With the advancement of urban land-use change theories and modeling techniques, a considerable number of models have been developed. The relatively young approach, agent-based modeling, provides urban land-use models with some new features and can help address the challenges faced by traditional models. Applications of agent-based models to study urban dynamics have increased steadily over the last twenty years. To offer a retrospective on the developments in agent-based models (ABMs) of urban residential choices, we review fifty-one relevant models that fall into three general categories: (i) urban land-use models based on classical theories; (ii) different stages of the urbanization process; and (iii) integrated agent-based and microsimulation models. We summarize and compare the main features of these fifty-one models within each category. This review focuses on three fundamental new features introduced byABMs. The first is agent heterogeneity with particular attention to the method of introducing heterogeneity in agents' attributes and behaviors. The second is the representation of land-market processes, namely preferences, resources constraints, competitive bidding, and endogenous relocation. The third is the method of measuring the extensive model outputs. In addition, we outline accompanying challenges to, and open questions for, incorporating these new features. We conclude that, by modeling agent heterogeneity and land markets, and by exploiting a much broader dimension of output, we will enhance our understanding of urban land-use change and are hopefully able to improve model fitness and robustness.

Suggested Citation

  • Qingxu Huang & Dawn C Parker & Tatiana Filatova & Shipeng Sun, 2014. "A Review of Urban Residential Choice Models Using Agent-Based Modeling," Environment and Planning B, , vol. 41(4), pages 661-689, August.
  • Handle: RePEc:sae:envirb:v:41:y:2014:i:4:p:661-689
    DOI: 10.1068/b120043p
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    References listed on IDEAS

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    3. Frederik Priem & Philip Stessens & Frank Canters, 2020. "Microsimulation of Residential Activity for Alternative Urban Development Scenarios: A Case Study on Brussels and Flemish Brabant," Sustainability, MDPI, vol. 12(6), pages 1-28, March.
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    6. Bernardo Alves Furtado, 2022. "PolicySpace2: Modeling Markets and Endogenous Public Policies," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 25(1), pages 1-8.
    7. Heng Liu & Lu Zhou & Diwei Tang, 2022. "Urban Expansion Simulation Coupled with Residential Location Selection and Land Acquisition Bargaining: A Case Study of Wuhan Urban Development Zone, Central China’s Hubei Province," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    8. Nayef Alghais & David Pullar & Elin Charles-Edwards, 2018. "Accounting for peoples’ preferences in establishing new cities: A spatial model of population migration in Kuwait," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-31, December.
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