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ABMland - a Tool for Agent-Based Model Development on Urban Land Use Change

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Modelling urban land use change can foster understanding of underlying processes and is increasingly realized using agent-based models (ABM) as they allow for explicitly coding land management decisions. However, urban land use change is the result of interactions of a variety of individuals as well as organisations. Thus, simulation models on urban land use need to include a diversity of agent types which in turn leads to complex interactions and coding processes. This paper presents the new ABMland tool which can help in this process: It is software for developing agent-based models for urban land use change within a spatially explicit and joint environment. ABMland allows for implementing agent-based models and parallel model development while simplifying the coding process. Six major agent types are already included as coupled models: residents, planners, infrastructure providers, businesses, developers and lobbyists. Their interactions are pre-defined and ensure valid communication during the simulation. The software is implemented in Java building upon Repast Simphony and other libraries.

Suggested Citation

  • Nina Schwarz & Daniel Kahlenberg & Dagmar Haase & Ralf Seppelt, 2012. "ABMland - a Tool for Agent-Based Model Development on Urban Land Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-8.
    • Handle: RePEc:jas:jasssj:2011-45-2
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    1. Cynthia Nikolai & Gregory Madey, 2009. "Tools of the Trade: A Survey of Various Agent Based Modeling Platforms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(2), pages 1-2.
    2. Claudio Cioffi-Revilla, 2010. "A Methodology for Complex Social Simulations," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-7.
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    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. SeHoon Lee & Jeong Hee Hong & Jang Won Bae & Il-Chul Moon, 2015. "Impact of Population Relocation to City Commerce: Micro-Level Estimation with Validated Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-5.
    3. Xu QuanLi & Yang Kun & Wang GuiLin & Yang YuLian, 2015. "Agent-based modeling and simulations of land-use and land-cover change according to ant colony optimization: a case study of the Erhai Lake Basin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 95-118, January.
    4. Ying Long & Yongping Zhang, 2015. "Land-use pattern scenario analysis using planner agents," Environment and Planning B, , vol. 42(4), pages 615-637, July.

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