Skip to main content
SpringerLink
Log in

Agent-based model of land system: Theory, application and modelling framework

  • Published:
Journal of Geographical Sciences Aims and scope Submit manuscript

Abstract

Land change science has become an interdisciplinary research direction for understanding human-natural coupling systems. As a process-oriented modelling approach, agent based model (ABM) plays an important role in revealing the driving forces of land change and understanding the process of land change. This paper starts from three aspects: The theory, application and modeling framework of ABM. First, we summarize the theoretical basis of ABM and introduce some related concepts. Then we expound the application and development of ABM in both urban land systems and agricultural land systems, and further introduce the case study of a model on Grain for Green Program in Hengduan Mountainous region, China. On the basis of combing the ABM modeling protocol, we propose the land system ABM modeling framework and process from the perspective of agents. In terms of urban land use, ABM research initially focused on the study of urban expansion based on landscape, then expanded to issues like urban residential separation, planning and zoning, ecological functions, etc. In terms of agricultural land use, ABM application presents more diverse and individualized features. Research topics include farmers’ behavior, farmers’ decision-making, planting systems, agricultural policy, etc. Compared to traditional models, ABM is more complex and difficult to generalize beyond specific context since it relies on local knowledge and data. However, due to its unique bottom-up model structure, ABM has an indispensable role in exploring the driving forces of land change and also the impact of human behavior on the environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Allan R J, 2010. Survey of Agent Based Modelling and Simulation Tools. Science & Technology Facilities Council.

  • An L, 2012. Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecological Modelling, 229: 25–36.

    Article  Google Scholar 

  • An L, Linderman M, Qi J et al., 2005. Exploring complexity in a human-environment system: An agent-based spatial model for multidisciplinary and multiscale integration. Annals of the Association of American Geographers, 95(1): 54–79.

    Article  Google Scholar 

  • An L, Zvoleff A, Liu J G et al., 2014. Agent-based modeling in coupled human and natural systems (CHANS): Lessons from a comparative analysis. Annals of the Association of American Geographers, 104(4): 723–745.

    Article  Google Scholar 

  • Ascensao F, Clevenger A, Santos-Reis M et al., 2013. Wildlife-vehicle collision mitigation: Is partial fencing the answer? An agent-based model approach. Ecological Modelling, 257: 36–43.

    Article  Google Scholar 

  • Bakker M M, Alam S J, van Dijk J et al., 2015. Land-use change arising from rural land exchange: An agent-based simulation model. Landscape Ecology, 30(2): 273–286.

    Article  Google Scholar 

  • Batty M, 2001a. Agent-based pedestrian modeling: Editorial. Environment and Planning B-Planning & Design, 28(3): 321–326.

    Article  Google Scholar 

  • Batty M, 2001b. Polynucleated urban landscapes. Urban Studies, 38(4): 635–655.

    Article  Google Scholar 

  • Batty M, Desyllas J, Duxbury E, 2003. The discrete dynamics of small-scale spatial events: Agent-based models of mobility in carnivals and street parades. International Journal of Geographical Information Science, 17(7): 673–697.

    Article  Google Scholar 

  • Benenson I, Torrens P M, 2004. Geosimulation: Automata-Based Modeling of Urban Phenomena. John Wiley & Sons.

  • Bert F E, Podesta G P, Rovere S L et al., 2011. An agent based model to simulate structural and land use changes in agricultural systems of the argentine pampas. Ecological Modelling, 222(19): 3486–3499.

    Article  Google Scholar 

  • Bone C, Dragicevic S, 2010. Simulation and validation of a reinforcement learning agent-based model for multi-stakeholder forest management. Computers Environment and Urban Systems, 34(2): 162–174.

    Article  Google Scholar 

  • Brady M, Kellermann K, Sahrbacher C et al., 2009. Impacts of decoupled agricultural support on farm structure, biodiversity and landscape mosaic: Some EU results. Journal of Agricultural Economics, 60(3): 563–585.

    Article  Google Scholar 

  • Bravo M, Briceno L, Cominetti R et al., 2010. An integrated behavioral model of the land-use and transport systems with network congestion and location externalities. Transportation Research Part B-Methodological, 44(4): 584–596.

    Article  Google Scholar 

  • Brown D G, Robinson D T, 2006. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and Society, 11(1): 22.

    Article  Google Scholar 

  • Brown D G, Verburg P H, Pontius R G et al., 2013. Opportunities to improve impact, integration, and evaluation of land change models. Current Opinion in Environmental Sustainability, 5(5): 452–457.

    Article  Google Scholar 

  • Cabral R B, Geronimo R C, Lim M T et al., 2010. Effect of variable fishing strategy on fisheries under changing effort and pressure: An agent-based model application. Ecological Modelling, 221(2): 362–369.

    Article  Google Scholar 

  • Castella J C, Trung T N, Boissau S, 2005. Participatory simulation of land-use changes in the northern mountains of Vietnam: The combined use of an agent-based model, a role-playing game, and a geographic information system. Ecology and Society, 10(1): 32.

    Article  Google Scholar 

  • Castella J C, Verburg P H, 2007. Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam. Ecological Modelling, 202(3/4): 410–420.

    Article  Google Scholar 

  • Chen X D, Lupi F, An L et al., 2012. Agent-based modeling of the effects of social norms on enrollment in payments for ecosystem services. Ecological Modelling, 229: 16–24.

    Article  Google Scholar 

  • Chen X D, Vina A, Shortridge A et al., 2014. Assessing the effectiveness of payments for ecosystem services: An agent-based modeling approach. Ecology and Society, 19(1): 15.

    Article  Google Scholar 

  • Dahal K R, Chow T E, 2014. An agent-integrated irregular automata model of urban land-use dynamics. International Journal of Geographical Information Science, 28(11): 2281–2303.

    Article  Google Scholar 

  • Dai E F, Ma L, 2018. Review on land change modeling approaches. Progress in Geography, 37(1): 152–162. (in Chinese)

    Article  Google Scholar 

  • Deadman P, Robinson D, Moran E et al., 2004. Colonist household decisionmaking and land-use change in the Amazon rainforest: An agent-based simulation. Environment and Planning B-Planning & Design, 31(5): 693–709.

    Article  Google Scholar 

  • Dion E, VanSchalkwyk L, Lambin E F, 2011. The landscape epidemiology of foot-and-mouth disease in South Africa: A spatially explicit multi-agent simulation. Ecological Modelling, 222(13): 2059–2072.

    Article  Google Scholar 

  • Farmer J D, Foley D, 2009. The economy needs agent-based modelling. Nature, 460(7256): 685–686.

    Article  Google Scholar 

  • Filatova T, Parker D, van der Veen A, 2009a. Agent-based urban land markets: Agent’s pricing behavior, land prices and urban land use change. JASSS: The Journal of Artificial Societies and Social Simulation, 12(1): 29.

    Google Scholar 

  • Filatova T, van der Veen A, Parker D C, 2009b. Land market interactions between heterogeneous agents in a heterogeneous landscape-tracing the macro-scale effects of individual trade-offs between environmental amenities and disamenities. Canadian Journal of Agricultural Economics: Revue Canadienne D Agroeconomie, 57(4): 431–457.

    Article  Google Scholar 

  • Filatova T, Verburg P H, Parker D C et al., 2013. Spatial agent-based models for socio-ecological systems: Challenges and prospects. Environmental Modelling & Software, 45: 1–7.

    Article  Google Scholar 

  • Green D G, Sadedin S, 2005. Interactions matter: Complexity in landscapes and ecosystems. Ecological Complexity, 2(2): 117–130.

    Article  Google Scholar 

  • Grimm V, 1999. Ten years of individual-based modelling in ecology: What have we learned and what could we learn in the future? Ecological Modelling, 115(2/3): 129–148.

    Article  Google Scholar 

  • Grimm V, Berger U, Bastiansen F et al., 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198(1/2): 115–126.

    Article  Google Scholar 

  • Grimm V, Revilla E, Berger U et al., 2005. Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310(5750): 987.

    Article  Google Scholar 

  • Haase D, Haase A, Kabisch N et al., 2012. Actors and factors in land-use simulation: The challenge of urban shrinkage. Environmental Modelling & Software, 35: 92–103.

    Article  Google Scholar 

  • Haase D, Lautenbach S, Seppelt R, 2010. Modeling and simulating residential mobility in a shrinking city using an agent-based approach. Environmental Modelling & Software, 25(10): 1225–1240.

    Article  Google Scholar 

  • Huang Q X, Parker D C, Sun S P et al., 2013. Effects of agent heterogeneity in the presence of a land-market: A systematic test in an agent-based laboratory. Computers Environment and Urban Systems, 41: 188–203.

    Article  Google Scholar 

  • Janssen M A, Alessa L N I, Barton M et al., 2008. Towards a community framework for agent-based modelling. Journal of Artificial Societies and Social Simulation, 11(2): 6.

    Google Scholar 

  • Johnson P A, Sieber R E, 2011. Negotiating constraints to the adoption of agent-based modeling in tourism planning. Environment and Planning B-Planning & Design, 38(2): 307–321.

    Article  Google Scholar 

  • Johnston K M, 2013. Agent-based Modeling in ArcGIS. California: Esri Press.

    Google Scholar 

  • Kocabas V, Dragicevic S, 2009. Agent-based model validation using Bayesian networks and vector spatial data. Environment and Planning B-Planning & Design, 36(5): 787–801.

    Article  Google Scholar 

  • Laatabi A, Marilleau N, Nguyen-Huu T et al., 2018. ODD+ 2D: An ODD based protocol for mapping data to empirical ABMs. Journal of Artificial Societies and Social Simulation, 21(2): 1–9.

    Article  Google Scholar 

  • Lambin E F, Geist H J, 2006. Land-use and land-cover change: Local processes and global impacts. Springer Science & Business Media.

  • Le Page C, Bazile D, Becu N et al., 2013. Agent-based modelling and simulation applied to environmental management. In: Edmonds B, Meyer R (eds.). Simulating Social Complexity: A Handbook. Springer Berlin Heidelberg, Berlin, Heidelberg, 499–540.

    Chapter  Google Scholar 

  • Le Q B, Park S J, Vlek P L G et al., 2008. Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system: I. Structure and theoretical specification. Ecological Informatics, 3(2): 135–153.

    Article  Google Scholar 

  • Li S Y, Li X, Liu X P et al., 2013. Simulation of spatial population dynamics based on labor economics and multi-agent systems: A case study on a rapidly developing manufacturing metropolis. International Journal of Geographical Information Science, 27(12): 2410–2435.

    Article  Google Scholar 

  • Li S Y, Liu X P, Li X et al., 2017. Simulation model of land use dynamics and application: Progress and prospects. Journal of Remote Sensing, 21(3): 329–340. (in Chinese)

    Google Scholar 

  • Li X, Liu X P, 2007. Defining agents’ behaviors to simulate complex residential development using multicriteria evaluation. Journal of Environmental Management, 85(4): 1063–1075.

    Article  Google Scholar 

  • Li X, Liu X P, 2008. Embedding sustainable development strategies in agent-based models for use as a planning tool. International Journal of Geographical Information Science, 22(1): 21–45.

    Article  Google Scholar 

  • Little L R, Punt A E, Mapstone B D et al., 2009. An agent-based model for simulating trading of multi-species fisheries quota. Ecological Modelling, 220(23): 3404–3412.

    Article  Google Scholar 

  • Liu X P, Li X, Yeh A G O, 2006. Multi-agent systems for simulating spatial decision behaviors and land-use dynamics. Science in China Series D-Earth Sciences, 49(11): 1184–1194.

    Article  Google Scholar 

  • Müller D, Munroe D K, 2014. Current and future challenges in land-use science. Journal of Land Use Science, 9(2): 133–142.

    Article  Google Scholar 

  • Macal C M, North M J, 2010. Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3): 151–162.

    Article  Google Scholar 

  • Magliocca N, Safirova E, McConnell V et al., 2011. An economic agent-based model of coupled housing and land markets (CHALMS). Computers, Environment and Urban Systems, 35(3): 183–191.

    Article  Google Scholar 

  • Mao X Y, Meng J J, Wang Q, 2014. Modeling the effects of tourism and land regulation on land-use change in tourist regions: A case study of the Lijiang River Basin in Guilin, China. Land Use Policy, 41: 368–377.

    Article  Google Scholar 

  • Muller B, Bohn F, Dressler G et al., 2013. Describing human decisions in agent-based models–ODD plus D, an extension of the ODD protocol. Environmental Modelling & Software, 48: 37–48.

    Article  Google Scholar 

  • Murray-Rust D, Robinson D T, Guillem E et al., 2014. An open framework for agent based modelling of agricultural land use change. Environmental Modelling & Software, 61: 19–38.

    Article  Google Scholar 

  • National Research Council (NRC), 2014. Advancing Land Change Modeling: Opportunities and Research Requirements. National Academies Press.

  • North M J, Macal C M, 2007. Managing Business Complexity: Discovering Strategic Solutions with Agent-based Modeling and Simulation. Oxford: Oxford University Press.

    Book  Google Scholar 

  • O’Sullivan D, Evans T, Manson S et al., 2016. Strategic directions for agent-based modeling: Avoiding the YAAWN syndrome. Journal of Land Use Science, 11(2): 177–187.

    Article  Google Scholar 

  • Parker D C, Brown D G, Polhill J G et al., 2008. Illustrating a new conceptual design pattern for agent-based models of land use via five case studies: The MR POTATOHEAD framework. In: Paredes A L, Iglesias C H (eds). Agent-Based Modelling in Natural Resource Management. Universidad de Valladolid, Valladolid, Spain, 23–51.

    Google Scholar 

  • Parker D C, Filatova T, 2008. A conceptual design for a bilateral agent-based land market with heterogeneous economic agents. Computers Environment and Urban Systems, 32(6): 454–463.

    Article  Google Scholar 

  • Parker D C, Manson S M, Janssen M A et al., 2003. Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2): 314–337.

    Article  Google Scholar 

  • Perez L, Dragicevic S, 2010. Modeling mountain pine beetle infestation with an agent-based approach at two spatial scales. Environmental Modelling & Software, 25(2): 223–236.

    Article  Google Scholar 

  • Robinson D T, Sun S P, Hutchins M et al., 2013. Effects of land markets and land management on ecosystem function: A framework for modelling exurban land-change. Environmental Modelling & Software, 45: 129–140.

    Article  Google Scholar 

  • Rounsevell M, Arneth A, Brown D et al., 2012. Incorporating human behaviour and decision making processes in land use and climate system models. GLP Report.

  • Rounsevell M D A, Arneth A, Alexander P et al., 2014. Towards decision-based global land use models for improved understanding of the Earth system. Earth System Dynamics, 5(1): 117–137.

    Article  Google Scholar 

  • Schluter M, Pahl-Wostl C, 2007. Mechanisms of resilience in common-pool resource management systems: An agent-based model of water use in a river basin. Ecology and Society, 12(2): 23.

    Article  Google Scholar 

  • Schmit C, Rounsevell M D A, 2006. Are agricultural land use patterns influenced by farmer imitation? Agriculture Ecosystems & Environment, 115(1–4): 113–127.

    Article  Google Scholar 

  • Schreinemachers P, Berger T, 2011. An agent-based simulation model of human-environment interactions in agricultural systems. Environmental Modelling & Software, 26(7): 845–859.

    Article  Google Scholar 

  • Schulze J, Müller B, Groeneveld J et al., 2017. Agent-based modelling of social-ecological systems: Achievements, challenges, and a way forward. Journal of Artificial Societies and Social Simulation, 20(2).

    Google Scholar 

  • Shin J K, Fossett M, 2008. Residential segregation by hill-climbing agents on the potential landscape. Advances in Complex Systems, 11(6): 875–899.

    Article  Google Scholar 

  • Sun S P, Parker D C, Huang Q X et al., 2014. Market impacts on land-use change: An agent-based experiment. Annals of the Association of American Geographers, 104(3): 460–484.

    Article  Google Scholar 

  • Sun Z L, Muller D, 2013. A framework for modeling payments for ecosystem services with agent-based models, Bayesian belief networks and opinion dynamics models. Environmental Modelling & Software, 45: 15–28.

    Article  Google Scholar 

  • Tan R H, Liu Y L, Zhou K H et al., 2015. A game-theory based agent-cellular model for use in urban growth simulation: A case study of the rapidly urbanizing Wuhan area of central China. Computers Environment and Urban Systems, 49: 15–29.

    Article  Google Scholar 

  • Tian G J, Qiao Z, 2014. Modeling urban expansion policy scenarios using an agent-based approach for Guangzhou Metropolitan Region of China. Ecology and Society, 19(3): 14.

    Article  Google Scholar 

  • Topping C J, Hoye T T, Olesen C R, 2010. Opening the black box: Development, testing and documentation of a mechanistically rich agent-based model. Ecological Modelling, 221(2): 245–255.

    Article  Google Scholar 

  • Turner B L, II, Lambin E F, Reenberg A, 2007. The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences of the United States of America, 104(52): 20666–20671.

    Article  Google Scholar 

  • Valbuena D, Bregt A K, McAlpine C et al., 2010a. An agent-based approach to explore the effect of voluntary mechanisms on land use change: A case in rural Queensland, Australia. Journal of Environmental Management, 91(12): 2615–2625.

    Article  Google Scholar 

  • Valbuena D, Verburg P, Veldkamp A et al., 2010b. Effects of farmers’ decisions on the landscape structure of a Dutch rural region: An agent-based approach. Landscape and Urban Planning, 97(2): 98–110.

    Article  Google Scholar 

  • Valbuena D, Verburg P H, Bregt A K et al., 2010c. An agent-based approach to model land-use change at a regional scale. Landscape Ecology, 25(2): 185–199.

    Article  Google Scholar 

  • van Vliet J, Bregt A K, Brown D G et al., 2016. A review of current calibration and validation practices in land-change modeling. Environmental Modelling & Software, 82: 174–182.

    Article  Google Scholar 

  • Verburg P H, Alexander P, Evans T et al., 2019. Beyond land cover change: Towards a new generation of land use models. Current Opinion in Environmental Sustainability, 38: 77–85.

    Article  Google Scholar 

  • Verburg P H, Crossman N, Ellis E C et al., 2015. Land system science and sustainable development of the earth system: A global land project perspective. Anthropocene, 12: 29–41.

    Article  Google Scholar 

  • Verburg P H, Erb K-H, Mertz O et al., 2013. Land system science: Between global challenges and local realities. Current Opinion in Environmental Sustainability, 5(5): 433–437.

    Article  Google Scholar 

  • Verburg P H, Schot P P, Dijst M J et al., 2004. Land use change modelling: Current practice and research priorities. GeoJournal, 61(4): 309–324.

    Article  Google Scholar 

  • Villamor G B, Noordwijk M, 2016. Gender specific land-use decisions and implications for ecosystem services in semi-matrilineal Sumatra. Global Environmental Change-Human and Policy Dimensions, 39: 69–80.

    Article  Google Scholar 

  • Wahyudi A, Liu Y, Corcoran J, 2019. Generating different urban land configurations based on heterogeneous decisions of private land developers: An agent-based approach in a developing country context. ISPRS International Journal of Geo-Information, 8(5): 229.

    Article  Google Scholar 

  • Wolfram S, 2002. A New Kind of Science. Wolfram Media Champaign.

  • Yang W S, 2019. Construction and application of an agent-based regional land use change model (APUS): A case study in Tongdu Town, Kunming [D]. Guangzhou: Sun Yat-sen University. (in Chinese)

    Google Scholar 

  • Yu Q Y, Wu W B, Tang H J et al., 2011. Complex system theory and agent-based modeling: Progresses in land change science. Acta Geographica Sinica, 66(11): 1518–1530. (in Chinese)

    Google Scholar 

  • Zhang H H, Zeng Y N, Bian L, 2010. Simulating multi-objective spatial optimization allocation of land use based on the integration of multi-agent system and genetic algorithm. International Journal of Environmental Research, 4(4): 765–776.

    Google Scholar 

  • Zhou C H, Ou Y, Ma T et al., 2009. Theoretical perspectives of CA-based geographical system modeling. Progress in Geography, 28(6): 833–838. (in Chinese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China

    Erfu Dai, Liang Ma, Yahui Wang & Miao Tong

  2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China

    Weishi Yang & Le Yin

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Erfu Dai, Liang Ma, Yahui Wang, Le Yin & Miao Tong

  4. School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, China

    Weishi Yang

Authors
  1. Erfu Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weishi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yahui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Le Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Miao Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erfu Dai.

Additional information

Foundation

National Natural Science Foundation of China, No.41571098, No.41530749; National Key R&D Program of China, No.2017YFC1502903, No.2018YFC1508805

Author

Dai Erfu (1972-), PhD and Professor, specialized in comprehensive study of physical geography, simulation of LUCC, and climate change.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, E., Ma, L., Yang, W. et al. Agent-based model of land system: Theory, application and modelling framework. J. Geogr. Sci. 30, 1555–1570 (2020). https://doi.org/10.1007/s11442-020-1799-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11442-020-1799-3

Keywords

Search

Navigation