A Multi-agent Model to Simulate Regional Land Use Change with an Application to the Poyang Lake Area of China

  • 1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    3 School of Computer, Taiyuan University of Science and Technology, Taiyuan 030024, China

Received date: 2012-04-16

  Revised date: 2012-05-21

  Online published: 2012-12-29

Supported by

Chinese R&D Program of “Development of a comprehensive monitoring and evaluation system for ecological compensation of typical ecologically vulnerable regions of China (2006BAC08B06)”, National Science Fund for Distinguished Young Scholars (40788001) and One Hundred Talents Program of the Chinese Academy of Sciences.


In many regions both urban expansion and rural development take place simultaneously, and for the purpose of understanding the dynamic process of land use/cover change (LUCC) in such large areas, this study develops a multi-agent based land use model. Taking the Poyang Lake area of China as a typical case, this study applies the mechanism of diffusion-limited aggregation to simulate the behavior of urban agents, while rural land use is illustrated with a bottom-up based model consisting of agent and environment layers. In the agent layer, each household agent makes its own decisions on land use, and at each time interval a government agent takes control of land use by implementing policies. According to incomes and the rate of migrant workers, household agents are divided into six categories, among which different decision rules are followed. For complex LUCC in the Poyang Lake area of China from 1985 to 2005, the artificial society model developed in this study yields results highly consistent with observations. Importantly, it is shown that governmental policies can impose significant effects on the decisions of individual household agents on land use and the multi-agent-based land use model developed in this study provides a robust means for assessing the effectiveness of governmental policies.

Cite this article

YAN Dan, HUANG Heqing, LIU Gaohuan, PAN Lihu, LIU Zhijia, . A Multi-agent Model to Simulate Regional Land Use Change with an Application to the Poyang Lake Area of China[J]. Journal of Resources and Ecology, 2012 , 3(4) : 349 -358 . DOI: 10.5814/j.issn.1674-764x.2012.04.008


Ai N S. 1993. Mandelbrot set and Hurst Phenomenon: The geographical revolution caused by fractal theory. Hefei: China Science and Technology University Press.

Anderson C J. 1986. Metropolis, Monte Carlo and the MANIAC. Los Alamos Science, 14: 96-108.

Balmann A. 1994. Farm-based modelling of a regional structural change: A cellular automata approach. European Review of Agricultural Economics, 24(1): 85–108.

Batty M, P A Longley. 1994. Fractal cities: A geometry of form and function. London: Academy Press.

Beltratti A, S Margarita, P Terna. 1996. Neural networks for economic and financial modeling. London, UK: International Thomson Computer Press.

Benenson I. 1998. Multi-agent simulation of residential dynamics in the city. Computers, Environment and Urban Systems, 22(1): 25-42.

Brown D G, R L Riolo, D Robinson, M North, W Rand. 2005. Spatial process and data models: Toward integration of agent-based models and GIS. Journal of Geographical Systems, 7: 1-23.

Brown D G, D T Robinson, Li A, et al. 2008. Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system. Geoforum, 39(2): 805-818.

Castella J C, S P Kam, Quang D D, et al. 2007. Combining top-down and, bottom-up modelling approaches of land use/cover change to support public policies: Application to sustainable management of natural resources in northern Vietnam. Land Use Policy, 24(3): 531-545.

Epstein J M, R Axtell. 1996. Growing artificial societies - social science from the bottom up. Cambridge, MA: MIT Press.

Evans T P, H Keley. 2004. Multi-scale analyses of a household level agent-based model of land cover change. Journal of Environmental Management, 72(1-2): 57 -72.

Fernandez L E, D G Brown, R W Marans, et al. 2005. Characterizing location preferences in an exurban population: Implications for agent-based modeling. Environment and Planning B-Planning & Design, 32(6): 799-820.

Frankhauser P. 1994. La Fractalite des Structures Urbanines. Paris: Economica.

GLP. 2005. GLP science plan and implementation strategy, IGBP Report No.53/IHDP Report No.19. IGBP Secretariat.

He C Y, O Norio, Zhang Q F, et al. 2008. Modeling dynamic urban expansion processes incorporating a potential model with cellular automata. Landscape and Urban Planning, 86(1): 79-91.

Holland J. 1995. Hidden order: How adaptation builds complexity. New York: Addsion- Wesley.

Huang H Q, Pan L H, Wang Q, Zhen L. 2010. An artificial society model of land use change in terms of households’ behaviors: Model development and application. Journal of Natural Resources, 25(3): 353-367. (in Chinese)

Huang H Q, W MacMillan. 2004. A generative bottom-up approach to the understanding of the development of rural societies. Agrifood Research Report, 68: 296-312.

Hurkmans R T W L, H de Moel, J C J H Aerts, P A Troch. 2008. Water balance versus land surface model in the simulation of Rhine River discharges. Water Resources Research, 44(1).

Li X, A G O Yeh. 2001. Integration of principal components analysis and cellular automata for spatial decision making and urban simulation. Science in China (Series D), 31(8): 683-690.

Liu X P, Li X, A G O Yeh. 2006. The spatial decision behaviors based on agent-based modeling and land use structure change simulation. Science in China (Series D), 36(11): 1027-1036.

MacMillan W, Huang H Q. 2008. An agent-based simulation model of a primitive agricultural society. Geoforum, 39:643-658.

Manson S M. 2005. Agent-based modeling and genetic programming for modeling land change in the Southern Yucatan Peninsular Region of Mexico. Agriculture Ecosystems & Environment, 111: 47-62.

Pan L H, Huang H Q, Jiang L G, Zhen L. 2010. A case study of the effects of wetland restoration policy with an artificial society model. Journal of Natural Resources, 25(12): 2007-2017. (in Chinese)

Parker D C, S M Manson, M A Janssen, 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.

Sanders L, D Pumain, H Mathian, et al. 1997. SIMPOP: A multiagent system for the study of urbanism. Environment and Planning B-Planning & Design, 24(2): 287-305.

Sasaki Y, P Box. 2003. Agent-based verification of von Thunen’s location theory. Journal of Artificial Societies and Social Simulation, 6(2).

Schelling T C. 1971. Dynamic models of segregation. Journal of Mathematical Sociology, 1: 143-186.

Sullivan D. 2000. Exploring the structure of space: Towards geo-computational theory. Working Paper Series, Centre for Advanced Spatial Analysis, London.

Tobler W. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2): 234-240.

Turner B L, R H Moss, D L Skole. 1993. Relating land use and global land-cover change: A proposal for an IGBP-HDP core project. HDP Report Number 5, International Geosphere-Biosphere Programme, Stockholm, Sweden.

Wang J J, Wu Z Q. 2009. Delimiting the stages of urbanization growth process: A method based on Northam’s theory and logistic growth model. Acta Geographica Sinica, 64(2): 177-188. (in Chinese)

White R, G Engelen. 1993. Cellular automata and fractal urban form: A cellular modeling approach to the evolution of urban land-use patterns. Environment and Planning A, 25(8): 1175-1199.

Windrum P, G Fagiolo, A Moneta. 2007. Empirical validation of agent-based models: Alternatives and prospects. Journal of Artificial Societies and Social Simulation, 10(2).

Witten T C, L M Sander. 1981. Diffusion-limited aggregation: A kinetic critical phenomenon. Physical Review Letters, 47: 1400-1403.

Xue L, Yang K Z. 2003. Research on urban evolution using agent-based simulation. Systems Engineering - Theory and Practice, 12: 1-17.

Zhen L, Cao S Y, Wei Y J, et al. 2009. Comparison of sustainability issues in two sensitive areas of China. Environmental Science & Policy, 12(8): 1153-1167.