Modelling urban expansion using a multi agentbased model in the city of Changsha

doi:10.1007/s11442-010-0540-z

Abstract

Abstract:

Although traditional urban expansion simulation models can simulate dynamic features, these models fail to address complex changes produced by different agents' behaviors. The paper has built up a set of spatial-temporal land resource allocation rules and developed a dynamic urban expansion model based on a multi-agent system, which can simulate the interaction among different agents, such as residents, peasants, and governments. This model is applied to simulate urban expansion process taking Changsha City, in China as a study area. The results show that this model can not only reflect basic characteristics of urban expansion, but also help explain the reasons for urban expansion process and understand the effect of agents' behavior on the expansion process, and provide insights into the causing factors behind the expansion. In addition, in contrast to simulation results with land use classification map from remote sensing images, the precision of the simulation reached over 68% with higher precision than cellular automata model according to the cell-by-cell comparison. The results suggest that the model can help to provide land use decision making support to government and urban planners.

Key words: multi-agent system, cellular automata, urban expansion, land resource allocation, Changsha

ZHANG Honghui, ZENG Yongnian, BIAN Ling, YU Xijun. Modelling urban expansion using a multi agentbased model in the city of Changsha[J].Journal of Geographical Sciences, 2010, 20(4): 540-556.

References

[1] Barredo J I, Kasanko M, McCormick M et al., 2003. Modelling dynamic spatial processes: Simulation of urban future scenarios through cellular automata. Landscape and Urban Planning, 64(3): 145–160.

[2] Batty M, Xie Y, 1994. From cells to cities. Environment and Planning B, 21(7): 531–548.

[3] Benenson I, Omer I, Hatna E, 2002. Entity-based modeling of urban residential dynamics: The case of Yaffo, Tel Aviv. Environment and Planning B, 29(4): 491–512.

[4] Boyce R R, Clark W A V, 1964. The concept of shape in geography. Geographical Review, 54: 561–572.

[5] Brown D G., Walker R, Manson S et al., 2004. Modeling land use and land cover change. In: Gutman G, Janetos A C,

[6] Justice C O et al. (eds.), Land Change Science: Observing, Monitoring and Understanding Trajectories of Change on the Earth’s Surface. New York: Springer, 395–409.

[7] Clarke K C, Hoppen S, Gaydos L, 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2): 247–261.

[8] Eastman J R, Kyem P A K, Toledano J et al., 1993. GIS and Decision Making. Worcester M A: Clark Labs for Cartographic Technology and Geographic Analysis.

[9] Ferrand N, 1996. Modeling and supporting multi-actor spatial planning using multi-agents systems. Proceedings of the Third NCGIA Conference on Integrating GIS and Environmental Modelling, Santa Fe, New Mexico, USA.

[10] Grimm N B, Grove J M, Pickett S T A et al., 2000. Integrated approaches to long-term studies of urban ecological systems. Bioscience, 50(7): 571–584.

[11] Herold M, Goldstein N C, Clarke K C, 2003. The spatiotemporal form of urban growth: Measurement, analysis and modeling. Remote Sensing of Environment, 86(3): 286–302.

[12] Howitt R E, 1995. Positive mathematical programming. American Journal of Agricultural Economics, 77 (2): 329–342.

[13] Li X, Liu X P, 2006. An extended cellular automaton using case-based reasoning for simulating urban development in a large complex region. International Journal of Geographical Information Science, 20(10): 1109–1136.

[14] 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.

[15] Li X, Yeh A G O, 2004. Data mining of cellular automata's transition rules. International Journal of Geographical Information Science, 18(8): 723–744.

[16] Ligtenberg A, Bregt A K, Lammeren R V, 2001. Multi-actor-based land use modeling: Spatial planning using agents. Landscape and Urban Planning, 56(1): 21–33.

[17] Lin G S L, Ho S P S, 2003. China’s land resources and land-use change: Insights from the 1996 land survey. Land Use Policy, 20(2): 87–107.

[18] Liu X P, Li X, Liu L et al., 2008. A bottom-up approach to discover transition rules of cellular automata using ant intelligence. International Journal of Geographical Information Science, 22(11): 1247–1269.

[19] Liu Xiaoping, Li Xia, Ai Bin et al., 2006. Multi-agent systems for simulating and planning land use development. Acta Geographica Sinica, 61(10): 1101–1112. (in Chinese)

[20] Liu J Y, Liu M L, Tian H Q et al., 2005. Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM data. Remote Sensing of Environment, 98(4): 442–456.

[21] McFadden D, 1974. Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed.), Frontiers in Econometrics. New York: Academic Press, 105–142.

[22] McFadden D, 1978. Modeling the choice of residential location in spatial interaction theory and planning models. In: Karlqvist A, Lundqvist L, Snickars F et al. (eds.), Spatial Interaction Theory and Planning Models. North Holland Amsterdam, 75–96.

[23] Mertens B, Lambin E F, 1997. Spatial modeling of deforestation in southern Cameroon. Applied Geography, 17 (2): 143–162.

[24] Monticino M, Miguel A, Baird C et al., 2007. Coupled human and natural systems: A multi-agent-based approach. Environmental Modelling & Software, 22(5): 656–663.

[25] Muller D, Zeller M, 2002. Land use dynamics in the central highlands of Vietnam: A spatial model combining village survey data with satellite imagery interpretation. Agricultural Economic, 27(3): 333–354.

[26] Otter H S, Veen A, Vriend H J, 2001. Abloom: Location behavior, spatial patterns, and agent-based modeling. Journal of Artificial Societies and Social Simulation, 4(4): 1–21.

[27] Paul M J, Meyer J L, 2001. Streams in the urban landscape. Annual Review of Ecology and Systematics, 32: 333–365.

[28] Quigley J M, 1976. Housing demand in the short run: analysis of polytomous choice. Exploration in Economic Research, 3(1): 76–102.

[29] Robinson D, Brown D G, Parker D et al., 2007. Comparison of empirical methods for building agent-based models in land use science. Journal of Land Use Science, 2(1): 31–55.

[30] Sklar F H, Costanza R, 1991. The Development of Dynamic Spatial Models for Landscape Ecology: A Review and Prognosis. New York: Springer-Verlag.

[31] Tao Haiyan, Li Xia, Chen Xiaoxiang et al., 2007. Method exploration of geographical spatial differentiation based on multi-agent: A case study of urban residential simulations. Acta Geographica Sinica, 62(6): 579–588. (in Chinese)

[32] Tietenberg T, 1992. Environmental and Nartural Resource Economics. New York: Harper Collins.

[33] Torrens P M, Benenson I, 2005. Geographic automata systems. International Journal of Geographical Information Science, 19(4): 385–412.

[34] Wu F, 2000. Modeling intrametropolitan location of foreign investment firms in a Chinese city. Urban Study, 37(13): 2441–2464.

[35] Yeh A G O, Li X, 1998. Sustainable land development model for rapid growth areas using GIS. International Journal of Geographical Information Science, 12(2): 169–189.