Resources Utilization and Management

Scenario Analysis of Urban Residential Land Use Utility Based on Multi-agents’ Spatial Decision

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  • 1. a. College of Economics and Land Management, b. Research Center of Rural Sustainable Development, Huazhong Agriculture University, Wuhan 430070, China;
    2. State Key Laboratory of Information Engineering in Surveying, Mapping and RS, Wuhan University, Wuhan 430079, China

Received date: 2010-12-08

  Revised date: 2011-05-24

  Online published: 2011-11-20

Abstract

This paper builds a multi-agents model to study the impact of the agents’ spatial decisions to the urban residential space evolution form and land use efficiency in the process of urban extension which contains one environment agent and three micro intelligent land use agents: residents, property developers and urban government. By the method of PEAS, the model induces the interactions among the three intelligent agents on the basis of the recognitions of their behavior characteristics and concludes that urban residential lands are developed in a priority sequence which is decided by the three intelligent agents’ spatial decisions and interactions. First, residents will choose favorable residential locations according to the law of consumption utility maximization. Then, property developers will choose these locations which can bring the maximized benefits. Residents and property developers’spatial decisions represent market mechanism which indicates the self organization of urban residential space in a degree. Furthermore, urban government agents will adjust the residential land developing priority sequence based on the comprehensive consideration of the social and ecological land use utility. So by adjusting the land use and environmental protection policies of urban government agent the model sets three scenarios which respectively represent the compact, relaxed and controlled modes of urban residential space extension and gets the residential developing priority function in every scenario. By the function, the model can get the preview of the evolution of residential space in every set scenario and provide land use planning policy guidance for urban government in advance. Taking Wuchang and Hongshan districts in Wuhan as the experimental areas, the paper compares the land use structure and land use efficiency in the process of the residential space evolution from 1998 to 2008 among the three scenarios and the actual situation respectively. The comparison indicates that land new development holds a lager proportion in the evolution process of the experimental residential space from 1998 to 2008, that is to say the land redevelopment level of old urban in experimental districts is not enough. In fact the government of Wuhan city had focused on residential new development of the suburban fringe areas before 2004, but the emphasis has been transferred to the old city transformation and land redevelopment after 2004, the point verifies the validity of the model to a certain degree. Compared with the model’s simulating results under different scenarios, the factual residential space evolution always has intersections with the three simulating results respectively, which means urban government may adjust its land use policy, natural environmental protection policy and so on under the influence of macroscopic environment in different periods. This is just one of the characteristics of Chinese real estate market. Excepting this, from the model simulating results, urban residents have attached increasing importance to the rights and interests of themselves, at the same time because of the action of market economic mechanism, the property developer also paid more attention to the favorite choices of urban residents; and the urban government gave more attention to the public willingness and the growth of resident welfare as well.

Cite this article

SHAN Yu-hong, ZHU Xin-yan . Scenario Analysis of Urban Residential Land Use Utility Based on Multi-agents’ Spatial Decision[J]. JOURNAL OF NATURAL RESOURCES, 2011 , 26(11) : 1832 -1841 . DOI: 10.11849/zrzyxb.2011.11.002

References

[1] 方美琪,张树人.复杂系统建模与仿真[M].北京:中国人民大学出版社,2005. [2] Arend Ligtenberg, Arnold K Bregt, Ronvan Lammern. Multi-actor-based land use modelling: Spatial planning using agents [J]. Landscape and Planning, 2002,56:21-33. [3] Michael Monticino, Miguel Acevedo, Baird Callicott. Coupled human and natural systems: A multi-agent-based approach [J]. Environmental Modelling & Software,2008,22:656-663. [4] Bura S. Multi-agent systems and the dynamics of a settlement system [J]. Geographical Analysis,1996,28:77-87. [5] Francisco Mart’nez, John Roy. A model for residential supply [J]. Annals of Regional Science,2004,38:531-550. [6] Deadman P, Gimblett R. A role for goal-oriented autonomous agents in modelling people-environment interactions in forest recreation [J]. Mathematical and Computer Modelling,1994,20(8):121-133. [7] Jaylson J Silveiraa, Aquino L Esp’ndolab, Pennab T J P. Agent-based model to rural-urban migration analysis [J]. Physica A, 2006, 364 :445-456. [8] Otter H S, Veen A, Vriend H J. ABLOoM: Location behaviour, spatial patterns, and agent-based modeling [J]. Journal of Artificial Societies and Social Simulation,2001,4(4):1-21. [9] Raphal Mathevet, Francois Bousquet, Christophe Le Page. Agent-based simulations of interactions between duck population, farming decisions and leasing of hunting rights in the Camargue (Southern France) [J]. Ecological Modelling,2003,165:107-126. [10] Sengupta R, Bennet D. Agent-based modelling environment for spatial decision support [J]. International Journal of Geographical Information Science,2003,17(2):157-180. [11] Steven M Manson. Agent-based modelling and genetic programming for modelling land change in the Southern Yucatan Peninsular Region of Mexico [J]. Agriculture, Ecosystems and Environment,2005,111:47-62. [12] Arend Ligtenberga, Monica Wachowicza, Arnold K Bregta. A design and application of a multi-agent system for simulation of multi-actor spatial planning [J]. Journal of Environmental Management,2004,72:43-55. [13] Benenson I. Multi-agent simulations of residential dynamics in the city [J]. Computers Environment and Urban Systems,1998, 22(1):25-42. [14] 刘小平,黎夏,艾彬,等.基于多智能体的土地利用模拟与规划模型[J].地理学报,2006,61(10):1101-1112. [15] 刘小平,黎夏,叶嘉安.基于多智能体系统的空间决策行为及土地利用格局演变的模拟[J].中国科学D辑:地球科学,2006,36 (11):1027-1036. [16] Bah A, Toure I, Le Page C, et al. An agent-based model to understand the multiple uses of land and resources around drillings in Sahel [J]. Mathematical and Computer Modelling,2006,44:513-534. [17] Jean-Christophe Castellaa, Suan Pheng Kamb, Dang Dinh Quangc, et al. 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 [J]. Land Use Policy,2008,24:531-545. [18] Stuart Russell, Peter Norving.人工智能——一种现代方法[M].姜哲,等译.北京:人民邮电出版社,2004. [19] 邓卫,宋扬.住宅经济学[M].北京:清华大学出版社,2008. [20] 王家庭,张换兆.中国城市土地集约利用——理论分析与实证研究[M].天津:南开大学出版社,2008. [21] Robert Costanza, et al. The value of the world’s ecosystem services and natural capital [J]. Nature,1997,387:253-260.
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