借助GIS 技术，本文构建了包含微观智能主体和环境主体的城市居住空间演化的多主体模型，旨在探讨城市居住空间增长的过程中市场机制和计划机制之间的相互作用和消长规律。模型分析总结了城市居住空间增长过程中城市居民、住宅开发商和城市政府的目标函数，认为城市空间扩张形态和土地集约利用水平主要取决于土地市场发育状态的不同阶段及城市政府的土地利用决策。通过调整城市政府主体的土地利用和环境政策设定了城市居住空间扩张的紧凑型、松散型和适度型3 种情景类型，以ArcGIS和VC为运行环境实现情景模型，为城市政府的土地利用规划政策提供事前指导。以武汉市的洪山和武昌两区为实验区，分别模拟了基于3 种规定情景下1998-2008 年期间的居住空间演化情况，并与实验区居住空间的实际演化情况进行了形态、土地利用结构变化和土地利用社会效用的概略对比分析，结论为实验区1998-2008 年的居住空间扩张基本属于适度型，但尚需要加强城市的内城市化。
Multi-agents model (MAS) is an effective tool for studying and simulating complex social and economic systems. MAS model itself does not have complicated modeling steps, but gives a modeling ideas and mechanisms of "from micro to macro and from bottom to up". In China, market mechanism and planning mechanisms are the major driving and regulation forces of urban residential space evolution. This paper builds an urban residential space expansion model based on GIS and MAS that contains micro intelligent agents and environment agent, aiming to investigate the interactions between the market mechanism and planning mechanism in the process of urban residential space transformation. On the basis of the cognition of the behavior characteristics of the market mechanism agents of urban residents and property developers, the model analyzes the impact of the two market mechanism agents on the evolution direction of urban residential space, and the paper points out that under the policies of state-owned urban land in China, the urban government's land supply decides the urban residential evolution patterns and the total benefits of residential land development. Thus, by adjusting the land use and environment protection policies of urban government, the model sets three policy scenarioes and achieves the preview of the evolution of residential space for each scenario, which can provide guidance for land use planning in advance. Wuchang and Hongshan districts in Wuhan city are chosen as the experimental areas. By the MAS model the paper compares the land use structure and land use benefits in the process of the residential space evolution from 1998 to 2008 among the three scenarioes and the actual situation respectively. Some main conclusions can be drawn as follows from the model’s outputs. Firstly, there are always intersections between the real residential space evolution and the model’s simulated results under different scenarioes, which means that because of the influence of macroscopic environment, urban government may adjust its land use policy, natural environment protection policy and so on in different periods. this is just one of the characteristics of Chinese real estate market. Secondly, urban residents’residential favor can affect the spatial form and the speed of urban residential space’s growth. The third is that compared with land redevelopment of the old urban area, newly developed land in inner suburban districts has a lager proportion in the evolution process of the residential space from 1998 to 2008 in the two experimental districts. In fact, the government of Wuhan city had focused on the development of new residential land in the suburban fringe areas before 2004, but the emphasis has been transferred to the old city transformation and land redevelopment after 2004.
[1] McDonald A D, Little L R, Gray R, et al. An agent-based modeling approach to evaluation of multi-ple-use management strategies for coastal marine ecosystems. Mathematics and Computers in Simulation, 2008,78(2-3): 401-411.
[2] Mathevet R, Bousquet F, Le Page C, et al. Agent-based simulations of interactions between duck population, farming decisions and leasing of hunting rights in the Camargue (Southern France). Ecological Modelling, 2003, 165(2-3): 107-126.
[3] Deadman P, Gimblett R. A role for goal-oriented autonomous agents in modeling people-environment interactions in forest recreation. Mathematical and Computer Modelling, 1994, 20(8): 121-133.
[4] Monticino M, Acevedo M, Callicott B, et al. Coupled human and natural systems: A multi-agent-based approach .Environmental Modelling & Software, 2008, 22(5): 656-663.
[5] Manson S M. Agent-based modeling and genetic programming for modeling land change in the Southern Yucata′n Peninsular Region of Mexico. Agriculture, Ecosystems and Environment, 2005, 111(1-4): 47–62.
[6] Ligtenberg A, Bregt A K, Lammern van R. Multi-actor- based land use modeling: Spatial planning using agents. Landscape and Planning, 2001, 56(1-2):21-33.
[7] Ligtenberga A, Wachowicza M, Bregt A K, et al. A design and application of a multi-agent system for simulation of multi-actor spatial planning. Journal of Environmental Management, 2004, 72(1-2): 43-55.
[8] Ligtenberg A, Lammeren van R, Bregt A K, et al. Validation of an agent-based model for spatial planning: A role-playing approach, Computers, Enviorment and Urban Systems, 2010, 34(5): 424-434.
[9] Francisco M, Roy J. A model for residential supply. The Annals of Regional Science, 2004, 38(3): 531-550.
[10] Bura S. Multi-agent systems and the dynamics of a settlement system. Geographical Analysis, 1996, 28(2): 161-178.
[11] Benenson I. Multi-agent simulations of residential dynamics in the city. Computers Environment and Urban Systems, 1998, 22(1): 25–42.
[12] Otter H S, Veen A, Vriend H J. ABLOoM: Location behaviour, spatial patterns, and agent-based modeling. J Artificial Soc Social Simul, 2001, 4(4): 1-21.
[13] 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. 地理学报, 2006, 61(10): 1101-1112.
[14] 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. 中国科学: D 辑, 2006, 36(11): 1027-1036
[15] Bah A, TouréI, Page C L, et al. An agent-based model to understand the multiple uses of land and resources around drillings in Sahel. Mathematical and Computer Modelling, 2006, 44(5-6): 513-534
[16] Castella J C, Kam S P, Quang D D, 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. Land Use Policy, 2007, 24(3): 531-545.
[17] Valbuena D, Verburg P H, Veldkamp A, et al. Effects of farmers’ decisions on the landscape structure of a Dutch rural region: An agent-based approach. Landscape and Urban Planning, 2010, 97(2): 98-110.
[18] Russell S, Norvig P. 人工智能: 一种现代方法. 姜哲等, 译.北京: 邮电出版社, 2004.
[19] 李伯含. 中国房地产业的市场结构与竞争行为研究[D]. 中共中央党校, 2006.
[20] 金丽国. 区域主体与空间经济自组织. 上海: 上海人民出版社, 2008.
[21] 邓卫, 宋扬. 住宅经济学. 北京: 清华大学出版社, 2008.
[22] 2006 年武汉市春交会住宅市场需求调查报告. 2006-6-2[2010-9-10]. http://news.wuhan.soufun.com/2006-06-02/722966_2.html,2006.6.2.
[23] 中国指数研究院. 2011 年一季度青岛消费者住房消费行为调查报告. 2011-4-1[2011-4-10].http://fdc.soufun.com/news/zt/201104/qingdaogoufang2011.htl.2011.4.1.
[24] 徐建华. 现代地理学中的数学方法. 北京: 高等教育出版社, 2002.
[25] 湖北省统计局武汉市2006 年住户调查资料. 2007-6-27[2010-9-12]. http://www.stats-hb.gov.cn/ structure/xxgk/tjfx/sztjfxzw_8196_1.htm,2007.6.27
[26] 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. 天津: 南开大学出版社, 2008.
[27] Costanza Robert, Arge Ralphd', Groot Rudolf de, et al. The value of the world’s ecosystem services and natural capital. Nature, 1997, 387(6630): 253-260.
