地理学报

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基于多智能体的居住空间格局演变的真实场景模拟

陶海燕,  黎夏,  陈晓翔   

  1. 中山大学地理科学与规划学院,广州 510275
  • 收稿日期:2008-09-26 修回日期:2009-01-10 出版日期:2009-06-25 发布日期:2010-04-15
  • 作者简介:陶海燕 (1966-), 女, 讲师, 博士, 主要研究方向: 多智能体地理模拟。E-mail: taohy@mail.sysu.edu.cn
  • 基金资助:

    国家自然科学基金重点项目 (40830532); 国家杰出青年基金项目 (40525002); 国家自然科学基金项目 (40301084)

Simulation for Evolvement of Residential Spatial Patterns in Real Scene Based on Multi-agent

TAO Haiyan,  LI Xia,  CHEN Xiaoxiang   

  1. Geography and Planning School, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2008-09-26 Revised:2009-01-10 Online:2009-06-25 Published:2010-04-15
  • Supported by:

    The Key Project for National Natural Science Foundation of China, No.40830532; National Outstanding Youth Foundation of NSF of China, No.40525002; National Natural Science Foundation of China, No.40301084

摘要:

多智能体建模方法为城市研究提供了一种新的研究思路。采用自下而上的多智能体方法构建真实场景的居住决策模型,并研究城市居住格局的形成和演变具有重要的理论意义和应用价值。但目前的多智能体模型通常把空间抽象为均质空间,无法反映真实的地理空间。通过对居住环境的"宜居性"评价,作为居民智能体对居住环境评价的影响因子。将多智能体模型与GIS相结合,为智能体模型提供一个异质的、动态变化的模拟环境。由此居民智能体根据自身的经济状况以及对居住环境的偏好不断地调整其在城市中的居住地,模拟出城市居住空间格局的演变过程。将模型应用于广州市海珠区,其模拟的住宅价格空间分布与实际情况相关系数在0.6以上,说明模拟结果与实际的情况比较吻合。模拟结果在一定程度上为理解和探讨居住空间格局的成因和动态变化提供帮助,为发展和验证城市理论提供一种重要的分析手段和模拟方法。

关键词: 多智能体, 居住空间, 居住环境, 地理信息系统, Swarm, 广州

Abstract:

Multi-agent modeling provides a new method for urban research. And it is of theoretical and practical significance to adopt multi-agent modeling, a bottom-up approach, to build up the actual residential decision model, and to research the formation and evolvement of the urban residential spatial patterns. However, many multi-agent models usually describe space as homogeneous space which is beyond expression of real geo-space. In order to simulate the urban residential patterns, some evaluation indices of urban residential suitability in the human-environment science are introduced. Integrating multi-agent model with GIS can provide a heterogeneous and dynamic simulated environment. The model based on multi-agent is constructed as follows: simulated inhabitants wander through the universe to find the optimal place of residence according to constraints as their incomes and preference of residential environment. Evaluation indices, such as traffic accessibility, life convenience and landscape beauty, have been chosen and quantified by GIS. The migration decision of agent depends on the current residential pressure. It is suggested that the probability of leaving its current location increases monotonically with an increase in the residential pressure. Current residential pressure of an agent is estimated by the pressure caused by both basic house price and house environmental price. On the Swarm platform, Java language and component of Swarm-GIS are used to realize the model. The proposed model is applied to simulate the formation of residential spatial patterns in Haizhu district of Guangzhou, Guangdong province. The results of simulation reveal that downtown area is still the first choice of housing location for middle and high income residents in Guangzhou, which shows a different residential mode from Western developed countries. The Spearman rank correlation coefficient of simulated house price and actual house price exceeds 0.6, which indicates that the simulation result can reflect the rules in real practice. Although some uncertainty remains in the model, such as the lack of unified criteria for residential environment evaluation, randomness of house choice and the simplification of the model, the simulation can help us to understand and explore causes and dynamics of residential space patterns, and provide an important analytical tool and simulation method to develop and verify urban theories.

Key words: multi-agent, residential space, residential environment, GIS, Swarm, Guangzhou