地理研究 ›› 2014, Vol. 33 ›› Issue (7): 1207-1216.doi: 10.11821/dlyj201407002

• 论文 • 上一篇    下一篇

城中村有序改造的群决策——以广州市城中村改造为例

陶海燕1,2, 周淑丽1, 卓莉1,2   

  1. 1. 中山大学地理科学与规划学院综合地理信息研究中心, 广州 510275;
    2. 广东省城市化与地理环境空间模拟重点实验室, 广州 510275
  • 收稿日期:2013-08-26 修回日期:2014-03-06 出版日期:2014-07-10 发布日期:2014-07-10
  • 通讯作者: 卓莉(1973-),女,湖南张家界人,副教授,主要研究方向为环境遥感与多智能体模拟。E-mail:zhuoli_sysu@163.com E-mail:zhuoli_sysu@163.com
  • 作者简介:陶海燕(1966-),女,江苏扬州人,副教授,主要研究方向为地图学与地理信息系统。E-mail:taohy@mail.sysu.edu.cn
  • 基金资助:
    广东省自然科学基金项目(S2013010012554);国家高技术研究发展计划(863计划)(2013AA122302)

Group decision-making on well-order renovation of urban villages:A case study of Guangzhou

TAO Haiyan1,2, ZHOU Shuli1, ZHUO Li1,2   

  1. 1. Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
    2. Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2013-08-26 Revised:2014-03-06 Online:2014-07-10 Published:2014-07-10

摘要: 公众参与已成为城市规划过程中的法定程序之一,因此研究公众参与下的城中村有序改造,对于探索中国城中村改造实施机制有着重大的理论意义和应用价值。公民个体由于信息的缺乏以及受到自身知识、阅历等的限制,通常只能对一定范围内的部分空间环境进行有效地观察,形成一定的感知,即仅仅能给出各自偏好下的对部分方案的评估序列,而传统的群决策方法不能很好地处理较大比例数据的缺失问题。采用基于马尔科夫链的MC 4 启发式算法,对多个部分序进行融合,得到一个初始序列,然后对该初始序列进行Kemeny 局部优化形成群决策结果。以广州市52 个全面改造的城中村改造为例,首先构造三种不同类型的城中村居民决策者和一类环境保护决策者;其次四类决策者个体分别从各自不同的利益角度出发,对其感知空间内的部分城中村改造的迫切程度进行评估并排序;然后采用Python 编程实现了Kemeny局部优化算法对四类决策者的评估序列进行融合,得到52 个城中村改造的群决策结果;群决策结果与个体决策方案之间的Kendall tau 距离为0.2873,说明该方法得到的群决策结果与个体决策者之间的决策具有较好的一致性。研究表明,该方法摒弃传统的个案剔除法以及各种各样的数据插补方法,充分利用隐藏在这些数据中的信息,保证数据的客观性和结果的正确性,可以为公众参与的民主决策提供定量化的方法,为公共政策的制定提供科学的决策支持。

关键词: 公众参与, 群决策, Kemeny准则, 局部优化, 城中村, 广州

Abstract: Public participation has become one of the city planning processes with legal procedures, so studying well-order renovation of urban villages based on public participant is of great value theoretically and practically for exploring the implement mechanism of rebuilding in China. However an individual decision-maker who is lack of information because of his/her self-interest, knowledge, experience and other restrictions, usually only perceives local space environment, and evaluates partial alternatives according to his preference, i.e., only can be given partial evaluation list. In order to research spatial group decision making based on incomplete information, Kemeny local optimization, which is proposed by Cynthia Dwork et al., is introduced. Steps of aggregation and optimization are as follows: firstly, find all elements in partial lists; then use a simple power-iteration algorithm to obtain a reasonable approximation to the stationary distribution of Markov chain, and the Markov chain ordering is the aggregated initial ordering; last, in order to improve consensus ranking, initial list has been locally Kemeny optimized. The normalized Kendall tau distance was used to evaluate the level of agreement from all the decision makers regarding all the possible alternatives in a given situation. With the example of 52 urban villages in Guangzhou, three different types of decision makers among villagers according to their main source of income and one type of environmentalists are introduced respectively, who are evaluating and ranking the urgency of urban villages rebuilding from their individual preference and perspective. Furthermore, a group decision making solution is obtained by using Kemeny local optimization algorithm. The method is realized by Python. The normalized Kendall tau distance of group solution to four individual solutions is 0.2873, which indicates that the result of group decision-making with all the individuals' decision making has a good consistency. The research demonstrates that the method is useful in making the most consistent group decision while comprehensively considering advices from different interest group, providing a quantitate method for public participation in democratic decision making and scientific decision support to public policy formulation.

Key words: public participant, group decision-making, Kemeny rule, local optimization, urban village, Guangzhou