地理研究

地理研究 ›› 2009, Vol. 28 ›› Issue (5): 1285-1296.doi: 10.11821/yj2009050015

• 地球信息科学 • 上一篇    下一篇

空间数据挖掘的地理案例推理方法及试验

杜云艳1, 温 伟1,2, 曹 锋1,3   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101;
    2. 山东科技大学,青岛 266510;
    3. 山西大学,太原 030006
  • 收稿日期:2008-12-14 修回日期:2009-03-09 出版日期:2009-09-25 发布日期:2009-09-25
  • 作者简介:杜云艳(1973-),女,河南内乡人,副研究员。主要从事GIS的空间数据挖掘方法研究以及空间数据集成研究。Email:duyy@lreis.ac.cn
  • 基金资助:

    国家863计划探索导向课题(2007AA12Z222),中科院知识创新项目(kzcx2-yw-304)和资源与环境信息系统国家重点实验室自主创新团队计划(088RA400SA)共同资助

A study on spatial data mining using Geo-CBR and its application

DU Yun-yan1, WEN Wei1,2, CAO Feng1,3   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, CAS, Beijing 100101, China;
    2. Shandong University of Science and Technology, Qingdao 266510, China;
    3. Shanxi University, Taiyuan 030006, China
  • Received:2008-12-14 Revised:2009-03-09 Online:2009-09-25 Published:2009-09-25
  • Supported by:

    国家863计划探索导向课题(2007AA12Z222),中科院知识创新项目(kzcx2-yw-304)和资源与环境信息系统国家重点实验室自主创新团队计划(088RA400SA)共同资助

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32 | 4

摘要：

从空间数据挖掘的角度谈地理案例推理方法,认为地理案例推理是面向问题的一种空间数据挖掘方法。针对这一思想进行了基于地理案例的空间数据挖掘具体算法介绍。首先在明确地理案例具体定义的基础上,给出了面向问题的空间数据挖掘地理案例界定和组织方法;其次,鉴于地理空间的自然地带性和区域分异性规律的影响,深入探讨了地理案例自身或其间所可能存在的相互依赖和相互制约关系,并给出了采用粗糙集方法进行地理案例内蕴空间关系的定量挖掘方法;第三,针对地理案例表达时考虑的空间特征和空间关系的不同,给出了三种状况下的空间相似性计算模型;最后,以土地利用这一典型的地学现象为例,给出具体实例,一方面进行土地利用问题的定量分析与推测;另一方面,通过实例展示地理案例推理方法在地学问题求解以及空间数据定量分析上的特点和优势。

关键词: 空间数据挖掘, 地理案例推理, 案例组织, 空间关系, 粗糙集

Abstract:

Currently, Geo-data-mining and knowledge discovering, a new kernel of GIS spatial analysis study, which help to break theoretic limitation of Geo-expert system and to reveal an innovative research roadmap for new era Geo-information sciences, represent latest trend in researching GIS. Various research communities have tried to apply or revise mathematic tools as probability theory, spatial statistic, fuzzy set and rule based induction method to studies concerning specific geo-scientific problems. According to the latest decade development in this study area, data mining method has absorbed, borrowed and revised latest mathematic tools and theories rising in AI study area; and focused both on theoretic research and its application in mining rules lying in spatial dataset. Development of Geo-data-mining couples tightly with AI and application mathematics by widely crossing and deeply fusing. CBR (Case Based Reasoning), a new AI method that expands knowledge capturing channels, encapsulating problems by case, solving new problem by referencing historical similar ones, storing and re-using successful cases, has advantages such as simplicity, flexibility, scalability, high efficiency, knowledge learning and accumulation, which enable CBR to analyse and reason complex geo-problems. This paper mainly discusses Geo-CBR from a spatial data mining view and deems it as a kind of problem oriented spatial data mining method. Firstly, a detailed Geo-CBR definition and its encapsulating method are given as well as discrimination between spatial data mining and problem oriented Geo-CBR. Then, considering physical geography zonal and regional variation effect, inter-dependent and mutually condition relationships between geo-cases are examined in depth. And a quantitative data-mining method to explore intrinsic spatial relationships from geo-cases is presented based on rough set theory. In addition, due to variation of spatial feature types and their spatial relationships in geo-case representative model, 3 categories of spatial similarity calculating models are derived. Finally, a pilot study for LU is provided with purposes of landuse problems quantitative analysis and deduction and demonstration of Geo-CBR's characteristics and advantages in solving and analysis spatial related problems.

Key words: spatial data mining, geographic case-based reasoning, case organization, spatial relationships