Journal of Geographical Sciences ›› 2010, Vol. 20 ›› Issue (5): 787-798.doi: 10.1007/s11442-010-0811-8

• Man-Land Relationship • Previous Articles     Next Articles

General multidimensional cloud model and its application on spatial clustering in Zhanjiang, Guangdong

DENG Yu1,4, LIU Shenghe1, ZHANG Wenting2, WANG Li3,4, WANG Jianghao1,4   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. School of Resources and Environment Science, Wuhan University, Wuhan 430079, China;
    3. Institute of Policy and Management, CAS, Beijing 100080, China;
    4. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2010-02-06 Revised:2010-04-16 Online:2010-10-15 Published:2010-10-15
  • Supported by:

    National Natural Science Foundation of China, No.40971102; Knowledge Innovation Project of the Chinese Academy of Sciences, No. KZCX2-YW-322; Special Grant for Postgraduates’ Scientific Innovation and Social Practice in 2008

Abstract:

Traditional spatial clustering methods have the disadvantage of “hardware division”, and can not describe the physical characteristics of spatial entity effectively. In view of the above, this paper sets forth a general multi-dimensional cloud model, which describes the characteristics of spatial objects more reasonably according to the idea of non-homogeneous and non-symmetry. Based on infrastructures’ classification and demarcation in Zhanjiang, a detailed interpretation of clustering results is made from the spatial distribution of membership degree of clustering, the comparative study of Fuzzy C-means and a coupled analysis of residential land prices. General multi-dimensional cloud model reflects the integrated characteristics of spatial objects better, reveals the spatial distribution of potential information, and realizes spatial division more accurately in complex circumstances. However, due to the complexity of spatial interactions between geographical entities, the generation of cloud model is a specific and challenging task.

Key words: multi-dimensional cloud, spatial clustering, data mining, membership degree, Zhanjiang