Acta Geodaetica et Cartographica Sinica >

2017 , Vol. 46 >Issue 10: 1534 - 1548

DOI: https://doi.org/10.11947/j.AGCS.2017.20170275

Towards a Scale-driven Theory for Spatial Clustering

  • LI Zhilin ,
  • LIU Qiliang ,
  • TANG Jianbo
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  • 1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China;
    2. Department of Geo-Informatics, Central South University, Changsha 410083, China;
    3. State-Provincial Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China

Received date: 2017-05-26

  Revised date: 2017-09-04

  Online published: 2017-10-26

Supported by

The National Natural Science Foundation of China (Nos. 41601410;41471383);The Natural Science Foundation of Hunan Province (No. 2017JJ3379)

Fold

Abstract

Spatial clustering plays a key role in exploratory geographical data analysis. It is important for investigating the distribution of geographical phenomena. Spatial clustering sometimes also serves as an important pre-processing for other geographical data analysis techniques. Although lots of attentions have been paid to spatial clustering, two serious obstacles remain to be tackled:①clusters will always be discovered in any geographical dataset by spatial clustering algorithms, even if the input dataset is a random dataset; ②users feel difficult to interpret the various clustering results obtained by using different parameters. It is hypothesized that scale is not handled well in clustering process. As a result, a scale-driven theory for spatial clustering is introduced in this study, based on the human recognition theory and the natural principle of multi-scale representation. Scale is modeled as parameter of a clustering model, and the scale dependency in spatial clustering is handled by constructing a hypothesis testing, and multi-scale significant clusters can be easily discovered by controlling the scale parameters in an objective manner.

Key words： spatial clustering; scale; natural principle; visual cognition; hypothesis testing

Cite this article

LI Zhilin , LIU Qiliang , TANG Jianbo . Towards a Scale-driven Theory for Spatial Clustering[J]. Acta Geodaetica et Cartographica Sinica, 2017 , 46(10) : 1534 -1548 . DOI: 10.11947/j.AGCS.2017.20170275

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