Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28483
Title: Modeling the effect of scale on clustering of spatial points
Authors: Liu, Q
Li, Z 
Deng, M
Tang, J
Mei, X
Keywords: Clustering
Spatial points
Spatial scale
The natural principle
Issue Date: 2015
Publisher: Elsevier Ltd
Source: Computers, environment and urban systems, 2015, v. 52, p. 81-92 How to cite?
Journal: Computers, Environment and Urban Systems 
Abstract: It has been established that spatial clustering patterns are scale-dependent. However, scale is still not explicitly handled in existing methods to detect clusters in spatial points; thus, users are often puzzled by the varied clustering results obtained by different spatial clustering methods and/or parameters. To handle the effect of scale on the cluster detection of spatial points, two kinds of scales are first specified in this study: scale of data and scale of analysis. These two kinds of scales are embodied by a set of three indictors: data resolution, spatial extent, and analysis resolution. Further, a scale-driven clustering model with these three scale indicators as parameters is statistically constructed based on the Natural Principle and graph theory. A comparative study of this scale-driven clustering model with existing methods is carried out with a simulated spatial dataset. It is found that only this new method is able to discover the multi-scale spatial clustering patterns defined in the benchmarks. Further, Carex lasiocarpa population data is used to illustrate the practical value of the proposed scale-driven clustering model.
URI: http://hdl.handle.net/10397/28483
ISSN: 0198-9715
DOI: 10.1016/j.compenvurbsys.2015.03.006
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