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Modeling the effect of scale on clustering of spatial points.
- Source :
-
Computers, Environment & Urban Systems . Jul2015, Vol. 52, p81-92. 12p. - Publication Year :
- 2015
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01989715
- Volume :
- 52
- Database :
- Academic Search Index
- Journal :
- Computers, Environment & Urban Systems
- Publication Type :
- Academic Journal
- Accession number :
- 102495761
- Full Text :
- https://doi.org/10.1016/j.compenvurbsys.2015.03.006