Modeling the Effect of Scale on Clustering of Spatial Points

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.