Spectral-spatial clustering of regionalized mixed data
Proceedings of IAMG 2019 - 20th Annual Conference of the International Association for Mathematical Geosciences
International Association for Mathematical Geosciences (IAMG)
School of Science
Spatial clustering groups similar spatial objects and/or patterns into distinct, spatially coherent clusters and is an important component of spatial data analysis in the earth sciences. Spectral clustering is a popular modern clustering algorithm, which relies on graph-theoretical concepts. It outperforms traditional clustering algorithms, but generally does not account for spatial relationships. The aim of this study is to improve the spatial awareness of the spectral clustering by utilising a dissimilarity matrix based on nonparametric higher order spatial statistics. The proposed model-free technique can identify meaningful spatial clusters (i.e., meaningful geographical subregions) from multivariate spatial data of different scales without the need to define a model of co-dependence.