Geostatistical R-mode clustering of regionalized variables
Author Identifier (ORCID)
Abstract
Clustering techniques for grouping georeferenced data points (Q-mode clustering) have advanced considerably, whereas the clustering of georeferenced variables themselves (R-mode clustering) remains largely unexplored. Traditional non-spatial R-mode clustering methods are generally inadequate in spatial contexts, as they ignore the spatial dependence structure of regionalized variables, which is a fundamental aspect of multivariate spatial data analysis. This paper proposes a geostatistical R-mode clustering framework that explicitly incorporates spatial correlation through a (dis)similarity measure derived from Matheron’s codispersion coefficient. This framework generalizes conventional non-spatial R-mode clustering approaches based on pairwise associations, such as the Pearson correlation coefficient. Applied to soil geochemical datasets, the method identifies two types of variable groupings: spatially coherent groups that remain stable across multiple spatial scales, and scale-dependent groups whose internal composition varies with the spatial scale considered. The proposed framework addresses a critical gap in multivariate spatial data analysis and provides a robust alternative to classical non-spatial R-mode clustering techniques for detecting spatially meaningful patterns.
Keywords
codispersion coefficient, geostatistics, R-mode clustering, silhouette index, spatial relationships
Document Type
Journal Article
Date of Publication
1-1-2026
E-ISSN
18748953
ISSN
18748961
Publication Title
Mathematical Geosciences
Publisher
Springer
School
School of Science
RAS ID
100049
Copyright
subscription content
Recommended Citation
Fouedjio, F., Arya, E., & Afrifa-Yamoah, E. (2026). Geostatistical R-Mode clustering of regionalized variables. Mathematical Geosciences. Advance online publication. https://doi.org/10.1007/s11004-026-10322-9