Abstract <p>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.</p> Graphical abstract <p></p>