ClustGeo: an R package for hierarchical clustering with spatial constraints

TL;DR

ClustGeo introduces a hierarchical clustering method that integrates spatial constraints by combining feature dissimilarities with geographical information, enhancing clustering quality with spatial contiguity.

Contribution

The paper presents a novel Ward-like hierarchical clustering algorithm that incorporates spatial constraints through a convex combination of dissimilarity matrices, adaptable to non-Euclidean data and non-uniform weights.

Findings

Effective integration of spatial constraints improves cluster contiguity.

Flexible dissimilarity measures accommodate various data types.

Application demonstrates practical utility on real spatial datasets.

Abstract

In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices $D_0$ and $D_1$ are inputted, along with a mixing parameter $\alpha \in [0,1]$. The dissimilarities can be non-Euclidean and the weights of the observations can be non-uniform. The first matrix gives the dissimilarities in the "feature space" and the second matrix gives the dissimilarities in the "constraint space". The criterion minimized at each stage is a convex combination of the homogeneity criterion calculated with $D_0$ and the homogeneity criterion calculated with $D_1$. The idea is then to determine a value of $\alpha$ which increases the spatial contiguity without deteriorating too much the quality of the solution based on the variables of interest i.e. those of the feature space. This procedure is illustrated on a real dataset using the R package ClustGeo.