ClustGeo: an R package for hierarchical clustering with spatial constraints

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.

Cited by in corpus (5)

• Applicability and Interpretability of Hierarchical Agglomerative Clustering With or Without Contiguity Constraints
• A review of regularised estimation methods and cross-validation in spatiotemporal statistics
• A Short-Term Predict-Then-Cluster Framework for Meal Delivery Services
• Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics
• Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence