# ClustGeo: an R package for hierarchical clustering with spatial constraints

arXiv: https://arxiv.org/abs/1707.03897 · HTML: https://mlsanity.com/paper/1707.03897/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [A Short-Term Predict-Then-Cluster Framework for Meal Delivery Services](https://mlsanity.com/paper/2501.08466/) (2025)
- [Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence](https://mlsanity.com/paper/2109.00539/) (2021)
- [ELFIS: Expert Learning for Fine-grained Image Recognition Using Subsets](https://mlsanity.com/paper/2303.09269/) (2023)
- [Spatial Clustering of Citizen Science Data Improves Downstream Species Distribution Models](https://mlsanity.com/paper/2412.15559/) (2024)

## Abstract (arXiv)

In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices D0 and D1 are inputted, along with a mixing parameter α ∈ [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 D0 and the homogeneity criterion calculated with D1. The idea is then to determine a value of α 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.