  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.
