We introduce a clustering method for time series based on tail dependence. Such a
method also considers spatial constraints by means of a suitable procedure merging temporal and spatial dependence via extreme-value copulas. The cluster composition depends on the choice of the hyper-parameter $\alpha \in (0, 1)$ used to calibrate the contribution of the spatial dependence to the overall dissimilarity. A novel heuristic approach to select $\alpha$ based on a suitable connectedness index associated to each cluster of the partition is proposed.
