  In several environmental applications data are functions of time, essentially
con- tinuous, observed and recorded discretely, and spatially correlated. Most
of the methods for analyzing such data are extensions of spatial statistical
tools which deal with spatially dependent functional data. In such framework,
this paper introduces a new clustering method. The main features are that it
finds groups of functions that are similar to each other in terms of their
spatial functional variability and that it locates a set of centers which
summarize the spatial functional variability of each cluster. The method
optimizes, through an iterative algorithm, a best fit criterion between the
partition of the curves and the representative element of the clusters, assumed
to be a variogram function. The performance of the proposed clustering method
was evaluated by studying the results obtained through the application on
simulated and real datasets.
