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In several environmental applications data are functions of time, essentiallycon- tinuous, observed and recorded discretely, and spatially correlated. Mostof the methods for analyzing such data are extensions of spatial statisticaltools which deal with spatially dependent functional data. In such framework,this paper introduces a new clustering method. The main features are that itfinds groups of functions that are similar to each other in terms of theirspatial functional variability and that it locates a set of centers whichsummarize the spatial functional variability of each cluster. The methodoptimizes, through an iterative algorithm, a best fit criterion between thepartition of the curves and the representative element of the clusters, assumedto be a variogram function. The performance of the proposed clustering methodwas evaluated by studying the results obtained through the application onsimulated and real datasets.

Revealing spatial variability structures of geostatistical functional data via Dynamic Clustering