Spatial variability clustering for spatially dependent functional data

This paper introduces a method for clustering spatially dependent functional data. The idea is to consider the contribution of each curve to the spatial variability. Thus, we define a spatial dispersion function associated to each curve and perform a k-means like clustering algorithm. The algorithm is based on the optimization of a fitting criterion between the spatial dispersion functions associated to each curve and the representative of the clusters. The performance of the proposed method is illustrated by an application on real data and a simulation study.     

Spatial prediction and spatial dependence monitoring on georeferenced data streams

Statistical Methods & Applications - This paper deals with the analysis of data streams recorded by georeferenced sensors. We focus on the problem of measuring the spatial dependence among the...