A regionalization method for spatial functional data based on variogram models: An application on environmental data

Abstract

This chapter proposes a Dynamic Clustering Algorithm (DCA) as a new regionalization method for spatial functional data. The method looks for the best partition optimizing a criterion of spatial association among functional data. Furthermore it is such that a summary of the variability structure of each cluster is discovered. The performance of the proposal is checked through an application on real data.