Revealing spatial variability structures of geostatistical functional data via Dynamic Clustering

TL;DR

This paper introduces a novel clustering method for spatially correlated functional data, effectively identifying groups with similar spatial variability and summarizing each with a representative variogram, validated on simulated and real datasets.

Contribution

The paper presents a new clustering approach that captures spatial functional variability and locates representative centers, improving analysis of spatially dependent functional data.

Findings

Effective in identifying spatial variability structures

Performs well on simulated datasets

Validates applicability on real environmental data

Abstract

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.