Clustering Spatial Functional Data
Vincent Vandewalle, Cristian Preda, Sophie Dabo‐Niang · Wiley series in probability and statistics · 2021
In this chapter, we present two approaches for clustering spatial functional data. The first one is the model-based clustering that uses the concept of density for functional random variables. The second one is the hierarchical clustering based on univariate statistics for functional data such as the functional mode or the functional mean. These two approaches take into account the spatial features of the data: two observations that are spatially close and share a common distribution of the associated random variables. The two methodologies are illustrated by an application to air quality data.