Bagging Voronoi classifiers for clustering spatial functional data

doi:10.1016/j.jag.2012.03.006

Bagging Voronoi classifiers for clustering spatial functional data

2013-06-01

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DOI：10.1016/j.jag.2012.03.006

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We propose a bagging strategy based on random Voronoi tessellations for the exploration of georeferenced functional data, suitable for different purposes (e.g., classification, regression, dimensional reduction, . . .). Urged by an application to environmental data contained in the Surface Solar Energy database, we focus in particular on the problem of clustering functional data indexed by the sites of a spatial finite lattice. We thus illustrate our strategy by implementing a specific algorithm whose rationale is to (i) replace the original data set with a reduced one, composed by local representatives of neighborhoods covering the entire investigated area; (ii) analyze the local representatives; (iii) repeat the previous analysis many times for different reduced data sets associated to randomly generated different sets of neighborhoods, thus obtaining many different weak formulations of the analysis; (iv) finally, bag together the weak analyses to obtain a conclusive strong analysis. Through an extensive simulation study, we show that this new procedure - which does not require an explicit model for spatial dependence - is statistically and computationally efficient. (C) 2012 Elsevier B.V. All rights reserved.

Keywords:

Spatial statistics

Functional data analysis

Voronoi tessellation

Clustering

Bagging

Irradiance data

Bagging Voronoi classifiers for clustering spatial functional data

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