Computational advances for spatio-temporal multivariate environmental models

Abstract

AbstractIn multivariate Geostatistics, the linear coregionalization model (LCM) has been widely used over the last decades, in order to describe the spatial dependence which characterizes two or more variables of interest. However, in spatio-temporal multiple modeling, the identification of the main elements of a space–time linear coregionalization model (ST-LCM), as well as of the latent structures underlying the analyzed phenomenon, represents a tough task. In this paper, some computational advances which support the selection of an ST-LCM are described, gathering all the necessary steps which allow the analyst to easily and properly detect the basic space–time components for the phenomenon under study. The implemented algorithm is applied on space–time air quality data measured in Scotland in 2017.