Geostatistics for Compositional Data with R (Hardcover, 1st ed. 2021)

This book provides a guided approach to the geostatistical modelling of compositional spatial data. These data are data in proportions, percentages or concentrations distributed in space which exhibit spatial correlation. The book can be divided into four blocks. The first block sets the framework and provides some background on compositional data analysis. Block two introduces compositional exploratory tools for both non-spatial and spatial aspects. Block three covers all necessary facets of multivariate spatial prediction for compositional data: variogram modelling, cokriging and validation. Finally, block four details strategies for simulation of compositional data, including transformations to multivariate normality, Gaussian cosimulation, multipoint simulation of compositional data, and common postprocessing techniques, valid for both Gaussian and multipoint methods. All methods are illustrated via applications to two types of data sets: one a large-scale geochemical survey, comprised of a full suite of geochemical variables, and the other from a mining context, where only the elements of greatest importance are considered. R codes are included for all aspects of the methodology, encapsulated in the R package "gmGeostats", available in CRAN.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Use R!
Release date:	November 2021
First published:	2021
Authors:	Raimon Tolosana-Delgado • Ute Mueller
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	259
Edition:	1st ed. 2021
ISBN-13:	978-3-03-082567-6
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > Ecological science, the Biosphere
LSN:	3-03-082567-1
Barcode:	9783030825676

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