Book Review Geostatistical Analysis of Compositional Data

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Compositional data are represented as vector variables with individual vector components ranging between zero and a positive maximum value representing a constant sum constraint, usually unity (or 100 percent). The earth sciences are flooded with spatial distributions of compositional data, such as concentrations of major ion constituents in natural waters (e.g. mole, mass, or volume fractions), mineral percentages, ore grades, or proportions of mutually exclusive categories (e.g. a water-oil-rock system). While geostatistical techniques have become popular in earth science applications since the 1970s, very little attention has been paid to the unique mathematical properties of geostatistical formulations involving compositional variables. ... continued below

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Carle, S F March 26, 2007.

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Compositional data are represented as vector variables with individual vector components ranging between zero and a positive maximum value representing a constant sum constraint, usually unity (or 100 percent). The earth sciences are flooded with spatial distributions of compositional data, such as concentrations of major ion constituents in natural waters (e.g. mole, mass, or volume fractions), mineral percentages, ore grades, or proportions of mutually exclusive categories (e.g. a water-oil-rock system). While geostatistical techniques have become popular in earth science applications since the 1970s, very little attention has been paid to the unique mathematical properties of geostatistical formulations involving compositional variables. The book 'Geostatistical Analysis of Compositional Data' by Vera Pawlowsky-Glahn and Ricardo Olea (Oxford University Press, 2004), unlike any previous book on geostatistics, directly confronts the mathematical difficulties inherent to applying geostatistics to compositional variables. The book righteously justifies itself with prodigious referencing to previous work addressing nonsensical ranges of estimated values and error, spurious correlation, and singular cross-covariance matrices.

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PDF-file: 9 pages; size: 0.1 Mbytes

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  • Journal Name: Vadose Zone Journal, vol. 6, no. 3, August 1, 2007, pp. 684-685; Journal Volume: 6; Journal Issue: 3

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  • Report No.: UCRL-JRNL-229488
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 940856
  • Archival Resource Key: ark:/67531/metadc900948

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  • March 26, 2007

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  • Sept. 27, 2016, 1:39 a.m.

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  • Dec. 2, 2016, 8:44 p.m.

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Carle, S F. Book Review Geostatistical Analysis of Compositional Data, article, March 26, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc900948/: accessed November 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.