LEADER 03826nam 22006615 450 001 9910510584803321 005 20250409152736.0 010 $a3-030-82568-X 024 7 $a10.1007/978-3-030-82568-3 035 $a(MiAaPQ)EBC6810818 035 $a(Au-PeEL)EBL6810818 035 $a(CKB)19919632600041 035 $a(OCoLC)1286622986 035 $a(PPN)258839570 035 $a(DE-He213)978-3-030-82568-3 035 $a(EXLCZ)9919919632600041 100 $a20211119d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGeostatistics for Compositional Data with R /$fby Raimon Tolosana-Delgado, Ute Mueller 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (275 pages) 225 1 $aUse R!,$x2197-5744 311 08$aPrint version: Tolosana-Delgado, Raimon Geostatistics for Compositional Data with R Cham : Springer International Publishing AG,c2021 9783030825676 320 $aIncludes bibliographical references and index. 327 $a1 Introduction -- 2 A review of compositional data analysis -- 3 Exploratory data analysis -- 4 Exploratory spatial analysis -- 5 Variogram Models -- 6 Geostatistical estimation -- 7 Cross-validation -- 8 Multivariate normal score transformation -- 9 Simulation -- 10 Compositional Direct Sampling Simulation -- 11 Evaluation and Postprocessing of Results -- A Matrix decompositions -- B Complete data analysis workflows -- Index. 330 $aThis book provides a guided approach to the geostatistical modelling of compositional spatial data. These data are data in proportions, percentages or concentrations distributed in space which exhibit spatial correlation. The book can be divided into four blocks. The first block sets the framework and provides some background on compositional data analysis. Block two introduces compositional exploratory tools for both non-spatial and spatial aspects. Block three covers all necessary facets of multivariate spatial prediction for compositional data: variogram modelling, cokriging and validation. Finally, block four details strategies for simulation of compositional data, including transformations to multivariate normality, Gaussian cosimulation, multipoint simulation of compositional data, and common postprocessing techniques, valid for both Gaussian and multipoint methods. All methods are illustrated via applications to two types of data sets: one a large-scale geochemical survey, comprised of a full suite of geochemical variables, and the other from a mining context, where only the elements of greatest importance are considered. R codes are included for all aspects of the methodology, encapsulated in the R package "gmGeostats", available in CRAN. 410 0$aUse R!,$x2197-5744 606 $aStatistics 606 $aStatistics 606 $aEcology 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aEcology 606 $aBiostatistics 615 0$aStatistics. 615 0$aStatistics. 615 0$aEcology. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aEcology. 615 24$aBiostatistics. 676 $a550.72 700 $aTolosana-Delgado$b Raimon$0521437 702 $aMueller$b Ute 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910510584803321 996 $aGeostatistics for compositional data with R$92906356 997 $aUNINA