Multivariable geostatistics in S: the gstat package

By Edzer J. Pebesma. Computational Geosciences, 2004

2,971 citations recorded by the source corpus.

Subfields: Building and Construction, Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/06534ab5320c23a9698d256afa54ca3943c86e71

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Citations in the corpus

Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom (2009, positive): “Global variograms were estimated and models fitted using Gstat (Pebesma and Wesseling 1998; Pebesma, 2004).”
Living with Collinearity in Local Regression Models (2012, methodology): “For each permutation of step 1, generate three predictor variables ( , , ) using geostatistical un-conditional co-simulations (e.g. see Pebesma, 2004).”
The bacterial biogeography of British soils. (2011, methodology): “First axes site scores were mapped using inverse distance weighting interpolation using the default settings within the gstat package (Pebesma, 2004).”
Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria (2014, methodology): “To achieve parameter surfaces of the coefficients on non-observed locations, geostatistical algorithms (Pebesma, 2004) can be employed (e.g., Yu et al., 2007).”
Potential climate change impacts on fire intensity and key wildfire suppression thresholds in Canada (2017, methodology): “Specifically, the krige function from the gstat package (Pebesma 2004) alongside the autofitVariogram function in the automap package (Hiemstra et al 2009).”
Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models (2013, methodology): “cal programming environment (R Development Core Team 2011) with package gstat (Pebesma 2004).”
Protocol for automating error removal from yield maps (2019, methodology): “Spatial (2)nRMSE = � 1 N ∑N i=1 � z � xi � − ẑ(xi) �2 1 N ∑N i=1 z � xi � × 100 (3)RDnRMSE = nRMSES1+S2 − nRMSES1 nRMSES1 × 100 1 3 interpolation and cross-validation were performed with the gstat (Pebesma 2004) library and parallelizing was carried out using the BiocParallel library (Morgan et al.”
Using model averaging to combine soil property rasters from legacy soil maps and from point data (2014, methodology): “, 2013) for fitting cubist models; “gstat” (Pebesma, 2004) for variogram fitting; and “raster” (Hijmans and van Etten, 2013) for handling raster layers and”
Continuous Time-Varying Kriging for Spatial Prediction of Functional Data: An Environmental Application (2010, methodology): “In a first stage of the analysis, and using the library gstat of R language (Pebesma, 2004), a LMC was fitted to the multivariable random field A = (α1, · · · , α65) composed by the coefficients of the Fourier basis used for smoothing each observed sample.”
How far does the tree affect the crop in agroforestry? New spatial analysis methods in a Faidherbia parkland (2020, methodology): “356 2.5 Geostatistics 357 The distance to tree effect, corresponding to the ‘Range’ parameter, was analyzed with 358 geostatistics in R, from the table of attributes supplied at the end of the geomatics chain, using 359 the libraries gstat (Pebesma, 2004) and sp (Bivand et al., 2008).”
Estimation of soil properties at the field scale from satellite data: a comparison between spatial and non‐spatial techniques (2014, methodology): “All geostatistical and regressive analyses were carried out with the Gstat (Pebesma, 2004) and geoR (Ribeiro & Diggle, 2001) packages, and PLSR analysis was carried out with the pls package (Mevik & Wehrens, 2007) of R software (R Development Core Team, 2011).”