A Practical Guide to Geostatistical Mapping

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Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018, methodology): “Some useful covariates to help explain the distribution of elements in stream sediments and soils have been previously prepared (Hengl, 2009) and include: > geochem <- readRDS(".”
Projecting changes in the distribution and productivity of living marine resources: A critical review of the suite of modelling approaches used in the large European project VECTORS (2016, background): “The simplest form of SDMs is the geo-statistical approaches that utilize spatial-autocorrelation (e.g. Hengl, 2009).”
Accuracy Assessment and Comparative Analysis of IDW, Spline and Kriging in Spatial Interpolation of Landform (Topography): An Experimental Study (2017, unknown): “The novelty Kriging has in the analysis of point data is the derivation and plotting of the semi-variance differences between the neighboring values [6].”