Applied Spatial Data Analysis with R. Springer

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Covariance Regression Analysis (2017, unknown): “Accordingly, spatial statistics attempts to explain the covariance structure of responses by their geographical locations (Cressie 1991; Bivand, Pebesma, and Gómez-Rubio 2008; Cressie and Wikle 2011).”
GRASS GIS: A multi-purpose open source GIS (2012, methodology): “…including Quantum GIS, Sextante (an analytical extension for gvSIG), statistical and geostatistical support (R Statistical Computing Environment, gstat, MATLAB), and rendering andmulti-dimensional visualization software (Furlanello et al., 2003; Bivand et al., 2008; Neteler et al., 2008).”
Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models (2013, methodology): “The standard deviation of SPE, σSPE, is ideally close to one indicating that the magnitude of the kriging variance is correct, and the variogram model is realistic (Bivand et al. 2008).”
Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale (2014, unknown): “The algorithm procedure was programmed with R software using functionalities of geoR and sp packages (Ribeiro and Diggle, 2001; Bivand et al., 2008).”
Neglected biological patterns in the residuals (2011, background): “Heteroscedasticity can also be an issue whenever data has either a temporal or spatial component (Bivand et al. 2008; Cryer and Chan 2008).”