Applied Spatial Data Analysis with R - Second Edition

By Roger S. Bivand, Edzer J. Pebesma, V. Gómez‐Rubio. Use R!, 2008

2,017 citations recorded by the source corpus.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/209562baa7d7a155c4f5ebfb30dbd84ad087e3b3

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In Management Science and Operations Research

Citations in the corpus

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).”
Simple Features for R: Standardized Support for Spatial Vector Data (2018, methodology): “Package sf (Pebesma, 2017) is an R package for reading, writing, handling and manipulating simple features in R, reimplementing the vector (points, lines, polygons) data handling functionality of packages sp (Pebesma and Bivand, 2005; Bivand et al., 2013), rgdal (Bivand et al.”
Competition, predation, and migration: Individual choice patterns of Serengeti migrants captured by hierarchical models (2014, positive): “…the concentration of nitrogen in the grass is inversely correlated with the mean NDVI (see Appendix C), we regression kriged (Hengl et al. 2007, Bivand et al. 2008) the data from the 148 points with a 9-year mean NDVI layer (2000–2009) to generate an accurate estimate of the spatial…”
Spatial modelling with R-INLA: A review (2018, methodology): “…structure given by (4) to R-INLA: 1) Through an ASCII ﬁle; 2) through a symmetric adjacency matrix of dimension n × n ; 3) by extracting the structure directly from the shapeﬁle using the R-packages maptools (Bivand and Lewin-Koh, 2017) and spdep (Bivand and Piras, 2015; Bivand et al., 2013a).”
Random Forest Spatial Interpolation (2020, background): “For more details see Bivand et al. [39].”
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).”
Potential species distribution modeling and the use of principal component analysis as predictor variables (2014, methodology): “Below, it is described in detail: 1) A randomness test was applied to the spatial records for each species (Bivand et al., 2008).”