Spatial Data Analysis

Methods for spatial data analysis with raster and vector data. Raster methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <> to get started. 'terra' is very similar to the 'raster' package; but 'terra' can do more, is easier to use, and it is faster.


Reference manual

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1.4-22 by Robert J. Hijmans, 4 days ago

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Authors: Robert J. Hijmans [cre, aut] , Roger Bivand [ctb] , Karl Forner [ctb] , Jeroen Ooms [ctb] , Edzer Pebesma [ctb]

Documentation:   PDF Manual  

Task views: Analysis of Spatial Data

GPL (>= 3) license

Imports methods, Rcpp

Suggests parallel, tinytest, ncdf4, sf, deldir, XML, raster

Linking to Rcpp

System requirements: C++11, GDAL (>= 2.2.3), GEOS (>= 3.4.0), PROJ (>= 4.9.3), sqlite3

Imported by ICvectorfields, Recocrop, Rsagacmd, dismo, fgdr, maptiles, mlr3spatial, rassta, raster, rasterVis, satellite.

Depended on by geodata, rts.

Suggested by Rquefts, Rwofost, belg, disdat, exactextractr, landscapemetrics, mapSpain, mapsf, motif, reproducible, sf, smoothr, spatialEco, stars.

Enhanced by sabre.

See at CRAN