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Fig. 6.4. Examples of environmental predictors used to interpolate HMCs.library(maptools) library(rgdal) # Download and extract grids: download.file("http://spatial-analyst.net/book/system/files/usgrids5km.zip", destfile=paste(getwd(), "usgrids5km.zip", sep="/")) grid.list <- c("dairp.asc", "dmino.asc", "dquksig.asc", "dTRI.asc", "gcarb.asc", "geomap.asc", "globedem.asc", "minotype.asc", "nlights03.asc", "sdroads.asc", "twi.asc", "vsky.asc", "winde.asc", "glwd31.asc") for(j in grid.list){ fname <- zip.file.extract(file=j, zipname="usgrids5km.zip") file.copy(fname, paste("./", j, sep=""), overwrite=TRUE) } # plot the predictors: library(adehabitat) image(as.kasc(list(geomap=import.asc("geomap.asc"), nlights03=import.asc("nlights03.asc"), dTRI=import.asc("dTRI.asc"), gcarb=import.asc("gcarb.asc"))))
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Testimonials"From a period in which geographic information systems, and later geocomputation and geographical information science, have been agenda setters, there seems to be interest in trying things out, in expressing ideas in code, and in encouraging others to apply the coded functions in teaching and applied research settings." Poll |
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