How to interpolate point value using kriging method with R and GRASS 6
ORDINARY KRIGING IN R WITH GRASS6 DATA
-- New development mid-2009:
- GRASS's Kriging wiki page
- v.krige_GSoC_2009 wxGUI Kriging project by Anne Ghisla
- Anne's module
- module by Mathieu Grelier
(WARNING!! Most of the code quoted here is very out of date, and simply does not work for current R/sp/gstat/spgrass6. Untried suggestions have been edited in, but without a test location, there is no guarantee that they will work! Roger Bivand, 5 April 2007)
Of all the methods we tried this is the most easy and (I suppose) exact too:
You have to have in your library the packages "gstat" and "spgrass6", you can download this last one directly from R using the command "install.packages". In GRASS we have a vector file named "giaciture_cat_clean3" and we want to do a prediction on this data... these are the commmands:
enter R from the GRASS prompt, and type:
library(spgrass6) #get vector points as SpatialPointsDataFrame #giaciture <- getSites6sp("giaciture_cat_clean3") RSB 070405 giaciture <- readVECT6("giaciture_cat_clean3") # RSB 070405 class(giaciture) #shows the class of "giaciture" (SpatialPointsDataFrame) # G <- gmeta6() #get region from GRASS to R RSB 070405
now if you want you can continue to work in R from GRASS or not...
#create a grid from the region settings of GRASS, it is very important # to have square cells, so you can set the region settings of GRASS or # you can give directly square dimensions using the values: # e.g."cells.dim=c(50,50)" #grd <- GridTopology(cellcentre.offset=c(G$west+(G$ewres/2) # ,G$south+(G$nsres/2)) # ,cellsize=c(G$ewres, G$nsres) # ,cells.dim=c(G$cols, G$rows)) RSB 070405 grd <- gmeta2grd() # RSB 070405 #create a SpatialGridDataFrame mask_SG <- SpatialGridDataFrame(grd, # ,data=list(k=rep(1, G$cols*G$rows)) RSB 070405 data=data.frame(k=rep(1, prod(slot(grd, "cells.dim")))), # RSB 070405 proj4string=CRS(proj4string(giaciture))) # RSB 070405 # proj4string(giaciture) and proj4string(mask_SG) must agree class(mask_SG) library(gstat) cvgm <- variogram(IMMERSIONE~1, data=giaciture, width=400, cutoff=4000) # RSB 070405 #create variogram, and "IMMERSIONE" #here is the our variable, the variable on wich we have to do the prediction, # ~ 1 select the type of kriging, this is the ordinary one efitted <- fit.variogram(cvgm, vgm(psill=5000, model="Exp", range=1500, nugget=8000)) # choose the model to fit variogram (here is exponential) and give the # estimated parameters of the variogram (partial sill, range and nugget) OK_pred <- krige(IMMERSIONE~1, data=giaciture, newdata=mask_SG, model=efitted) # RSB 070405 # make the kriging prediction names(OK_pred) #show the name of variable kriged writeRAST6(OK_pred, "OK_pred", zcol="var1.pred") # RSB 070405 #write a raster file and save it in GRASS, now you can open it from there.
special thanks to Roger Bivand, ever ready to lend a hand!