How to interpolate point value using kriging method with R and GRASS 6: Difference between revisions
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(+cat) |
(tried to fix a bit) |
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enter in R from GRASS and digit: | enter in R from GRASS and digit: | ||
<pre> | <pre> | ||
>library(spgrass6) | > library(spgrass6) | ||
#get vector points as SpatialPointsDataFrame: | #get vector points as SpatialPointsDataFrame: | ||
>giaciture <- | > giaciture <- readVECT6("giaciture_cat_clean3", ignore.stderr=TRUE) | ||
#shows the class of "giaciture" (SpatialPointsDataFrame): | #shows the class of "giaciture" (SpatialPointsDataFrame): | ||
>class(giaciture) | > class(giaciture) | ||
#get region from GRASS to R: | #get region from GRASS to R: | ||
>G <- gmeta6() | > G <- gmeta6() | ||
</pre> | </pre> | ||
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<pre> | <pre> | ||
#create a grid from the region settings of GRASS | #create a grid from the region settings of GRASS: | ||
> grd <- gmeta2grd(ignore.stderr=TRUE) | |||
>grd <- | |||
#create a SpatialGridDataFrame: | #create a SpatialGridDataFrame: | ||
>mask_SG <- SpatialGridDataFrame(grd,data=list(k=rep(1,G$cols*G$rows)),\ | > mask_SG <- SpatialGridDataFrame(grd,data=list(k=rep(1,G$cols*G$rows)),\ | ||
proj4string=CRS(G$proj4)) | |||
>library(gstat) | > class(mask_SG) | ||
> library(gstat) | |||
#create variogram, and "IMMERSIONE" here is the our variable, the variable on | #create variogram, and "IMMERSIONE" here is the our variable, the variable on | ||
#which we have to do the prediction, ~ 1 select the type of kriging, this | #which we have to do the prediction, ~ 1 select the type of kriging, this | ||
#is the ordinary one: | #is the ordinary one: | ||
>cvgm <- variogram(IMMERSIONE~1,locations=giaciture,width=400,cutoff=4000) | > cvgm <- variogram(IMMERSIONE~1,locations=giaciture,width=400,cutoff=4000) | ||
#choose the model to fit variogram (here is exponential) and give the | #choose the model to fit variogram (here is exponential) and give the | ||
#estimated parameters of the variogram (partial sill, range and nugget): | #estimated parameters of the variogram (partial sill, range and nugget): | ||
>efitted <- fit.variogram(cvgm,vgm(psill=5000,model="Exp",range=1500,nugget=8000)) | > efitted <- fit.variogram(cvgm,vgm(psill=5000,model="Exp",range=1500,nugget=8000)) | ||
# make the kriging prediction: | # make the kriging prediction: |
Revision as of 21:12, 31 July 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 in R from GRASS and digit:
> library(spgrass6) #get vector points as SpatialPointsDataFrame: > giaciture <- readVECT6("giaciture_cat_clean3", ignore.stderr=TRUE) #shows the class of "giaciture" (SpatialPointsDataFrame): > class(giaciture) #get region from GRASS to R: > G <- gmeta6()
now if you want you can continue to work in R from GRASS or not...
#create a grid from the region settings of GRASS: > grd <- gmeta2grd(ignore.stderr=TRUE) #create a SpatialGridDataFrame: > mask_SG <- SpatialGridDataFrame(grd,data=list(k=rep(1,G$cols*G$rows)),\ proj4string=CRS(G$proj4)) > class(mask_SG) > library(gstat) #create variogram, and "IMMERSIONE" here is the our variable, the variable on #which we have to do the prediction, ~ 1 select the type of kriging, this #is the ordinary one: > cvgm <- variogram(IMMERSIONE~1,locations=giaciture,width=400,cutoff=4000) #choose the model to fit variogram (here is exponential) and give the #estimated parameters of the variogram (partial sill, range and nugget): > efitted <- fit.variogram(cvgm,vgm(psill=5000,model="Exp",range=1500,nugget=8000)) # make the kriging prediction: >OK_pred <- krige(IMMERSIONE~ 1,locations=giaciture,newdata=mask_SG,model=efitted) >names(OK_pred) #show the name of variable kriged #write a raster file and save it in GRASS, now you can open it from there: >writeRast6sp(OK_pred,"OK_pred",zcol="var1.pred",NODATA=-9999)
that's all!
special thanks to Roger Bivand, even ready to lend a hand!