How to interpolate point value using kriging method with R and GRASS 6: Difference between revisions
(tried to fix a bit) |
No edit summary |
||
(6 intermediate revisions by 4 users not shown) | |||
Line 1: | Line 1: | ||
=== 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 {{addonCmd|v.krige}} module | |||
* {{addonCmd|v.autokrige}} 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 Most of the code quoted here is very out of date, and simply does not work for current R/sp/gstat/spgrass6.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: | 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... | 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: | these are the commmands: | ||
enter | enter R from the GRASS prompt, and type: | ||
<pre> | <pre> | ||
library(spgrass6) | |||
#get vector points as SpatialPointsDataFrame | #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 | |||
</pre> | </pre> | ||
Line 21: | Line 35: | ||
<pre> | <pre> | ||
#create a grid from the region settings of GRASS: | #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 | #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 | |||
#write a raster file and save it in GRASS, now you can open it from there | 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. | |||
</pre> | </pre> | ||
that's all! | that's all! | ||
special thanks to Roger Bivand, | special thanks to Roger Bivand, ever ready to lend a hand! | ||
=== More Help === | |||
* A [http://casoilresource.lawr.ucdavis.edu/drupal/node/438 nice working example] for recent versions of R/sp/gstat/spgrass6. | |||
* [[Kriging]] | |||
[[Category:Documentation]] | [[Category:Documentation]] | ||
[[Category:R]] | |||
[[Category: Tutorial]] | |||
[[Category: HowTo]] |
Latest revision as of 09:49, 9 August 2013
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 v.krige module
- v.autokrige 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.
that's all!
special thanks to Roger Bivand, ever ready to lend a hand!
More Help
- A nice working example for recent versions of R/sp/gstat/spgrass6.
- Kriging