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PROGRAM:

NAME

v.krige - Performs ordinary or block kriging for vector maps.

KEYWORDS

vector, interpolation, raster, kriging

SYNOPSIS

v.krige
v.krige --help
v.krige input=name column=name [output=name] [package=string]
[model=string[,string,...]] [block=integer] [range=integer] [nugget=integer]
[sill=integer] [output_var=name] [--overwrite] [--help] [--verbose] [--quiet]
[--ui]

Flags:
--overwrite
Allow output files to overwrite existing files

--help
Print usage summary

--verbose
Verbose module output

--quiet
Quiet module output

--ui
Force launching GUI dialog

Parameters:
input=name [required]
Name of input vector map
Name of point vector map containing sample data

column=name [required]
Name of attribute column with numerical value to be interpolated

output=name
Name for output raster map
If omitted, will be <input name>_kriging

package=string
R package to use
Options: gstat
Default: gstat

model=string[,string,...]
Variogram model(s)
Leave empty to test all models (requires automap)
Options: Nug, Exp, Sph, Gau, Exc, Mat, Ste, Cir, Lin, Bes, Pen, Per, Hol, Log, Pow,
Spl, Leg, Err, Int

block=integer
Block size (square block)
Block size. Used by block kriging.

range=integer
Range value
Automatically fixed if not set

nugget=integer
Nugget value
Automatically fixed if not set

sill=integer
Sill value
Automatically fixed if not set

output_var=name
Name for output variance raster map
If omitted, will be <input name>_kriging.var

DESCRIPTION

v.krige allows performing Kriging operations in GRASS GIS environment, using R software
functions in background.

NOTES

v.krige is just a front-end to R. The options and parameters are the same offered by
packages automap and gstat.

Kriging, like other interpolation methods, is fully dependent on input data features.
Exploratory analysis of data is encouraged to find out outliers, trends, anisotropies,
uneven distributions and consequently choose the kriging algorithm that will give the most
acceptable result. Good knowledge of the dataset is more valuable than hundreds of
parameters or powerful hardware. See Isaaks and Srivastava’s book, exhaustive and clear
even if a bit outdated.

Dependencies
R software >= 2.x

rpy2
Python binding to R. Note! rpy version 1 is not supported.

R packages automap, gstat, rgrass7 and rgeos.
automap is optional (provides automatic variogram fit). Install the packages via R
command line (or your preferred GUI):
install.packages("rgeos", dep=T)
install.packages("gstat", dep=T)
install.packages("rgrass7", dep=T)
install.packages("automap", dep=T)

Notes for Debian GNU/Linux
Install the dependiencies. Attention! python-rpy IS NOT SUITABLE.:
aptitude install R python-rpy2
To install R packages, use either R’s functions listed above (as root or as user), either
the Debian packages [5], add to repositories’ list for 32bit or 64bit (pick up the
suitable line):
deb http://debian.cran.r-project.org/cran2deb/debian-i386 testing/
deb http://debian.cran.r-project.org/cran2deb/debian-amd64 testing/
and get the packages via aptitude:
aptitude install r-cran-gstat r-cran-rgrass7

Notes for Windows
Compile GRASS GIS following this guide. You could also use Linux in a virtual machine. Or
install Linux in a separate partition of the HD. This is not as painful as it appears,
there are lots of guides over the Internet to help you.

Computation time issues
Please note that although high number of input data points and/or high region resolution
contribute to a better output, both will also slow down the kriging calculation.

EXAMPLES

Kriging example based on elevation map (Spearfish data set).

Part 1: random sampling of 2000 vector points from known elevation map. Each point will
receive the elevation value from the elevation raster, as if it came from a point survey.
# reduce resolution for this example
g.region raster=elevation -p res=100
v.random output=rand2k_elev npoints=2000
v.db.addtable map=rand2k_elev columns="elevation double precision"
v.what.rast map=rand2k_elev raster=elevation column=elevation
Part 2: remove points lacking elevation attributes. Points sampled at the border of the
elevation map didn’t receive any value. v.krige has no preferred action to cope with no
data values, so the user must check for them and decide what to do (remove points, fill
with the value of the nearest point, fill with the global/local mean...). In the following
line of code, points with no data are removed from the map.
v.extract input=rand2k_elev output=rand2k_elev_filt where="elevation not NULL"
Check the result of previous line ("number of NULL attributes" must be 0):
v.univar map=rand2k_elev_filt type=point column=elevation
Part 3: reconstruct DEM through kriging. The simplest way to run v.krige from CLI is using
automatic variogram fit (note: requires R’s automap package). Output map name is optional,
the modules creates it automatically appending "_kriging" to the input map name and also
checks for overwrite. If output_var is specified, the variance map is also created.
Automatic variogram fit is provided by R package automap. The variogram models tested by
the fitting functions are: exponential, spherical, Gaussian, Matern, M.Stein’s
parametrisation. A wider range of models is available from gstat package and can be tested
on the GUI via the variogram plotting. If a model is specified in the CLI, also sill,
nugget and range values are to be provided, otherwise an error is raised (see second
example of v.krige command).
# automatic variogram fit
v.krige input=rand2k_elev_filt column=elevation \
output=rand2k_elev_kriging output_var=rand2k_elev_kriging_var
# define variogram model, create variance map as well
v.krige input=rand2k_elev_filt column=elevation \
output=rand2k_elev_filt_kriging output_var=rand2k_elev_filt_kriging_var \
model=Mat sill=2500 nugget=0 range=1000
Or run wxGUI, to interactively fit the variogram and explore options:
v.krige
Calculate prediction error:
r.mapcalc "rand2k_elev_kriging_pe = sqrt(rand2k_elev_kriging_var)"
r.univar map=elevation
r.univar map=rand2k_elev_kriging
r.univar map=rand2k_elev_kriging_pe
The results show high errors, as the kriging techniques (ordinary and block kriging) are
unable to handle a dataset with a trend, like the one used in this example: elevation is
higher in the southwest corner and lower on northeast corner. Universal kriging can give
far better results in these cases as it can handle the trend. It is available in R package
gstat and will be part in a future v.krige release.

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