r.random.cells(1grass) GRASS GIS User's Manual r.random.cells(1grass)
NAME
r.random.cells - Generates random cell values with spatial dependence.
KEYWORDS
raster, sampling, random, autocorrelation
SYNOPSIS
r.random.cells
r.random.cells --help
r.random.cells output=name distance=float [ncells=integer] [seed=in-
teger] [--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:
output=name [required]
Name for output raster map
distance=float [required]
Maximum distance of spatial correlation (value >= 0.0)
ncells=integer
Maximum number of cells to be created
Options: 1-
seed=integer
Random seed, default [random]
DESCRIPTION
r.random.cells generates a random sets of raster cells that are at
least distance apart. The cells are numbered from 1 to the numbers of
cells generated, all other cells are NULL (no data). Random cells will
not be generated in areas masked off.
Detailed parameter description
output
Random cells. Each random cell has a unique non-zero cell value
ranging from 1 to the number of cells generated. The heuristic for
this algorithm is to randomly pick cells until there are no cells
outside of the chosen cell’s buffer of radius distance.
distance
Determines the minimum distance the centers of the random cells
will be apart.
seed
Specifies the random seed that r.random.cells will use to generate
the cells. If the random seed is not given, r.random.cells will get
a seed from the process ID number.
NOTES
The original purpose for this program was to generate independent ran-
dom samples of cells in a study area. The distance value is the amount
of spatial autocorrelation for the map being studied.
EXAMPLES
Random cells in given distances
North Carolina sample dataset example:
g.region n=228500 s=215000 w=630000 e=645000 res=100 -p
r.random.cells output=random_500m distance=500
Limited number of random points
Here is another example where we will create given number of vector
points with the given minimal distances. Let’s star with setting the
region (we use large cells here):
g.region raster=elevation
g.region rows=20 cols=20 -p
Then we generate random cells and we limit their count to 20:
r.random.cells output=random_cells distance=1500 ncells=20 seed=200
Finally, we convert the raster cells to points using r.to.vect module:
r.to.vect input=random_cells output=random_points type=point
An example of the result is at the Figure below on the left in compari-
son with the result without the cell limit on the right.
Additionally, we can use v.perturb module to add random spatial devia-
tion to their position so that they are not perfectly aligned with the
grid. We cannot perturb the points too much, otherwise we might seri-
ously break the minimal distance we set earlier.
v.perturb input=random_points output=random_points_moved parameters=50 seed=200
In the above examples, we were using fixed seed. This is advantageous
when we want to generate (pseudo) random data, but we want to get re-
producible results at the same time.
Figure: Generated cells with limited number of cells (upper left), de-
rived vector points (lower left), cells without a count limit (upper
right) and corresponding vector points (lower right)
REFERENCES
Random Field Software for GRASS GIS by Chuck Ehlschlaeger
As part of my dissertation, I put together several programs that help
GRASS (4.1 and beyond) develop uncertainty models of spatial data. I
hope you find it useful and dependable. The following papers might
clarify their use:
• Ehlschlaeger, C.R., Shortridge, A.M., Goodchild, M.F., 1997.
Visualizing spatial data uncertainty using animation. Comput-
ers & Geosciences 23, 387-395.
doi:10.1016/S0098-3004(97)00005-8
• Modeling Uncertainty in Elevation Data for Geographical Analy-
sis, by Charles R. Ehlschlaeger, and Ashton M. Shortridge.
Proceedings of the 7th International Symposium on Spatial Data
Handling, Delft, Netherlands, August 1996.
• Dealing with Uncertainty in Categorical Coverage Maps: Defin-
ing, Visualizing, and Managing Data Errors, by Charles
Ehlschlaeger and Michael Goodchild. Proceedings, Workshop on
Geographic Information Systems at the Conference on Information
and Knowledge Management, Gaithersburg MD, 1994.
• Uncertainty in Spatial Data: Defining, Visualizing, and Manag-
ing Data Errors, by Charles Ehlschlaeger and Michael Goodchild.
Proceedings, GIS/LIS’94, pp. 246-253, Phoenix AZ, 1994.
SEE ALSO
r.random.surface, r.random, v.random, r.to.vect, v.perturb
AUTHOR
Charles Ehlschlaeger; National Center for Geographic Information and
Analysis, University of California, Santa Barbara.
SOURCE CODE
Available at: r.random.cells source code (history)
Accessed: unknown
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