Provided by: grass-doc_7.0.3-1build1_all 

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 [seed=integer] [--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)
seed=integer
Random seed (SEED_MIN >= value >= SEED_MAX) (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 0 (zero). 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 random samples of cells in a study
area. The distance value is the amount of spatial autocorrelation for the map being studied.
EXAMPLE
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
# optionally set 0 to NULL (masked off areas)
r.null random_500m setnull=0
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. Computers & Geosciences 23, 387-395.
doi:10.1016/S0098-3004(97)00005-8
• Modeling Uncertainty in Elevation Data for Geographical Analysis, 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: Defining, 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 Managing 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
AUTHOR
Charles Ehlschlaeger; National Center for Geographic Information and Analysis, University of California,
Santa Barbara.
Last changed: $Date: 2015-04-21 16:00:05 +0200 (Tue, 21 Apr 2015) $
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