r.random.cells.1grass man page

r.random.cells — Generates random cell values with spatial dependence.


raster, sampling, random, autocorrelation


r.random.cells --help
r.random.cells output=name distance=float  [seed=integer]   [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]



Allow output files to overwrite existing files


Print usage summary


Verbose module output


Quiet module output


Force launching GUI dialog


output=name [required]

Name for output raster map

distance=float [required]

Maximum distance of spatial correlation (value >= 0.0)


Random seed (SEED_MIN >= value >= SEED_MAX) (default [random])


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


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.


Determines the minimum distance the centers of the random cells will be apart.


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.


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.


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


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


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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