## NAME

**r.random.cells** - Generates random cell values with spatial dependence.
## KEYWORDS

raster
## SYNOPSIS

**r.random.cells**

**r.random.cells help**

**r.random.cells** **output**=*string* **distance**=*float* [**seed**=*integer*] [--**overwrite**]
### Flags:

**--overwrite**
- Force overwrite of output files

### Parameters:

**output**=*string*
- Name of indepent cells map
**distance**=*float*
- Input value: max. distance of spatial correlation (value(s) >= 0.0)
**seed**=*integer*
- Input value: random seed (SEED_MIN >= value >= SEED_MAX), default [random]

## DESCRIPTION

*r.random.cells* generates a random sets of cells that are at
least `distance` apart. The cells are numbered from 1 to the
numbers of cells generated. Random cells will not be generated in areas
masked off.
## PARAMETERS

**output ** Output map: 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** Input value(s) [default 0.0]: **distance** determines the
minimum distance the centers of the random cells will be apart.

**seed** Input value [default: random]: 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. The amount of spatial
autocorrelation can be determined by using *r.2Dcorrelogram* with
*r.2Dto1D*, or *r.1Dcorrelogram*. With **distance** set to
zero, the **output** map will number each non-masked cell from 1 to the
number of non-masked cells in the study region.
## REFERENCES

Random Field Software for GRASS 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:

"Visualizing Spatial Data
Uncertainty Using Animation (final draft)," by Charles R.
Ehlschlaeger, Ashton M. Shortridge, and Michael F. Goodchild. Submitted to
Computers in GeoSciences in September, 1996, accepted October, 1996 for
publication in June, 1997.

"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.1Dcorrelogram,
r.2Dcorrelogram,
r.2Dto1D,
r.random.surface,
r.random.model,
r.random
*
## AUTHOR

Charles Ehlschlaeger; National Center for Geographic Information and
Analysis, University of California, Santa Barbara.

*Last changed: $Date: 2006/04/20 21:31:23 $*

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