r.random.cellsgrass - Online in the Cloud

PROGRAM:

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.

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