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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