NAME

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

KEYWORDS

       raster, random, cell

SYNOPSIS

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

   Flags:
       --overwrite
           Allow output files to overwrite existing files

       --verbose
           Verbose module output

       --quiet
           Quiet module output

   Parameters:
       output=name
           Name for output raster map

       distance=float
           Maximum distance of spatial correlation (value(s) >= 0.0)

       seed=integer
           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.

   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.

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:

                      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: 2011-10-07 12:53:04 -0700 (Fri, 07 Oct 2011) $

       Full index

       © 2003-2013 GRASS Development Team

GRASS 6.4.3                                                                               r.random.cells(1grass)