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