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   [ncells=integer]   [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)

       ncells=integer
           Maximum number of cells to be created

           Options: 1-
       seed=integer
           Random seed, 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 NULL (no data). 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

   Random cells in given distances
       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

   Limited number of random points
       Here is another example where we will create given  number  of  vector  points  with  the  given  minimal
       distances.  Let’s star with setting the region (we use large cells here):
       g.region raster=elevation
       g.region rows=20 cols=20 -p
       Then we generate random cells and we limit their count to 20:
       r.random.cells output=random_cells distance=1500 ncells=20 seed=200
       Finally, we convert the raster cells to points using r.to.vect module:
       r.to.vect input=random_cells output=random_points type=point
       An  example  of  the  result is at the Figure below on the left in comparison with the result without the
       cell limit on the right.

       Additionally, we can use v.perturb module to add random spatial deviation to their position so that  they
       are  not  perfectly  aligned  with  the  grid.  We cannot perturb the points too much, otherwise we might
       seriously break the minimal distance we set earlier.
       v.perturb input=random_points output=random_points_moved parameters=50 seed=200
       In the above examples, we were using fixed seed. This is advantageous when we want to  generate  (pseudo)
       random data, but we want to get reproducible results at the same time.

         Figure:  Generated cells with limited number of cells (upper left), derived vector points (lower left),
       cells without a count limit (upper right) and corresponding vector points (lower right)

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, v.random, r.to.vect, v.perturb

AUTHOR

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

SOURCE CODE

       Available at: r.random.cells source code (history)

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual