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.

EXAMPLES

   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)

       Accessed: Monday Apr 01 03:07:49 2024

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       © 2003-2024 GRASS Development Team, GRASS GIS 8.3.2 Reference Manual

GRASS 8.3.2                                                                               r.random.cells(1grass)