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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.

       Last changed: $Date: 2015-10-10 22:01:15 +0200 (Sat, 10 Oct 2015) $

SOURCE CODE

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

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