r.random.cells
Generates random cell values with spatial dependence.
r.random.cells output=name distance=float [ncells=integer] [seed=integer] [--overwrite] [--verbose] [--quiet] [--qq] [--ui]
Example:
r.random.cells output=name distance=float
grass.script.run_command("r.random.cells", output, distance, ncells=None, seed=None, overwrite=False, verbose=False, quiet=False, superquiet=False)
Example:
gs.run_command("r.random.cells", output="name", distance=float)
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
Allowed values: 1-
seed=integer
Seed value for the random number generator
Using the same seed ensures identical results, while a randomly generated seed produces different outcomes in each run.
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--qq
Very quiet module output
--ui
Force launching GUI dialog
output : str, required
Name for output raster map
Used as: output, raster, name
distance : float, required
Maximum distance of spatial correlation (value >= 0.0)
ncells : int, optional
Maximum number of cells to be created
Allowed values: 1-
seed : int, optional
Seed value for the random number generator
Using the same seed ensures identical results, while a randomly generated seed produces different outcomes in each run.
overwrite: bool, optional
Allow output files to overwrite existing files
Default: False
verbose: bool, optional
Verbose module output
Default: False
quiet: bool, optional
Quiet module output
Default: False
superquiet: bool, optional
Very quiet module output
Default: False
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)
Latest change: Thursday Mar 06 19:23:31 2025 in commit 5fd4234