- Generates random cell values with spatial dependence.
r.random.cells output=name distance=float [ncells=integer] [seed=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
- Allow output files to overwrite existing files
- Print usage summary
- Verbose module output
- Quiet module output
- Force launching GUI dialog
- output=name [required]
- Name for output raster map
- distance=float [required]
- Maximum distance of spatial correlation (value >= 0.0)
- Maximum number of cells to be created
- Options: 1-
- Random seed (SEED_MIN >= value >= SEED_MAX) (default [random])
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.
- 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.
- Determines the minimum distance the centers of the random cells
will be apart.
- 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
The original purpose for this program was to generate independent
random samples of cells in a study area. The distance
the amount of spatial autocorrelation for the map being studied.
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
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 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 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)
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
- 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
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.
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
in Spatial Data: Defining, Visualizing, and Managing Data
Errors, by Charles Ehlschlaeger and Michael
Goodchild. Proceedings, GIS/LIS'94, pp. 246-253, Phoenix AZ,
Charles Ehlschlaeger; National Center for Geographic Information and
Analysis, University of California, Santa Barbara.
Last changed: $Date: 2015-10-10 13:01:15 -0700 (Sat, 10 Oct 2015) $
Available at: r.random.cells source code (history)
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