r.random.cells.1grass - Man Page

--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

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]

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.

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 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)