Flags:

-z

Create 3D output

-a

Generate n points for each individual area (requires restrict parameter)

-b

Do not build topology
Advantageous when handling a large number of points

--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 vector map

npoints=integer [required]

Number of points to be created

restrict=name

Name of input vector map
Restrict points to areas in input vector

layer=string

Layer number or name (’-1’ for all layers)
A single vector map can be connected to multiple database tables. This number determines which table to use. When used with direct OGR access this is the layer name.
Default: -1

cats=range

Category values
Example: 1,3,7-9,13

where=sql_query

WHERE conditions of SQL statement without ’where’ keyword
Example: income < 1000 and population >= 10000

zmin=float

Minimum z height (needs -z flag or column name)
Default: 0.0

zmax=float

Maximum z height (needs -z flag or column name)
Default: 0.0

seed=integer

The seed to initialize the random generator. If not set the process ID is used

column=name

Name of column for z values
Writes z values to column

column_type=string

Type of column for z values
Options: integer, double precision
Default: double precision

Restriction to vector areas

If the -a flag is given, the requested number of points is generated for each individual area. For example, if 20 points should be generated and the input map has 100 individual areas, 2000 points will be generated in total.

Attributes attached to restrict vector map are also transferred if the layer parameter is defined > 0, see example below.

Generating random points in 2D

v.random output=binary_random npoints=20 zmin=0 zmax=1 column=’binary’
v.db.select binary_random
cat|binary
1|0.63495
2|0.233421
3|0.489302
4|0.748264
5|0.505556
6|0.32975
[...]
v.univar -d binary_random
Calculating geometric distances between 20 primitives...
[...]
minimum: 148.515
maximum: 16572.8
[...]

Generating random points in 2D with binary attributes

v.random output=binary_random npoints=20 zmin=0 zmax=1 column=’binary’ column_type=integer
v.db.select binary_random
cat|binary
1|0
2|0
3|0
4|0
5|1
6|0
[...]

Generating random points in 3D

v.random seed=52 output=height_random npoints=40 zmin=110 zmax=170 -z
v.univar -d height_random
Calculating geometric distances between 40 primitives...
[...]
minimum: 334.889
maximum: 18351.9
range: 18017
sum: 5.38425e+06
mean: 7266.2
mean of absolute values: 7266.2
population standard deviation: 3563.95
[...]
skewness: 0.34703

Random points with different X, Y, and Z coordinates

Generating random points in selected areas

v.random restrict=zipcodes_wake output=zipcodes_local_random_n3 npoints=3 where="ZIPNAME = ’RALEIGH’" -a
# visualization
d.mon wx0
d.vect zipcodes_wake
d.vect zipcodes_wake fcolor=yellow where="ZIPNAME = ’RALEIGH’"
d.vect zipcodes_local_random_n3 color=red icon=basic/circle

Generating random adjacent areas

g.region vector=nc_state
v.random output=randpoints6k npoints=6000
v.voronoi input=randpoints6k output=randareas6k
v.info -t randareas6k
v.category randareas6k option=print
# plot vector areas
d.mon wx0
d.vect randareas6k -c

Random adjacent areas from random points (here: used as centroids)

To eventually obtain isolated areas, selected areas can be extracted with v.extract.

These vector areas can also be rasterized:

# rasterize areas
# note: rastermaps must result in at least 6k pixel in this example
g.region vector=nc_state res=500 -p -a
v.to.rast randareas6k out=randareas6k use=cat
r.colors randareas6k color=random
d.rast randareas6k

Random sampling from raster map

g.region -p raster=elevation
v.random output=random_samples npoints=20
v.db.addtable map=random_samples columns=’cat INTEGER, sample DOUBLE PRECISION’
v.what.rast map=random_samples raster=elevation column=sample
v.db.select random_samples
cat|sample
1|103.9935
2|129.1266
3|96.01388
[...]

Random sampling from vector map

g.region -p vector=geology
v.random output=random_samples npoints=20
v.db.addtable map=random_samples columns=’cat integer, geology varchar(100)’
v.what.vect map=random_samples column=geology query_map=geology query_layer=1 query_column=GEO_NAME
v.db.select random_samples
cat|geology
1|PzZm
2|
3|Zatm
[...]

Stratified random sampling: Random sampling from vector map by attribute

g.region -p raster=landclass96
r.to.vect -v input=landclass96 output=landclass96 type=area
v.random restrict=landclass96 output=random_samples npoints=20 where="label = ’forest’" layer=1
v.db.select map=random_samples
cat|landclass96_cat|landclass96_label
1|5|forest
2|5|forest
3|5|forest
...

Random points only sampled in forested areas (stratified random sampling)

Stratified random sampling: Random sampling from vector map with spatial constraints

g.region -p raster=landclass96
r.to.vect -v input=landclass96 output=landclass96 type=area
v.random restrict=landclass96 output=random_samples npoints=2 where="label = ’water’" layer=1 -a

Two random points sampled in each individual water body (stratified random sampling)