**-g**- Print only class breaks (without min and max)
**--help**- Print usage summary
**--verbose**- Verbose module output
**--quiet**- Quiet module output
**--ui**- Force launching GUI dialog

**map**=*name***[required]**- Name of vector map
- Or data source for direct OGR access
**layer**=*string*- Layer number or name
- Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.
- Default:
*1* **column**=*name***[required]**- Column name or expression
**where**=*sql_query*- WHERE conditions of SQL statement without 'where' keyword
- Example: income < 1000 and population >= 10000
**algorithm**=*string***[required]**- Algorithm to use for classification
- Options:
*int, std, qua, equ, dis* **int**: simple intervals**std**: standard deviations**qua**: quantiles**equ**: equiprobable (normal distribution)**nbclasses**=*integer***[required]**- Number of classes to define

The *equal interval* algorithm simply divides the range max-min
by the number of breaks to determine the interval between class breaks.

The *quantiles* algorithm creates classes which all contain
approximately the same number of observations.

The *standard deviations* algorithm creates class breaks which
are a combination of the mean +/- the standard deviation. It calculates
a scale factor (<1) by which to multiply the standard deviation in
order for all of the class breaks to fall into the range min-max of the
data values.

The *equiprobabilites* algorithm creates classes that would be
equiprobable if the distribution was normal. If some of the class breaks
fall outside the range min-max of the data values, the algorithm prints
a warning and reduces the number of breaks, but the probabilities used
are those of the number of breaks asked for.

The *discont* algorithm systematically searches discontinuities
in the slope of the cumulated frequencies curve, by approximating this
curve through straight line segments whose vertices define the class
breaks. The first approximation is a straight line which links the two
end nodes of the curve. This line is then replaced by a two-segmented
polyline whose central node is the point on the curve which is farthest
from the preceding straight line. The point on the curve furthest from
this new polyline is then chosen as a new node to create break up one of
the two preceding segments, and so forth. The problem of the difference
in terms of units between the two axes is solved by rescaling both
amplitudes to an interval between 0 and 1. In the original algorithm,
the process is stopped when the difference between the slopes of the two
new segments is no longer significant (alpha = 0.05). As the slope is
the ratio between the frequency and the amplitude of the corresponding
interval, i.e. its density, this effectively tests whether the frequencies
of the two newly proposed classes are different from those obtained by
simply distributing the sum of their frequencies amongst them in proportion
to the class amplitudes. In the GRASS implementation, the algorithm
continues, but a warning is printed.

v.class map=communes column=pop algo=qua nbclasses=5

v.class map=communes column=pop/area algo=std nbclasses=5

d.vect.thematic -l map=communes2 column=pop/area \ breaks=`v.class -g map=communes2 column=pop/area algo=std nbcla=5` \ colors=0:0:255,50:100:255,255:100:50,255:0:0,156:0:0

*Last changed: $Date: 2018-06-11 17:46:28 -0700 (Mon, 11 Jun 2018) $*

Available at: v.class source code (history)

Main index | Vector index | Topics index | Keywords index | Graphical index | Full index

© 2003-2018 GRASS Development Team, GRASS GIS 7.5.svn Reference Manual