v.class  Classifies attribute data, e.g. for thematic mapping
vector, classification, attribute table, statistics
v.class
v.class help
v.class [
g]
map=
name [
layer=
string]
column=
name [
where=
sql_query]
algorithm=
string nbclasses=
integer [
help]
[
verbose] [
quiet] [
ui]
 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
v.class classifies vector attribute data into classes, for example for
thematic mapping. Classification can be on a column or on an expression
including several columns, all in the table linked to the vector map. The user
indicates the number of classes desired and the algorithm to use for
classification. Several algorithms are implemented for classification: equal
interval, standard deviation, quantiles, equal probabilities, and a
discontinuities algorithm developed by JeanPierre Grimmeau at the Free
University of Brussels (ULB). It can be used to pipe class breaks into
thematic mapping modules such as
d.vect.thematic (see example below);
The
equal interval algorithm simply divides the range maxmin 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 minmax 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 minmax 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 twosegmented 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.
Classify column pop of map communes into 5 classes using quantiles:
v.class map=communes column=pop algo=qua nbclasses=5
This example uses population and area to calculate a population density and to
determine the density classes:
v.class map=communes column=pop/area algo=std nbclasses=5
The following example uses the output of d.class and feeds it directly into
d.vect.thematic:
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
v.univar, d.vect.thematic
Moritz Lennert
Last changed: $Date: 20180616 10:49:17 +0200 (Sat, 16 Jun 2018) $
Available at: v.class source code (history)
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