v.class(1grass) GRASS GIS User's Manual v.class(1grass)
NAME
v.class - Classifies attribute data, e.g. for thematic mapping
KEYWORDS
vector, classification, attribute table, statistics
SYNOPSIS
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]
Flags:
-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
Parameters:
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 ac-
cess 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
DESCRIPTION
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 classi-
fication: equal interval, standard deviation, quantiles, equal proba-
bilities, and a discontinuities algorithm developed by Jean-Pierre
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);
NOTES
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 com-
bination 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 val-
ues.
The equiprobabilites algorithm creates classes that would be equiproba-
ble 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 poly-
line 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 differ-
ence 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 correspond-
ing interval, i.e. its density, this effectively tests whether the fre-
quencies of the two newly proposed classes are different from those ob-
tained 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.
EXAMPLE
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
SEE ALSO
v.univar, d.vect.thematic
AUTHOR
Moritz Lennert
SOURCE CODE
Available at: v.class source code (history)
Accessed: unknown
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GRASS 7.8.7 v.class(1grass)
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