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v.class(1)		    GRASS GIS User's Manual		    v.class(1)

       v.class	- Classifies attribute data, e.g. for thematic mapping

       vector, classification, attribute table,	statistics

       v.class --help
       v.class	[-g]  map=name	[layer=string]	column=name  [where=sql_query]
       algorithm=string	nbclasses=integer   [--help]   [--verbose]   [--quiet]

	   Print only class breaks (without min	and max)

	   Print usage summary

	   Verbose module output

	   Quiet module	output

	   Force launching GUI dialog

       map=nameA [required]
	   Name	of vector map
	   Or data source for direct OGR access

	   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=nameA [required]
	   Column name or expression

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

       algorithm=stringA [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=integerA [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 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);

       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-

       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.

       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` \

	v.univar, d.vect.thematic

       Moritz Lennert

       Available at: v.class source code (history)

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       A(C) 2003-2020 GRASS Development	Team, GRASS GIS	7.8.3 Reference	Manual

GRASS 7.8.3							    v.class(1)


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