Provided by: grass-doc_7.6.1-3_all

**NAME**

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

**KEYWORDS**

vector, classification, attribute table, statistics

**SYNOPSIS**

v.classv.class--helpv.class[-g]map=name[layer=string]column=name[where=sql_query]algorithm=stringnbclasses=integer[--help] [--verbose] [--quiet] [--ui]Flags:-gPrint only class breaks (without min and max)--helpPrint usage summary--verboseVerbose module output--quietQuiet module output--uiForce launching GUI dialogParameters:map=name[required]Name of vector map Or data source for direct OGR accesslayer=stringLayer 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:1column=name[required]Column name or expressionwhere=sql_queryWHERE conditions of SQL statement without ’where’ keyword Example: income < 1000 and population >= 10000algorithm=string[required]Algorithm to use for classification Options:int,std,qua,equ,disint: simple intervalsstd: standard deviationsqua: quantilesequ: equiprobable (normal distribution)nbclasses=integer[required]Number of classes to define

**DESCRIPTION**

v.classclassifies 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 Jean-Pierre Grimmeau at the Free University of Brussels (ULB). It can be used to pipe class breaks into thematic mapping modules such asd.vect.thematic(see example below);

**NOTES**

Theequalintervalalgorithm simply divides the range max-min by the number of breaks to determine the interval between class breaks. Thequantilesalgorithm creates classes which all contain approximately the same number of observations. Thestandarddeviationsalgorithm 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. Theequiprobabilitesalgorithm 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. Thediscontalgorithm 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.

**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 intod.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 LennertLastchanged:$Date:2018-06-1202:46:28+0200(Tue,12Jun2018)$

**SOURCE** **CODE**

Available at: v.class source code (history) Main index | Vector index | Topics index | Keywords index | Graphical index | Full index © 2003-2019 GRASS Development Team, GRASS GIS 7.6.1 Reference Manual