NAME

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

KEYWORDS

       vector, statistics

SYNOPSIS

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

   Flags:
       -g
           Print only class breaks (without min and max)

       --verbose
           Verbose module output

       --quiet
           Quiet module output

   Parameters:
       map=name
           Name of input vector map

       layer=integer
           Layer number
           A single vector map can be connected to multiple database tables. This number determines which  table
           to use.
           Default: 1

       column=string
           Column name or expression

       where=sql_query
           WHERE conditions of SQL statement without 'where' keyword
           Example: income = 10000

       algorithm=string
           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
           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 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  as
       d.thematic.area (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  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.

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.thematic.area:
       d.thematic.area -l map=communes2 data=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.thematic.area

AUTHOR

       Moritz Lennert

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       © 2003-2013 GRASS Development Team