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

       Full index

       © 2003-2013 GRASS Development Team