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 access 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 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.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 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 cumulative 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.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: Friday Apr 04 01:20:11 2025

       Main index | Vector index | Topics index | Keywords index | Graphical index | Full index

       © 2003-2025 GRASS Development Team, GRASS GIS 8.4.1 Reference Manual

GRASS 8.4.1                                                                                      v.class(1grass)