1v.class(1) Grass User's Manual v.class(1)
2
6 v.class - Classifies attribute data, e.g. for thematic mapping
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9 vector, classification, attribute table, statistics
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12 v.class
13 v.class --help
14 v.class [-g] map=name [layer=string] column=name [where=sql_query]
15 algorithm=string nbclasses=integer [--help] [--verbose] [--quiet]
16 [--ui]
17 Flags:
19 -g
20 Print only class breaks (without min and max)
21 --help
23 Print usage summary
24 --verbose
26 Verbose module output
27 --quiet
29 Quiet module output
30 --ui
32 Force launching GUI dialog
33 Parameters:
35 map=name [required]
36 Name of vector map
37 Or data source for direct OGR access
38 layer=string
40 Layer number or name
41 Vector features can have category values in different layers. This
42 number determines which layer to use. When used with direct OGR
43 access this is the layer name.
44 Default: 1
45 column=name [required]
47 Column name or expression
48 where=sql_query
50 WHERE conditions of SQL statement without ’where’ keyword
51 Example: income < 1000 and population >= 10000
52 algorithm=string [required]
54 Algorithm to use for classification
55 Options: int, std, qua, equ, dis
56 int: simple intervals
57 std: standard deviations
58 qua: quantiles
59 equ: equiprobable (normal distribution)
60 nbclasses=integer [required]
62 Number of classes to define
63
65 v.class classifies vector attribute data into classes, for example for
66 thematic mapping. Classification can be on a column or on an expression
67 including several columns, all in the table linked to the vector map.
68 The user indicates the number of classes desired and the algorithm to
69 use for classification. Several algorithms are implemented for classi‐
70 fication: equal interval, standard deviation, quantiles, equal proba‐
71 bilities, and a discontinuities algorithm developed by Jean-Pierre
72 Grimmeau at the Free University of Brussels (ULB). It can be used to
73 pipe class breaks into thematic mapping modules such as d.vect.thematic
74 (see example below);
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77 The equal interval algorithm simply divides the range max-min by the
78 number of breaks to determine the interval between class breaks.
79 The quantiles algorithm creates classes which all contain approximately
81 the same number of observations.
82 The standard deviations algorithm creates class breaks which are a com‐
84 bination of the mean +/- the standard deviation. It calculates a scale
85 factor (<1) by which to multiply the standard deviation in order for
86 all of the class breaks to fall into the range min-max of the data val‐
87 ues.
88 The equiprobabilites algorithm creates classes that would be equiproba‐
90 ble if the distribution was normal. If some of the class breaks fall
91 outside the range min-max of the data values, the algorithm prints a
92 warning and reduces the number of breaks, but the probabilities used
93 are those of the number of breaks asked for.
94 The discont algorithm systematically searches discontinuities in the
96 slope of the cumulated frequencies curve, by approximating this curve
97 through straight line segments whose vertices define the class breaks.
98 The first approximation is a straight line which links the two end
99 nodes of the curve. This line is then replaced by a two-segmented poly‐
100 line whose central node is the point on the curve which is farthest
101 from the preceding straight line. The point on the curve furthest from
102 this new polyline is then chosen as a new node to create break up one
103 of the two preceding segments, and so forth. The problem of the differ‐
104 ence in terms of units between the two axes is solved by rescaling both
105 amplitudes to an interval between 0 and 1. In the original algorithm,
106 the process is stopped when the difference between the slopes of the
107 two new segments is no longer significant (alpha = 0.05). As the slope
108 is the ratio between the frequency and the amplitude of the correspond‐
109 ing interval, i.e. its density, this effectively tests whether the fre‐
110 quencies of the two newly proposed classes are different from those
111 obtained by simply distributing the sum of their frequencies amongst
112 them in proportion to the class amplitudes. In the GRASS implementa‐
113 tion, the algorithm continues, but a warning is printed.
114
116 Classify column pop of map communes into 5 classes using quantiles:
117 v.class map=communes column=pop algo=qua nbclasses=5
118 This example uses population and area to calculate a population density
119 and to determine the density classes:
120 v.class map=communes column=pop/area algo=std nbclasses=5
121 The following example uses the output of d.class and feeds it directly
122 into d.vect.thematic:
123 d.vect.thematic -l map=communes2 column=pop/area \
124 breaks=`v.class -g map=communes2 column=pop/area algo=std nbcla=5` \
125 colors=0:0:255,50:100:255,255:100:50,255:0:0,156:0:0
126
128 v.univar, d.vect.thematic
129
131 Moritz Lennert
132 Last changed: $Date: 2018-06-12 02:46:28 +0200 (Tue, 12 Jun 2018) $
134
136 Available at: v.class source code (history)
137 Main index | Vector index | Topics index | Keywords index | Graphical
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140 © 2003-2019 GRASS Development Team, GRASS GIS 7.6.0 Reference Manual
142GRASS 7.6.0 v.class(1)