v.univargrass - Online in the Cloud

PROGRAM:

NAME

v.univar - Calculates univariate statistics of vector map features.
Variance and standard deviation is calculated only for points if specified.

KEYWORDS

vector, statistics, univariate statistics, attribute table, geometry

SYNOPSIS

v.univar
v.univar --help
v.univar [-gewd] map=name [layer=string] [type=string[,string,...]] [column=name]
[where=sql_query] [percentile=integer] [--help] [--verbose] [--quiet] [--ui]

Flags:
-g
Print the stats in shell script style

-e
Calculate extended statistics

-w
Weigh by line length or area size

-d
Calculate geometric distances instead of attribute statistics

--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

type=string[,string,...]
Input feature type
Options: point, line, boundary, centroid, area
Default: point,line,area

column=name
Name of attribute column

where=sql_query
WHERE conditions of SQL statement without ’where’ keyword
Example: income < 1000 and inhab >= 10000

percentile=integer
Percentile to calculate (requires extended statistics flag)
Options: 0-100
Default: 90

DESCRIPTION

v.univar calculates univariate statistics of vector map features. This includes the
number of features counted, minimum, maximum values, and range. Variance and standard
deviation is calculated only for points if type=point is defined.

Extended statistics (-e) adds median, 1st and 3rd quartiles, and 90th percentile to the
output.

NOTES

When using the -d flag, univariate statistics of vector geometry are calculated. Depending
on the selected vector type, distances are calculated as follows:

· type=point: point distances are considered;

· type=line: the first point of each line is considered;

· type=area: the centroid of each area is considered.

EXAMPLE

The example is based on the North Carolina sample dataset:
g.region raster=elevation -p
v.random output=samples npoints=100
v.db.addtable map=samples columns="heights double precision"
v.what.rast map=samples rast=elevation column=heights
v.db.select map=samples
Calculate height attribute statistics:
v.univar -e samples column=heights type=point
number of features with non NULL attribute: 100
number of missing attributes: 0
number of NULL attributes: 0
minimum: 57.2799
maximum: 148.903
range: 91.6235
sum: 10825.6
mean: 108.256
mean of absolute values: 108.256
population standard deviation: 20.2572
population variance: 410.356
population coefficient of variation: 0.187123
sample standard deviation: 20.3593
sample variance: 414.501
kurtosis: -0.856767
skewness: 0.162093
1st quartile: 90.531
median (even number of cells): 106.518
3rd quartile: 126.274
90th percentile: 135.023
Compare to statistics of original raster map:
r.univar -e elevation
total null and non-null cells: 2025000
total null cells: 0
Of the non-null cells:
----------------------
n: 2025000
minimum: 55.5788
maximum: 156.33
range: 100.751
mean: 110.375
mean of absolute values: 110.375
standard deviation: 20.3153
variance: 412.712
variation coefficient: 18.4057 %
sum: 223510266.558102
1st quartile: 94.79
median (even number of cells): 108.88
3rd quartile: 126.792
90th percentile: 138.66
Calculate distance between sampling points statistics:
v.univar -d samples column=heights type=point
number of primitives: 100
number of non zero distances: 4851
number of zero distances: 0
minimum: 69.9038
maximum: 18727.7
range: 18657.8
sum: 3.51907e+07
mean: 7254.33
mean of absolute values: 7254.33
population standard deviation: 3468.53
population variance: 1.20307e+07
population coefficient of variation: 0.478132
sample standard deviation: 3468.89
sample variance: 1.20332e+07
kurtosis: -0.605406
skewness: 0.238688

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