v.univar(1)

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] format=name [--help] [--verbose] [--quiet] [--ui]

  Flags:

-g
    Print the stats in shell script style [deprecated]
    This flag is deprecated and will be removed in a future release. Use format=shell instead.

-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 population >= 10000

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

format=name [required]
    Output format
    Options: plain, shell, json
    Default: plain
    plain: Human readable text output
    shell: shell script style text output
    json: JSON (JavaScript Object Notation)

DESCRIPTION
v.univar calculates univariate statistics on (by default) an attribute of, or, through the -d flag on distance between, vector map features. Attributes are read per feature and per category value. This means that if the map contains several features with the same category value, the attribute is read as many times as there are features. On the other hand, if a feature has more than one category value, each attribute value linked to each of the category values of the feature is read. For statistics on one attribute per category value, instead of one attribute per feature and per category, see v.db.univar.

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

NOTES
When using the -d flag, univariate statistics of distances between vector features are calculated. The distances from all features to all other features are used. Since the distance from feature A to feature B is the same like the distance from feature B to feature A, that distance is considered only once, i.e. all pairwise distances between features are used. Depending on the selected vector type, distances are calculated as follows:

    •   type=point: point distances are considered;

    •   type=line: line to line distances are considered;

    •   type=area: not supported, use type=centroid instead (and see v.distance for calculating distances between areas)

EXAMPLES
The examples are based on the North Carolina sample dataset.

  Example dataset preparation

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 statistic of distance between sampling points

v.univar -d samples 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

  Output in JSON format

v.univar -e samples column=heights type=point format=json

will output the results in JSON format:

{
    "n": 1832,
    "missing": 0,
    "nnull": 0,
    "min": 166.946991,
    "max": 2729482.25,
    "range": 2729315.3030090001,
    "sum": 78876146.145385057,
    "mean": 43054.664926520229,
    "mean_abs": 43054.664926520229,
    "population_stddev": 132689.08650029532,
    "population_variance": 17606393676.282852,
    "population_coeff_variation": 3.0818747916573215,
    "sample_stddev": 132725.31560308655,
    "sample_variance": 17616009401.938931,
    "kurtosis": 139.15698418811229,
    "skewness": 9.7065048189730767,
    "first_quartile": 3699.3234859999998,
    "median": 10308.4453125,
    "third_quartile": 29259.074218999998,
    "percentiles": [
        {
            "percentile": 90,
            "value": 86449.734375
        }
    ]
}

SEE ALSO
db.univar, r.univar, v.db.univar, v.distance, v.neighbors, v.qcount

AUTHORS
Radim Blazek, ITC-irst

extended by:
Hamish Bowman, University of Otago, New Zealand
Martin Landa

SOURCE CODE

Available at: v.univar source code (history)

Accessed: Saturday May 09 13:03:04 2026

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