v.univar — Calculates univariate statistics of vector map features.
Variance and standard deviation is calculated only for points if specified.
vector, statistics, univariate statistics, attribute table, geometry
v.univar [-gewd] map=name [layer=string] [type=string[,string,...]] [column=name] [where=sql_query] [percentile=integer] [--help] [--verbose] [--quiet] [--ui]
Print the stats in shell script style
Calculate extended statistics
Weigh by line length or area size
Calculate geometric distances instead of attribute statistics
Print usage summary
Verbose module output
Quiet module output
Force launching GUI dialog
- map=nameÂ [required]
Name of vector map
Or data source for direct OGR access
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.
Input feature type
Options: point, line, boundary, centroid, area
Name of attribute column
WHERE conditions of SQL statement without ’where’ keyword
Example: income < 1000 and population >= 10000
Percentile to calculate (requires extended statistics flag)
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.
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)
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
db.univar, r.univar, v.db.univar, v.distance, v.neighbors, v.qcount
Radim Blazek, ITC-irst
Hamish Bowman, University of Otago, New Zealand
Available at: v.univar source code (history)
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