DESCRIPTION

v.db.univar calculates basic univariate statistics for numeric attributes in a vector attribute table. It will calculate minimum, maximum, range, mean, standard deviation, variance, coefficient of variation, quartiles, median, and 90th percentile.

v.db.univar uses db.univar which in turn uses db.select to get the attribute values on which it calculates the statistics. This means that statistics are calculated based on the entries in the attribute table, not based on the features in the map. One attribute value is read from each line in the attribute table, whether there are no, one or several features with the category value referenced by that line, or whether any features have more than one category value. For feature-based, instead of attribute table-based, univariate statistics on attributes see v.univar. NOTES A database connection must be defined for the selected vector layer.

EXAMPLES

Univariate statistics on attribute table column

In this example, the 30 years precipitation data table is statistically analysed (North Carolina sample dataset) and univariate statistics performed: 
# show columns of attribute table connected to precipitation map
v.info -c precip_30ynormals

# univariate statistics on 30 years annual precipitation in NC
v.db.univar precip_30ynormals column=annual
 Number of values: 136
 Minimum: 947.42
 Maximum: 2329.18
 Range: 1381.76
 Mean: 1289.31147058823
 [...]

Univariate statistics on randomly sampled data points

In this example, random points are sampled from the elevation map (North Carolina sample dataset) and univariate statistics performed: 
g.region raster=elevation -p
v.random output=samples n=100
v.db.addtable samples column="heights double precision"
v.what.rast samples raster=elevation column=heights
v.db.select samples

v.db.univar samples column=heights

SEE ALSO

db.univar, r.univar, v.univar, db.select, d.vect.thematic, v.random

AUTHOR

Michael Barton, Arizona State University

and authors of r.univar.sh (Markus Neteler et al.)

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