NAME
v.db.univar - Calculates univariate statistics on selected table column for a GRASS vector map.
KEYWORDS
vector,
statistics,
attribute table
SYNOPSIS
v.db.univar
v.db.univar --help
v.db.univar [-eg] map=name [layer=string] column=name [where=sql_query] [percentile=float[,float,...]] [format=string] [--help] [--verbose] [--quiet] [--ui]
Flags:
- -e
- Extended statistics (quartiles and 90th percentile)
- -g
- Print stats in shell script style
- --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
- column=name [required]
- Name of attribute column on which to calculate statistics (must be numeric)
- where=sql_query
- WHERE conditions of SQL statement without 'where' keyword
- Example: income < 1000 and population >= 10000
- percentile=float[,float,...]
- Percentile to calculate (requires extended statistics flag)
- Options: 0-100
- Default: 90
- format=string
- Output format
- Options: plain, json, shell
- plain: Plain text output
- json: JSON (JavaScript Object Notation)
- shell: Shell script style for Bash eval
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.
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
[...]
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
This uses the JSON output of the module which is passed using
a pipe (in Bash or other unix-like shell) to the
jq tool
which selects just the relevant statistic.
v.db.univar precip_30ynormals column=annual format=json | jq .statistics.mean
db.univar,
r.univar,
v.univar,
db.select,
d.vect.thematic,
v.random
GRASS SQL interface
Michael Barton, Arizona State University
and authors of r.univar.sh (Markus Neteler et al.)
SOURCE CODE
Available at:
v.db.univar source code
(history)
Latest change: Thursday Jan 26 14:10:26 2023 in commit: cdd84c130cea04b204479e2efdc75c742efc4843
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GRASS Development Team,
GRASS GIS 8.4.1dev Reference Manual