t.vect.univar(1grass) GRASS GIS User's Manual t.vect.univar(1grass)
NAME
t.vect.univar - Calculates univariate statistics of attributes for
each registered vector map of a space time vector dataset
KEYWORDS
temporal, statistics, vector, time
SYNOPSIS
t.vect.univar
t.vect.univar --help
t.vect.univar [-eu] input=name [output=name] [layer=string] col-
umn=name [twhere=sql_query] [where=sql_query] [type=string]
[separator=character] [--overwrite] [--help] [--verbose] [--quiet]
[--ui]
Flags:
-e
Calculate extended statistics
-u
Suppress printing of column names
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog
Parameters:
input=name [required]
Name of the input space time vector dataset
output=name
Name for output file
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 ac-
cess this is the layer name.
Default: 1
column=name [required]
Name of attribute column
twhere=sql_query
WHERE conditions of SQL statement without ’where’ keyword used in
the temporal GIS framework
Example: start_time > ’2001-01-01 12:30:00’
where=sql_query
WHERE conditions of SQL statement without ’where’ keyword
Example: income < 1000 and population >= 10000
type=string
Input feature type
Options: point, line, boundary, centroid, area
Default: point
separator=character
Field separator character between the output columns
Special characters: pipe, comma, space, tab, newline
Default: pipe
DESCRIPTION
The module t.vect.univar computes univariate statistics of a space time
vector dataset based on a single attribute row.
EXAMPLE
The example is based on the t.vect.observe.strds example; so create the
precip_stations space time vector dataset and after run the following
command:
t.vect.univar input=precip_stations col=month
id|start|end|n|nmissing|nnull|min|max|range|mean|mean_abs|population_stddev|population_variance|population_coeff_variation|sample_stddev|sample_variance|kurtosis|skewness
precip_stations_monthly@climate_2009_2012|2009-01-01 00:00:00|2009-02-01 00:00:00|132|0|4|-2.31832|7.27494|9.59326|3.44624|3.5316|1.79322|3.21564|0.520341|1.80005|3.24019|0.484515|-0.338519
precip_stations_monthly@climate_2009_2012|2009-02-01 00:00:00|2009-03-01 00:00:00|132|0|4|-0.654152|7.90613|8.56028|5.47853|5.48844|1.73697|3.01708|0.317051|1.74359|3.04011|0.875252|-1.0632
....
precip_stations_monthly@climate_2009_2012|2012-10-01 00:00:00|2012-11-01 00:00:00|132|0|4|9.67596|18.4654|8.78945|14.945|14.945|1.90659|3.6351|0.127574|1.91386|3.66285|-0.0848967|-0.700833
precip_stations_monthly@climate_2009_2012|2012-11-01 00:00:00|2012-12-01 00:00:00|132|0|4|3.56755|10.6211|7.05357|7.72153|7.72153|1.33684|1.78715|0.173132|1.34194|1.8008|0.90434|-0.863935
precip_stations_monthly@climate_2009_2012|2012-12-01 00:00:00|2013-01-01 00:00:00|132|0|4|3.04325|11.6368|8.5935|8.20147|8.20147|1.78122|3.17275|0.217183|1.78801|3.19697|-0.177991|-0.501295
SEE ALSO
t.create, t.info
AUTHOR
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
SOURCE CODE
Available at: t.vect.univar source code (history)
Accessed: unknown
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