table of contents
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]
column=name [twhere=sql_query]
[where=sql_query] [type=string]
[separator=character] [--overwrite] [--help]
[--verbose] [--quiet] [--ui]
Flags:¶
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 access 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: Saturday Jul 27 17:09:15 2024
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