t.vect.univar.1grass - Man Page
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
- -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 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 Nov 09 07:56:33 2024
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