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:

-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

AUTHOR

Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

SOURCE CODE

Available at: t.vect.univar source code (history)