t.rast.aggregategrass - Online in the Cloud

PROGRAM:

NAME

t.rast.aggregate - Aggregates temporally the maps of a space time raster dataset by a
user defined granularity.

KEYWORDS

temporal, aggregation, raster, time

SYNOPSIS

t.rast.aggregate
t.rast.aggregate --help
t.rast.aggregate [-ns] input=name output=name basename=string granularity=string
method=string [offset=integer] [nprocs=integer] [sampling=name[,name,...]]
[where=sql_query] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:
-n
Register Null maps

-s
Use start time - truncated according to granularity - as suffix (overrides offset
option)

--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 raster dataset

output=name [required]
Name of the output space time raster dataset

basename=string [required]
Basename of the new generated output maps
Either a numerical suffix or the start time (s-flag) separated by an underscore will
be attached to create a unique identifier

granularity=string [required]
Aggregation granularity, format absolute time "x years, x months, x weeks, x days, x
hours, x minutes, x seconds" or an integer value for relative time

method=string [required]
Aggregate operation to be performed on the raster maps
Options: average, count, median, mode, minimum, min_raster, maximum, max_raster,
stddev, range, sum, variance, diversity, slope, offset, detcoeff, quart1, quart3,
perc90, quantile, skewness, kurtosis
Default: average

offset=integer
Offset that is used to create the output map ids, output map id is generated as:
basename_ (count + offset)
Default: 0

nprocs=integer
Number of r.series processes to run in parallel
Default: 1

sampling=name[,name,...]
The method to be used for sampling the input dataset
Options: equal, overlaps, overlapped, starts, started, finishes, finished, during,
contains
Default: contains

where=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’

DESCRIPTION

t.rast.aggregate temporally aggregates space time raster datasets by a specific temporal
granularity. This module support absolute and relative time. The temporal granularity of
absolute time can be seconds, minutes, hours, days, weeks, months or years. Mixing of
granularities eg. "1 year, 3 months 5 days" is not supported. In case of relative time the
temporal unit of the input space time raster dataset is used. The granularity must be
specified with an integer value.

This module is sensitive to the current region and mask settings, hence spatial extent and
spatial resolution. In case the registered raster maps of the input space time raster
dataset have different spatial resolutions, the default nearest neighbor resampling method
is used for runtime spatial aggregation.

NOTES

The raster module r.series is used internally. Hence all aggregate methods of r.series are
supported. See the r.series manual page for details.

This module will shift the start date for each aggregation process depending on the
provided temporal granularity. The following shifts will performed:

· granularity years: will start at the first of January, hence 14-08-2012 00:01:30
will be shifted to 01-01-2012 00:00:00

· granularity months: will start at the first day of a month, hence 14-08-2012 will
be shifted to 01-08-2012 00:00:00

· granularity weeks: will start at the first day of a week (Monday), hence
14-08-2012 01:30:30 will be shifted to 13-08-2012 01:00:00

· granularity days: will start at the first hour of a day, hence 14-08-2012 00:01:30
will be shifted to 14-08-2012 00:00:00

· granularity hours: will start at the first minute of a hour, hence 14-08-2012
01:30:30 will be shifted to 14-08-2012 01:00:00

· granularity minutes: will start at the first second of a minute, hence 14-08-2012
01:30:30 will be shifted to 14-08-2012 01:30:00

The specification of the temporal relation between the aggregation intervals and the
raster map layers is always formulated from the aggregation interval viewpoint. Hence, the
relation contains has to be specified to aggregate map layer that are temporally located
in an aggregation interval.

Parallel processing is supported in case that more than one interval is available for
aggregation computation. Internally several r.series modules will be started, depending on
the number of specified parallel processes (nprocs) and the number of intervals to
aggregate.

The flag -s allows storing a date as map name suffix rather than using consecutive
numbering. See the examples below for details.

EXAMPLES

Aggregation of monthly data into yearly data
In this example the user is going to aggregate monthly data into yearly data, running:
t.rast.aggregate input=tempmean_monthly output=tempmean_yearly
basename=tempmean_year
granularity="1 years" method=average
t.support input=tempmean_yearly
title="Yearly precipitation"
description="Aggregated precipitation dataset with yearly resolution"
t.info tempmean_yearly
+-------------------- Space Time Raster Dataset -----------------------------+
| |
+-------------------- Basic information -------------------------------------+
| Id: ........................ tempmean_yearly@climate_2000_2012
| Name: ...................... tempmean_yearly
| Mapset: .................... climate_2000_2012
| Creator: ................... lucadelu
| Temporal type: ............. absolute
| Creation time: ............. 2014-11-27 10:25:21.243319
| Modification time:.......... 2014-11-27 10:25:21.862136
| Semantic type:.............. mean
+-------------------- Absolute time -----------------------------------------+
| Start time:................. 2009-01-01 00:00:00
| End time:................... 2013-01-01 00:00:00
| Granularity:................ 1 year
| Temporal type of maps:...... interval
+-------------------- Spatial extent ----------------------------------------+
| North:...................... 320000.0
| South:...................... 10000.0
| East:.. .................... 935000.0
| West:....................... 120000.0
| Top:........................ 0.0
| Bottom:..................... 0.0
+-------------------- Metadata information ----------------------------------+
| Raster register table:...... raster_map_register_514082e62e864522a13c8123d1949dea
| North-South resolution min:. 500.0
| North-South resolution max:. 500.0
| East-west resolution min:... 500.0
| East-west resolution max:... 500.0
| Minimum value min:.......... 7.370747
| Minimum value max:.......... 8.81603
| Maximum value min:.......... 17.111387
| Maximum value max:.......... 17.915511
| Aggregation type:........... average
| Number of registered maps:.. 4
|
| Title: Yearly precipitation
| Monthly precipitation
| Description: Aggregated precipitation dataset with yearly resolution
| Dataset with monthly precipitation
| Command history:
| # 2014-11-27 10:25:21
| t.rast.aggregate input="tempmean_monthly"
| output="tempmean_yearly" basename="tempmean_year" granularity="1 years"
| method="average"
|
| # 2014-11-27 10:26:21
| t.support input=tempmean_yearly
| title="Yearly precipitation"
| description="Aggregated precipitation dataset with yearly resolution"
+----------------------------------------------------------------------------+

Different aggregations and map name suffix variants
Examples of resulting naming schemes for different aggregations when using the -s flag:

Weekly aggregation
t.rast.aggregate input=daily_temp output=weekly_avg_temp
basename=weekly_avg_temp method=average granularity="1 weeks"
t.rast.list weekly_avg_temp
name|mapset|start_time|end_time
weekly_avg_temp_2003_01|climate|2003-01-03 00:00:00|2003-01-10 00:00:00
weekly_avg_temp_2003_02|climate|2003-01-10 00:00:00|2003-01-17 00:00:00
weekly_avg_temp_2003_03|climate|2003-01-17 00:00:00|2003-01-24 00:00:00
weekly_avg_temp_2003_04|climate|2003-01-24 00:00:00|2003-01-31 00:00:00
weekly_avg_temp_2003_05|climate|2003-01-31 00:00:00|2003-02-07 00:00:00
weekly_avg_temp_2003_06|climate|2003-02-07 00:00:00|2003-02-14 00:00:00
weekly_avg_temp_2003_07|climate|2003-02-14 00:00:00|2003-02-21 00:00:00
Variant with -s flag:
t.rast.aggregate -s input=daily_temp output=weekly_avg_temp
basename=weekly_avg_temp method=average granularity="1 weeks"
t.rast.list weekly_avg_temp
name|mapset|start_time|end_time
weekly_avg_temp_2003_01_03|climate|2003-01-03 00:00:00|2003-01-10 00:00:00
weekly_avg_temp_2003_01_10|climate|2003-01-10 00:00:00|2003-01-17 00:00:00
weekly_avg_temp_2003_01_17|climate|2003-01-17 00:00:00|2003-01-24 00:00:00
weekly_avg_temp_2003_01_24|climate|2003-01-24 00:00:00|2003-01-31 00:00:00
weekly_avg_temp_2003_01_31|climate|2003-01-31 00:00:00|2003-02-07 00:00:00
weekly_avg_temp_2003_02_07|climate|2003-02-07 00:00:00|2003-02-14 00:00:00
weekly_avg_temp_2003_02_14|climate|2003-02-14 00:00:00|2003-02-21 00:00:00

Monthly aggregation
t.rast.aggregate -s input=daily_temp output=monthly_avg_temp
basename=monthly_avg_temp method=average granularity="1 months"
t.rast.list monthly_avg_temp
name|mapset|start_time|end_time
monthly_avg_temp_2003_01|climate|2003-01-01 00:00:00|2003-02-01 00:00:00
monthly_avg_temp_2003_02|climate|2003-02-01 00:00:00|2003-03-01 00:00:00
monthly_avg_temp_2003_03|climate|2003-03-01 00:00:00|2003-04-01 00:00:00
monthly_avg_temp_2003_04|climate|2003-04-01 00:00:00|2003-05-01 00:00:00
monthly_avg_temp_2003_05|climate|2003-05-01 00:00:00|2003-06-01 00:00:00
monthly_avg_temp_2003_06|climate|2003-06-01 00:00:00|2003-07-01 00:00:00

Yearly aggregation
t.rast.aggregate -s input=daily_temp output=yearly_avg_temp
basename=yearly_avg_temp method=average granularity="1 years"
t.rast.list yearly_avg_temp
name|mapset|start_time|end_time
yearly_avg_temp_2003|climate|2003-01-01 00:00:00|2004-01-01 00:00:00
yearly_avg_temp_2004|climate|2004-01-01 00:00:00|2005-01-01 00:00:00

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