t.rast.aggregate(1grass) GRASS GIS User's Manual t.rast.aggregate(1grass)
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 [-n] input=name output=name basename=string [suf-
fix=string] granularity=string method=string [offset=integer]
[nprocs=integer] [file_limit=integer] [sampling=name[,name,...]]
[where=sql_query] [--overwrite] [--help] [--verbose] [--quiet]
[--ui]
Flags:
-n
Register Null maps
--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
suffix=string
Suffix to add at basename: set ’gran’ for granularity, ’time’ for
the full time format, ’num’ for numerical suffix with a specific
number of digits (default %05)
Default: gran
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, maxi-
mum, max_raster, stddev, range, sum, variance, diversity, slope,
offset, detcoeff, quart1, quart3, perc90, quantile, skewness, kur-
tosis
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
file_limit=integer
The maximum number of open files allowed for each r.series process
Default: 1000
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 rela-
tive 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 res-
olutions, 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 de-
tails.
This module will shift the start date for each aggregation process de-
pending 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 (Mon-
day), 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 in-
tervals and the raster map layers is always formulated from the aggre-
gation interval viewpoint. Hence, the relation contains has to be spec-
ified to aggregate map layer that are temporally located in an aggrega-
tion interval.
Parallel processing is supported in case that more than one interval is
available for aggregation computation. Internally several r.series mod-
ules will be started, depending on the number of specified parallel
processes (nprocs) and the number of intervals to aggregate.
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 suffix option:
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 suffix set to granularity:
t.rast.aggregate input=daily_temp output=weekly_avg_temp \
basename=weekly_avg_temp suffix=gran 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 input=daily_temp output=monthly_avg_temp \
basename=monthly_avg_temp suffix=gran 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 input=daily_temp output=yearly_avg_temp \
basename=yearly_avg_temp suffix=gran 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
SEE ALSO
t.rast.aggregate.ds, t.rast.extract, t.info, r.series, g.region,
r.mask
Temporal data processing Wiki
AUTHOR
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
SOURCE CODE
Available at: t.rast.aggregate source code (history)
Accessed: unknown
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GRASS 7.8.7 t.rast.aggregate(1grass)
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