.TH t.rast.aggregate 1grass "" "GRASS 7.8.5" "GRASS GIS User's Manual"
.SH NAME
\fI\fBt.rast.aggregate\fR\fR  \- Aggregates temporally the maps of a space time raster dataset by a user defined granularity.
.SH KEYWORDS
temporal, aggregation, raster, time
.SH SYNOPSIS
\fBt.rast.aggregate\fR
.br
\fBt.rast.aggregate \-\-help\fR
.br
\fBt.rast.aggregate\fR [\-\fBn\fR] \fBinput\fR=\fIname\fR \fBoutput\fR=\fIname\fR \fBbasename\fR=\fIstring\fR  [\fBsuffix\fR=\fIstring\fR]  \fBgranularity\fR=\fIstring\fR \fBmethod\fR=\fIstring\fR  [\fBoffset\fR=\fIinteger\fR]   [\fBnprocs\fR=\fIinteger\fR]   [\fBfile_limit\fR=\fIinteger\fR]   [\fBsampling\fR=\fIname\fR[,\fIname\fR,...]]   [\fBwhere\fR=\fIsql_query\fR]   [\-\-\fBoverwrite\fR]  [\-\-\fBhelp\fR]  [\-\-\fBverbose\fR]  [\-\-\fBquiet\fR]  [\-\-\fBui\fR]
.SS Flags:
.IP "\fB\-n\fR" 4m
.br
Register Null maps
.IP "\fB\-\-overwrite\fR" 4m
.br
Allow output files to overwrite existing files
.IP "\fB\-\-help\fR" 4m
.br
Print usage summary
.IP "\fB\-\-verbose\fR" 4m
.br
Verbose module output
.IP "\fB\-\-quiet\fR" 4m
.br
Quiet module output
.IP "\fB\-\-ui\fR" 4m
.br
Force launching GUI dialog
.SS Parameters:
.IP "\fBinput\fR=\fIname\fR \fB[required]\fR" 4m
.br
Name of the input space time raster dataset
.IP "\fBoutput\fR=\fIname\fR \fB[required]\fR" 4m
.br
Name of the output space time raster dataset
.IP "\fBbasename\fR=\fIstring\fR \fB[required]\fR" 4m
.br
Basename of the new generated output maps
.br
Either a numerical suffix or the start time (s\-flag) separated by an underscore will be attached to create a unique identifier
.IP "\fBsuffix\fR=\fIstring\fR" 4m
.br
Suffix to add at basename: set \(cqgran\(cq for granularity, \(cqtime\(cq for the full time format, \(cqnum\(cq for numerical suffix with a specific number of digits (default %05)
.br
Default: \fIgran\fR
.IP "\fBgranularity\fR=\fIstring\fR \fB[required]\fR" 4m
.br
Aggregation granularity, format absolute time \(dqx years, x months, x weeks, x days, x hours, x minutes, x seconds\(dq or an integer value for relative time
.IP "\fBmethod\fR=\fIstring\fR \fB[required]\fR" 4m
.br
Aggregate operation to be performed on the raster maps
.br
Options: \fIaverage, count, median, mode, minimum, min_raster, maximum, max_raster, stddev, range, sum, variance, diversity, slope, offset, detcoeff, quart1, quart3, perc90, quantile, skewness, kurtosis\fR
.br
Default: \fIaverage\fR
.IP "\fBoffset\fR=\fIinteger\fR" 4m
.br
Offset that is used to create the output map ids, output map id is generated as: basename_ (count + offset)
.br
Default: \fI0\fR
.IP "\fBnprocs\fR=\fIinteger\fR" 4m
.br
Number of r.series processes to run in parallel
.br
Default: \fI1\fR
.IP "\fBfile_limit\fR=\fIinteger\fR" 4m
.br
The maximum number of open files allowed for each r.series process
.br
Default: \fI1000\fR
.IP "\fBsampling\fR=\fIname[,\fIname\fR,...]\fR" 4m
.br
The method to be used for sampling the input dataset
.br
Options: \fIequal, overlaps, overlapped, starts, started, finishes, finished, during, contains\fR
.br
Default: \fIcontains\fR
.IP "\fBwhere\fR=\fIsql_query\fR" 4m
.br
WHERE conditions of SQL statement without \(cqwhere\(cq keyword used in the temporal GIS framework
.br
Example: start_time > \(cq2001\-01\-01 12:30:00\(cq
.SH DESCRIPTION
\fIt.rast.aggregate\fR temporally aggregates space time raster
datasets by a specific temporal granularity. This module support
\fIabsolute\fR and \fIrelative time\fR. The temporal granularity
of absolute time can be \fIseconds, minutes, hours, days, weeks,
months\fR or \fIyears\fR. Mixing of granularities eg. \(dq1 year, 3
months 5 days\(dq 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.
.PP
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.
.SH NOTES
The raster module \fIr.series\fR is used internally. Hence all
aggregate methods of \fIr.series\fR are supported. See the
r.series manual page for details.
.PP
This module will shift the start date for each aggregation process
depending on the provided temporal granularity. The following shifts
will performed:
.RS 4n
.IP \(bu 4n
\fIgranularity years\fR: will start at the first of January,
hence 14\-08\-2012 00:01:30 will be shifted to 01\-01\-2012 00:00:00
.IP \(bu 4n
\fIgranularity months\fR: will start at the first day of a month,
hence 14\-08\-2012 will be shifted to 01\-08\-2012 00:00:00
.IP \(bu 4n
\fIgranularity weeks\fR: 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
.IP \(bu 4n
\fIgranularity days\fR: 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
.IP \(bu 4n
\fIgranularity hours\fR: 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
.IP \(bu 4n
\fIgranularity minutes\fR: 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
.RE
.PP
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 \fIcontains\fR
has to be specified to aggregate map layer that are temporally located
in an aggregation interval.
.PP
Parallel processing is supported in case that more than one interval is
available for aggregation computation. Internally several
\fIr.series\fR modules will be started, depending on the number of
specified parallel processes (\fInprocs\fR) and the number of
intervals to aggregate.
.PP
.SH EXAMPLES
.SS Aggregation of monthly data into yearly data
In this example the user is going to aggregate monthly data into yearly
data, running:
.br
.nf
\fC
t.rast.aggregate input=tempmean_monthly output=tempmean_yearly \(rs
                 basename=tempmean_year \(rs
                 granularity=\(dq1 years\(dq method=average
t.support input=tempmean_yearly \(rs
          title=\(dqYearly precipitation\(dq \(rs
          description=\(dqAggregated precipitation dataset with yearly resolution\(dq
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=\(dqtempmean_monthly\(dq
 |     output=\(dqtempmean_yearly\(dq basename=\(dqtempmean_year\(dq granularity=\(dq1 years\(dq
 |     method=\(dqaverage\(dq
 |
 | # 2014\-11\-27 10:26:21
 | t.support input=tempmean_yearly \(rs
 |        title=\(dqYearly precipitation\(dq \(rs
 |        description=\(dqAggregated precipitation dataset with yearly resolution\(dq
 +\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-+
\fR
.fi
.SS Different aggregations and map name suffix variants
Examples of resulting naming schemes for different aggregations when
using the \fBsuffix\fR option:
.SS Weekly aggregation
.br
.nf
\fC
t.rast.aggregate input=daily_temp output=weekly_avg_temp \(rs
  basename=weekly_avg_temp method=average granularity=\(dq1 weeks\(dq
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
\fR
.fi
Variant with \fBsuffix\fR set to granularity:
.br
.nf
\fC
t.rast.aggregate input=daily_temp output=weekly_avg_temp \(rs
  basename=weekly_avg_temp suffix=gran method=average \(rs
  granularity=\(dq1 weeks\(dq
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
\fR
.fi
.SS Monthly aggregation
.br
.nf
\fC
t.rast.aggregate input=daily_temp output=monthly_avg_temp \(rs
  basename=monthly_avg_temp suffix=gran method=average \(rs
  granularity=\(dq1 months\(dq
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
\fR
.fi
.SS Yearly aggregation
.br
.nf
\fC
t.rast.aggregate input=daily_temp output=yearly_avg_temp \(rs
  basename=yearly_avg_temp suffix=gran method=average \(rs
  granularity=\(dq1 years\(dq
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
\fR
.fi
.SH SEE ALSO
\fI
t.rast.aggregate.ds,
t.rast.extract,
t.info,
r.series,
g.region,
r.mask
\fR
.PP
Temporal data processing Wiki
.SH AUTHOR
Sören Gebbert, Thünen Institute of Climate\-Smart Agriculture
.SH SOURCE CODE
.PP
Available at: t.rast.aggregate source code (history)
.PP
Main index |
Temporal index |
Topics index |
Keywords index |
Graphical index |
Full index
.PP
© 2003\-2020
GRASS Development Team,
GRASS GIS 7.8.5 Reference Manual