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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     [suffix=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,  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

       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 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.

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

       Last changed: $Date: 2016-09-19 12:29:41 +0200 (Mon, 19 Sep 2016) $

SOURCE CODE

       Available at: t.rast.aggregate source code (history)

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