t.rast.neighbors.1grass man page

t.rast.neighbors — Performs a neighborhood analysis for each map in a space time raster dataset.

Keywords

temporal, aggregation, raster, time

Synopsis

t.rast.neighbors
t.rast.neighbors --help
t.rast.neighbors [-n] input=name output=name [where=sql_query] [size=integer] method=string basename=string [nprocs=integer] [--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
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’
size=integer
Neighborhood size
Default: 3
method=string [required]
Aggregate operation to be performed on the raster maps
Options: average, median, mode, minimum, maximum, range, stddev, sum, count, variance, diversity, interspersion, quart1, quart3, perc90, quantile
Default: average
basename=string [required]
Basename of the new generated output maps
A numerical suffix separated by an underscore will be attached to create a unique identifier
nprocs=integer
Number of r.neighbor processes to run in parallel
Default: 1

Description

t.rast.neighbors performs r.neighbors computations on the maps of a space time raster dataset (STRDS). This module supports a subset of options that are available in r.neighbors. The size of the neighborhood and the aggregation method can be chosen.

The user must provide an input and an output space time raster dataset and the basename of the resulting raster maps. The resulting STRDS will have the same temporal resolution as the input dataset. All maps will be processed using the current region settings.

The user can select a subset of the input space time raster dataset for processing using a SQL WHERE statement. The number of CPU’s to be used for parallel processing can be specified with the nprocs option, to speedup the computation on multi-core system.

Example

To smooth the maps contained into a space time dataset run:

t.rast.neighbors input=tempmean_monthly output=smooth_tempmean_monthly \
                 basename=tmean_smooth size=5 method=average nprocs=4
# show some info about the new space time dataset
t.info smooth_tempmean_monthly
 +-------------------- Space Time Raster Dataset -----------------------------+
 |                                                                            |
 +-------------------- Basic information -------------------------------------+
 | Id: ........................ smooth_tempmean_monthly@climate_2000_2012
 | Name: ...................... smooth_tempmean_monthly
 | Mapset: .................... climate_2000_2012
 | Creator: ................... lucadelu
 | Temporal type: ............. absolute
 | Creation time: ............. 2014-11-27 11:41:36.444579
 | Modification time:.......... 2014-11-27 11:41:39.978232
 | Semantic type:.............. mean
 +-------------------- Absolute time -----------------------------------------+
 | Start time:................. 2009-01-01 00:00:00
 | End time:................... 2013-01-01 00:00:00
 | Granularity:................ 1 month
 | 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_ea1c9a83524e41a784d72744b08c6107
 | 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:.......... -6.428905
 | Minimum value max:.......... 18.867296
 | Maximum value min:.......... 4.247691
 | Maximum value max:.......... 28.767953
 | Aggregation type:........... None
 | Number of registered maps:.. 48
 |
 | Title:
 | Monthly precipitation
 | Description:
 | Dataset with monthly precipitation
 | Command history:
 | # 2014-11-27 11:41:36
 | t.rast.neighbors input="tempmean_monthly"
 |     output="smooth_tempmean_monthly" basename="tmean_smooth" size="5"
 |     method="average" nprocs="4"
 |
 +----------------------------------------------------------------------------+
# now compare the values between original data and the smoothed one
t.rast.list input=smooth_tempmean_monthly columns=name,start_time,min,max
t.rast.list input=smooth_tempmean_monthly columns=name,start_time,min,max
name|start_time|min|max
tmean_smooth_1|2009-01-01 00:00:00|-3.361714|7.409861
tmean_smooth_2|2009-02-01 00:00:00|-1.820261|7.986794
tmean_smooth_3|2009-03-01 00:00:00|2.912971|11.799684
...
tmean_smooth_46|2012-10-01 00:00:00|9.38767|18.709297
tmean_smooth_47|2012-11-01 00:00:00|1.785653|10.911189
tmean_smooth_48|2012-12-01 00:00:00|1.784212|11.983857
t.rast.list input=tempmean_monthly columns=name,start_time,min,max
name|start_time|min|max
2009_01_tempmean|2009-01-01 00:00:00|-3.380823|7.426054
2009_02_tempmean|2009-02-01 00:00:00|-1.820261|8.006386
2009_03_tempmean|2009-03-01 00:00:00|2.656992|11.819274
...
2012_10_tempmean|2012-10-01 00:00:00|9.070884|18.709297
2012_11_tempmean|2012-11-01 00:00:00|1.785653|10.911189
2012_12_tempmean|2012-12-01 00:00:00|1.761019|11.983857

See Also

r.neighbors, t.rast.aggregate.ds, t.rast.extract, t.info, g.region, r.mask

Author

Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

Last changed: $Date: 2016-01-13 00:30:14 +0100 (Wed, 13 Jan 2016) $

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