t.rast.neighbors(1)

NAME
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 [-cenr] input=name output=name [where=sql_query] [region_relation=string] [selection=name] [size=integer] method=string [weighting_function=string] [weighting_factor=float] [weight=name] [quantile=float[,float,...]] basename=string [suffix=name] [semantic_labels=string] [nprocs=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

  Flags:

-c
    Use circular neighborhood

-e
    Extend existing space time raster dataset

-n
    Register Null maps

-r
    Ignore the current region settings and use the raster map regions

--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’

region_relation=string
    Process only maps with this spatial relation to the current computational region
    Options: overlaps, contains, is_contained

selection=name
    Name of an input raster map to select the cells which should be processed

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

weighting_function=string
    Weighting function
    Options: none, gaussian, exponential, file
    Default: none
    none: No weighting
    gaussian: Gaussian weighting function
    exponential: Exponential weighting function
    file: File with a custom weighting matrix

weighting_factor=float
    Factor used in the selected weighting function (ignored for weighting_function=none and file)

weight=name
    Text file containing weights

quantile=float[,float,...]
    Quantile to calculate for method=quantile
    Options: 0.0-1.0

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

suffix=name
    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

semantic_labels=string
    Set semantic labels
    Options: input, method
    Default: input
    input: copy semantic labels from input to output
    method: append method name to input label if existing, otherwise use method name

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 the options that are available in r.neighbors.

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. With the -e flag, resulting maps can be registered in an existing STRDS, that e.g. may have been created with a previous run of t.rast.neighbors. All maps will be processed using the current region settings unless the -r flag is selected. In the latter case, the computaional region is set to each raster map selected from the input STRDS.

The user can select a subset of the input space time raster dataset for processing using a SQL WHERE statement or using the region_relation for spatial selection of raster maps. For the spatial map selection the current computational region is used, even when the -r flag is given. 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.

Semantic labels are needed to relate output raster maps to input raster maps. E.g. with method=stddev, the user needs to know the spatial extent, the time stamp and the semantic label to determine which stddev map corresponds to which input map.

EXAMPLE
To smooth the maps contained in 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 the original and the smoothed dataset
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

SOURCE CODE

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

Latest change: Tuesday Jan 27 12:40:13 2026 in commit: 5e8b13d47dd4fc81efa5abefe90c34f7a8291fb9

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