Temporal data processing in GRASS GIS
The temporal enabled GRASS introduces three new data types that
are designed to handle time series data:
- Space time raster datasets (strds) are designed to
manage raster map time series. Modules that process strds have the
naming prefix t.rast.
- Space time 3D raster datasets (str3ds) are designed to
manage 3D raster map time series. Modules that process str3ds have
the naming prefix t.rast3d.
- Space time vector datasets (stvds) are designed to
manage vector map time series. Modules that process stvds have the
naming prefix t.vect.
These new data types can be managed, analyzed and processed with
temporal modules that are based on the GRASS GIS temporal framework.
Space time datasets are stored in a temporal database. A core principle
of the temporal framework is that temporal databases are mapset
specific. A new temporal database is created when a temporal command is
invoked in a mapset that does not contain any temporal databases yet.
For example, when a mapset was recently created.
Therefore, as space-time datasets are mapset specific, they can only
register raster, 3D raster or vector maps from the same mapset.
By default, space-time datasets can not register maps from other
mapsets. This is a security measure, since the registration of maps in
a space-time dataset will always modify the metadata of the registered
map. This is critical if:
- The user has no write access to the maps from other mapsets
he/she wants to register
- If registered maps are removed from other mapsets, the temporal
database will not be updated and will contain ghost maps
SQLite3 or PostgreSQL are supported as temporal database backends.
Temporal databases stored in other mapsets can be accessed as long as
those other mapsets are in the user's current mapset search path
(managed with g.mapsets
). Access to
space-time datasets from other mapsets is read-only. They can not be
modified or removed.
Connection settings are performed with t.connect.
By default, a SQLite3 database is
created in the current mapset to store all space-time datasets and
registered time series maps in that mapset.
New space-time datasets are created in the temporal database with
t.create. The name of the new dataset, the
type (strds, str3ds, stvds), the title and the description must be
provided for creation. Optionally, the temporal type (absolute,
relative) and the semantic information can be provided.
The module t.register is designed to
register raster, 3D raster and vector maps in the temporal database and
in the space-time datasets. It supports different input options. Maps
to register can be provided as a comma separated string at the command
line, or in an input file. The module supports the definition of time
stamps (time instances or intervals) for each map in the input file.
With t.unregister maps can be
unregistered from space-time datasets and from the temporal database.
Use only temporal commands like t.register
to attach a time stamp to raster,
3D raster and vector maps. The commands r.timestamp, r3.timestamp and
v.timestamp should not be used because they only modify the metadata of
the map in the spatial database, but they do not register maps in the
temporal database. However, maps with timestamps attached by means of
*.timestamp modules can be registered in space-time datasets using the
The module t.remove will remove the
space-time datasets from the temporal database and optionally all
registered maps. It will take care of multiple map registration, hence
if maps are registered in several space-time datasets in the current
mapset. Use t.support to modify the
metadata of space time datasets or to update the metadata that is
derived from registered maps. This module also checks for removed and
modified maps and updates the space-time datasets accordingly. Rename a
space-time dataset with t.rename.
To print information about space-time datasets or registered maps, the
module t.info can be used.
t.list will list all space-time datasets and
registered maps in the temporal database.
The module t.topology was designed to
compute and check the temporal topology of space-time datasets.
Moreover, the module t.sample samples input
space-time dataset(s) with a sample space-time dataset and prints the
result to standard output. Different sampling methods are supported and
can be combined.
List of general management modules:
The focus of the temporal GIS framework is the processing and analysis
of raster time series. Hence, the majority of the temporal modules are
designed to process space-time raster datasets (strds). However, there
are several modules to process space-time 3D raster datasets and
space-time vector datasets as well.
Querying and map calculation
Maps registered in a space-time raster dataset can be listed using
. This module supports several
methods to list maps and uses SQL queries to determine how these maps
are selected and sorted. Subsets of space-time raster datasets can be
extracted with t.rast.extract
allows performing additional mapcalc operations on the selected raster
Several modules in the temporal framework have a where option.
This option allows performing different selections of maps registered
in the temporal database and in space-time datasets. The columns that
can be used to perform these selections are: id, name, creator,
mapset, temporal_type, creation_time, start_time, end_time, north,
south, west, east, nsres, ewres, cols, rows, number_of_cells, min and
max. Note that for vector time series, i.e. stvds, some of the
columns that can be queried to list/select vector maps differ from
those for space-time raster datasets (check with
Moreover, there is v.what.strds, that
uploads space-time raster dataset values at positions of vector points,
to the attribute table of the vector map.
Aggregation and accumulation analysis
The temporal framework supports the aggregation of space-time raster
datasets. It provides three modules to perform aggregation using
different approaches. To aggregate a space-time raster dataset using a
temporal granularity like 4 months, 7 days and so on, use
. The module
aggregating a space-time raster dataset using the time intervals of the
maps of another space-time dataset (raster, 3D raster and vector). A
simple interface to r.series
is the module
that processes the whole
input space-time raster dataset or a subset of it.
Space-time raster datasets can be exported with
as a compressed tar
archive. Such archives can be then imported using
The module t.rast.to.rast3 converts
space-time raster datasets into space-time voxel cubes. All 3D raster
modules can be used to process such voxel cubes. This conversion allows
the export of space-time raster datasets as netCDF files that include
time as one dimension.
Statistics and gap filling
Several space-time vector dataset modules were developed to allow the
handling of vector time series data.
The space-time 3D raster dataset modules are doing exactly the same as
their raster pendants, but with 3D raster map layers:
- Gebbert, S., Pebesma, E. 2014. TGRASS: A temporal GIS for field based environmental modeling.
Environmental Modelling & Software 53, 1-12 (DOI)
- preprint PDF
- Gebbert, S., Pebesma, E. 2017. The GRASS GIS temporal framework. International Journal of
Geographical Information Science 31, 1273-1292 (DOI)
- Gebbert, S., Leppelt, T., Pebesma, E., 2019. A topology based spatio-temporal map algebra for big data analysis.
Data 4, 86. (DOI)
data processing (Wiki)
- Vaclav Petras, Anna Petrasova, Helena Mitasova, Markus Neteler,
FOSS4G 2014 workshop:
data handling and visualization in GRASS GIS
- GEOSTAT 2012 GRASS Course
Temporal data processing in GRASS GIS source code
Latest change: Sun Feb 20 21:30:25 2022 in commit: 8d90d53a5087d8ab5c5399b01d879b6c2e2df6b8
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GRASS Development Team,
GRASS GIS 8.0.3dev Reference Manual