temporalintro(1grass) GRASS GIS User's Manual temporalintro(1grass)
Temporal data processing in GRASS GIS
The temporal enabled GRASS introduces three new data types that are de-
signed 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 vec-
tor map time series. Modules that process stvds have the naming
prefix t.vect.
These new data types can be managed, analyzed and processed with tempo-
ral modules that are based on the GRASS GIS temporal framework.
Temporal data management in general
Space time datasets are stored in a temporal database. A core principle
of the temporal framework is that temporal databases are mapset spe-
cific. 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 (man-
aged 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. Option-
ally, the temporal type (absolute, relative) and the semantic informa-
tion 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 inter-
vals) for each map in the input file. With t.unregister maps can be
unregistered from space-time datasets and from the temporal database.
Important
Use only temporal commands like t.register to attach a time stamp to
raster, 3D raster and vector maps. The commands r.timestamp, r3.time-
stamp 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 existing timestamp.
The module t.remove will remove the space-time datasets from the tempo-
ral 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 de-
rived 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:
• t.connect
• t.create
• t.rename
• t.remove
• t.register
• t.unregister
• t.info
• t.list
• t.sample
• t.support
• t.topology
Modules to visualize space-time datasets and temporal data
• g.gui.animation
• g.gui.timeline
• g.gui.mapswipe
• g.gui.tplot
Modules to process space-time raster datasets
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
t.rast.list. This module supports several methods to list maps and uses
SQL queries to determine how these maps are selected and sorted. Sub-
sets of space-time raster datasets can be extracted with t.rast.extract
that allows performing additional mapcalc operations on the selected
raster maps.
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, tempo-
ral_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 t.vect.list --help).
• t.rast.extract
• t.rast.gapfill
• t.rast.mapcalc
• t.rast.colors
• t.rast.neighbors
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 dif-
ferent approaches. To aggregate a space-time raster dataset using a
temporal granularity like 4 months, 7 days and so on, use t.rast.aggre-
gate. The module t.rast.aggregate.ds allows 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 t.rast.series that processes the whole input
space-time raster dataset or a subset of it.
• t.rast.aggregate
• t.rast.aggregate.ds
• t.rast.series
• t.rast.accumulate
• t.rast.accdetect
Export/import conversion
Space-time raster datasets can be exported with t.rast.export as a com-
pressed tar archive. Such archives can be then imported using
t.rast.import.
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.
• t.rast.export
• t.rast.import
• t.rast.out.vtk
• t.rast.to.rast3
• r3.out.netcdf
Statistics and gap filling
• t.rast.univar
• t.rast.gapfill
Modules to manage, process and analyze STR3DS and STVDS
Several space-time vector dataset modules were developed to allow the
handling of vector time series data.
• t.vect.extract
• t.vect.import
• t.vect.export
• t.vect.observe.strds
• t.vect.univar
• t.vect.what.strds
• t.vect.db.select
The space-time 3D raster dataset modules are doing exactly the same as
their raster pendants, but with 3D raster map layers:
• t.rast3d.list
• t.rast3d.extract
• t.rast3d.mapcalc
• t.rast3d.univar
See also
• Gebbert, S., Pebesma, E. 2014. TGRASS: A temporal GIS for field
based environmental modeling. Environmental Modelling & Soft-
ware 53, 1-12 (DOI) - preprint PDF
• Gebbert, S., Pebesma, E. 2017. The GRASS GIS temporal frame-
work. 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)
• Temporal data processing (Wiki)
• Vaclav Petras, Anna Petrasova, Helena Mitasova, Markus Neteler,
FOSS4G 2014 workshop:
Spatio-temporal data handling and visualization in GRASS GIS
• GEOSTAT 2012 GRASS Course
SOURCE CODE
Available at: Temporal data processing in GRASS GIS source code (his-
tory)
Accessed: unknown
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