Ubuntu Manpage: The temporal enabled GRASS introduces three new datatypes that are designed to handle time

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Temporal data processing in GRASS GIS

The temporal enabled GRASS introduces three new datatypes 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.

Temporal data management in general
Space time datasets are stored in a temporal database. SQLite3 or PostgreSQL are supported
as SQL database back end. Temporal databases stored in other mapsets can be used as long
as they are in the user’s current mapset search path (managed with g.mapsets).

Connection settings are performed with t.connect. As default a sqlite3 database will be
created in the current mapset that stores all space time datasets and registered time
series maps.

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. Optional the temporal type (absolute, relative) and semantic
information can be provided.

The module t.remove will remove the space time datasets from the temporal database. 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.

The module t.register is designed to register raster, 3D raster and vector maps in the
temporal database and optionally in a space time dataset. 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 the temporal database.

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.

To compute and check the temporal topology of a space time datasets the module t.topology
was designed. The module t.sample samples input space time dataset(s) with a sample space
time dataset and print the result to standard output. Several different sample methods
are supported that 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

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. However, there are several modules to process space time 3D raster
datasets and space time vector datasets.

Querying and map calculation
Registered maps of a space time raster datasets can be listed using t.rast.list. This
module supports several methods how the maps should be listed using SQL queries do
determine how they are selected and sorted. Subsets of space time raster datasets can be
extracted with t.rast.extract that allows additionally to perform mapcalc operations on
the selected raster maps.

• t.rast.extract

• t.rast.gapfill

• t.rast.mapcalc

• t.rast.colors

• t.rast.neighbors
Additionally, there is v.what.strds.

Aggregation and accumulation analysis
The temporal framework support the aggregation of space time raster datasets. It provides
three modules to perform aggregation using different approaches. To aggregate a space time
raster map using a temporal granularity like 4 months, 7 days and so on use
t.rast.aggregate. The module t.rast.aggregate.ds allows the aggregation of raster map
series using the intervals of the maps (raster, 3D raster and vector) of a 2. space time
dataset. 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 compressed tar archive.
Such archives can be 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.gapfill

• t.rast.univar

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 & Software 53, 1-12. (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 TGRASS Course

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