temporalintro(1)

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.

  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 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.

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.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 existing timestamp.

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:

    •   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. Subsets 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, 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 t.vect.list --help).

    •   t.rast.extract

    •   t.rast.gapfill

    •   t.rast.mapcalc

    •   t.rast.colors

    •   t.rast.neighbors

Similar to the where option, selected modules like t.rast.univar support a region_relation option to limit computations to maps of the space time raster dataset that have a given spatial relation to the current computational region. Supported spatial relations are:

    •   "overlaps": process only maps that spatially overlap ("intersect") with the current computational region

    •   "is_contained": process only maps that are fully within the current computational region

    •   "contains": process only maps that contain (fully cover) the current computational region
Space time raster datasets may contain raster maps with varying spatial extent like for example series of scenes of satellite images. For such cases the region_relation option can be useful.

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 t.rast.aggregate. 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 compressed 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 & 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)

    •   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 (history)

Accessed: Sunday Dec 10 00:34:57 2023

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