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:

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:

Modules to visualize space-time datasets and temporal data¶

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).

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.

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.

Statistics and gap filling¶

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.

See also¶