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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

           ·   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 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 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  can
       be  used.   t.list  will  list all space-time datasets and registered maps in the temporal

       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.list

           ·   t.sample


           ·   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

       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

       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 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

           ·   t.rast.export

           ·   t.rast.import

           ·   t.rast.out.vtk


           ·   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

       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
               Spatio-temporal data handling and visualization in GRASS GIS

           ·   GEOSTAT 2012 TGRASS Course


       Available at: Temporal data processing in GRASS GIS source code (history)

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