1temporalintro(1) Grass User's Manual temporalintro(1)
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6 The temporal enabled GRASS introduces three new data types that are
7 designed to handle time series data:
8 · Space time raster datasets (strds) are designed to manage
10 raster map time series. Modules that process strds have the
11 naming prefix t.rast.
12 · Space time 3D raster datasets (str3ds) are designed to manage
14 3D raster map time series. Modules that process str3ds have the
15 naming prefix t.rast3d.
16 · Space time vector datasets (stvds) are designed to manage vec‐
18 tor map time series. Modules that process stvds have the naming
19 prefix t.vect.
20 These new data types can be managed, analyzed and processed with tempo‐
21 ral modules that are based on the GRASS GIS temporal framework.
22 Temporal data management in general
24 Space time datasets are stored in a temporal database. A core principle
25 of the temporal framework is that temporal databases are mapset spe‐
26 cific. A new temporal database is created when a temporal command is
27 invoked in a mapset that does not contain any temporal databases yet.
28 For example, when a mapset was recently created.
29 Therefore, as space-time datasets are mapset specific, they can only
31 register raster, 3D raster or vector maps from the same mapset.
32 By default, space-time datasets can not register maps from other
34 mapsets. This is a security measure, since the registration of maps in
35 a space-time dataset will always modify the metadata of the registered
36 map. This is critical if:
37 · The user has no write access to the maps from other mapsets
39 he/she wants to register
40 · If registered maps are removed from other mapsets, the temporal
42 database will not be updated and will contain ghost maps
43 SQLite3 or PostgreSQL are supported as temporal database backends.
44 Temporal databases stored in other mapsets can be accessed as long as
45 those other mapsets are in the user’s current mapset search path (man‐
46 aged with g.mapsets). Access to space-time datasets from other mapsets
47 is read-only. They can not be modified or removed.
48 Connection settings are performed with t.connect. By default, a
50 SQLite3 database is created in the current mapset to store all
51 space-time datasets and registered time series maps in that mapset.
52 New space-time datasets are created in the temporal database with
54 t.create. The name of the new dataset, the type (strds, str3ds, stvds),
55 the title and the description must be provided for creation. Option‐
56 ally, the temporal type (absolute, relative) and the semantic informa‐
57 tion can be provided.
58 The module t.register is designed to register raster, 3D raster and
60 vector maps in the temporal database and in the space-time datasets. It
61 supports different input options. Maps to register can be provided as a
62 comma separated string at the command line, or in an input file. The
63 module supports the definition of time stamps (time instances or inter‐
64 vals) for each map in the input file. With t.unregister maps can be
65 unregistered from space-time datasets and from the temporal database.
66 Important
68 Use only temporal commands like t.register to attach a time stamp to
69 raster, 3D raster and vector maps. The commands r.timestamp, r3.time‐
70 stamp and v.timestamp should not be used because they only modify the
71 metadata of the map in the spatial database, but they do not register
72 maps in the temporal database. However, maps with timestamps attached
73 by means of *.timestamp modules can be registered in space-time
74 datasets using the existing timestamp.
75 The module t.remove will remove the space-time datasets from the tempo‐
77 ral database and optionally all registered maps. It will take care of
78 multiple map registration, hence if maps are registered in several
79 space-time datasets in the current mapset. Use t.support to modify the
80 metadata of space time datasets or to update the metadata that is
81 derived from registered maps. This module also checks for removed and
82 modified maps and updates the space-time datasets accordingly. Rename a
83 space-time dataset with t.rename.
84 To print information about space-time datasets or registered maps, the
86 module t.info can be used. t.list will list all space-time datasets
87 and registered maps in the temporal database.
88 The module t.topology was designed to compute and check the temporal
90 topology of space-time datasets. Moreover, the module t.sample samples
91 input space-time dataset(s) with a sample space-time dataset and prints
92 the result to standard output. Different sampling methods are supported
93 and can be combined.
94 List of general management modules:
96 · t.connect
98 · t.create
100 · t.rename
102 · t.remove
104 · t.register
106 · t.unregister
108 · t.info
110 · t.list
112 · t.sample
114 · t.support
116 · t.topology
118 Modules to visualize space-time datasets and temporal data
120 · g.gui.animation
121 · g.gui.timeline
123 · g.gui.mapswipe
125 · g.gui.tplot
127 Modules to process space-time raster datasets
129 The focus of the temporal GIS framework is the processing and analysis
130 of raster time series. Hence, the majority of the temporal modules are
131 designed to process space-time raster datasets (strds). However, there
132 are several modules to process space-time 3D raster datasets and
133 space-time vector datasets as well.
134 Querying and map calculation
136 Maps registered in a space-time raster dataset can be listed using
137 t.rast.list. This module supports several methods to list maps and uses
138 SQL queries to determine how these maps are selected and sorted. Sub‐
139 sets of space-time raster datasets can be extracted with t.rast.extract
140 that allows performing additional mapcalc operations on the selected
141 raster maps.
142 Several modules in the temporal framework have a where option. This
144 option allows performing different selections of maps registered in the
145 temporal database and in space-time datasets. The columns that can be
146 used to perform these selections are: id, name, creator, mapset, tempo‐
147 ral_type, creation_time, start_time, end_time, north, south, west,
148 east, nsres, ewres, cols, rows, number_of_cells, min and max. Note that
149 for vector time series, i.e. stvds, some of the columns that can be
150 queried to list/select vector maps differ from those for space-time
151 raster datasets (check with t.vect.list --help).
152 · t.rast.extract
154 · t.rast.gapfill
156 · t.rast.mapcalc
158 · t.rast.colors
160 · t.rast.neighbors
162 Moreover, there is v.what.strds, that uploads space-time raster dataset
164 values at positions of vector points, to the attribute table of the
165 vector map.
166 Aggregation and accumulation analysis
168 The temporal framework supports the aggregation of space-time raster
169 datasets. It provides three modules to perform aggregation using dif‐
170 ferent approaches. To aggregate a space-time raster dataset using a
171 temporal granularity like 4 months, 7 days and so on, use t.rast.aggre‐
172 gate. The module t.rast.aggregate.ds allows aggregating a space-time
173 raster dataset using the time intervals of the maps of another
174 space-time dataset (raster, 3D raster and vector). A simple interface
175 to r.series is the module t.rast.series that processes the whole input
176 space-time raster dataset or a subset of it.
177 · t.rast.aggregate
179 · t.rast.aggregate.ds
181 · t.rast.series
183 · t.rast.accumulate
185 · t.rast.accdetect
187 Export/import conversion
189 Space-time raster datasets can be exported with t.rast.export as a com‐
190 pressed tar archive. Such archives can be then imported using
191 t.rast.import.
192 The module t.rast.to.rast3 converts space-time raster datasets into
194 space-time voxel cubes. All 3D raster modules can be used to process
195 such voxel cubes. This conversion allows the export of space-time
196 raster datasets as netCDF files that include time as one dimension.
197 · t.rast.export
199 · t.rast.import
201 · t.rast.out.vtk
203 · t.rast.to.rast3
205 · r3.out.netcdf
207 Statistics and gap filling
209 · t.rast.univar
210 · t.rast.gapfill
212 Modules to manage, process and analyze STR3DS and STVDS
214 Several space-time vector dataset modules were developed to allow the
215 handling of vector time series data.
216 · t.vect.extract
218 · t.vect.import
220 · t.vect.export
222 · t.vect.observe.strds
224 · t.vect.univar
226 · t.vect.what.strds
228 · t.vect.db.select
230 The space-time 3D raster dataset modules are doing exactly the same as
231 their raster pendants, but with 3D raster map layers:
232 · t.rast3d.list
234 · t.rast3d.extract
236 · t.rast3d.mapcalc
238 · t.rast3d.univar
240 See also
242 · Gebbert, S., Pebesma, E. 2014. TGRASS: A temporal GIS for field
243 based environmental modeling. Environmental Modelling & Soft‐
244 ware 53, 1-12 (DOI) - preprint PDF
245 · Gebbert, S., Pebesma, E. 2017. The GRASS GIS temporal frame‐
247 work. International Journal of Geographical Information Science
248 31, 1273-1292 (DOI)
249 · Gebbert, S., Leppelt, T., Pebesma, E., 2019. A topology based
251 spatio-temporal map algebra for big data analysis. Data 4, 86.
252 (DOI)
253 · Temporal data processing (Wiki)
255 · Vaclav Petras, Anna Petrasova, Helena Mitasova, Markus Neteler,
257 FOSS4G 2014 workshop:
258 Spatio-temporal data handling and visualization in GRASS GIS
259 · GEOSTAT 2012 GRASS Course
261
263 Available at: Temporal data processing in GRASS GIS source code (his‐
264 tory)
265 Main index | Temporal index | Topics index | Keywords index | Graphical
267 index | Full index
268 © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual
270GRASS 7.8.2 temporalintro(1)