r.timeseries.locationslocations=Map name for the managed locations [locations_subunits=Map name for subunits of the managed locations] locations_url=string [layer=string] [type=string[,string,...]] [where=sql_query] [snap=float] [method=string] [keepass_file=Input KeePass file to get DB credentials from] [keepass_title=string] [continuous_subdivision_map=name] [align=name] domain_id=integer [class_number=integer] [round_to_closest=string] [nprocs=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog
Parameters:
locations=Map name for the managed locations[required]
Map name for the managed locations
locations_subunits=Map name for subunits of the managed locations
Map name for subunits of the managed locations
locations_url=string[required]
URL to the OGR data source to import
Default:
layer=string
Layer name of the OGR data source to import
type=string[,string,...]
Input feature type
Options: point, line, boundary, centroid, area
Default: point,line,boundary,centroid,area
where=sql_query
WHERE conditions of SQL statement without 'where' keyword
Example: income < 1000 and population >= 10000
snap=float
Snap vertices in input vector map to this number of map units
Default: -1
method=string
Method for generating subunits
Options: percentile, linear, database, map_units
keepass_file=Input KeePass file to get DB credentials from
Input KeePass file to get DB credentials from
keepass_title=string
Title of the KeePass entry to get MS SQL credentials from
continuous_subdivision_map=name
Name of input raster map
align=name
Raster map to align pixels with
domain_id=integer[required]
Domain ID of locations to import
class_number=integer
Number of classes for subdividing locations
round_to_closest=string
Closest unit to round class boundaries to (integer or float)
nprocs=integer
Number of threads for parallel computing
0: use OpenMP default; >0: use nprocs; <0: use MAX-nprocs
r.timeseries.locations subdivides locations (geometries) into
coherent sub-units based on a continuous raster map and break values for
subdivisions. It is written for locations in NVEs time series database.
The tool supports the following methods to create subdivision maps:
percentile: creates sub-units from the continuous_subdivision_map
using user-given percentiles for intervals.
linear: creates sub-units from the continuous_subdivision_map
using linear scaled intervals.
database: creates sub-units from intervals that are pre-defined in
the database using the continuous_subdivision_map.
map_units: creates sub-units from the unique categories of the
continuous_subdivision_map. The continuous_subdivision_map
should thus contain integer values. Floating point values will be
rounded according to quantization rules in r.stats