Flags:¶

-d

Dissolve common boundaries (default is no)

-t

Do not copy attributes (see also ’new’ parameter)

-r

Reverse selection

--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:¶

input=name [required]

Name of input vector map
Or data source for direct OGR access

layer=string

Layer number or name (’-1’ for all layers)
A single vector map can be connected to multiple database tables. This number determines which table to use. When used with direct OGR access this is the layer name.
Default: 1

type=string[,string,...]

Types to be extracted
Input feature type
Options: point, line, boundary, centroid, area, face
Default: point,line,boundary,centroid,area,face

cats=range

Category values
Example: 1,3,7-9,13

where=sql_query

WHERE conditions of SQL statement without ’where’ keyword
Example: income < 1000 and population >= 10000

output=name [required]

Name for output vector map

file=name

Input text file with category numbers/number ranges to be extracted
If ’-’ given reads from standard input

random=integer

Number of random categories matching vector objects to extract
Number must be smaller than unique cat count in layer

new=integer

Desired new category value (enter -1 to keep original categories)
If new >= 0, attributes is not copied
Default: -1

dissolve_column=name

Name of attribute column for dissolving areas
Preserves category values

Extract areas by category number with dissolving #1:¶

v.extract -d cats=1,2,3,4 input=soils_wake output=soil_groupa type=area new=0

produces a new vector soil_groupa, containing those areas from vector soils which have category numbers 1 thru 4; any common boundaries are dissolved, and all areas in the new map will be assigned category number 0.

Extract areas by category number with dissolving #2:¶

v.extract -d cats=1-4 input=soils_wake output=soil_groupa type=area new=-1

produces a new vector map soil_groupa containing the areas from vector soils which have categories 1 thru 4. Any common boundaries are dissolved, all areas in the new map will retain their original category numbers 1 thru 4, since new was set to -1.

Extract all areas and assign the same category to all:¶

v.extract input=soils_wake output=soil_groupa type=area new=1

produces a new vector map soil_groupa containing all areas from soils. No common boundaries are dissolved, all areas of the new map will be assigned category number 1.

Extract vectors with SQL:¶

v.extract input=markveggy.shp output=markveggy.1 new=13 \


  where="(VEGTYPE = ’Wi’) or (VEGTYPE = ’PS’) or (PRIME_TYPE=’Wi’)"

produces a new vector map with category number 13 if the SQL statement is fulfilled.

Extract vector features which have the given field empty:¶

v.extract input=lakes output=lakes_gaps where="FTYPE is NULL"

Extract vector features which have the given field not empty:¶

v.extract input=lakes output=lakes_ftype where="FTYPE not NULL"

Reverse extracting (behaves like selective vector objects deleting):¶

Remove meteorological stations from map which are located above 1000m:

# check what to delete:
v.db.select precip_30ynormals where="elev > 1000"
# perform reverse selection
v.extract -r input=precip_30ynormals output=precip_30ynormals_lowland \


  where="elev > 1000"
# verify
v.db.select precip_30ynormals_lowland

Dissolving based on column attributes:¶

# check column names:
v.info -c zipcodes_wake
# reclass based on desired column:
v.reclass input=zipcodes_wake output=zipcodes_wake_recl_nam column=ZIPNAME
# verify:
v.info -c zipcodes_wake_recl_nam
v.db.select zipcodes_wake_recl_nam
# dissolve:
v.extract -d input=zipcodes_wake_recl_nam output=zipcodes_wake_regions

This produces a new vector map with common boundaries dissolved where the reclassed attributes of adjacent (left/right) areas are identical.

Extract 3 random areas from geology map¶

v.extract input=geology output=random_geology type=area random=3

This creates a new map with three random categories matching areas. Note that there may be more than one feature with the same category.