---
description: Vector data processing in GRASS
index: vector
---

# Vector data processing in GRASS

## Vector maps in general

A "vector map" is a data layer consisting of a number of sparse features
in geographic space. These might be data points (drill sites), lines
(roads), polygons (park boundary), volumes (3D CAD structure), or some
combination of all these. Typically each feature in the map will be tied
to a set of attribute layers stored in a database (road names, site ID,
geologic type, etc.). As a general rule these can exist in 2D or 3D
space and are independent of the GIS's computation region.

## Attribute management

The default database driver used by GRASS 8 is SQLite. GRASS
handles multiattribute vector data by default. The *db.\** set of
commands provides basic SQL support for attribute management, while the
*v.db.\** set of commands operates on vector maps.

Note: The list of available database drivers can vary in various binary
distributions of GRASS, for details see [SQL support in GRASS](sql.md).

## Vector data import and export

The [v.in.ogr](v.in.ogr.md) module offers a common interface for many
different vector formats. Additionally, it offers options such as
on-the-fly creation of new projects or extension of the default region
to match the extent of the imported vector map. For special cases, other
import modules are available, e.g. [v.in.ascii](v.in.ascii.md) for input
from a text file containing coordinate and attribute data, and
[v.in.db](v.in.db.md) for input from a database containing coordinate
and attribute data. With [v.external](v.external.md) external maps can
be virtually linked into a mapset, only pseudo-topology is generated but
the vector geometry is not imported. The *v.out.\** set of commands
exports to various formats. To import and export only attribute tables,
use [db.in.ogr](db.in.ogr.md) and [db.out.ogr](db.out.ogr.md).

GRASS vector map exchange between different projects (same
projection) can be done in a lossless way using the [v.pack](v.pack.md)
and [v.unpack](v.unpack.md) modules.

The naming convention for vector maps requires that map names start with
a character, not a number (map name scheme:
\[A-Za-z\]\[A-Za-z0-9\_\]\*).

## Metadata

The [v.info](v.info.md) display general information such as metadata and
attribute columns about a vector map including the history how it was
generated. Each map generating command stores the command history into
the metadata (query with [v.info -h mapname](v.info.md)). Metadata such
as map title, scale, organization etc. can be updated with
[v.support](v.support.md).

## Vector map operations

GRASS vector map processing is always performed on the full map. If this
is not desired, the input map has to be clipped to the current region
beforehand ([v.in.region](v.in.region.md), [v.overlay](v.overlay.md),
[v.select](v.select.md)).

## Vector model and topology

GRASS is a topological GIS. This means that adjacent geographic
components in a single vector map are related. For example in a
non-topological GIS if two areas shared a common border that border
would be digitized two times and also stored in duplicate. In a
topological GIS this border exists once and is shared between two areas.
Topological representation of vector data helps to produce and maintain
vector maps with clean geometry as well as enables certain analyses that
can not be conducted with non-topological or spaghetti data. In GRASS,
topological data are referred to as level 2 data and spaghetti data is
referred to as level 1.

Sometimes topology is not necessary and the additional memory and space
requirements are burdensome to a particular task. Therefore two modules
allow for working level 1 (non-topological) data within GRASS. The
[v.in.ascii](v.in.ascii.md) module allows users to input points without
building topology. This is very useful for large files where memory
restrictions may cause difficulties. The other module which works with
level 1 data is [v.surf.rst](v.surf.rst.md) which enables spatial
approximation and topographic analysis from a point or isoline file.

In GRASS, the following vector object types are defined:

- point: a point;
- line: a directed sequence of connected vertices with two endpoints
  called nodes;
- boundary: the border line to describe an area;
- centroid: a point within a closed ring of boundaries;
- area: the topological composition of a closed ring of boundaries and a
  centroid;
- face: a 3D area;
- kernel: a 3D centroid in a volume (not yet implemented);
- volume: a 3D corpus, the topological composition of faces and kernel
  (not yet implemented).

Lines and boundaries can be composed of multiple vertices.

Area topology also holds information about isles. These isles are
located within that area, not touching the boundaries of the outer area.
Isles are holes inside the area, and can consist of one or more areas.
They are used internally to maintain correct topology for areas.

The [v.type](v.type.md) module can be used to convert between vector
types if possible. The [v.build](v.build.md) module is used to generate
topology. It optionally allows the user to extract erroneous vector
objects into a separate map. Topological errors can be corrected either
manually within [wxGUI vector digitizer](wxGUI.vdigit.md) or, to some
extent, automatically in [v.clean](v.clean.md). A dedicated vector
editing module is [v.edit](v.edit.md) which supports global and local
editing operations. Adjacent polygons can be found by
[v.to.db](v.to.db.md) (see 'sides' option).

Many operations including extraction, queries, overlay, and export will
only act on features which have been assigned a category number.
Typically a centroid will hold the attribute data for the area with
which the centroid is associated. Boundaries are not typically given a
category ID as it would be ambiguous as to which area either side of it
the attribute data would belong to. An exception might be when the
boundary between two crop-fields is the center-line of a road, and the
category information is an index to the road name. For everyday use
boundaries and centroids can be treated as internal data types and the
user can work directly and more simply with the "area" type.

## Vector object categories and attribute management

GRASS vectors can be linked to one or many database management systems
(DBMS). The *db.\** set of commands provides basic SQL support for
attribute management, while the *v.db.\** set of commands operates on a
table linked to a vector map.

- **Categories**  
  Categories are used to categorize vector objects and link attribute(s)
  to each category. Each vector object can have zero, one or several
  categories. Category numbers do not have to be unique for each vector
  object, several vector objects can share the same category.  
  Category numbers are stored both within the geometry file for each
  vector object and within the (optional) attribute table(s) (usually
  the "cat" column). It is not required that attribute table(s) hold an
  entry for each category, and attribute table(s) can hold information
  about categories not present in the vector geometry file. This means
  that e.g. an attribute table can be populated first and then vector
  objects can be added to the geometry file with category numbers. Using
  [v.category](v.category.md), category numbers can be printed or
  maintained.  

- **Layers**  
  Layers are a characteristic of the vector feature (geometries) file.
  As mentioned above, categories allow the user to give either a unique
  id to each feature or to group similar features by giving them all the
  same id. Layers allow several parallel, but different groupings of
  features in a same map. The practical benefit of this system is that
  it allows placement of thematically distinct but topologically related
  objects into a single map, or for linking time series attribute data
  to a series of locations that did not change over time.  
  For example, one can have roads with one layer containing the unique
  id of each road and another layer with ids for specific routes that
  one might take, combining several roads by assigning the same id.
  While this example can also be dealt with in an attribute table,
  another example of the use of layers that shows their specific
  advantage is the possibility to identify the same geometrical features
  as representing different thematic objects. For example, in a map with
  a series of polygons representing fields in which at the same time the
  boundaries of these fields have a meaning as linear features, e.g. as
  paths, one can give a unique id to each field as area in layer 1, and
  a unique id in layer 2 to those boundary lines that are paths. If the
  paths will always depend on the field boundaries (and might change if
  these boundaries change) then keeping them in the same map can be
  helpful. Trying to reproduce the same functionality through attributes
  is much more complicated.  
  If a vector object has zero categories in a layer, then it does not
  appear in that layer. In this fashion some vector objects may appear
  in some layers but not in others. Taking the example of the fields and
  paths, only some boundaries, but not all, might have a category value
  in layer 2. With *option=report*, [v.category](v.category.md) lists
  available categories in each layer.  
  Optionally, each layer can (but does not have to) be linked to an
  attribute table. The link is made by the category values of the
  features in that layer which have to have corresponding entries in the
  specified key column of the table. Using
  [v.db.connect](v.db.connect.md) connections between layers and
  attribute tables can be listed or maintained.  
  In most modules, the first layer (*layer=1*) is active by default.
  Using *layer=-1* one can access all layers.
- **SQL support**  
  By default, SQLite is used as the attribute database. Also other
  supported DBMS backends (such as SQLite, PostgreSQL, MySQL etc.)
  provide full SQL support as the SQL statements are sent directly to
  GRASS' database management interface (DBMI). Only the DBF driver
  provides just very limited SQL support (as DBF is not an SQL DB). SQL
  commands can be directly executed with [db.execute](db.execute.md),
  [db.select](db.select.md) and the other db.\* modules.

When creating vector maps from scratch, in general an attribute table
must be created and the table must be populated with one row per
category (using [v.to.db](v.to.db.md)). However, this can be performed
in a single step using [v.db.addtable](v.db.addtable.md) along with the
definition of table column types. Column adding and dropping can be done
with [v.db.addcolumn](v.db.addcolumn.md) and
[v.db.dropcolumn](v.db.dropcolumn.md). A table column can be renamed
with [v.db.renamecolumn](v.db.renamecolumn.md). To drop a table from a
map, use [v.db.droptable](v.db.droptable.md). Values in a table can be
updated with [v.db.update](v.db.update.md). Tables can be joined with
with [v.db.join](v.db.join.md).

## Editing vector attributes

To manually edit attributes of a table, the map has to be queried in
'edit mode' using [d.what.vect](d.what.vect.md). To bulk process
attributes, it is recommended to use SQL ([db.execute](db.execute.md)).

## Geometry operations

The module [v.in.region](v.in.region.md) saves the current region
extents as a vector area. Split vector lines can be converted to
polylines by [v.build.polylines](v.build.polylines.md). Long lines can
be split by [v.split](v.split.md) and [v.segment](v.segment.md). Buffer
and circles can be generated with [v.buffer](v.buffer.md) and
[v.parallel](v.parallel.md). [v.generalize](v.generalize.md) is module
for generalization of GRASS vector maps. 2D vector maps can be changed
to 3D using [v.drape](v.drape.md) or [v.extrude](v.extrude.md). If
needed, the spatial position of vector points can be perturbed by
[v.perturb](v.perturb.md). The [v.type](v.type.md) command changes
between vector types (see list above). Projected vector maps can be
reprojected with [v.proj](v.proj.md). Unprojected maps can be geocoded
with [v.transform](v.transform.md). Based on the control points,
[v.rectify](v.rectify.md) rectifies a vector map by computing a
coordinate transformation for each vector object. Triangulation and
point-to-polygon conversions can be done with
[v.delaunay](v.delaunay.md), [v.hull](v.hull.md), and
[v.voronoi](v.voronoi.md). The [v.random](v.random.md) command generated
random points.

## Vector overlays and selections

Geometric overlay of vector maps is done with [v.patch](v.patch.md),
[v.overlay](v.overlay.md) and [v.select](v.select.md), depending on the
combination of vector types. Vectors can be extracted with
[v.extract](v.extract.md) and reclassified with
[v.reclass](v.reclass.md).

## Vector statistics

Statistics can be generated by [v.qcount](v.qcount.md),
[v.sample](v.sample.md), [v.normal](v.normal.md),
[v.univar](v.univar.md), and [v.vect.stats](v.vect.stats.md). Distances
between vector objects are calculated with [v.distance](v.distance.md).

## Vector-Raster-DB conversion

The [v.to.db](v.to.db.md) transfers vector information into database
tables. With [v.to.points](v.to.points.md), [v.to.rast](v.to.rast.md)
and [v.to.rast3](v.to.rast3.md) conversions are performed. Note that a
raster mask will not be respected since it is only applied when
*reading* an existing GRASS raster map.

## Vector queries

Vector maps can be queried with [v.what](v.what.md) and
[v.what.vect](v.what.vect.md).

## Vector-Raster queries

Raster values can be transferred to vector maps with
[v.what.rast](v.what.rast.md) and [v.rast.stats](v.rast.stats.md).

## Vector network analysis

GRASS provides support for vector network analysis. The following
algorithms are implemented:

- Network preparation and maintenance: [v.net](v.net.md)
- Shortest path: [d.path](d.path.md) and [v.net.path](v.net.path.md)
- Shortest path between all pairs of nodes
  [v.net.allpairs](v.net.allpairs.md)
- Allocation of sources (create subnetworks, e.g. police station zones):
  [v.net.alloc](v.net.alloc.md)
- Iso-distances (from centers): [v.net.iso](v.net.iso.md)
- Computes bridges and articulation points:
  [v.net.bridge](v.net.bridge.md)
- Computes degree, centrality, betweeness, closeness and eigenvector
  centrality measures: [v.net.centrality](v.net.centrality.md)
- Computes strongly and weakly connected components:
  [v.net.components](v.net.components.md)
- Computes vertex connectivity between two sets of nodes:
  [v.net.connectivity](v.net.connectivity.md)
- Computes shortest distance via the network between the given sets of
  features: [v.net.distance](v.net.distance.md)
- Computes the maximum flow between two sets of nodes:
  [v.net.flow](v.net.flow.md)
- Computes minimum spanning
  tree:[v.net.spanningtree](v.net.spanningtree.md)
- Minimum Steiner trees (star-like connections, e.g. broadband cable
  connections): [v.net.steiner](v.net.steiner.md)
- Finds shortest path using timetables:
  [v.net.timetable](v.net.timetable.md)
- Traveling salesman (round trip): [v.net.salesman](v.net.salesman.md)

Vector directions are defined by the digitizing direction (a--\>--b).
Both directions are supported, most network modules provide parameters
to assign attribute columns to the forward and backward direction.

## Vector networks: Linear referencing system (LRS)

LRS uses linear features and distance measured along those features to
positionate objects. There are the commands
[v.lrs.create](v.lrs.create.md) to create a linear reference system,
[v.lrs.label](v.lrs.label.md) to create stationing on the LRS,
[v.lrs.segment](v.lrs.segment.md) to create points/segments on LRS, and
[v.lrs.where](v.lrs.where.md) to find line id and real km+offset for
given points in vector map using linear reference system.

The [LRS tutorial](lrs.md) explains further details.

## Interpolation and approximation

Some of the vector modules deal with spatial or volumetric approximation
(also called interpolation): [v.kernel](v.kernel.md),
[v.surf.idw](v.surf.idw.md), [v.surf.rst](v.surf.rst.md), and
[v.vol.rst](v.vol.rst.md).

## Lidar data processing

Lidar point clouds (first and last return) are imported from text files
with [v.in.ascii](v.in.ascii.md) or from LAS files with
[v.in.pdal](v.in.pdal.md). Both modules recognize the -b flag to not
build topology. Outlier detection is done with [v.outlier](v.outlier.md)
on both first and last return data. Then, with
[v.lidar.edgedetection](v.lidar.edgedetection.md), edges are detected
from last return data. The building are generated by
[v.lidar.growing](v.lidar.growing.md) from detected edges. The resulting
data are post-processed with
[v.lidar.correction](v.lidar.correction.md). Finally, the DTM and DSM
are generated with [v.surf.bspline](v.surf.bspline.md) (DTM: uses the
'v.lidar.correction' output; DSM: uses last return output from outlier
detection).  
In addition, [v.decimate](v.decimate.md) can be used to decimates a
point cloud.

## See also

- [Introduction to raster data processing](rasterintro.md)
- [Introduction to 3D raster data (voxel) processing](raster3dintro.md)
- [Introduction to image processing](imageryintro.md)
- [Introduction into temporal data processing](temporalintro.md)
- [Introduction to database management](databaseintro.md)
- [Projections and spatial transformations](projectionintro.md)

## SOURCE CODE

Available at: [vectorintro source code](https://github.com/OSGeo/grass/tree/main/vector)
([history](https://github.com/OSGeo/grass/commits/main/vector))  
Accessed: Friday May 15 03:21:30 2026
