v.generalize - Performs vector based
generalization.
vector, generalization, simplification, smoothing, displacement,
network generalization, topology, geometry
v.generalize
v.generalize --help
v.generalize [-lt] input=name
[layer=string] [type=string[,string,...]]
output=name [error=name]
method=string threshold=float
[look_ahead=integer] [reduction=float]
[slide=float] [angle_thresh=float]
[degree_thresh=integer] [closeness_thresh=float]
[betweeness_thresh=float] [alpha=float]
[beta=float] [iterations=integer]
[cats=range] [where=sql_query]
[--overwrite] [--help] [--verbose] [--quiet]
[--ui]
- -l
-
Disable loop support
Do not modify end points of lines forming a closed loop
- -t
-
Do not copy attributes
- --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
- 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,...]
-
Input feature type
Options: line, boundary, area
Default: line,boundary,area
- output=name [required]
-
Name for output vector map
- error=name
-
Error map with failed generalizations
Lines and boundaries causing errors (collapsed to a point or topology
errors)
- method=string [required]
-
Generalization algorithm
Options: douglas, douglas_reduction, lang, reduction, reumann, boyle,
sliding_averaging, distance_weighting, chaiken, hermite, snakes, network,
displacement
douglas: Douglas-Peucker Algorithm
douglas_reduction: Douglas-Peucker Algorithm with reduction parameter
lang: Lang Simplification Algorithm
reduction: Vertex Reduction Algorithm eliminates points close to each
other
reumann: Reumann-Witkam Algorithm
boyle: Boyle’s Forward-Looking Algorithm
sliding_averaging: McMaster’s Sliding Averaging Algorithm
distance_weighting: McMaster’s Distance-Weighting Algorithm
chaiken: Chaiken’s Algorithm
hermite: Interpolation by Cubic Hermite Splines
snakes: Snakes method for line smoothing
network: Network generalization
displacement: Displacement of lines close to each other
- threshold=float [required]
-
Maximal tolerance value
Options: 0-1000000000
- look_ahead=integer
-
Look-ahead parameter
Default: 7
- reduction=float
-
Percentage of the points in the output of ’douglas_reduction’
algorithm
Options: 0-100
Default: 50
- slide=float
-
Slide of computed point toward the original point
Options: 0-1
Default: 0.5
- angle_thresh=float
-
Minimum angle between two consecutive segments in Hermite method
Options: 0-180
Default: 3
- degree_thresh=integer
-
Degree threshold in network generalization
Default: 0
- closeness_thresh=float
-
Closeness threshold in network generalization
Options: 0-1
Default: 0
- betweeness_thresh=float
-
Betweeness threshold in network generalization
Default: 0
- alpha=float
-
Snakes alpha parameter
Default: 1.0
- beta=float
-
Snakes beta parameter
Default: 1.0
- iterations=integer
-
Number of iterations
Default: 1
- 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
v.generalize is a module for the generalization of GRASS
vector maps. This module consists of algorithms for line simplification,
line smoothing, network generalization and displacement (new methods may be
added later).
The cats and where options are used only if a
layer > 0 is specified, otherwise, those options are ignored. Be
aware that the default is layer=-1, meaning that all layers are
processed, ignoring the cats and where options.
If type=area is selected, boundaries of selected areas will
be generalized, and the options cats, where, and layer
will be used to select areas.
(Line) simplification is a process of reducing the complexity of
vector features. The module transforms a line into another line consisting
of fewer vertices, that still approximate the original line. Most of the
algorithms described below select a subset of points on the original
line.
(Line) smoothing is a "reverse" process which takes as
input a line and produces a smoother approximate of the original. In some
cases, this is achieved by inserting new vertices into the original line,
and can total up to 4000% of the number of vertices in the original. In such
an instance, it is always a good idea to simplify the line after
smoothing.
Smoothing and simplification algorithms implemented in this module
work line by line, i.e. simplification/smoothing of one line does not affect
the other lines; they are treated separately. For isolated loops formed by a
single line/boundary, he first and the last point of each line/boundary can
be translated and/or deleted, unless the -l flag is used to disable
loop support.
Lines and boundaries are not translated if they would collapse to
a single point. Boundaries are not translated if they would intersect with
themselves or other boundaries. Such erroneous features are written to an
optional error vector map. Overlaying the error map over the
generalized map indicates the kind of error. Lines/boundaries collapsing to
a point are written out as points, boundaries violating topology are written
out as boundaries. The error map can be overlaid over the generalized
map to understand why some features were not generalized.
Simplification can fail for many boundaries if the simplification
parameters would result in a large reduction of vertices. If many
lines/boundaries could not be simplified, try different parameters that
would cause a lower degree of simplification.
v.generalize contains following line simplification
algorithms:
- Douglas-Peucker Algorithm
- Douglas-Peucker Reduction Algorithm
- Lang Algorithm
- Vertex Reduction
- Reumann-Witkam Algorithm
Different algorithms require different parameters, but all the algorithms have
one parameter in common: the threshold parameter, given in map units
(for latitude-longitude locations: in decimal degree). In general, the degree
of simplification increases with the increasing value of threshold.
- Douglas-Peucker - "Quicksort" of line simplification, the
most widely used algorithm. Input parameters: input,
threshold. For more information, see:
http://geomalgorithms.com/a16-_decimate-1.html.
- Douglas-Peucker Reduction Algorithm is essentially the same
algorithm as the algorithm above, the difference being that it takes an
additional reduction parameter which denotes the percentage of the
number of points on the new line with respect to the number of points on
the original line. Input parameters: input, threshold,
reduction.
- Lang - Another standard algorithm. Input parameters: input,
threshold, look_ahead. For an excellent description, see:
http://www.sli.unimelb.edu.au/gisweb/LGmodule/LGLangVisualisation.htm.
- Vertex Reduction - Simplest among the algorithms. Input parameters:
input, threshold. Given a line, this algorithm removes the
points of this line which are closer to each other than threshold.
More precisely, if p1 and p2 are two consecutive points, and the distance
between p2 and p1 is less than threshold, it removes p2 and repeats
the same process on the remaining points.
- Reumann-Witkam - Input parameters: input, threshold.
This algorithm quite reasonably preserves the global characteristics of
the lines. For more information, see for example:
http://psimpl.sourceforge.net/reumann-witkam.html.
Douglas-Peucker and Douglas-Peucker Reduction
Algorithm use the same method to simplify the lines. Note that
v.generalize input=boundary_county output=boundary_county_dp20 method=douglas threshold=20
is equivalent to
v.generalize input=boundary_county output=boundary_county_dp_red20_100 \
method=douglas_reduction threshold=20 reduction=100
However, in this case, the first method is faster. Also observe
that douglas_reduction never outputs more vertices than
douglas, and that, in general, douglas is more efficient than
douglas_reduction. More importantly, the effect of
v.generalize input=boundary_county output=boundary_county_dp_red0_30 \
method=douglas_reduction threshold=0 reduction=30
is that ’out’ contains approximately only 30% of
points of ’in’.
The following smoothing algorithms are implemented in
v.generalize:
- Boyle’s Forward-Looking Algorithm - The position of each
point depends on the position of the previous points and the point
look_ahead ahead. look_ahead consecutive points. Input
parameters: input, look_ahead.
- McMaster’s Sliding Averaging Algorithm - Input Parameters:
input, slide, look_ahead. The new position of each
point is the average of the look_ahead points around. Parameter
slide is used for linear interpolation between old and new position
(see below).
- McMaster’s Distance-Weighting Algorithm - Takes the weighted
average of look_ahead consecutive points where the weight is the
reciprocal of the distance from the point to the currently smoothed point.
The parameter slide is used for linear interpolation between the
original position of the point and newly computed position where value 0
means the original position. Input parameters: input, slide,
look_ahead.
- Chaiken’s Algorithm - "Inscribes" a line touching
the original line such that the points on this new line are at least
threshold apart. Input parameters: input, threshold.
This algorithm approximates the given line very well.
- Hermite Interpolation - This algorithm takes the points of the
given line as the control points of hermite cubic spline and approximates
this spline by the points approximately threshold apart. This
method has excellent results for small values of threshold, but in
this case it produces a huge number of new points and some simplification
is usually needed. Input parameters: input, threshold,
angle_thresh. Angle_thresh is used for reducing the number
of the points. It denotes the minimal angle (in degrees) between two
consecutive segments of a line.
- Snakes is the method of minimisation of the "energy" of a
line. This method preserves the general characteristics of the lines but
smooths the "sharp corners" of a line. Input parameters
input, alpha, beta. This algorithm works very well
for small values of alpha and beta (between 0 and 5). These
parameters affect the "sharpness" and the curvature of the
computed line.
One of the key advantages of Hermite Interpolation is the fact that the
computed line always passes through the points of the original line, whereas
the lines produced by the remaining algorithms never pass through these
points. In some sense, this algorithm outputs a line which
"circumscribes" the input line.
On the other hand, Chaiken’s Algorithm outputs a
line which "inscribes" a given line. The output line always
touches/intersects the centre of the input line segment between two
consecutive points. For more iterations, the property above does not hold,
but the computed lines are very similar to the Bezier Splines. The
disadvantage of the two algorithms given above is that they increase the
number of points. However, Hermite Interpolation can be used as
another simplification algorithm. To achieve this, it is necessary to set
angle_thresh to higher values (15 or so).
One restriction on both McMasters’ Algorithms is that
look_ahead parameter must be odd. Also note that these algorithms
have no effect if look_ahead = 1.
Note that Boyle’s, McMasters’ and
Snakes algorithm are sometimes used in the signal processing to
smooth the signals. More importantly, these algorithms never change the
number of points on the lines; they only translate the points, and do not
insert any new points.
Snakes Algorithm is (asymptotically) the slowest among the
algorithms presented above. Also, it requires quite a lot of memory. This
means that it is not very efficient for maps with the lines consisting of
many segments.
The displacement is used when the lines overlap and/or are close
to each other at the current level of detail. In general, displacement
methods move the conflicting features apart so that they do not interact and
can be distinguished.
This module implements an algorithm for displacement of linear
features based on the Snakes approach. This method generally yields
very good results; however, it requires a lot of memory and is not very
efficient.
Displacement is selected by method=displacement. It uses
the following parameters:
- threshold - specifies critical distance. Two features interact if
they are closer than threshold apart.
- alpha, beta - These parameters define the rigidity of lines.
For larger values of alpha, beta (>=1), the algorithm
does a better job at retaining the original shape of the lines, possibly
at the expense of displacement distance. If the values of alpha,
beta are too small (<=0.001), then the lines are moved
sufficiently, but the geometry and topology of lines can be destroyed.
Most likely the best way to find the good values of alpha,
beta is by trial and error.
- iterations - denotes the number of iterations the interactions
between the lines are resolved. Good starting points for values of
iterations are between 10 and 100.
The lines affected by the algorithm can be specified by the layer,
cats and where parameters.
Used for selecting "the most important" part of the
network. This is based on the graph algorithms. Network generalization is
applied if method=network. The algorithm calculates three centrality
measures for each line in the network and only the lines with the values
greater than thresholds are selected. The behaviour of algorithm can be
altered by the following parameters:
- degree_thresh - algorithm selects only the lines which share a
point with at least degree_thresh different lines.
- closeness_thresh - is always in the range (0, 1]. Only the lines
with the closeness centrality value at least closeness_thresh apart
are selected. The lines in the centre of a network have greater values of
this measure than the lines near the border of a network. This means that
this parameter can be used for selecting the centre(s) of a network. Note
that if closeness_thresh=0 then everything is selected.
- betweeness_thresh - Again, only the lines with a betweeness
centrality measure at least betweeness_thresh are selected. This
value is always positive and is larger for large networks. It denotes to
what extent a line is in between the other lines in the network. This
value is large for the lines which lie between other lines and lie on the
paths between two parts of a network. In the terminology of road networks,
these are highways, bypasses, main roads/streets, etc.
All three parameters above can be presented at the same time. In that case, the
algorithm selects only the lines which meet each criterion.
Also, the outputed network may not be connected if the value of
betweeness_thresh is too large.
Simplification of county boundaries with DP method (North Carolina
sample dataset), threshold given in mapset units (here: meters):
v.generalize input=boundary_county output=boundary_county_dp20 \
method=douglas threshold=20 error=boundary_county_dp20_leftover
Figure: Vector simplification example (spatial subset:
original map shown in black, simplified map with 26% remaining vertices
shown in red)
Smoothing of road network with Chaiken method (North Carolina
sample dataset), threshold given in mapset units (here: meters):
v.generalize input=roads output=roads_chaiken method=chaiken \
threshold=1 error=roads_chaiken_leftover
Figure: Vector smoothing example (spatial subset:
original map shown in black, smoothed map with 500% increased number of
vertices shown in red)
v.clean, v.dissolve
v.generalize Tutorial (GRASS-Wiki)
Daniel Bundala, Google Summer of Code 2007, Student
Wolf Bergenheim, Mentor
Partial rewrite: Markus Metz
Available at: v.generalize source code (history)
Accessed: Sunday Jan 22 07:37:01 2023
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