1v.generalize(1) GRASS GIS User's Manual v.generalize(1)
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6 v.generalize - Performs vector based generalization.
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9 vector, generalization, simplification, smoothing, displacement, net‐
10 work generalization, topology, geometry
11
13 v.generalize
14 v.generalize --help
15 v.generalize [-lt] input=name [layer=string]
16 [type=string[,string,...]] output=name [error=name] method=string
17 threshold=float [look_ahead=integer] [reduction=float]
18 [slide=float] [angle_thresh=float] [degree_thresh=integer]
19 [closeness_thresh=float] [betweeness_thresh=float] [alpha=float]
20 [beta=float] [iterations=integer] [cats=range] [where=sql_query]
21 [--overwrite] [--help] [--verbose] [--quiet] [--ui]
22 Flags:
24 -l
25 Disable loop support
26 Do not modify end points of lines forming a closed loop
27 -t
29 Do not copy attributes
30 --overwrite
32 Allow output files to overwrite existing files
33 --help
35 Print usage summary
36 --verbose
38 Verbose module output
39 --quiet
41 Quiet module output
42 --ui
44 Force launching GUI dialog
45 Parameters:
47 input=name [required]
48 Name of input vector map
49 Or data source for direct OGR access
50 layer=string
52 Layer number or name (’-1’ for all layers)
53 A single vector map can be connected to multiple database tables.
54 This number determines which table to use. When used with direct
55 OGR access this is the layer name.
56 Default: -1
57 type=string[,string,...]
59 Input feature type
60 Options: line, boundary, area
61 Default: line,boundary,area
62 output=name [required]
64 Name for output vector map
65 error=name
67 Error map with failed generalizations
68 Lines and boundaries causing errors (collapsed to a point or topol‐
69 ogy errors)
70 method=string [required]
72 Generalization algorithm
73 Options: douglas, douglas_reduction, lang, reduction, reumann,
74 boyle, sliding_averaging, distance_weighting, chaiken, hermite,
75 snakes, network, displacement
76 douglas: Douglas-Peucker Algorithm
77 douglas_reduction: Douglas-Peucker Algorithm with reduction parame‐
78 ter
79 lang: Lang Simplification Algorithm
80 reduction: Vertex Reduction Algorithm eliminates points close to
81 each other
82 reumann: Reumann-Witkam Algorithm
83 boyle: Boyle’s Forward-Looking Algorithm
84 sliding_averaging: McMaster’s Sliding Averaging Algorithm
85 distance_weighting: McMaster’s Distance-Weighting Algorithm
86 chaiken: Chaiken’s Algorithm
87 hermite: Interpolation by Cubic Hermite Splines
88 snakes: Snakes method for line smoothing
89 network: Network generalization
90 displacement: Displacement of lines close to each other
91 threshold=float [required]
93 Maximal tolerance value
94 Options: 0-1000000000
95 look_ahead=integer
97 Look-ahead parameter
98 Default: 7
99 reduction=float
101 Percentage of the points in the output of ’douglas_reduction’ algo‐
102 rithm
103 Options: 0-100
104 Default: 50
105 slide=float
107 Slide of computed point toward the original point
108 Options: 0-1
109 Default: 0.5
110 angle_thresh=float
112 Minimum angle between two consecutive segments in Hermite method
113 Options: 0-180
114 Default: 3
115 degree_thresh=integer
117 Degree threshold in network generalization
118 Default: 0
119 closeness_thresh=float
121 Closeness threshold in network generalization
122 Options: 0-1
123 Default: 0
124 betweeness_thresh=float
126 Betweeness threshold in network generalization
127 Default: 0
128 alpha=float
130 Snakes alpha parameter
131 Default: 1.0
132 beta=float
134 Snakes beta parameter
135 Default: 1.0
136 iterations=integer
138 Number of iterations
139 Default: 1
140 cats=range
142 Category values
143 Example: 1,3,7-9,13
144 where=sql_query
146 WHERE conditions of SQL statement without ’where’ keyword
147 Example: income < 1000 and population >= 10000
148
150 v.generalize is a module for the generalization of GRASS vector maps.
151 This module consists of algorithms for line simplification, line
152 smoothing, network generalization and displacement (new methods may be
153 added later).
154 The cats and where options are used only if a layer > 0 is specified,
156 otherwise, those options are ignored. Be aware that the default is
157 layer=-1, meaning that all layers are processed, ignoring the cats and
158 where options.
159 If type=area is selected, boundaries of selected areas will be general‐
161 ized, and the options cats, where, and layer will be used to select ar‐
162 eas.
163
165 (Line) simplification is a process of reducing the complexity of vector
166 features. The module transforms a line into another line consisting of
167 fewer vertices, that still approximate the original line. Most of the
168 algorithms described below select a subset of points on the original
169 line.
170 (Line) smoothing is a "reverse" process which takes as input a line and
172 produces a smoother approximate of the original. In some cases, this is
173 achieved by inserting new vertices into the original line, and can to‐
174 tal up to 4000% of the number of vertices in the original. In such an
175 instance, it is always a good idea to simplify the line after smooth‐
176 ing.
177 Smoothing and simplification algorithms implemented in this module work
179 line by line, i.e. simplification/smoothing of one line does not affect
180 the other lines; they are treated separately. For isolated loops formed
181 by a single line/boundary, he first and the last point of each
182 line/boundary can be translated and/or deleted, unless the -l flag is
183 used to disable loop support.
184 Lines and boundaries are not translated if they would collapse to a
186 single point. Boundaries are not translated if they would intersect
187 with themselves or other boundaries. Such erroneous features are writ‐
188 ten to an optional error vector map. Overlaying the error map over the
189 generalized map indicates the kind of error. Lines/boundaries collaps‐
190 ing to a point are written out as points, boundaries violating topology
191 are written out as boundaries. The error map can be overlaid over the
192 generalized map to understand why some features were not generalized.
193 SIMPLIFICATION
195 Simplification can fail for many boundaries if the simplification pa‐
196 rameters would result in a large reduction of vertices. If many
197 lines/boundaries could not be simplified, try different parameters that
198 would cause a lower degree of simplification.
199 v.generalize contains following line simplification algorithms:
201 • Douglas-Peucker Algorithm
203 • Douglas-Peucker Reduction Algorithm
205 • Lang Algorithm
207 • Vertex Reduction
209 • Reumann-Witkam Algorithm
211 Different algorithms require different parameters, but all the algo‐
212 rithms have one parameter in common: the threshold parameter, given in
213 map units (for latitude-longitude locations: in decimal degree). In
214 general, the degree of simplification increases with the increasing
215 value of threshold.
216 ALGORITHM DESCRIPTIONS
218 • Douglas-Peucker - "Quicksort" of line simplification, the most
219 widely used algorithm. Input parameters: input, threshold. For
220 more information, see:
221 http://geomalgorithms.com/a16-_decimate-1.html.
222 • Douglas-Peucker Reduction Algorithm is essentially the same al‐
224 gorithm as the algorithm above, the difference being that it
225 takes an additional reduction parameter which denotes the per‐
226 centage of the number of points on the new line with respect to
227 the number of points on the original line. Input parameters:
228 input, threshold, reduction.
229 • Lang - Another standard algorithm. Input parameters: input,
231 threshold, look_ahead. For an excellent description, see:
232 http://www.sli.unimelb.edu.au/gisweb/LGmodule/LGLangVisualisa‐
233 tion.htm.
234 • Vertex Reduction - Simplest among the algorithms. Input parame‐
236 ters: input, threshold. Given a line, this algorithm removes
237 the points of this line which are closer to each other than
238 threshold. More precisely, if p1 and p2 are two consecutive
239 points, and the distance between p2 and p1 is less than thresh‐
240 old, it removes p2 and repeats the same process on the remain‐
241 ing points.
242 • Reumann-Witkam - Input parameters: input, threshold. This al‐
244 gorithm quite reasonably preserves the global characteristics
245 of the lines. For more information, see for example:
246 http://psimpl.sourceforge.net/reumann-witkam.html.
247 Douglas-Peucker and Douglas-Peucker Reduction Algorithm use the same
248 method to simplify the lines. Note that
249 v.generalize input=boundary_county output=boundary_county_dp20 method=douglas threshold=20
250 is equivalent to
251 v.generalize input=boundary_county output=boundary_county_dp_red20_100 \
252 method=douglas_reduction threshold=20 reduction=100
253 However, in this case, the first method is faster. Also observe that
254 douglas_reduction never outputs more vertices than douglas, and that,
255 in general, douglas is more efficient than douglas_reduction. More im‐
256 portantly, the effect of
257 v.generalize input=boundary_county output=boundary_county_dp_red0_30 \
258 method=douglas_reduction threshold=0 reduction=30
259 is that ’out’ contains approximately only 30% of points of ’in’.
260 SMOOTHING
262 The following smoothing algorithms are implemented in v.generalize:
263 • Boyle’s Forward-Looking Algorithm - The position of each point
265 depends on the position of the previous points and the point
266 look_ahead ahead. look_ahead consecutive points. Input parame‐
267 ters: input, look_ahead.
268 • McMaster’s Sliding Averaging Algorithm - Input Parameters: in‐
270 put, slide, look_ahead. The new position of each point is the
271 average of the look_ahead points around. Parameter slide is
272 used for linear interpolation between old and new position (see
273 below).
274 • McMaster’s Distance-Weighting Algorithm - Takes the weighted
276 average of look_ahead consecutive points where the weight is
277 the reciprocal of the distance from the point to the currently
278 smoothed point. The parameter slide is used for linear interpo‐
279 lation between the original position of the point and newly
280 computed position where value 0 means the original position.
281 Input parameters: input, slide, look_ahead.
282 • Chaiken’s Algorithm - "Inscribes" a line touching the original
284 line such that the points on this new line are at least thresh‐
285 old apart. Input parameters: input, threshold. This algorithm
286 approximates the given line very well.
287 • Hermite Interpolation - This algorithm takes the points of the
289 given line as the control points of hermite cubic spline and
290 approximates this spline by the points approximately threshold
291 apart. This method has excellent results for small values of
292 threshold, but in this case it produces a huge number of new
293 points and some simplification is usually needed. Input param‐
294 eters: input, threshold, angle_thresh. Angle_thresh is used
295 for reducing the number of the points. It denotes the minimal
296 angle (in degrees) between two consecutive segments of a line.
297 • Snakes is the method of minimisation of the "energy" of a line.
299 This method preserves the general characteristics of the lines
300 but smooths the "sharp corners" of a line. Input parameters in‐
301 put, alpha, beta. This algorithm works very well for small
302 values of alpha and beta (between 0 and 5). These parameters
303 affect the "sharpness" and the curvature of the computed line.
304 One of the key advantages of Hermite Interpolation is the fact that the
305 computed line always passes through the points of the original line,
306 whereas the lines produced by the remaining algorithms never pass
307 through these points. In some sense, this algorithm outputs a line
308 which "circumscribes" the input line.
309 On the other hand, Chaiken’s Algorithm outputs a line which "inscribes"
311 a given line. The output line always touches/intersects the centre of
312 the input line segment between two consecutive points. For more itera‐
313 tions, the property above does not hold, but the computed lines are
314 very similar to the Bezier Splines. The disadvantage of the two algo‐
315 rithms given above is that they increase the number of points. How‐
316 ever, Hermite Interpolation can be used as another simplification algo‐
317 rithm. To achieve this, it is necessary to set angle_thresh to higher
318 values (15 or so).
319 One restriction on both McMasters’ Algorithms is that look_ahead param‐
321 eter must be odd. Also note that these algorithms have no effect if
322 look_ahead = 1.
323 Note that Boyle’s, McMasters’ and Snakes algorithm are sometimes used
325 in the signal processing to smooth the signals. More importantly,
326 these algorithms never change the number of points on the lines; they
327 only translate the points, and do not insert any new points.
328 Snakes Algorithm is (asymptotically) the slowest among the algorithms
330 presented above. Also, it requires quite a lot of memory. This means
331 that it is not very efficient for maps with the lines consisting of
332 many segments.
333 DISPLACEMENT
335 The displacement is used when the lines overlap and/or are close to
336 each other at the current level of detail. In general, displacement
337 methods move the conflicting features apart so that they do not inter‐
338 act and can be distinguished.
339 This module implements an algorithm for displacement of linear features
341 based on the Snakes approach. This method generally yields very good
342 results; however, it requires a lot of memory and is not very effi‐
343 cient.
344 Displacement is selected by method=displacement. It uses the following
346 parameters:
347 • threshold - specifies critical distance. Two features interact
349 if they are closer than threshold apart.
350 • alpha, beta - These parameters define the rigidity of lines.
352 For larger values of alpha, beta (>=1), the algorithm does a
353 better job at retaining the original shape of the lines, possi‐
354 bly at the expense of displacement distance. If the values of
355 alpha, beta are too small (<=0.001), then the lines are moved
356 sufficiently, but the geometry and topology of lines can be de‐
357 stroyed. Most likely the best way to find the good values of
358 alpha, beta is by trial and error.
359 • iterations - denotes the number of iterations the interactions
361 between the lines are resolved. Good starting points for values
362 of iterations are between 10 and 100.
363 The lines affected by the algorithm can be specified by the layer, cats
364 and where parameters.
365 NETWORK GENERALIZATION
367 Used for selecting "the most important" part of the network. This is
368 based on the graph algorithms. Network generalization is applied if
369 method=network. The algorithm calculates three centrality measures for
370 each line in the network and only the lines with the values greater
371 than thresholds are selected. The behaviour of algorithm can be al‐
372 tered by the following parameters:
373 • degree_thresh - algorithm selects only the lines which share a
375 point with at least degree_thresh different lines.
376 • closeness_thresh - is always in the range (0, 1]. Only the
378 lines with the closeness centrality value at least close‐
379 ness_thresh apart are selected. The lines in the centre of a
380 network have greater values of this measure than the lines near
381 the border of a network. This means that this parameter can be
382 used for selecting the centre(s) of a network. Note that if
383 closeness_thresh=0 then everything is selected.
384 • betweeness_thresh - Again, only the lines with a betweeness
386 centrality measure at least betweeness_thresh are selected.
387 This value is always positive and is larger for large networks.
388 It denotes to what extent a line is in between the other lines
389 in the network. This value is large for the lines which lie be‐
390 tween other lines and lie on the paths between two parts of a
391 network. In the terminology of road networks, these are high‐
392 ways, bypasses, main roads/streets, etc.
393 All three parameters above can be presented at the same time. In that
394 case, the algorithm selects only the lines which meet each criterion.
395 Also, the outputed network may not be connected if the value of betwee‐
397 ness_thresh is too large.
398
400 SIMPLIFICATION EXAMPLE
401 Simplification of county boundaries with DP method (North Carolina sam‐
402 ple dataset), threshold given in mapset units (here: meters):
403 v.generalize input=boundary_county output=boundary_county_dp20 \
404 method=douglas threshold=20 error=boundary_county_dp20_leftover
405 Figure: Vector simplification example (spatial subset: original map
406 shown in black, simplified map with 26% remaining vertices shown in
407 red)
408 SMOOTHING EXAMPLE
410 Smoothing of road network with Chaiken method (North Carolina sample
411 dataset), threshold given in mapset units (here: meters):
412 v.generalize input=roads output=roads_chaiken method=chaiken \
413 threshold=1 error=roads_chaiken_leftover
414 Figure: Vector smoothing example (spatial subset: original map shown in
415 black, smoothed map with 500% increased number of vertices shown in
416 red)
417
419 v.clean, v.dissolve
420 v.generalize Tutorial (GRASS-Wiki)
422
424 Daniel Bundala, Google Summer of Code 2007, Student
425 Wolf Bergenheim, Mentor
426 Partial rewrite: Markus Metz
427
429 Available at: v.generalize source code (history)
430 Accessed: Saturday Jan 21 21:16:03 2023
432 Main index | Vector index | Topics index | Keywords index | Graphical
434 index | Full index
435 © 2003-2023 GRASS Development Team, GRASS GIS 8.2.1 Reference Manual
437GRASS 8.2.1 v.generalize(1)