1v.generalize(1) Grass User's Manual v.generalize(1)
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6 v.generalize - Performs vector based generalization.
7
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 If type=area is selected, boundaries of selected areas will be general‐
156 ized, and the options cats, where, and layer will be used to select
157 areas.
158
160 (Line) simplification is a process of reducing the complexity of vector
161 features. The module transforms a line into another line consisting of
162 fewer vertices, that still approximate the original line. Most of the
163 algorithms described below select a subset of points on the original
164 line.
165 (Line) smoothing is a "reverse" process which takes as input a line and
167 produces a smoother approximate of the original. In some cases, this is
168 achieved by inserting new vertices into the original line, and can
169 total up to 4000% of the number of vertices in the original. In such an
170 instance, it is always a good idea to simplify the line after smooth‐
171 ing.
172 Smoothing and simplification algorithms implemented in this module work
174 line by line, i.e. simplification/smoothing of one line does not affect
175 the other lines; they are treated separately. For isolated loops formed
176 by a single line/boundary, he first and the last point of each
177 line/boundary can be translated and/or deleted, unless the -l flag is
178 used to disable loop support.
179 Lines and boundaries are not translated if they would collapse to a
181 single point. Boundaries are not translated if they would intersect
182 with themselves or other boundaries. Such erroneous features are writ‐
183 ten to an optional error vector map. Overlaying the error map over the
184 generalized map indicates the kind of error. Lines/boundaries collaps‐
185 ing to a point are written out as points, boundaries violating topology
186 are written out as boundaries. The error map can be overlaid over the
187 generalized map to understand why some features were not generalized.
188 SIMPLIFICATION
190 Simplification can fail for many boundaries if the simplification
191 parameters would result in a large reduction of vertices. If many
192 lines/boundaries could not be simplified, try different parameters that
193 would cause a lower degree of simplification.
194 v.generalize contains following line simplification algorithms:
196 · Douglas-Peucker Algorithm
198 · Douglas-Peucker Reduction Algorithm
200 · Lang Algorithm
202 · Vertex Reduction
204 · Reumann-Witkam Algorithm
206 · Remove Small Lines/Areas
208 Different algorithms require different parameters, but all the algo‐
209 rithms have one parameter in common: the threshold parameter, given in
210 map units (for latitude-longitude locations: in decimal degree). In
211 general, the degree of simplification increases with the increasing
212 value of threshold.
213 ALGORITHM DESCRIPTIONS
215 · Douglas-Peucker - "Quicksort" of line simplification, the most
216 widely used algorithm. Input parameters: input, threshold. For
217 more information, see:
218 http://geomalgorithms.com/a16-_decimate-1.html.
219 · Douglas-Peucker Reduction Algorithm is essentially the same
221 algorithm as the algorithm above, the difference being that it
222 takes an additional reduction parameter which denotes the per‐
223 centage of the number of points on the new line with respect to
224 the number of points on the original line. Input parameters:
225 input, threshold, reduction.
226 · Lang - Another standard algorithm. Input parameters: input,
228 threshold, look_ahead. For an excellent description, see:
229 http://www.sli.unimelb.edu.au/gisweb/LGmodule/LGLangVisualisa‐
230 tion.htm.
231 · Vertex Reduction - Simplest among the algorithms. Input parame‐
233 ters: input, threshold. Given a line, this algorithm removes
234 the points of this line which are closer to each other than
235 threshold. More precisely, if p1 and p2 are two consecutive
236 points, and the distance between p2 and p1 is less than thresh‐
237 old, it removes p2 and repeats the same process on the remain‐
238 ing points.
239 · Reumann-Witkam - Input parameters: input, threshold. This
241 algorithm quite reasonably preserves the global characteristics
242 of the lines. For more information, see for example:
243 http://psimpl.sourceforge.net/reumann-witkam.html.
244 Douglas-Peucker and Douglas-Peucker Reduction Algorithm use the same
245 method to simplify the lines. Note that
246 v.generalize input=boundary_county output=boundary_county_dp20 method=douglas threshold=20
247 is equivalent to
248 v.generalize input=boundary_county output=boundary_county_dp_red20_100 \
249 method=douglas_reduction threshold=20 reduction=100
250 However, in this case, the first method is faster. Also observe that
251 douglas_reduction never outputs more vertices than douglas, and that,
252 in general, douglas is more efficient than douglas_reduction. More
253 importantly, the effect of
254 v.generalize input=boundary_county output=boundary_county_dp_red0_30 \
255 method=douglas_reduction threshold=0 reduction=30
256 is that ’out’ contains approximately only 30% of points of ’in’.
257 SMOOTHING
259 The following smoothing algorithms are implemented in v.generalize:
260 · Boyle’s Forward-Looking Algorithm - The position of each point
262 depends on the position of the previous points and the point
263 look_ahead ahead. look_ahead consecutive points. Input parame‐
264 ters: input, look_ahead.
265 · McMaster’s Sliding Averaging Algorithm - Input Parameters:
267 input, slide, look_ahead. The new position of each point is
268 the average of the look_ahead points around. Parameter slide is
269 used for linear interpolation between old and new position (see
270 below).
271 · McMaster’s Distance-Weighting Algorithm - Takes the weighted
273 average of look_ahead consecutive points where the weight is
274 the reciprocal of the distance from the point to the currently
275 smoothed point. The parameter slide is used for linear interpo‐
276 lation between the original position of the point and newly
277 computed position where value 0 means the original position.
278 Input parameters: input, slide, look_ahead.
279 · Chaiken’s Algorithm - "Inscribes" a line touching the original
281 line such that the points on this new line are at least thresh‐
282 old apart. Input parameters: input, threshold. This algorithm
283 approximates the given line very well.
284 · Hermite Interpolation - This algorithm takes the points of the
286 given line as the control points of hermite cubic spline and
287 approximates this spline by the points approximately threshold
288 apart. This method has excellent results for small values of
289 threshold, but in this case it produces a huge number of new
290 points and some simplification is usually needed. Input param‐
291 eters: input, threshold, angle_thresh. Angle_thresh is used
292 for reducing the number of the points. It denotes the minimal
293 angle (in degrees) between two consecutive segments of a line.
294 · Snakes is the method of minimisation of the "energy" of a line.
296 This method preserves the general characteristics of the lines
297 but smooths the "sharp corners" of a line. Input parameters
298 input, alpha, beta. This algorithm works very well for small
299 values of alpha and beta (between 0 and 5). These parameters
300 affect the "sharpness" and the curvature of the computed line.
301 One of the key advantages of Hermite Interpolation is the fact that the
302 computed line always passes through the points of the original line,
303 whereas the lines produced by the remaining algorithms never pass
304 through these points. In some sense, this algorithm outputs a line
305 which "circumscribes" the input line.
306 On the other hand, Chaiken’s Algorithm outputs a line which "inscribes"
308 a given line. The output line always touches/intersects the centre of
309 the input line segment between two consecutive points. For more itera‐
310 tions, the property above does not hold, but the computed lines are
311 very similar to the Bezier Splines. The disadvantage of the two algo‐
312 rithms given above is that they increase the number of points. How‐
313 ever, Hermite Interpolation can be used as another simplification algo‐
314 rithm. To achieve this, it is necessary to set angle_thresh to higher
315 values (15 or so).
316 One restriction on both McMasters’ Algorithms is that look_ahead param‐
318 eter must be odd. Also note that these algorithms have no effect if
319 look_ahead = 1.
320 Note that Boyle’s, McMasters’ and Snakes algorithm are sometimes used
322 in the signal processing to smooth the signals. More importantly,
323 these algorithms never change the number of points on the lines; they
324 only translate the points, and do not insert any new points.
325 Snakes Algorithm is (asymptotically) the slowest among the algorithms
327 presented above. Also, it requires quite a lot of memory. This means
328 that it is not very efficient for maps with the lines consisting of
329 many segments.
330 DISPLACEMENT
332 The displacement is used when the lines overlap and/or are close to
333 each other at the current level of detail. In general, displacement
334 methods move the conflicting features apart so that they do not inter‐
335 act and can be distinguished.
336 This module implements an algorithm for displacement of linear features
338 based on the Snakes approach. This method generally yields very good
339 results; however, it requires a lot of memory and is not very effi‐
340 cient.
341 Displacement is selected by method=displacement. It uses the following
343 parameters:
344 · threshold - specifies critical distance. Two features interact
346 if they are closer than threshold apart.
347 · alpha, beta - These parameters define the rigidity of lines.
349 For larger values of alpha, beta (>=1), the algorithm does a
350 better job at retaining the original shape of the lines, possi‐
351 bly at the expense of displacement distance. If the values of
352 alpha, beta are too small (<=0.001), then the lines are moved
353 sufficiently, but the geometry and topology of lines can be
354 destroyed. Most likely the best way to find the good values of
355 alpha, beta is by trial and error.
356 · iterations - denotes the number of iterations the interactions
358 between the lines are resolved. Good starting points for values
359 of iterations are between 10 and 100.
360 The lines affected by the algorithm can be specified by the layer, cats
361 and where parameters.
362 NETWORK GENERALIZATION
364 Used for selecting "the most important" part of the network. This is
365 based on the graph algorithms. Network generalization is applied if
366 method=network. The algorithm calculates three centrality measures for
367 each line in the network and only the lines with the values greater
368 than thresholds are selected. The behaviour of algorithm can be
369 altered by the following parameters:
370 · degree_thresh - algorithm selects only the lines which share a
372 point with at least degree_thresh different lines.
373 · closeness_thresh - is always in the range (0, 1]. Only the
375 lines with the closeness centrality value at least close‐
376 ness_thresh apart are selected. The lines in the centre of a
377 network have greater values of this measure than the lines near
378 the border of a network. This means that this parameter can be
379 used for selecting the centre(s) of a network. Note that if
380 closeness_thresh=0 then everything is selected.
381 · betweeness_thresh - Again, only the lines with a betweeness
383 centrality measure at least betweeness_thresh are selected.
384 This value is always positive and is larger for large networks.
385 It denotes to what extent a line is in between the other lines
386 in the network. This value is large for the lines which lie
387 between other lines and lie on the paths between two parts of a
388 network. In the terminology of road networks, these are high‐
389 ways, bypasses, main roads/streets, etc.
390 All three parameters above can be presented at the same time. In that
391 case, the algorithm selects only the lines which meet each criterion.
392 Also, the outputed network may not be connected if the value of betwee‐
394 ness_thresh is too large.
395
397 SIMPLIFICATION EXAMPLE
398 Simplification of county boundaries with DP method (North Carolina sam‐
399 ple dataset), threshold given in mapset units (here: meters):
400 v.generalize input=boundary_county output=boundary_county_dp20 \
401 method=douglas threshold=20 error=boundary_county_dp20_leftover
402 Figure: Vector simplification example (spatial subset: original map
403 shown in black, simplified map with 26% remaining vertices shown in
404 red)
405 SMOOTHING EXAMPLE
407 Smoothing of road network with Chaiken method (North Carolina sample
408 dataset), threshold given in mapset units (here: meters):
409 v.generalize input=roads output=roads_chaiken method=chaiken \
410 threshold=1 error=roads_chaiken_leftover
411 Figure: Vector smoothing example (spatial subset: original map shown in
412 black, smoothed map with 500% increased number of vertices shown in
413 red)
414
416 v.clean, v.dissolve
417 v.generalize Tutorial (GRASS-Wiki)
419
421 Daniel Bundala, Google Summer of Code 2007, Student
422 Wolf Bergenheim, Mentor
423 Partial rewrite: Markus Metz
424 Last changed: $Date: 2017-05-07 22:50:11 +0200 (Sun, 07 May 2017) $
426
428 Available at: v.generalize source code (history)
429 Main index | Vector index | Topics index | Keywords index | Graphical
431 index | Full index
432 © 2003-2019 GRASS Development Team, GRASS GIS 7.4.4 Reference Manual
434GRASS 7.4.4 v.generalize(1)