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**NAME**

v.generalize- Vector based generalization.

**KEYWORDS**

vector, generalization, simplification, smoothing, displacement, network generalization

**SYNOPSIS**

v.generalizev.generalizehelpv.generalize[-cr]input=nameoutput=name[type=string[,string,...]]method=stringthreshold=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] [layer=integer] [cats=range] [where=sql_query] [--overwrite] [--verbose] [--quiet]Flags:-cCopy attributes-rThis does nothing. It is retained for backwards compatibility--overwriteAllow output files to overwrite existing files--verboseVerbose module output--quietQuiet module outputParameters:input=nameName of input vector mapoutput=nameName for output vector maptype=string[,string,...] Feature type Options:line,boundary,areaDefault:line,boundary,areamethod=stringGeneralization algorithm Options:douglas,douglas_reduction,lang,reduction,reumann,boyle,sliding_averaging,distance_weighting,chaiken,hermite,snakes,network,displacementdouglas: Douglas-Peucker Algorithmdouglas_reduction: Douglas-Peucker Algorithm with reduction parameterlang: Lang Simplification Algorithmreduction: Vertex Reduction Algorithm eliminates points close to each otherreumann: Reumann-Witkam Algorithmboyle: Boyle's Forward-Looking Algorithmsliding_averaging: McMaster's Sliding Averaging Algorithmdistance_weighting: McMaster's Distance-Weighting Algorithmchaiken: Chaiken's Algorithmhermite: Interpolation by Cubic Hermite Splinessnakes: Snakes method for line smoothingnetwork: Network generalizationdisplacement: Displacement of lines close to each otherthreshold=floatMaximal tolerance value Options:0-1000000000look_ahead=integerLook-ahead parameter Default:7reduction=floatPercentage of the points in the output of 'douglas_reduction' algorithm Options:0-100Default:50slide=floatSlide of computed point toward the original point Options:0-1Default:0.5angle_thresh=floatMinimum angle between two consecutive segments in Hermite method Options:0-180Default:3degree_thresh=integerDegree threshold in network generalization Default:0closeness_thresh=floatCloseness threshold in network generalization Options:0-1Default:0betweeness_thresh=floatBetweeness threshold in network generalization Default:0alpha=floatSnakes alpha parameter Default:1.0beta=floatSnakes beta parameter Default:1.0iterations=integerNumber of iterations Default:1layer=integerLayer number A single vector map can be connected to multiple database tables. This number determines which table to use. Default:1cats=rangeCategory values Example: 1,3,7-9,13where=sql_queryWHERE conditions of SQL statement without 'where' keyword Example: income = 10000

**DESCRIPTION**

v.generalizeis 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). For more examples and nice pictures, seetutorialIftype=areais selected, boundaries of selected areas will be generalized, and the optionscats,where, andlayerwill be used to select areas.

**NOTES**

(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. Also, the first and the last point of each line is never translated and/or deleted.SIMPLIFICATIONv.generalizecontains following line simplification algorithms: Douglas-Peucker Algorithm Douglas-Peucker Reduction Algorithm Lang Algorithm Vertex Reduction Reumann-Witkam Algorithm Remove Small Lines/Areas Different algorithms require different parameters, but all the algorithms have one parameter in common: thethresholdparameter. In general, the degree of simplification increases with the increasing value ofthreshold.ALGORITHMDESCRIPTIONSDouglas-Peucker- "Quicksort" of line simplification, the most widely used algorithm. Input parameters:input,threshold. For more information, see: http://geometryalgorithms.com/Archive/algorithm_0205/algorithm_0205.htm.Douglas-PeuckerReductionAlgorithmis essentially the same algorithm as the algorithm above, the difference being that it takes an additionalreductionparameter 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.VertexReduction- 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 thanthreshold. More precisely, if p1 and p2 are two consecutive points, and the distance between p2 and p1 is less thanthreshold, it removes p2 and repeats the same process on the remaining points.Reuman-Witkam- Input parameters:input,threshold. This algorithm quite reasonably preserves the global characteristics of the lines. For more information, see: http://www.ifp.uni- stuttgart.de/lehre/vorlesungen/GIS1/Lernmodule/Lg/LG_de_6.html (german).Douglas-PeuckerandDouglas-PeuckerReductionAlgorithmuse 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 thatdouglas_reductionnever outputs more vertices thandouglas, and that, in general,douglasis more efficient thandouglas_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'.SMOOTHINGThe following smoothing algorithms are implemented inv.generalize:Boyle'sForward-LookingAlgorithm- The position of each point depends on the position of the previous points and the pointlook_aheadahead.look_aheadconsecutive points. Input parameters:input,look_ahead.McMaster'sSlidingAveragingAlgorithm- Input Parameters:input,slide,look_ahead. The new position of each point is the average of thelook_aheadpoints around. Parameterslideis used for linear interpolation between old and new position (see below).McMaster'sDistance-WeightingAlgorithm- Takes the weighted average oflook_aheadconsecutive points where the weight is the reciprocal of the distance from the point to the currently smoothed point. The parameterslideis 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'sAlgorithm- "Inscribes" a line touching the original line such that the points on this new line are at leastthresholdapart. Input parameters:input,threshold. This algorithm approximates the given line very well.HermiteInterpolation- This algorithm takes the points of the given line as the control points of hermite cubic spline and approximates this spline by the points approximatelythresholdapart. This method has excellent results for small values ofthreshold, 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_threshis used for reducing the number of the points. It denotes the minimal angle (in degrees) between two consecutive segments of a line.Snakesis 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 parametersinput,alpha,beta. This algorithm works very well for small values ofalphaandbeta(between 0 and 5). These parameters affect the "sharpness" and the curvature of the computed line. One of the key advantages ofHermiteInterpolationis 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'sAlgorithmoutputs 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,HermiteInterpolationcan be used as another simplification algorithm. To achieve this, it is necessary to setangle_threshto higher values (15 or so). One restriction on both McMasters' Algorithms is thatlook_aheadparameter must be odd. Also note that these algorithms have no effect iflook_ahead=1. Note thatBoyle's,McMasters'andSnakesalgorithm 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.SnakesAlgorithm 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.DISPLACEMENTThe 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 theSnakesapproach. This method generally yields very good results; however, it requires a lot of memory and is not very efficient. Displacement is selected bymethod=displacement. It uses the following parameters:threshold- specifies critical distance. Two features interact if they are closer thanthresholdapart.alpha,beta- These parameters define the rigidity of lines. For larger values ofalpha,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 ofalpha,betaare 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 ofalpha,betais by trial and error.iterations- denotes the number of iterations the interactions between the lines are resolved. Good starting points for values ofiterationsare between 10 and 100. The lines affected by the algorithm can be specified by thelayer,catsandwhereparameters.NETWORKGENERALIZATIONUsed 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 leastdegree_threshdifferent lines.closeness_thresh- is always in the range (0, 1]. Only the lines with the closeness centrality value at leastcloseness_threshapart 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 leastbetweeness_threshare 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 ofbetweeness_threshis too large.

**SEE** **ALSO**

v.clean,v.dissolvev.generalize Tutorial (from GRASS-Wiki)

**AUTHORS**

Daniel Bundala, Google Summer of Code 2007, Student Wolf Bergenheim, MentorLastchanged:$Date:2013-02-1514:04:18-0800(Fri,15Feb2013)$Full index © 2003-2013 GRASS Development Team