![]() To Define the Lines Simplification ParametersThis page presents the procedures to perform the lines simplification. The SPRING presents three algorithms options to simpli lines:
IMPORTANT: It is worth to remind you that all algorithms use subjective criterias, which can be translated as tolerances to be selected by the user in the SPRING interface. Thus, it is advised that the user checks the tolerances difference impacts in each method over the data. The SPRING suggests a set of default values which are considered conservatives for each scale change desired. If the user is not sure about what values to use, the user should adopt the default values suggested by the system. Another important aspect is related to the topology. These simplification methods act over the lines without being worried with previously created topological relationships. Because of this, the operation has to be followed by poligonalization and nodes adjusting operations. Defining the simplification parameters
Importing and Exporting data. Douglas-PeuckerThis is the most used method in geographical information systems. It was initially designed to solve the high number of points resulting from the graphical to the digital format data conversion. The Douglas-Peucker method is based on the following idea: if no line point is placed further than a given vertical distance to the line segment connecting the line extremes, than this line segment is good enough to represent the line. This method is considered a generalization global technique, because it analyses each line as a whole. The figure presented next shows the result of the Douglas-Peucker algorithm.
Area/Perimeter RatioThis method uses exactly the same global analyses procedure for each line used in the Douglas-Peucker method. The only difference consists in the adoption of the area/perimeter ratio computed as a function of the tolerance selected by the user. The usage of the area/perimeter ratio allows that triangles formed by three consecutive points that have a very small acute angle in the second point, can be detected in a more efficient way than in the Douglas-Peucker method. Accumulated DistanceThe accumulated distance method is an adaptation of the Li-Openshaw vectorial implementation algorithm that uses as a criteria the smallest visible object concept. This method accumulates the distances while the line is followed up to a certain limit, removing all accumulated points in this section. Thus this is a very simple method, but, different than the other two, it does not analyse the line as a whole.
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