Lecture11

Lecture11 - Analytical and Computer Cartography Lecture 11 Generalization and Structure-to-Structure Transformations Generalization Transformations

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Unformatted text preview: Analytical and Computer Cartography Lecture 11: Generalization and Structure-to-Structure Transformations Generalization Transformations z Conversion of data collected at higher resolutions to lower resolution z Change (reduction) in extent due to scale change (e.g. zoom) z Less data and less detail z Simplicity -> clarity z Can be lossless or lossy Less detail and fewer features Generalization: Line to line transformations z Problem of "line character" z Algorithmic resampling i.e. reduce # of points in finite sample z Algorithmic reconstruction z Enhancement Algorithms (Reviewed by McMaster) z N-th Point retention z Equidistant Resampling z Douglas-Peucker Douglas-Peucker aka Ramer–Douglas–Peucker algorithm, the iterative end-point fit algorithm or the split-and-merge algorithm Pseudocode function DouglasPeucker(PointList, epsilon) //Find the point with the maximum distance dmax = 0 index = 0 for i = 2 to (length(PointList) - 1) d = OrthogonalDistance(PointList[i], Line(PointList[1], PointList[end])) if d > dmax index = i dmax = d end end //If max distance is greater than epsilon, recursively simplify if dmax >= epsilon //Recursive call recResults1 = DouglasPeucker(PointList[1...index], epsilon) recResults1 = DouglasPeucker(PointList[1....
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This note was uploaded on 12/28/2011 for the course GEOG 128 taught by Professor Staff during the Fall '08 term at UCSB.

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Lecture11 - Analytical and Computer Cartography Lecture 11 Generalization and Structure-to-Structure Transformations Generalization Transformations

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