Assessment of Line-Generalization Algorithms Using Characteristic Points

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Abstract
Line generalization, used to reduce scale in mapping, caricatures an original line by retaining points essential to its shape—called characteristic, or critical, points—and discarding others. This study describes experiments utilizing base lines of perceptually selected critical points to evaluate three common line-generalization algorithms: nth-point elimination, a perpendicular routine, and the Douglas algorithm. Base-line correspondence to computer generalizations was measured by graphical overlay, areal offset, commonly held points, and mean number of times respondents judged particular points to be critical. Visual comparison of an original line and its computer generalization followed. The Douglas algorithm's line generalizations proved more faithful to original lines than those of perpendicular point selection and nth-point elimination.

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