Scale, Sinuosity, and Point Selection in Digital Line Generalization

Abstract

This paper examines some assumptions and results of cartographic line simplification in the digital realm, focusing upon two major aspects of map generalization-scale-specificity and the concept of characteristic points. These are widely regarded as critical controls to generalization but, in our estimation, they are rarely well considered or properly applied. First, a look at how scale and shape are treated in various research papers identifies some important conceptual and methodological issues that either have been misconstrued or inadequately treated. We then conduct an empirical analysis with a set of line generalization experiments that control resolution, detail, and sinuosity using four source datasets. The tests yield about 100 different versions of two island coastlines digitized at two scales, exploring systematically the consequences of linking scale with spatial resolution as well as a variety of point selection strategies. The generalized results are displayed (at scale and enlarged) along with some basic statistics. We conclude with a set of observations on the need for more careful and robust evaluations of strategies for point selection and for at-scale verification of visual quality. The results indicate that regardless of which methods are used, sensitivity to scale and sinuosity can and must be built into all digital map generalization toolkits.