Assessment of Line-Generalization Algorithms Using Characteristic Points

White, Ellen R.

doi:10.1559/152304085783914703

Cited by 83 publications

(43 citation statements)

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“…As expected, the Douglas algorithm produces less areal displacement at nearly all simplification levels. In a previous psychological experiment, the Douglas algorithm was also found to be superior in selecting critical points, or those points representing the significant geomorphological characteristics along the line (White 1985 Johannsen, Jenks, perpendicular distance, and nth point routines all produce more areal displacement at nearly all simplification levels. At slight levels of reduction (10 to 40 percent of coordinates eliminated), both Reumann and Opheim are comparable to the Douglas routine.…”

Section: Angularity and Curoilinearitymentioning

confidence: 93%

“…Initially, these efforts concentrated on the development of new simplification algorithms, but recently some progress has been made in developing techniques for evaluating both line simplification and the mathematical characteristics of the digital line (McMaster 1986;Jenks 1985;White 1985;Buttenfield 1986). The evaluation o F simplification algorithms presented in this paper is based on the measures developed by McMaster (1986), and previously applied in McMaster (1983).…”

Section: Linear Simplificationmentioning

confidence: 99%

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The Geometric Properties of Numerical Generalization

McMaster

1987

Geographical Analysis

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Section: Angularity and Curoilinearitymentioning

confidence: 93%

Section: Linear Simplificationmentioning

confidence: 99%

The Geometric Properties of Numerical Generalization

McMaster

1987

Geographical Analysis

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“…Smoothing operators produce a line with a more aesthetically pleasing caricature [3]. Detailed explanations and criticisms about line simplification and smoothing algorithms could be obtained from the numerous review papers produced by, for example, White [4], Weibel [5], McMaster [6], Brassel and Weibel [7], Thapa [8], Li [9,10] and Visvalingam and Williamson [11].…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Cartographic Simplification of Streams and Lakes Using Deviation Angles and Error Bands

Gökgöz

Sen

Memduhoğlu

et al. 2015

IJGI

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Multi-representation databases (MRDBs) are used in several geographical information system applications for different purposes. MRDBs are mainly obtained through model and cartographic generalizations. Simplification is the essential operator of cartographic generalization, and streams and lakes are essential features in hydrography. In this study, a new algorithm was developed for the simplification of streams and lakes. In this algorithm, deviation angles and error bands are used to determine the characteristic vertices and the planimetric accuracy of the features, respectively. The algorithm was tested using a high-resolution national hydrography dataset of Pomme de Terre, a sub-basin in the USA. To assess the performance of the new algorithm, the Bend Simplify and Douglas-Peucker algorithms, the medium-resolution hydrography dataset of the sub-basin, and Töpfer's radical law were used. For quantitative analysis, the vertex numbers, the lengths, and the sinuosity values were computed. Consequently, it was shown that the new algorithm was able to meet the main requirements (i.e., accuracy, legibility and aesthetics, and storage).

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“…Among numerous candidates, the well-known Douglas method (Douglas and Peucker 1973) will be adapted for use on the sphere. This method was selected because of its popularity and because of what seems to be general agreement regarding its ability to produce results at least as good as, if not superior to most others (White 1985;McMaster 1987). Moreover, the notion of point inclusion, which underlies the Douglas method, is valid for spherical as well as planar data.…”

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confidence: 99%

Line Generalization on the Sphere

Burt¹

1989

Geographical Analysis

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In the literature discussing methods for processing cartographic lines, it seem!; almost axiomatic that the line data lie on a plane. For example, in Buttenfield'r; comprehensive review of line treatment, all the techniques described are planar (Buttenfield 1985). One is almost tempted to infer that geographic data become cartographic only after projection to a map plane. Given the dominance of map!; over globes for display, this is entirely understandable. There are, however, instances when it is advantageous to generalize line data on the sphere, with map projection subsequent to that processing. The intent of this note is to present arguments in favor of spherical generalization and to develop a spherical adaptation of the popular planar method due to Douglas and Ptoicker (Douglas and Peuckei-1973). We will also show that there can be a significant computational advantage in generalizing prior to projection. We do not discuss the need for generalization per se-it is assumed that line generalization is desirable for the usual reasons of visual clarity and/or economy of data.There are two areas where generalization on the sphere is appropriate and where data should be maintained in spherical coordinates. The first category is comprised of analytical applications, where the spherical line information is incorporated in other spherical processing. (This is in contrast to a display application, discussed below.) A good example of analytical use is the one that motivated this paper, namely, the production of global climate maps from point data. In smd-scale isarithmic mapping, a strong case can be made for interpolation on the sphere (see Willmott, Rowe, and Philpot 1985). In this approach isolines are created on the sphere by passing point data through a spherical interpolation and contour lacing procedure (e.g., Wahba 1981; Renka 1984; Lawson 1984). Because the line data are needed to construct the isolines (clipping contours to coastlines) they are most easily processed in the same coordinate system as the isolines themselves. If an interpolation method is thought of as a spatial model, the line data might be seen providing boundary conditions. When the interpolation model is formulated on the sphere, it is only natural that the boundary conditions should also be spherical. Interpolation is but one example of the analytical use of line data; others that come to mind are calculating areas and volume on the sphere (e.g., to compute spatial averages over continents).The second application of spherical line generalization is displaydriven, in which a line archive would be produced for repeated mapping at constant scale. This application arises because the level of generalization required is influenced more by

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Assessment of Line-Generalization Algorithms Using Characteristic Points

Cited by 83 publications

References 0 publications

The Geometric Properties of Numerical Generalization

The Geometric Properties of Numerical Generalization

A New Algorithm for Cartographic Simplification of Streams and Lakes Using Deviation Angles and Error Bands

Line Generalization on the Sphere

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