Abstract
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).
Highlights
The idea that we can abstract and portray geographic information at multiple scales in map form has existed for thousands of years
The flexibility of Multi-representation databases (MRDBs) lies in its ability to derive different types of maps, using models and cartographic generalization methods
The tolerance values for the Bend Simplify algorithm are suggested by Wilmer and Brewer [20], Stanislawski and Buttenfield [21] and Stanislawski et al [22]
Summary
The idea that we can abstract and portray geographic information at multiple scales in map form has existed for thousands of years. National Mapping Agencies (NMAs) maintain spatial databases at different levels of detail—Databases that store multiple representations of the same geographic phenomena. In many geographical information system (GIS) applications, users need to visualize and inspect data at different scales, which requires different representations to be stored at different levels of detail. The flexibility of MRDBs lies in its ability to derive different types of maps, using models and cartographic generalization methods. In this respect, automated spatial data generalization techniques are as important as data modeling, management, and distribution. Cartographic generalization of foreground data has to be achieved in real-time, requiring flexible, on-the-fly generalization algorithms [2]
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