Generalising roads on large-scale maps : a comparison of two algorithms

Visvalingam, Maheswari; Williamson, Peter John

Abstract

This paper reports the results of an in-depth study which investigated two algorithms for line simplification and caricatural generalisation, namely those developed by Douglas and Peucker, and Visvalingam respectively. The use of large-scale data for man-designed objects, such as roads, has led to a better understanding of the properties of these algorithms and of their utility within the spectrum of scale-free mapping. The Douglas-Peucker algorithm is better at minimal
simplification. The large-scale data for roads makes it apparent that Visvalingam's technique is not only capable of removing entire scale-related features, but that it does so in a manner which preserves the shape of retained features. With this technique it is possible to achieve balanced generalisations of an entire map, consisting of several complex lines , using a single tolerance value. This expedites the construction of scale-free databases. The results also suggest that it may be easier to formulate concepts for automatic segmentation of in-line features using large-scale road data and Visvalingam's algorithm. The paper also suggests how an even better generalisation, particularly for abstraction of centre lines, may be produced
by the inclusion of additional rules when filtering data tagged by Visvalingam's algorithm.

Citation

Visvalingam, M., & Williamson, P. J. Generalising roads on large-scale maps : a comparison of two algorithms

• Working Paper Type: Discussion Paper
• Deposit Date: Mar 10, 2014
• Journal: Cartography and geographic information systems
• Electronic ISSN: 1545-0465
• Volume: 22
• Pages: 25
• Series Title: C.I.S.R.G. Discussion papers (University of Hull Cartographic Information Systems Research Group)
• Series Number: 13
• Book Title: Generalising roads on large-scale maps : a comparison of two algorithms
• Keywords: Cartography -- Data processing, Computer science
• Public URL: https://hull-repository.worktribe.com/output/459313