Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8901
Title: A comparative study of various strategies to concatenate road segments into strokes for map generalization
Authors: Zhou, Q
Li, Z 
Keywords: chi-square test
Marascuilo procedure
road network
road segment
stroke
Issue Date: 2012
Publisher: Taylor & Francis
Source: International journal of geographical information science, 2012, v. 26, no. 4, p. 691-715 How to cite?
Journal: International journal of geographical information science 
Abstract: The study of road networks has been a topic of interest for some time. A road network in a database is often represented by intersections and segments. However, in many cases (e.g., traffic flow analysis and map generalization), one needs to consider individual roads as a whole, instead of individual segments. Thus, it is sometimes very desirable to concatenate road segments into long lines - 'strokes' as they are called in the literature. For stroke building, a number of strategies are available and the effectiveness of using these strategies needs to be evaluated. This article presents a comparative analysis of 17 such strategies, including 3 of the geometric approach, 1 of the thematic approach, and 13 of the hybrid approach for road network generalization purposes. Three sets of real-life data with different patterns are used to test these strategies. Corresponding road maps at smaller scales are used as benchmarks and a new measure called the accuracy rate is proposed to indicate the correctness of the concatenated strokes. The results show that if only the geometric approach is considered, the every-best-fit strategy performs best; if thematic attributes are also added, road class can be more effective than road name. Also significance tests (the chi-square test and the Marascuilo procedure) are carried out to give all pairwise comparisons of these strategies. The results indicate that 45 of the 136 pairs of strategies have statistically significant differences; the purely geometry-based every-best-fit performs significantly better than the purely geometry-based self-fit; and the inclusion of thematic attributes, especially road class, sometimes improves the accuracy rate but the improvement is not significant.
URI: http://hdl.handle.net/10397/8901
ISSN: 1365-8816
EISSN: 1362-3087
DOI: 10.1080/13658816.2011.609990
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