Generalizing linear stream features to preserve sinuosity for analysis and display: A pilot study in multi-scale data science

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Abstract

Cartographic generalization can impact geometric properties of geospatial data and subsequent analyses. This study evaluates simplification methods with the goal of preserving geometric details, such as sinuosity. We evaluate two recently developed line simplification algorithms that introduce Steiner points: Raposo’s Spatial Means, and Kronenfeld’s new area-preserving segment collapse algorithm, and compare them with several well-known algorithms. Results indicate the area-preserving segment collapse algorithm optimally simplifies linear stream features with minimal horizontal displacement and the best retention of sinuosity.

Additional publication details

Publication type Conference Paper
Publication Subtype Abstract or summary
Title Generalizing linear stream features to preserve sinuosity for analysis and display: A pilot study in multi-scale data science
Year Published 2018
Language English
Publisher Cartography and Geographic Information Society and the University Consortium on Geographic Information Science
Contributing office(s) Center for Geospatial Information Science (CEGIS)
Description 9 p.
Larger Work Type Book
Larger Work Subtype Conference publication
Larger Work Title Conference Proceedings, 22nd International Research Symposium on Computer-based Cartography and GIScience
First page 111
Last page 119
Conference Title 22nd International Research Symposium on Computer-based Cartography and GIScience
Conference Location Madison, Wisconsin, USA
Conference Date May 22-24, 2018