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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.
| 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 |
| Google Analytic Metrics | Metrics page |