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

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.