title: eulerAPE: drawing area-proportional 3-Venn diagrams using ellipses.
abstract: Venn diagrams with three curves are used extensively in various medical and scientific disciplines to visualize relationships between data sets and facilitate data analysis. The area of the regions formed by the overlapping curves is often directly proportional to the cardinality of the depicted set relation or any other related quantitative data. Drawing these diagrams manually is difficult and current automatic drawing methods do not always produce appropriate diagrams. Most methods depict the data sets as circles, as they perceptually pop out as complete distinct objects due to their smoothness and regularity. However, circles cannot draw accurate diagrams for most 3-set data and so the generated diagrams often have misleading region areas. Other methods use polygons to draw accurate diagrams. However, polygons are non-smooth and non-symmetric, so the curves are not easily distinguishable and the diagrams are difficult to comprehend. Ellipses are more flexible than circles and are similarly smooth, but none of the current automatic drawing methods use ellipses. We present eulerAPE as the first method and software that uses ellipses for automatically drawing accurate area-proportional Venn diagrams for 3-set data. We describe the drawing method adopted by eulerAPE and we discuss our evaluation of the effectiveness of eulerAPE and ellipses for drawing random 3-set data. We compare eulerAPE and various other methods that are currently available and we discuss differences between their generated diagrams in terms of accuracy and ease of understanding for real world data.
authors.affiliation: School of Computing, University of Kent, Canterbury, Kent, United Kingdom.
authors.forename: Luana
authors.initials: L
authors.lastname: Micallef
authors.affiliation: School of Computing, University of Kent, Canterbury, Kent, United Kingdom.
authors.forename: Peter
authors.initials: P
authors.lastname: Rodgers
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D066264:Datasets as Topic; D008962:Models, Theoretical