RT Journal Article
SR Electronic
T1 Pattern-Driven Navigation in 2D Multiscale Visual Spaces with Scalable Insets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 301036
DO 10.1101/301036
A1 Fritz Lekschas
A1 Michael Behrisch
A1 Benjami Bach
A1 Peter Kerpedjiev
A1 Nils Gehlenborg
A1 Hanspeter Pfister
YR 2018
UL http://biorxiv.org/content/early/2018/04/15/301036.abstract
AB We present Scalable Insets, a technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visual spaces such as gigapixel images, matrices, or maps. Exploration of many but sparsely-distributed patterns in multiscale visual spaces is challenging as visual representations change across zoom levels, context and navigational cues get lost upon zooming, and navigation is time consuming. Our technique visualizes annotated patterns too small to be identifiable at certain zoom levels using insets, i.e., magnified thumbnail views of the annotated pattern. Insets support users in searching, comparing, and contextualizing patterns, while reducing the amount of navigation needed. They are dynamically placed either within the viewport or along the boundary of the viewport to offer a compromise between locality and context preservation. Annotated patterns are interactively clustered by location and type. They are visually represented as an aggregated inset to provide scalable exploration within a single viewport. A controlled user study with 18 participants found improved performance in visual search (up to 45% faster) and comparison of pattern types (up to 32 percentage points more accurate) compared to a baseline technique. A second study with 6 experts in the field of genomics showed that Scalable Insets are easy to learn and effective in a biological data exploration scenario.