SpatialRugs: A compact visualization of space and time for analyzing collective movement data. (December 2021)

• Record Type: Journal Article
• Authors: Buchmüller, Juri F.; Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.; Dimara, Evanthia
• Is Part Of: Computers & graphics. Volume 101(2021)
• Page Start: 23
• Page End: 34
• Publication Date: 2021-12
• Subjects: Computers and graphics -- Information visualization -- Collective behavior visualization -- Spatiotemporal data; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2021.08.003
• Languages: English
• ISSNs: 0097-8493

Abstract

Abstract: Compact visualization techniques such as dense pixel displays find application in displaying spatio-temporal datasets in a space-efficient way. While mostly focusing on feature development, the depiction of spatial distributions of the movers in these techniques is often traded against better scalability towards the number of moving objects. We propose SpatialRugs, a technique that can be applied to reintroduce spatial positions in such approaches by applying 2D colormaps to determine object locations and which enables users to follow spatio-temporal developments even in non-spatial representations. Geared towards collective movement datasets, we evaluate the applicability of several color maps and discuss limitations. To mitigate perceptional artifacts, we also present and evaluate a custom, time-aware color smoothing method. Graphical abstract: Highlights: 2D colormaps can be used to map spatial positions to colors. Non-Spatial visualizations can potentially be extended to reflect spatial relations. Large-scale spatiotemporal data can be visualized space-efficiently using SpatialRugs. A time-aware color smoothing helps mitigating perceptual color map artifacts.