IEEE VIS 2024 Content: UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization

UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization

Nikolaus Piccolotto - TU Wien, Vienna, Austria

Markus Wallinger - TU Wien, Vienna, Austria

Silvia Miksch - Institute of Visual Computing and Human-Centered Technology, Vienna, Austria

Markus Bögl - TU Wien, Vienna, Austria

Room: Bayshore V

2024-10-16T14:15:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T14:15:00Z
Exemplar figure, described by caption below
The main component of our visualization approach is UnDRground Tubes, which presents glyphs in a grid and connects them by lines according to their set memberships.
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Keywords

Geographical data, multivariate data, set visualization, visual cluster analysis.

Abstract

In various scientific and industrial domains, analyzing multivariate spatial data, i.e., vectors associated with spatial locations, is common practice. To analyze those datasets, analysts may turn to methods such as Spatial Blind Source Separation (SBSS). Designed explicitly for spatial data analysis, SBSS finds latent components in the dataset and is superior to popular non-spatial methods, like PCA. However, when analysts try different tuning parameter settings, the amount of latent components complicates analytical tasks. Based on our years-long collaboration with SBSS researchers, we propose a visualization approach to tackle this challenge. The main component is UnDRground Tubes (UT), a general-purpose idiom combining ideas from set visualization and multidimensional projections. We describe the UT visualization pipeline and integrate UT into an interactive multiple-view system. We demonstrate its effectiveness through interviews with SBSS experts, a qualitative evaluation with visualization experts, and computational experiments. SBSS experts were excited about our approach. They saw many benefits for their work and potential applications for geostatistical data analysis more generally. UT was also well received by visualization experts. Our benchmarks show that UT projections and its heuristics are appropriate.