Exploring vast spatial datasets often requires to drill down in order to inspect details, thus leading to a loss of contextual overview. An additional challenge rises if the visualized data is of multivariate nature, which we encounter in various domains such as healthcare, nutrition, crime reports, or social networks. Existing overview-plus-detail approaches do provide context but only limited support for multivariate data and often suffer from distortion. In this paper, we dynamically integrate star glyphs as insets into the spatial representation of multivariate data thus providing overview while inspecting details. Star glyphs pose an efficient and space saving method to visualize multivariate data, which qualifies them as integrated data representative. Furthermore, we demonstrate the usefulness of our approach in two use cases: The spatial exploration of multivariate crime data collected in San Francisco and the exploration of multivariate whisky data.
Dominik Jäckle, Johannes Fuchs, Daniel Keim, "Star Glyph Insets for Overview Preservation of Multivariate Data" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Visualization and Data Analysis, 2016, https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-506