Visual Boosting Techniques for Spatiotemporal Dense Pixel Visualizations
Julius Rauscher, Frederik L. Dennig, Udo Schlegel, Daniel A. Keim, Tobias Schreck
TLDR
This paper introduces visual boosting techniques to identify and mitigate linearization artifacts in dense pixel visualizations of spatiotemporal data.
Key contributions
- Proposes a measure-driven visual analytics approach for spatiotemporal dense pixel visualizations.
- Captures visual artifacts from 2D-to-1D linearization using neighborhood preservation measures.
- Renders these artifacts with visual boosting techniques like glyphs, halos, and hatching.
- Demonstrates improved reliability in distinguishing genuine spatial patterns from artifacts.
Why it matters
Spatiotemporal data analysis is critical, but dense pixel visualizations often suffer from linearization artifacts. This paper provides a robust method to identify and visualize these distortions, enhancing the reliability of insights derived from such visualizations. It helps analysts trust their data patterns more.
Original Abstract
The analysis of spatiotemporal data is essential in domains such as epidemiology and environmental monitoring, where understanding the interplay between spatially distributed phenomena and their temporal evolution is critical. Dense pixel visualizations offer a compact, effective overview of spatiotemporal dynamics. However, the necessary linearization of 2D geographic space into a 1D ordering inevitably introduces structural distortions that manifest as visual artifacts. We propose a measure-driven visual analytics approach that captures visual artifacts through neighborhood preservation measures for 1D orderings and renders them using visual boosting techniques such as glyphs, halos, and hatching. We demonstrate our approach through a usage scenario analyzing COVID-19 incidence data across German districts, showing that interactive, measure-driven boosting enables analysts to reliably distinguish genuine spatial patterns from linearization artifacts.
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