Visual Boosting Techniques for Spatiotemporal Dense Pixel Visualizations
MCML Authors
Abstract
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.
inproceedings RDS+26
EuroVA @EuroVis 2026
International Workshop on Visual Analytics at the Eurographics Conference on Visualization. Nottingham, UK, Jun 08-12, 2026.Authors
J. Rauscher • F. L. Dennig • U. Schlegel • D. A. Keim • T. SchreckLinks
DOIResearch Area
BibTeXKey: RDS+26