Visual Boosting Techniques for Spatiotemporal Dense Pixel Visualizations

TL;DR

This paper presents a measure-driven visual analytics approach using boosting techniques to identify and distinguish genuine spatial patterns from artifacts in dense spatiotemporal pixel visualizations.

Contribution

It introduces a novel method combining neighborhood preservation measures with visual boosting to improve artifact detection in dense visualizations.

Findings

Interactive boosting helps distinguish real patterns from artifacts.

The approach effectively analyzes COVID-19 data across districts.

Visual artifacts are quantitatively captured and visually enhanced.

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.