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.

翻译：时空数据分析在流行病学和环境监测等领域至关重要，理解空间分布现象及其时间演变的相互作用是关键。密集像素可视化提供了一种紧凑且有效的时空动态概览。然而，将二维地理空间线性化为一维排序不可避免地会引入结构扭曲，表现为视觉伪影。我们提出一种基于度量的视觉分析方法，通过邻域保持度量捕获一维排序中的视觉伪影，并利用字形、光晕和阴影线等视觉增强技术对其进行呈现。通过分析德国各地区COVID-19发病率数据的使用场景，我们展示了这种方法：交互式、基于度量的增强使分析师能够可靠地区分真实空间模式与线性化伪影。