Enhancing Line Density Plots with Outlier Control and Bin-based Illumination
For visualization analysts, this provides an interactive method to explore dense line datasets by balancing trend and outlier visibility with minimal color distortion.
The paper addresses the problem of visualizing dense line-based datasets (e.g., trajectories, time series) where standard density plots obscure path continuity and rare anomalies. They propose a bin-based illumination model with an outlierness metric to highlight dominant trends or outliers, achieving lower CIEDE2000 color distortion than standard shading and supporting interactive updates for up to 10,000 lines.
Density plots effectively summarize large numbers of points, which would otherwise lead to severe overplotting in, for example, a scatter plot. However, when applied to line-based datasets, such as trajectories or time series, density plots alone are insufficient, as they disrupt path continuity, obscuring smooth trends and rare anomalies. We propose a bin-based illumination model that decouples structure from density to enhance flow and reveal sparse outliers while preserving the original colormap. We introduce a bin-based outlierness metric to rank trajectories. Guided by this ranking, we construct a structural normal map and apply locally-adaptive lighting in the luminance channel to highlight chosen patterns -- from dominant trends to atypical paths -- with acceptable color distortion. Our interactive method enables analysts to prioritize main trends, focus on outliers, or strike a balance between the two. We demonstrate our method on several real-world datasets, showing it reveals details missed by simpler alternatives, achieves significantly lower CIEDE2000 color distortion than standard shading, and supports interactive updates for up to 10,000 lines.