Enhancing Line Density Plots with Outlier Control and Bin-based Illumination

TL;DR

This paper introduces a bin-based illumination and outlier control technique for line density plots, improving the visualization of trajectories by highlighting trends and anomalies while maintaining color fidelity.

Contribution

It presents a novel bin-based outlier metric and an interactive lighting model that enhances flow visualization and outlier detection in line density plots.

Findings

Reveals details missed by simpler methods

Achieves lower color distortion than standard shading

Supports interactive visualization of up to 10,000 lines

Abstract

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.