Blue Noise Plots

TL;DR

Blue Noise Plots are a new visualization method for univariate data that reduces overlap and visual clutter by optimizing dot placement to maintain a minimum distance, improving clarity and aesthetics.

Contribution

We introduce Blue Noise Plots, a novel 2D dot plot technique that replaces random jitter with optimized placement to enhance data visualization clarity.

Findings

Blue Noise Plots reduce overlap compared to jitter plots.

User studies show improved aesthetic appeal.

Quantitative analysis confirms better data separation.

Abstract

We propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often one-dimensional strip plots are used to depict such data, one of their main problems is visual clutter which results from overlap. To reduce this overlap, jitter plots were introduced, whereby an additional, non-encoding plot dimension is introduced, along which the data point representing dots are randomly perturbed. Unfortunately, this randomness can suggest non-existent clusters, and often leads to visually unappealing plots, in which overlap might still occur. To overcome these shortcomings, we introduce BlueNoise Plots where random jitter along the non-encoding plot dimension is replaced by optimizing all dots to keep a minimum distance in 2D i. e., Blue Noise. We evaluate the effectiveness as well as the aesthetics of Blue Noise Plots through both, a quantitative and a qualitative user study. The Python implementation of Blue Noise Plots is available here.