  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.
