Enabling fundamental understanding of Nature with novel binning methods for 2D histograms

TL;DR

This paper introduces a novel method for plotting 2D histograms with arbitrary bin shapes, enhancing visualization and understanding of 2D distributions across various applications.

Contribution

The authors develop a new approach for 2D histograms that supports arbitrary bin shapes, including tilings and maps, addressing longstanding visualization issues.

Findings

Our method outperforms traditional histograms in various visualization tasks.

It provides better thematic, visual, and conceptual alignment with underlying data.

The approach advances scientific progress in data visualization.

Abstract

Context. Visualization of 2D distributions is an essential task, commonly done with a 2D histogram. The histogram is built by subdividing the sample space into regions and color-coding the number of samples in each region. Aims. We aim to solve long-standing problems with common 2D histogram methods: lack of thematic, visual, and conceptual unity with underlying data, and general stagnation in the field. Methods. We develop a new method for plotting 2D histograms with arbitrary bin shapes, including aperiodic tilings and geographic maps. We apply the method to several common plot types from the literature. Results. We find our method performs best across all tasks, solving the problems and propelling the scientific progress forward.