Gatherplot: A Non-Overlapping Scatterplot

TL;DR

Gatherplots are an innovative visualization technique that extends scatterplots to effectively address overplotting issues, allowing clearer perception of data groupings without aggregation, validated by user studies.

Contribution

This paper introduces gatherplots, a new scatterplot extension that manages overplotting while preserving individual data identities, enhancing data distribution assessment.

Findings

Gatherplots improve speed of data distribution assessment.

Gatherplots increase accuracy in interpreting data groups.

User study confirms gatherplots outperform jittered scatterplots.

Abstract

Scatterplots are a common tool for exploring multidimensional datasets, especially in the form of scatterplot matrices (SPLOMs). However, scatterplots suffer from overplotting when categorical variables are mapped to one or two axes, or the same continuous variable is used for both axes. Previous methods such as histograms or violin plots use aggregation, which makes brushing and linking difficult. To address this, we propose gatherplots, an extension of scatterplots to manage the overplotting problem. Gatherplots are a form of unit visualization, which avoid aggregation and maintain the identity of individual objects to ease visual perception. In gatherplots, every visual mark that maps to the same position coalesces to form a packed entity, thereby making it easier to see the overview of data groupings. The size and aspect ratio of marks can also be changed dynamically to make it easier to compare the composition of different groups. In the case of a categorical variable vs. a categorical variable, we propose a heuristic to decide bin sizes for optimal space usage. To validate our work, we conducted a crowdsourced user study that shows that gatherplots enable people to assess data distribution more quickly and more correctly than when using jittered scatterplots.