De-cluttering Scatterplots with Integral Images

TL;DR

This paper introduces a GPU-accelerated algorithm that de-clutters scatterplots by transforming density distributions to improve visual clarity and scalability, while preserving data neighborhood relations.

Contribution

It presents a novel integral-image-based regularization method for scatterplots, including a parallel GPU implementation and visualization techniques, enhancing interactive data analysis.

Findings

The algorithm effectively reduces overplotting in large scatterplots.

User study shows improved visual clarity and interpretability.

GPU implementation enables real-time interaction with large datasets.

Abstract

Scatterplots provide a visual representation of bivariate data (or 2D embeddings of multivariate data) that allows for effective analyses of data dependencies, clusters, trends, and outliers. Unfortunately, classical scatterplots suffer from scalability issues, since growing data sizes eventually lead to overplotting and visual clutter on a screen with a fixed resolution, which hinders the data analysis process. We propose an algorithm that compensates for irregular sample distributions by a smooth transformation of the scatterplot's visual domain. Our algorithm evaluates the scatterplot's density distribution to compute a regularization mapping based on integral images of the rasterized density function. The mapping preserves the samples' neighborhood relations. Few regularization iterations suffice to achieve a nearly uniform sample distribution that efficiently uses the available screen space. We further propose approaches to visually convey the transformation that was applied to the scatterplot and compare them in a user study. We present a novel parallel algorithm for fast GPU-based integral-image computation, which allows for integrating our de-cluttering approach into interactive visual data analysis systems.