Scatterplots

Citation Author(s):
Hennes
Rave
University of Münster, Germany
Vladimir
Molchanov
University of Münster, Germany
Lars
Linsen
University of Münster, Germany
Submitted by:
Hennes Rave
Last updated:
Tue, 04/02/2024 - 15:07
DOI:
10.21227/p2me-9d43
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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.

Instructions: 

This dataset contains scatterplots used in our paper and user study. The first line contains metadata, like the number of datapoints. The remaining rows contain the x- and y-coordinates.

Funding Agency: 
Deutsche Forschungsgemeinschaft (DFG)
Grant Number: 
MO 3050/2-3 and CRC 1450 - 431460824