Impacts of aspect ratio on task accuracy in parallel coordinates

TL;DR

This study investigates how the aspect ratio of axes in static parallel coordinates plots affects task accuracy, revealing that certain aspect ratios improve positive correlation estimation and providing design recommendations.

Contribution

The paper systematically evaluates the impact of aspect ratio on PCP task accuracy and offers practical guidelines for visualization design choices.

Findings

Aspect ratio significantly influences correlation estimation accuracy.

ARs greater than 1:1 improve positive correlation estimation.

Recommendations for PCP design based on data and task characteristics.

Abstract

Parallel coordinates plots (PCPs) are a widely used visualization method, particularly for exploratory analysis. Previous studies show that PCPs perform much more poorly for estimating positive correlation than for estimating negative correlation, but it is not clear if this is affected by the aspect ratio (AR) of the axes pairs. In this paper, we present the results from an evaluation of the effect of the aspect ratio of axes in static (non-interactive) PCPs for two tasks: a) linear correlation estimation and b) value tracing. For both tasks we find strong evidence that AR influences accuracy, including ARs greater than 1:1 being much more performant for estimation of positive correlations. We provide a set of recommendations for visualization designers using PCPs for correlation or value-tracing tasks, based on the data characteristics and expected use cases.