Maybe, Maybe Not: A Survey on Uncertainty in Visualization

TL;DR

This survey emphasizes the importance of representing uncertainty in visualizations, analyzing the entire pipeline from data collection to inference, and exploring future directions for effective uncertainty visualization.

Contribution

It provides a comprehensive overview of uncertainty visualization, evaluates challenges across the visualization pipeline, and discusses future research directions.

Findings

Uncertainty impacts all stages of the visualization pipeline.

Authors employ diverse methods to process and design uncertainty.

Future paths include improved techniques for uncertainty portrayal.

Abstract

Understanding and evaluating uncertainty play a key role in decision-making. When a viewer studies a visualization that demands inference, it is necessary that uncertainty is portrayed in it. This paper showcases the importance of representing uncertainty in visualizations. It provides an overview of uncertainty visualization and the challenges authors and viewers face when working with such charts. I divide the visualization pipeline into four parts, namely data collection, preprocessing, visualization, and inference, to evaluate how uncertainty impacts them. Next, I investigate the authors' methodologies to process and design uncertainty. Finally, I contribute by exploring future paths for uncertainty visualization.