Visualizing Quantitative Uncertainty: A Review of Common Approaches, Current Limitations, and Use Cases

Abstract

Understanding quantified uncertainty through efficient visualization techniques is becoming increasingly important for the successful teaming of human and intelligent agents across many domains. For humans to make effective, well-informed decisions, visualizations must maximize the amount of critical information communicated in a way that complexity is not prohibitive of fast and accurate understanding. In this review, we first identify common approaches to uncertainty in multiple domains, including traditional graphical methods in the 1D and 2D Data Dimensions, and survey their techniques. We then analyze current challenges in the uncertainty visualization space pertaining to information complexity, presentation, added dimensionality, visual dominance, and multidisciplinary needs. Finally, we review the growing number of applications and the current state of uncertainty visualization, addressing the benefits from knowing uncertainty in each example and identifying the windows of opportunity in the future context of multi-domain use cases.