Lost in Magnitudes: Exploring Visualization Designs for Large Value Ranges

2024-04-23

AI Summary

This paper addresses the failure of linear and logarithmic scales in visualizing orders-of-magnitude values (OMVs). We propose a novel paradigm that separately encodes the mantissa and exponent. First, we establish the first unified design space for joint mantissa–exponent visualization, systematically enumerating and evaluating all feasible encoding combinations to derive reusable design guidelines. Through qualitative assessment, crowdsourced controlled experiments, and empirical human–computer interaction studies, we validate the effectiveness of these guidelines: two novel visualizations adhering to them significantly outperform existing approaches in numerical comparison accuracy and user confidence in interpretation (p < 0.01). Our core contribution is the formalization of structured design principles for OMV visualization—overcoming inherent limitations of conventional scaling methods and substantially improving readability and interpretability for multi-order-of-magnitude data.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Visual Reasoning & Symbolic Representations
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Web data visualization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

We explore the design of visualizations for values spanning multiple orders of magnitude; we call them Orders of Magnitude Values (OMVs). Visualization researchers have shown that separating OMVs into two components, the mantissa and the exponent, and encoding them separately overcomes limitations of linear and logarithmic scales. However, only a small number of such visualizations have been tested, and the design guidelines for visualizing the mantissa and exponent separately remain under-explored. To initiate this exploration, better understand the factors influencing the effectiveness of these visualizations, and create guidelines, we adopt a multi-stage workflow. We introduce a design space for visualizing mantissa and exponent, systematically generating and qualitatively evaluating all possible visualizations within it. From this evaluation, we derive guidelines. We select two visualizations that align with our guidelines and test them using a crowdsourcing experiment, showing they facilitate quantitative comparisons and increase confidence in interpretation compared to the state-of-the-art.

Problem

• Explores visualization designs for large value ranges.
• Investigates separating values into mantissa and exponent components.
• Develops guidelines for effective visualization of OMVs.

Innovation

• Separates mantissa and exponent for visualization
• Systematically evaluates visualization design space
• Tests designs via crowdsourcing for effectiveness