A Design Space for Visualizations with Large Value Ranges

Figure description: Our design space encompasses four dimensions: MARKS (green), DATA (blue), VISUAL CHANNELS (red), and TASKS (yellow).The image illustrates an example of using our design space as an interactive table, where a mark is selected, and visual channels are assigned to data attributes. Grey cells are invalid, according to the visualization literature. After checking for integrity constraints, a visualization is generated to perform the tasks. Note the ⊕ signs (top left); enable the assignment of the E+M scale to positions.

What is Order of Magnitude Values (OMVs)?

OMVs are quantitative attributes that span four or more orders of magnitude (e.g., from thousands to billions). They are common in domains such as finance, social media, health, environmental monitoring, and science.


We explore the design space for the static visualization of datasets with quantitative attributes that vary over multiple orders of magnitude—we call these attributes Orders of Magnitude Values (OMVs)—, and provide design guidelines and recommendations on effective visual encodings for OMVs. Current charts rely on linear or logarithmic scales to visualize values, leading to limitations in performing simple tasks for OMVs. In particular, linear scales prevent the reading of smaller magnitudes and their comparisons, while logarithmic scales are challenging for the general public to understand. Our design space leverages the approach of dividing OMVs into two different parts: mantissa and exponent, in a way similar to the scientific notation. This separation allows for a visual encoding of both parts. For our exploration, we use four datasets, each with two attributes: an OMV, divided into mantissa and exponent, and a second attribute, that is nominal, ordinal, time, or quantitative. We start from the original design space described by the Grammar of Graphics and systematically generate all possible visualizations for these datasets, employing different marks and visual channels. We refine this design space by enforcing integrity constraints from visualization and graphical perception literature. Through a qualitative assessment of all viable combinations, we discuss the most effective visualizations for OMVs, focusing on channel and task effectiveness. The article’s main contributions are 1) the presentation of the design space of OMVs, 2) the generation of a large number of OMV visualizations, among which some are novel and effective, 3) the refined definition of a scale that we call E+M for OMVs, and 4) guidelines and recommendations for designing effective OMV visualizations. We also present a visualization tool to interactively explore viable combinations of marks and visual channels for mantissa and exponent across different datasets, demonstrating the potential of various design combinations for effectively representing OMVs. These efforts aim to enrich visualization systems to better support data with OMVs and guide future research.

Guidelines for Effective OMVs Visualizations

Examples of Visualizations in our Design Space

The 168 visualizations used in our evaluation are available at https://www.omvs-designspace.com/ and in the supplemental material


The full paper is currently under review and accessible in arxiv.

Study materials

All supplemental materials are available on osf.io, released under a CC BY 4.0 license.


Katerina Batziakoudi: a.batziakoudi@berger-levrault.com
Florent Cabric: florent.cabric@inria.fr
Stéphanie Rey: Stephanie.REY@berger-levrault.com
Jean-Daniel Fekete: Jean-Daniel.Fekete@inria.fr