A Design Space for Visualizations with Large Differences Between Largest and Smallest Elements

Core Concepts

Designers have developed many approaches to overcome the challenge of designing visualizations where the smallest data items can be clearly seen from a high level. This paper presents a design space for visualization with large scale-item ratios, which includes three dimensions and eight subdimensions. The authors demonstrate the descriptive and generative power of the design space by using it to code a corpus of 54 examples and identify missed opportunities within the corpus.

Abstract

The paper presents a design space for visualization with large scale-item ratios, which is the relationship between the largest scale and the smallest item in a visualization. Designing visualizations in this context can be challenging, as the smallest items may not be visible or clearly separable.
The design space includes three main dimensions:

Scales: This dimension describes the number of scales, how the scales differ in mapping, whether they share encodings, and how they are associated.
Navigation: This dimension covers the interaction capabilities, including the type of navigation (zooming, panning, or both), the mode (physical or digital), and whether it relies on visceral time.
Familiarity: This dimension describes whether the visualization compares unfamiliar objects to familiar ones to help users understand the scale.

The authors demonstrate the descriptive power of the design space by using it to code a corpus of 54 examples, both from academic literature and practitioner work. They also identify five strategies that partition the examples based on shared approaches for design space choices.
The authors further demonstrate the generative power of the design space by analyzing missed opportunities within the corpus, where different design space choices could have improved the visualizations. For example, they suggest that some examples could have benefited from using physical navigation, employing more simultaneous and separate scales, using different encodings on different scales, or incorporating visceral time and familiarity.
Overall, the design space provides a structured framework for reasoning about and generating visualizations with large scale-item ratios.

Stats

The scale-item ratio is the ratio between the size of the largest scale and the size of the smallest item, both in display space.
The authors define value as the magnitude of data items in data space, and a mapping as a transformation from a value to a discretized display space position.
A scale is a region of the discretized display space that depicts a mapping, and it ranges from minimum to maximum positions in display space.
A size is the difference between a scale or an item's minimum and maximum positions in display space.

Quotes

"The scale-item ratio is the relationship between the largest scale and the smallest item in a visualization. Designing visualizations when this ratio is large can be challenging, and designers have developed many approaches to overcome this challenge."
"We present a design space for visualization with large scale-item ratios. The design space includes three dimensions, with eight total subdimensions."

Key Insights Distilled From

A Design Space for Visualization with Large Scale-Item Ratios

by Mara Solen,T... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01485.pdf

Deeper Inquiries

How could the design space be extended to cover a broader range of visualization design challenges beyond large scale-item ratios?

To extend the design space to cover a broader range of visualization design challenges, one approach could be to introduce additional dimensions that address different aspects of visualization design. For example, dimensions could be added to capture aspects such as interactivity levels, data complexity, user engagement strategies, or data types. By incorporating these new dimensions, the design space can provide a more comprehensive framework for analyzing and categorizing various visualization design scenarios beyond just large scale-item ratios.

What are the potential limitations or drawbacks of the strategies identified in the paper, and how might they be addressed in future designs?

One potential limitation of the strategies identified in the paper is that they may not encompass all possible design approaches or variations in visualization design. To address this limitation, future designs could consider expanding the set of strategies to include more nuanced or specialized approaches. Additionally, the strategies could be refined to account for overlapping or hybrid design scenarios that may not fit neatly into the existing categories. By continuously evaluating and updating the strategies based on evolving design practices, designers can ensure that the strategies remain relevant and effective in guiding visualization design decisions.

How could the design space and strategies be integrated into visualization design tools or workflows to support designers in creating effective visualizations for large scale-item ratio scenarios?

Integrating the design space and strategies into visualization design tools or workflows can provide valuable guidance and structure for designers working on large scale-item ratio scenarios. Design tools could incorporate the dimensions of the design space as parameters for defining visualization requirements, allowing designers to input specific criteria and constraints for their projects. The strategies could be implemented as design templates or presets within the tools, enabling designers to quickly apply proven approaches to their visualizations. Additionally, the design space and strategies could be integrated into design workflows as checkpoints or decision-making frameworks, helping designers systematically evaluate and refine their designs based on established principles and best practices. By embedding the design space and strategies into design tools and workflows, designers can benefit from a structured and systematic approach to creating effective visualizations for scenarios with large scale-item ratios.