Papers

Steve Franconeri’s 2018 talk at the Open Vis conference is a good introduction to how models of human perception can help us design better visualizations. This is a completely automatically generated summary of it. I believe it is based on an LLM reading the captions of the video.

Steve Franconeri. Thinking with Data Visualizations, Fast and Slow. Open Vis 2018 Conference Talk. (video)

I asked Google Gemini desktop (using the Argon model) how I could obtain the transcript (preferably from the closed captioning). It returned with this summary. I include it unedited, and unchecked. Read at your own risk. I do recommend watching the video.

Munzner’s nested model is a way of thinking about visualization validation that leads to processes for design and evaluation. It divides visualization design into four levels, each affecting the ones below (or inside) it (hence “nested”). While it was originally framed as a way to validate visualization research, it generalizes to practical visualization design and beyond.

Design Factors for Summary Visualization in Visual Analytics

This paper is a survey of approaches to how summarization is used in visualization. It provides a 4-way categorization of summarization into aggregate, project, subsample, and filter. It looks at a large number of examples to identify trends in how the methods are used.

The essay Design and Redesign by Viegas and Wattenberg is an important bridge between the design culture (of design and redesign) and data visualization. It has a great discussion of how designers use critique and redesign, and how this can be used (and abused) in visualization. At the time (2015) it was trying to bring design culture to visualization, and the importance of visualization to designers.

I really like the book Discussing Design. Here is an AI generated summary of what is in it. The authors give away the first chapter. This summary can help you decide if you want to read more than that.

The “Problem Space” paper describes a way to think about the problem a visualization is trying to solve, rather than the solution. It proposes six aspects to consider, named for the “5Ws and an H” from journalism: Who, What, Where, Why, When, and How.

We often think of interaction as a solution to visualization problems, as it has many upsides. However, there are costs to the user (beyond the obvious costs in terms of design, implementation and deployment). The paper “A Framework of Interaction Costs in Information Visualization” by Heidi Lam gives a nice way to think about these costs. It’s an adaptation of earlier work in HCI, but she shows how appropriate it is. She validates the framework by applying it in a broad survey of visualization papers.

Many (if not all) visualizations are about making comparisons in the data. I’ve spent a lot of effort to come up with ways of thinking about comparisons, or (if you prefer) to think about visualization in terms of comparison. These were summarized in a 2017 paper (it has a 2018 publication date) “Considerations for Visualizing Comparison”.

The paper An Algebraic Process for Visualization Design by Gordon Kindlmann and Carlos Scheidegger is something that influenced my thinking a lot. It is a great example of visualization formalism that has a big and practical payoff in how we think about visualizations. It came back to mind recently.

Thinking with Data Visualizations, Fast and Slow: Franconeri 2018 Video

The Nested Model(s): A Reader's Guide

Design Factors for Summary Visualization in Visual Analytics

Design and Redesign

Summary: Discussing Design (Book)

A Problem Space for Designing Visualizations

A Framework of Interaction Costs in Information Visualization

Considerations for Visualizing Comparison

Paper: An Algebraic Process for Visualization Design