The topic for this week’s readings is Abstraction. There are a bunch of readings here, but its an important topic, and you need to see different ways to think about task abstraction because no one approach is sufficient. You need to use all of them.
Reading 1 is a historical introduction to abstractions (task and data). Readings 2 and 3 get at data abstraction. Readings 4,5 and 6 are three different ways to look at task abstraction. Each differently useful.
Yes, this is a big reading. But I don’t know what to cut. This is good practice for reading for the gist, rather than the details.
This is a classic. Possibly one of the most influencial papers in the field. It’s old, and newer things are far more extensive. And the field has moved on from 1996 in many ways. But the initial thinking of abstracting data and task separately, and suggesting what those abstractions might be, really started here. The information seeking mantra is a classic notion. This paper is dated enough that it can be hard to read - but it is short.
What: Data Abstraction (Chapter 2 from Munzner’s Visualization Analysis and Design) (Munzner-02-DataAbstraction.pdf 1.1mb)
A fairly dry description of the types of data. Don’t worry about trying to remember all the terms - you can always look them up when you encounter them again.
Despite it’s length, the chapter skips a key concept: level of measurement for scales. You might have learned this in a stats class, but please understand the difference between “scale types” (nominal, ordinal, interval, ratio). Usable Stats has a simple introduction.
Forms and Functions (Chapter 2 of The Functional Art) (theFunctionalArtCh2.pdf 8.2mb).
Cairo’s thinking about “the shape of data” is another way to think about data abstraction in a less academic way.
Why: Task Abstraction (Chapter 3 from Munzner’s Visualization Analysis and Design) (Munzner-03-TaskAbstraction.pdf 0.4mb)
Figuring out how to think about tasks is important. This chapter (and the research paper it is derived from) focuses too much on trying to put every task in a neat organization. What’s important is to think about tasks. This is one way to do it, and it will help you learn to think about tasks. Don’t get too bogged down in all of her categories.
We’re reading the book chapter, not the paper. If you’re going to work in the field, you might want to look at the paper A Multi-Level Typology of Abstract Visualization Tasks by Brehmer and Munzner, IEEE InfoVis 2013. The chapter is better, although the paper is notable for its extensive references and careful use of the terminology. If you want to read one paper, I recommend the Schulz et. al paper below for contrast.
This paper takes a quite different approach to Munzner in thinking about tasks. It came out at the same time as the paper behind the book chapter. It was literally in the same session of the conference. I actually find this to be a more useful way to think about task - it’s not as encyclopedic, but that’s a feature.
An important paper because it tried to break down “analysis work” into low enough level tasks that can be named, and therefore designed for and evaluated. It is not a encyclopedic as things that come later - but that is a feature. In practice, we need to describe our task, well enough that we can design to address it. Having an encyclopedic taxonomy is useful for many reasons (it provides a vocabulary, a way to see similarities and differences, …). But its not the only thing.
There is a more recent paper that basically takes this taxonomy and uses it to organization the perceptual science behind visualization. A Survey of Perception-Based Visualization Studies by Task gives a concise overview of Amar, Eagan & Stasko - and then uses it to organize the literature of perceptual studies to connect them to these tasks. You can look at that paper instead of the original (I was tempted to use it as a replacement), but the survey part does get a bit long.
Two optional papers. One shows the application of abstraction, while the second gives a more specific taxonomy.
This is a research paper, but it’s an unusual one. It’s easy to dismiss this paper as marketing for Tableau - but it really does give a sense of how good abstractions can help in choosing appropriate visualizations. It is timely, since Tableau will come up in class.
Sarikaya, A. and Gleicher, M. Scatterplots: Tasks, Data, and Designs. IEEE Transactions on Visualization and Computer Graphics, 24(1) — Jan 2018 . (web page)
An recent paper that Alper (a former student) and I wrote. It focuses on a specific (but ubiquitous) kind of visualization, but thinks through the tasks and shows how thinking about the data properties and tasks helps suggest designs. I like this paper, but I am biased.