The Week in Vis 03 (Mon, Sep 16 – Fri, Sep 20): Abstraction

by Mike Gleicher on September 13, 2019

Week in Vis 3 Mon, Sep 16-Fri, Sep 20

We’ve finished our second week – which means we’ve been through a “regular” week. Hopefully, you are getting with the weekly rhythm.  Readings for class Monday, online discussion post before Tuesday, quiz and seek and find due on Friday. Now we also have Design Challenges (with the first phase of the first challenge due on Wednesday).

Content-wise, this week, we’ll talk about abstraction, as a mechanism to understand how to apply visualization to data. We’ll talk briefly about tasks, and focus on data abstraction. We’ll also talk about how data type influences design (mainly in the ICE on Tuesday) especially for the standard data types. This is good preparation for Design Challenge 1 (which is starting this week).

If you haven’t already looked over Design Challenge 1, you should do so. While Wednesday’s deadline is soft (see the description), you probably should get started. Friday in class we will have an (optional) tableau tutorial.

In terms of online discussions, your focus should be Online Discussion 3 and Seek and Find 3, although we leave last week’s discussions open so you can continue to discuss.

 

Readings for the Week

The topic for this week’s readings is Abstraction – especially data abstraction.

  1. Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information visualizations. In Proceedings 1996 IEEE Symposium on Visual Languages (pp. 336–343). (doi) (web pdf)

    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.

  2. 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.

  3. 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. I recommend the Schulz et. al paper below for contrast.

  4. 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.

Optional

  1. Mackinlay, J., Hanrahan, P., & Stolte, C. (2007). Show me: automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1137–44. (DOI) (Mackinlay2007-ShowMe.pdf 0.6mb)

    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.

  2. Schulz, H.-J., Nocke, T., Heitzler, M., & Schumann, H. (2013). A Design Space of Visualization Tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2366–2375. (doi) (web pdf)

    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.

  3. 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.

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