The Week in Vis 03 (Mon, Sep 17 – Fri, Sep 21): Abstraction

by Mike Gleicher on September 13, 2018

Class Meetings
  • Mon, Sep 17 – Lecture:Abstractions
  • Wed, Sep 19 – ICE: Data and Task
  • Fri, Sep 21 – OPT:DC1Data
Week Deadlines

Based on the critique in class, I tried to improve the format a bit so that the deadlines are more explicit. Of course, you could review the Part of Class page to remind yourself of the weekly deadlines.

Another change based on class critique: I want to be clearer that the readings are required for Monday, preferably before class. That means that if you only start the readings when you get the “week in vis” post on Friday, you don’t have a lot of time. But, you can always look ahead at the “all readings” page to see what the readings are ahead of time.

This week includes a design challenge – a 4 week “project”, with a deadline each week (on Friday). Look over the project description page. For this week, you need to pick a data set to work with. For Friday’s (optional) class period, we will look over data sets. If you want to use a data set that is not on the approved list, you need to bring it Friday so that we can approve it. If you don’t want to bring your own data set, it may be a useful discussion to listen in, since ideas for the assignment may come up.

The actual course content this week is about abstraction – a key element of visualization, because it lets us get beyond the specifics of a situation and draw from similar ones. We’ll talk about data abstraction (how to think about data in a manner that gets beyond its meaning) and task abstraction (how to think about what the viewer might be trying to do so we can help them). A warning: there are many ways to think about tasks abstractly. We’ll just see a few in the readings.

In lecture Monday, we’ll discuss data and task abstractions. For the in-class exercise on Wednesday, we’ll practice describing data and tasks abstractly. And hopefully, do more critique practice. Friday’s (optional) class is to look at data sets for DC1 (as described above).

You may want to look at this week’s learning goals Learning Goals 3: Week 3 – Abstraction.

Readings (due Mon, Sep 17 – preferably before class)

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.

Optional

This used to be required – both because it makes the value of abstractions concrete, as well as gives some of the ideas behind Tableau.

  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. Plus, we’ll probably use Tableau this semester, so learning about it is a good idea.

    Because Tableau is such a direct implementation of the “building blocks” theory of visualization, it provides a great way to experiment with it.

Understanding task is really key to doing visualization well. These papers are strongly recommended because they provide another perspective on task as well as show how task analysis can be helpful.

  1. 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 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.
  2. 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 my 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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