Module 2: Building Blocks of Visualizations (Sep 15-26)

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We will look at how we can consider visualizations in terms of the building blocks of data and task abstractions and visual encodings. We will see how this allows designing and analyzing visualizations. We will practice design process (including critique). Students will begin to design and analyze visualizations (pen-and-paper).

Introduction

Our approach to designing and understanding visualizations is to build them up from basic building blocks. This module, we’ll learn about those building blocks.

This module, we’ll speed through 4 important topics: data abstraction, task abastraction, encodings, and critique (the readings for critique were last week). And, we’ll add some initial exposure to some of the things we’ll need to actually start working with data and making visualizations next week

This is another heavy reading week - next week, we’ll start making visualizations, so there will be less reading and more doing.

We’ll take a lecture to introduce Tableau (a commercial data analysis/visualization tool) - not just because we encourage you to use it, but also because it is an interesting embodiment of the concepts we are discussing.

Summary

The parts of the assignment (detailed below) can be done any time during the 2 weeks of the module.

  • This module has a substantial amount of readings (and less other work). We recommend doing this reading sooner, rather than later - you will get more out of the lectures and in class exercises if you’ve done the readings.
  • The two design exercises are due at the end of the module. The first (critique practice) is independent of other things in the module, so you can do it first.
  • There is a content survey and a class survey.
  • There is a seek and find.

Recommended Schedule:

  • do the “main readings” early in the module - you will get more out of the 1st wednesday lecture if you’ve seen the abstraction readings, and the 2nd monday lecture if you’ve seen the endoding readings.
  • do the first design exercise in the first week.
  • do the second design exercise in the second week.
  • do the seek and find and surveys in the second week.

Module Learning Outcomes (Goals)

By completing this module, students should

  1. have practice with critique to enable learning from examples going forward.
  2. have a vocabulary for describing data (data abstraction) to help us connect it to visual representations.
  3. understand the many different ways that we might talk about task, in ways that help us design visualizations that address tasks.
  4. be able to describe a range of different visual encodings and describe visualizations in terms of these building blocks.

Readings

There are 3 different topics this week: data abstractions, task abstractions, and encodings. Each could have a nearly infinite amount of reading. I want you to read the essentials - then some optional stuff later.

In each topic, start with the chapters of the Munzner book since she’ll give you a good overview. Readings beyond that will give you alternate perspectives.

Readings Part 1: Data Abstractions

Data abstractions should be easy/familiar to anyone with any CS / Data Science / Stats background. It’s good to refer the terminilogy But it is fairly dry stuff.

  • (required) Tamara Munzner. What: Data Abstraction. Chapter 2 from Munzner's Visualization Analysis and Design. (Canvas File) (video) (UW Library)
  • Despite its 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). Scribbr has a simple introduction.
  • A plan to make a “data abstraction cheat sheet” - but I haven’t yet

Readings Part 2: Task Abstractions

Describing what we’re trying to do in a visualization is much trickier. There are many different ways to look at it. In fact, one of the optional papers (below) tries to organize them. My paper takes a broader perspective. Munzner’s take is another broad take. The two other papers are key historical papers. With all of these, it’s important to get the essence of how to think about task - and less the details of their specific ways of describing it.

  • (required) Michael Gleicher, Maria Riveiro, Tatiana von Landesberger, Oliver Deussen, Remco Chang and Christina Gillman. A Problem Space for Designing Visualizations. IEEE Computer Graphics and Applications, Volume 43, Number 4, page 111-120 — Jul 2023. (doi) (web pdf) - The point here is that task is not everything. This is a light read - you can skip over the examples if you want.
  • (required) Tamara Munzner. Why: Task Abstraction. Chapter 3 from Munzner's Visualization Analysis and Design. (Canvas File) (video) (UW Library)
  • (required) Ben Shneiderman. The eyes have it: a task by data type taxonomy for information visualizations. Proceedings of the 1996 IEEE Symposium on Visual Languages (pp. 336–343). (doi) (url) - This is a hugely historically important paper from a key person (Ben Schneiderman) very early in the development of the field (1996).
  • (required) Amar, Eagan and Stasko. Low-Level Components of Analytic Activity in Information Visualization. Proceedings InfoVis 2005. (doi) (web pdf) - This is a very important early paper. It is much more focused than the others, and gets referred to a lot.

Readings Part 3: Encodings

Encodings are the ways we connect data to task by having visual elements for the data. The goal here is to get the basic concepts - in time, we’ll get some of the “science” of how to choose them. The optional readings (below) will get more into the specifics.

Munzner will give you a good overview (although, she splits it across two chapters). Cairo will give you his less formal perspective. Looking at (but not reading in detail) the (historical) Cleveland and McGill paper will give you a sense of where the “scientific study” of encodings began. The Bertini web posting will give a counter-point to the discussions of effectiveness.

  • (required) Tamara Munzner. Marks and Channels. Chapter 5 from Munzner's Visualization Analysis & Design. (Canvas File) (video) (UW Library)
  • (required) Tamara Munzner. Arrange Tables. Chapter 7 from Munzner's Visualization Analysis & Design. (Canvas File) (UW Library)
  • (required) Alberto Cairo. Basic Principles of Visualization. Chapter 5 of The Truthful Art. (Canvas File) (UW Library)
  • You should skim over one of these two (one is a shorter summary of the other). The rigorous study of encodings is something we’ll come back to (when we talk about perception and empicism). But for now, I want you to look at one of these historical papers to have a sense of where it all started. Don’t read it for details (the main takeaways are in other things we will read) - just get a sense of what it is so when people say “Cleveland and McGill” you know why.
    • (required) Cleveland and McGill. Graphical Perception and Graphical Methods for Analyzing Scientific Data**. Science 229(4716), 1985. (Canvas File) (url) - this is a shorter version
    • (alternate) Cleveland and McGill. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Society 79 (387), 1984. (Canvas File) (url) - this is the more complete one
  • (optional - but recommended) Enrico Bertini. Beyond Precision: Expressiveness in Visualization. Fell in Love With Data Substack Posting. (url)

Optional Readings

There is a lot to read already - because we’re trying to get 3 important topics in 2 weeks. But if you want to see some of the more “research paper” like things…

This is a historical paper that doesn’t fall into any category. It is an early example of trying to automatically create charts (visualization recommendations). It is notable for many reasons - but for one, it introduces the idea of thinking in terms of encodings, and the ordered list of encodings by data types - that is still used to this day! It’s amazing that Jock Mackinlay nailed it so early on.

  • (optional) Jock Mackinlay. Automating the Design of Graphical Presentations of Relational Information. ACM Transactions on Graphics, 1986. (doi) (web pdf)

Optional: Critique

In class, I will discuss Tufte and the Challenger and John Snow. If you want to actually read what he said about them…

  • (optional) Eduard Tufte. Visual Statistical Thinking. Chapter 2 from Tufte’s Visual Explanations. (Canvas File)

Optional: Task Abstractions

Why are there so many different task schemes? The first paper tries to organize them to explain why they are different. The second gets out how to decide which ones are good.

  • (optional) Alexander Rind, Wolfgang Aigner, Markus Wagner, Silvia Miksch, and Tim Lammarsch. Task Cube: A three-dimensional conceptual space of user tasks in visualization design and evaluation. Information Visualization 15(4) (October 2016), 288–300. (doi) (web pdf)
  • (optional) N. Kerracher and J. Kennedy. Constructing and Evaluating Visualisation Task Classifications: Process and Considerations. Computer Graphics Forum 36, 3 (2017), 47–59. (doi)

Here are more examples of task schemes. A very general one that influenced my thinking, and a specific one that tries to explore a very specific chart type (scatterplots) to connect tasks and designs.

  • (optional) Schulz, H.-J., Nocke, T., Heitzler, M., & Schumann, H.. A Design Space of Visualization Tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2366–2375, Dec 2013. (doi) (web pdf)
  • (optional) Sarikaya, A. and Gleicher, M.. Scatterplots: Tasks, Data, and Designs. IEEE Transactions on Visualization and Computer Graphics, 24(1), Jan 2018. (url)

Optional: Encodings

There are many more modern papers that try to evaluate encodings. Cleveland and McGill’s papers are mainly important because they inspired so much later work. Here are a few worth starting with. There are many, many more. We’ll come back to this when we talk about perceptual science and empiricsm.

  • (optional) Jeffrey Heer, Michael Bostock. Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. (web pdf)
  • (optional) Caitlyn M. McColeman, Fumeng Yang, Timothy F. Brady, and Steven Franconeri. Rethinking the Ranks of Visual Channels. IEEE Transactions on Visualization and Computer Graphics 28, 1 (January 2022). (doi)

Optional: Chart Typographies

I pose encodings as an alternative to long lists of chart types. If you want to see lists of chart types…

Lecture Plan

  • Monday 1 (Sep 15) Critique practice - we’ll take a lecture to focus on critique. You read about it last module. It is a valuable skill, so it is worth it to take the time to do it. ICE for critique practice.
  • Wednesday 1 (Sep 17) Data and task abstractions - you’ll have read about them, but we’ll take some class time to discuss. ICE for task abstraction practice.
  • Monday 2 (Sep 22) Encodings - you’ll have seen them in the readings, but we’ll practice putting pieces together. ICE for encodings practice.
  • Wednesday 2 (Sep 24) Tableau “tutorial” - I’ll take a lecture to show you how to work with Tableau (since you might want to try it). Even if you aren’t going to use Tableau, it is interesting because it is an explicit application of the theory we are discussing.

There is a drawing/discussing exercise that I like to do that I might try to squeeze in as well. So much to do, so little time…

Assignments

Everything is due the last day of the module (Friday, September 26). We expect you to work on the assignments over the whole time period of the module.