Reading and initial posting due: Monday, March 2, 11:59pm
Turn-in link: Reading 12 on Canvas
Color is only one of the visual variables. Now we can consider the rest. Except that two of them (the two spatial dimensions, X and Y) are a huge topic unto themselves.
The starting point for this discussion is Munzner Chapter 5 (which you should have read already). You might want to scan through it a bit.
The other starting point would be to read some of Bertin’s original introduction of the concept of the visual variables. But, I don’t have a good reading of that. I haven’t read the original myself (yet).
For this week, there will be a set of readings about two slightly disjoint topics:
- What are the different encodings, and what do we know about their properties? (and how do we know it) – This is the world of Graphical Perception, which is experiencing a resurgence in visualization.
- How do we deal with the position dimensions? This starts to get at layout.
For Monday, March 2, do all of the following:
- If you’re a 638 student, pick any one from 1-3 (you may pick more). If you’re an 838 student, read #3 and either 1 or 2.
- Read #4 Munzner chapter 7. (and optionally skim Ch. 8)
- Read #6 Ware, chapter 3. I was going to say this can be for Thursday, but it goes well with the others since it gets at the machinery involved.
- Read #7 Tufte (OK, this one can be for Thursday)
Yeah, this is more than I wanted to give in one reading – but to make up for it, there won’t be a reading for March 4. (I just couldn’t decide how to divide it up!)
For the discussion… Please make an initial posting by Monday, March 2. I don’t have a good concrete prompt here but…
These readings basically express the idea of “how do I map my data to marks on the page” (where do those marks go, what color, form, … do they have, …). In a sense all visualization might be mapped to this (at least making static visualizations). What I hope is forming in your head is that there is a principled way to figure what mappings are more or less appropriate. Discuss this – how are these pieces all fitting together? how do standard designs emerge as solutions? What kinds of things don’t fit in?
The readings:
You need to experience where the whole graphical perception thing got started. There are different versions of the paper, you may read either one.
- (short) Cleveland and McGill. Graphical Perception and Graphical Methods for Analyzing Scientific Data. Science 229(4716), 1985. (online library)
- (long) Cleveland and McGill. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistician, 79(387) 1984. (online library)
Jeff Heer and Michael Bostock re-created these results using crowdsourcing (many more participants, but much higher variance for several reasons). This paper is nice for many reasons, but a relevant one is that it’s a more modern presentation of (basically) the same results.
- Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. Jeffrey Heer, Michael Bostock ACM Human Factors in Computing Systems (CHI), 203–212, 2010 PDF (607.4 KB) | Best Paper Nominee
Normally, I’d pick a few other experimental papers to add, but we’ll get to those later in the semester. For now, just know that there are a lot more experiments helping us understand encodings.
For Layout, there are two chapters in Munzner (the third layout chapter we’ll get to when we talk about graphs), a Chapter in Ware, and a Chapter of Tufte (which is short).
- Chapter 7 “Arrange Tables” – in Munzner VAD
- Chapter 8 “Arrange Spatial Data” in Munzner – actually, for our purposes, we can summarize this chapter in one sentence for now “sometimes, you want to map position to position.” – we’ll come back to this chapter later. But you might want to scan through it now.
- Structuring 2 Dimensional Space – Chapter 3 of Visual Thinking for Design by Colin Ware. (the textbook, available online).
- Layering and Separation, Chapter 3 of Envisioning Information by Tufte. (in protected reader beware 14Mb file).
