Initial Posting Due: Tue, Sep 26 at (Canvas Link)
Readings
This week, the topic is Encodings. The Visual channels to which we can map data. These can be thought of as the building blocks from which visualizations are constructed. We’ll read about different encodings, and hopefully get a sense of why you might choose one over the other. And you’ll look at some standard designs and try to understand how they are put together from encodings.
The primary readings are three chapters that discuss the different encodings, and a classic paper they all refer to:
- Marks and Channels (Chapter 5 from Munzner’s Visualization Analysis & Design) (Munzner-05-MarksAndChannels.pdf 366 kb)
A nice discussion of the main encodings, with information of how they differ and how to choose.
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Arrange Tables (Chapter 7 from Munzner’s Visualization Analysis & Design) (Munzner-07-ArrangeTables.pdf 586 kb)
Position encodings are extra important and potentially more complex, so they get their own chapter. This chapter is particularly interesting because Munzner shows us how to break down a lot of standard (and some not so standard) charts into basic encodings. (note that we’ve skipped over Chapters 4 and 6 – we’ll come back to these).
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Basic Principles of Visualization (Chapter 5 of The Truthful Art) (theTruthfulArtCh5.pdf 10.2 mb)
In some ways, this is redundant with Munzner – but I like it as a different perspective, less formal and academic. It provides some thoughts on how to make practical use of the research literature (which we will look at).
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Cleveland and McGill. Graphical Perception and Graphical Methods for Analyzing Scientific Data. Science 229(4716), 1985. (online library) (copy on Canvas)
This paper is referred to by Munzner, Cairo, and, well, everyone else. It’s the first rigorous attempt to understand how people perform at reading encodings. I think it’s important to see the original paper, so you know what they are talking about.
There are many more recent papers that continue the tradition of trying to rigorously and empirically determine what works and doesn’t work. It’s become a whole genre. We’ll read a lot more. (one is in the optional list)
Another part of learning about encodings is to use them as a way to understand how standard charts are made. In fact, we can analyze the graphs and charts we are used to by breaking them into their constituent encodings and understanding the encodings. As part of the “reading” for this week, I’d like you to look at the variety of chart types that get used, and start to think of them in terms of their encodings.
Here are a few places to look for catalogs of visualization types (this is the same list as last time):
- The Data Visualization Project – a nicely done visual catalog of known visualization types. I like this a lot. A mix of obscure and standard designs. The discussions are useful.
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A tour through the Visualization Zoo. By Heer, Bostock, and Ogievetsky. There are various versions around. But, the one in ACM Queue online (here) is good enough. This is a more academic paper, that has various forms.
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The D3 examples page has hundreds of examples of things – some standard, some not-so-standard.
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The visualization taxonomy developed for the MassVis project groups many standard designs. It tries to organize the different designs.
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The periodic table of visualization methods and the Data Visualization Catalogue also have lots of examples of different types of visualizations.
Optional:
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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
This paper is interesting since it recreates most of Cleveland and McGill as a Mechanical Turk study, with a much broader population. The presentation is much more modern (and easier to interpret). This could be a replacement for the original.
Online Discussion
Initial Posting Due: Tue, Sep 26 at (Canvas Link)
For this week, there are a few key topics:
- What are the different visual channels that we can use to encode data?
- How do we assemble these encodings to make visualizations?
- How are standard designs assembled from these basic building blocks of encodings?
We didn’t spend as much time (yet) on how to choose appropriate encodings – this week is mainly about what they are.
For your two postings, please do the following:
- Give examples of appropriate and inappropriate encoding choices. For different encodings, what might it be good or bad for?
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Take a complex visualization (using something from a previous seek and find is good): break it down into its encodings. Which visual channels are used, and what are they used for? (a good discussion topic: are these encodings good). Please either give a link to the visualization (especially if it’s a previous seek and find), or post a picture of it so we know what you’re referring to.
As usual, discuss these with your online group.
