Readings 04: Encoding

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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.

Unfortunately, I don’t have a way to let you read the original source where the idea of basic encodings/visual variables were introduced (see Bertin's Books (Semiology of Graphics)).

The primary readings are three chapters that discuss the different encodings, and a classic paper they all refer to:

  1. (required)  Marks and Channels (Chapter 5 from Munzner’s Visualization Analysis & Design) (Munzner-05-MarksAndChannels.pdf 0.4mb)

    A nice discussion of the main encodings, with information of how they differ and how to choose.

  2. (required)  Arrange Tables (Chapter 7 from Munzner’s Visualization Analysis & Design) (Munzner-07-ArrangeTables.pdf 0.6mb)

    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).

  3. (required)  Basic Principles of Visualization (Chapter 5 of The Truthful Art) (theTruthfulArtCh5.pdf 10.2mb)

    In some ways, this is redundant with Munzner - but I like it as a different perspective, less formal and less academic. It provides some thoughts on how to make practical use of the research literature (which we will look at).

  4. (required)  Cleveland and McGill. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods/ Journal of the American Statistical Society 79 (387), 1984. (online library) (ClevelandMcGill84.pdf 3.2mb)

    or

    (alternate)  Cleveland and McGill. Graphical Perception and Graphical Methods for Analyzing Scientific Data. Science 229(4716), 1985. (online library) (ClevelandMcGill85.pdf 1.3mb)

    The first one (84) is the famous “Cleveland and McGill” paper; the second one is a shorter version that was published in a broader venue. You can read either one. Either one will feel dated - this work is a historical foundation of the field.

    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. It is OK to skim it to get the general gist and ideas.

    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 see more when we talk about evaluation and perception. See Heer&Bostock (optional, below) for a more modern take on this paper.

  5. (required)  Michael Gleicher. Video Lecture on TreeMaps made for CS765 in 2020. The one lecture is broken into three parts. (the whole thing is 35 minutes). This may also give you an idea of what the lectures were like when they were online.

    A Three Part Series on TreeMaps (and part whole encodings):

    1. Treemaps 1 - Part/Whole Encodings (10:51)
    2. Treemaps 2 - Encoding Part Whole Relationships (9:17)
    3. Treemaps 3 - Details and Algorithms (14:22)

Optional

  • (optional)  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)

    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, but I think its important to see the original.

  • (optional)  Visual Representation from Semiology of Graphics by Sheelagh Carpendale. Lecture slides/notes.

    A lot of the idea of encodings come from [Bertin](Bertin's Books (Semiology of Graphics)), but it’s too hard to read the original sources. Sheelagh Carpendale (a well known Vis professor) provides a great discussion in her slides that mix modern examples with Bertin. It’s too bad that it’s just slides.

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

    In an amazing 1986 system, Jock Mackinlay tried to automatically create charts from data. One of the key insights was to think about visualizations in terms of the basic encodings, which let him reason about these basic building blocks. His intuitions of what encodings were better/worse for different tasks was the beginning of trying to formalize this. He based this on his intuitions - but experiments show that he wasn’t too far off.

    In 1986 he was systematizing the design of visualizations - he had to have a systematic way to design, since he wanted to do it automatically! His approach is exactly what we are doing: considering visualizations in terms of encodings that can be reasoned about. Amazingly far ahead of its time.

  • (optional)  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) (pdf)

    This is from last week’s optional list. However, it shows how abstraction and encoding can be put together to make a cool system: Tableau. It is timely, since we are using Tableau in class.

These are late adds for 2022 - should be added in the future

  • (optional)  Younghoon Kim and Jeffrey Heer. 2018. Assessing Effects of Task and Data Distribution on the Effectiveness of Visual Encodings. Computer Graphics Forum 37, 3 (2018), 157–167. (DOI) (PDF)

  • (optional)  Caitlyn M. McColeman, Fumeng Yang, Timothy F. Brady, and Steven Franconeri. 2022. **Rethinking the Ranks of Visual Channels. IEEE Transactions on Visualization and Computer Graphics 28, 1 (January 2022), 707–717. DOI: https://doi.org/10.1109/TVCG.2021.3114684

    Graphical perception considering more realistic factors, a very different task, and other metrics of quality.

Part of the idea of class is to avoid trying to think of visualization as a huge catalog of discrete designs. There are definitely efforts to do this. And they will come up again when we talk about implementation. Here are a few examples:

  • (optional)  Data Vis Project - really nice visual compendium, with useful discussions of each.

  • (optional)  The Data Visualisation Catalogue - the index are smaller icons, but the description pages are rich with resources.

  • (optional)  D3 Gallery - examples of things made using D3. Since just about anything can be made in D3, this covers a pretty good range. OK, 3D stuff and scientific data sets are conspicuously missing, but this is expected.

  • (optional)  VTK Gallery - examples of things made using VTK (a toolkit for scientific data visualization). This is a place to see the kinds of things missing from the D3 Gallery. It’s not an encyclopedia - it’s just meant as a counterpoint to the others that ignore this whole side of visualization.

  • (optional)  Harris, Robert. Information Graphics: A Comprehensive Illustrated Reference. Oxford University Press, 2000. (UW Library Link).

    I used this in class as an example of what not to do. It’s quite dated, but also quite encyclopedic. Who knew there were so many different kinds of charts worthy of names? It’s worth a look for amusement. Note that this book is from 2000 (!) before a lot of the current academic organization and rigor had been brought to the field. To make it easier for students in class, I have put a copy of the library’s online version on Canvas at: (Information+Graphics.pdf 58.8mb).