Initial posting due: Wednesday, April 22nd, 11:59pm (note: this is Wednesday, not Monday, since I was slow at getting the assignment out). Additional postings required later.
Turn in Link: Reading 21 on Canvas
Uncertainty is a really important topic in Visualization. Really important. It’s really hard. And I don’t have any readings that I am totally happy with.
There are 6 things listed here. You need to read 1 or 2 (probably 1, since 2 is long – but feel free to pick it if you find the topic interesting). Then read either 3 or 4 (quickly – just get the ideas of how to present uncertainty, not necessarily the details of the experiment). Similarly, skim #5.
The Readings
The first paper is short (it’s an extended abstract), but it gets at a lot of the issues (in an unexpected way).
1. Boukhelifa, N., & Duke, D. J. (2009). Uncertainty visualization: why might it fail? In Proceedings of the 27th international conference extended abstracts on Human factors in computing systems – CHI EA ’09 (p. 4051). New York, New York, USA: ACM Press. doi:10.1145/1520340.1520616 (ACM) (PDF in Box)
In contrast, this is a thorough survey – too much for me to ask everyone to read, but it has a nice diversity.
2. Ken Brodlie, Osorio, R. A., & Lopes, A. (2012). Expanding the Frontiers of Visual Analytics and Visualization. In J. Dill, R. Earnshaw, D. Kasik, J. Vince, & P. C. Wong (Eds.), Expanding the Frontiers of Visual Analytics and Visualization (pp. 81–109). London: Springer London. doi:10.1007/978-1-4471-2804-5 (Springer) (PDF in Box)
I like this next paper because it gets at a variety of different ways to show uncertainty, and points at some of the different strategies. The evaluation aspect is less important for class.
3. MacEachren, A. M., Roth, R. E., O’Brien, J., Li, B., Swingley, D., & Gahegan, M. (2012). Visual Semiotics & Uncertainty Visualization: An Empirical Study. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2496–2505. doi:10.1109/TVCG.2012.279 (PDF)
This one focuses on a single kind of visual technique, but goes a little deeper…
4. Wood, J., Isenberg, P., Isenberg, T., Dykes, J., Boukhelifa, N., & Slingsby, A. (2012). Sketchy Rendering for Information Visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2749–2758. doi:10.1109/TVCG.2012.262 (web)
We just wrote a paper that gets at the issues in a different way, but it isn’t published yet – and I am not sure its ready for people to read. So, instead, I’ll point to our recent paper which deals with a very common case of uncertainty visualization, and one of the most standard visualizations. (we have discussed this in class, early on)
5. Correll, M., & Gleicher, M. (2014). Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2142–2151. doi:10.1109/TVCG.2014.2346298 (web)
The statisticians have a lot to say about how we should think about uncertainty, especially in experiments. This paper gets at many of the issues (it is statisticians explaining to psychologists what they should do).
6. Cumming, G., & Finch, S. (n.d.). Inference by eye: confidence intervals and how to read pictures of data. The American Psychologist, 60(2), 170–80. doi:10.1037/0003-066X.60.2.170 (pdf)
What you need to read…
Everyone must read #1 or #2. Each person should read 3 or 4 – but don’t worry about the details of the experiments. (hopefully within each discussion group, there will be a mix). Everyone should look at 5 (but again, get the gist, don’t worry about the details of the experiments). 6 is optional.
For your initial posting, give a sense of the kinds of challenges in visualizing uncertain data. And then describe how the methods in the technique paper you read (3,4) address these. In the follow on discussion, think about how the range of techniques you see (in 3,4,5 and other places) both make use of the various concepts from class, but also, might be applied to the various problems we’ve talked about.
