Due Date: Initial Reading and Posting Due Monday April 17; additional postings by Friday April 21, Discussions close Friday April 28.
Hand in: Canvas (LINK)
When you have lots of “stuff” (too many items, too many variables, items that are too large) visualization gets hard. The objective for this week is to get you to understand the ways in which scale (things getting big) makes visualization hard, and to get some sense of what to do about it.
Since the Design Challenges are going on, expectations are adjusted a bit – even though this is a cool topic from many directions (like how do we apply machine learning to reduce and organize the data so its easier to see and what happens at the limits of perception). Hopefully, we’ll at least touch on the coolness…
The Munzner chapter are short. And to be honest, they are not the strongest part of the book.
The readings…
- Munzner Chapter 13 (LINK) – on reducing items (although, she mixes reducing items and dimensions)
- Munzner Chapter 14 (LINK) – on focus + context
- Ellis, Geoffrey, and Alan Dix. “A Taxonomy of Clutter Reduction for Information Visualisation.” IEEE Transactions on Visualization and Computer Graphics, 2007, 1216–23.
http://www.computer.org/portal/web/csdl/doi/10.1109/TVCG.2007.70535.
http://core.ac.uk/download/pdf/70210.pdf - Elmqvist, Niklas, and Jean-Daniel Fekete. “Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines.” IEEE Transactions on Visualization and Computer Graphics 16, no. 3 (2010): 439–54. doi:10.1109/TVCG.2009.84.
https://engineering.purdue.edu/~elm/projects/hieragg/hieragg.pdf
And an extra one… the following week (April 28th) I am going to France, in part to present a paper on video focus+context. The project page is here (the video examples are at the bottom). You don’t have to read the paper, but watch the videos to figure out what it does (see the required postings).
1, 2 and 5 are required. I’d like you to (at least) skim through 3 and/or 4 to get a sense of the range of problems and solutions they discuss. (with the DC3 going on, less expectation to read everything)
Discussion Points:
- For the initial posting, discuss the different ways “scale” makes visualization hard. Try to think of the different ways data scales, and why this makes visualizing challenging. Try to give examples. Munzner will give you ideas, Ellis and Dix will help organize them, and Elmqvist and Fekete will show you some solutions.
- In a second posting: the “Zooming on All Actors” is not a vis paper (it’s a graphics/multimedia paper). But, it builds on Vis ideas (you should figure it out from the videos). Why can it be viewed as a visualization scale problem? How have the things from the readings inspired the solution? How else might what we’ve learned in this class be applied to this problem?
- Hopefully, those two (plus the ideas in class) will give you enough to think about and discuss.