User:Pauloppenheim/Reading Notes/Lec-2010-01-20

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Lec 1 - 2010-01-20 - The Purpose of Visualization

Chapter 1: Information Visualization, In Readings in Information Visualization. Card, et al.

  • this is a really jacked up scan, hopefully not missing too much
  • the slap in the face of big academic words: "perceptualization". Wouldn't "rendering" suffice?
  • Q: "What do you think of Card et al.'s distinction between "scientific visualization" that deals with physical scientific data (e.g., air flow over an airplane wing) and often has a natural spatial mapping versus "information visualization" that deals with abstract data" A: most of that section sounds like bullshit.
    • quote: "Scientific visualization is visualization applied to scientific data, and information visualization is visualization applied to abstract data."
    • Wait, lemme catch up with you. What is the difference in the definition of "information" and "data"? I would say those words are synonyms, but by the logic of the authors, Scientific visualization : scientific data, and information visualization : abstract data. So information = abstract? I would imagine the implication from the wording of the science one would be information visualization : information data, which makes no sense. Hence, their distinction is word salad.
    • aside from the snarky "hey guys, write a little better" commentary, I do agree in some sense that the inherent geometric relations of physical systems give a bit of a natural nudge to those visualizations, insofar as there is a clear geometric property of the physical system. I can't imagine quantum mechanics or the 6-dimensional dances of bees are much easier to depict in a 2D projection than any other complex data.
  • quote p10: "The results could be a briefing, a short paper, or even a decision or action." ROFLMAO! no kidding? We might actually do more than churn butter all day? In general the "knowledge crystallization" section feels like a misplaced attempt at a knowledge work action taxonomy.
  • p15 "Cost-of-Knowledge Characteristic Function" sounds ideal but somewhat specious. How do you know how long it will take a user to do something? All users? 99th pctile?
  • this is really overwordy, repetitive, and hard to read. The great internet filter has made me impatient.
  • p18 is this really a half page discussion on whether to put the variable names as column or row labels? Wait, the rest of the page (functions, multidimensionality) bothers me too. (multidimensional can mean many things and usually needs qualification, despite what this book says.) (I may have finally spent too much time with databases if I am this sensitive about these things.)
  • p20 Nominal, Ordinal, Quantitative - this is relevant to my interests. Reminds me of Haskell typeclasses, and any other type systems, but a weird division. I'm curious why these 3, and only these 3.
  • Derived values is intuitive, but "derived structure" is a concept I wasn't aware of.
  • This "mapping data to visual form" section is pretty good, and typing / transformations led in well to "visual structures" demonstrating what typings / graphings are effective.
  • "Controlled processing" and "automatic processing", and hints at new ways to hack our brains via vision. This sounds fun!
  • p30 table 1.23 - we call this a "cheat sheet".
  • p33 - viewpoint controls - finally getting into the tradeoffs of overwhelming vs too detailed and ways to avoid taking too much out of view and thus into human working memory.
  • When they quote (Norman, 1993) are they talking about Donald Norman, author of "The Design of Everyday Things"?
  • Now that I'm done, I think the general thrust of "we use visualizations as a hack to make us smarter, and must be careful to avoid becoming stupider" is awesome. I think the approach (focusing solely on abstract data vis after discussing "scientific vis" as something different) is somewhat stilted. (outdated?) The approach discounts visualization methods of abstract concepts that closely match physical systems, which I find to be the most useful. For instance, the operating systems diagram for an LRU cache is typically a circle that marks slots. That diagram method isn't discussed in this survey.

Decision to launch the Challenger, In Visual Explanations. Tufte

  • those, sir, are excellent diagrams.
  • aaaand you open by ripping into all of the analysis others have done as an intro to your analysis. Your ideas are intriguing to me and I wish to subscribe to your newsletter.
  • describes the importance of signature on the doc; this isn't just about vis, it's about communication and comprehension, the heart of what we're after.
  • really tears into the charts and how the link to temperature is awful, and the failure highlighted was blow-by, when erosion was allegedly the most devastating.
  • simply take all raw data, do a sort-by (derived structure from previous doc!) on interesting factor, show results, add derived values if desired, then graph. In this case, the results are a no-brainer.

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