Who’s who Class time Structure Week 1: Intro, Marks and Channels • 6 weeks, Sep 15 - Oct 20 • Instructor: Tamara Munzner • participation – 1 3-hr session per week – UBC Computer Science – attendance and discussion in class, 16% • standard week • tell me in advance if you’ll miss class (and why) • tell when you recover if you were ill – foundations lecture/discussion: 90 min • homework, 84% – break: 15 min – 6 assignments, 14% each • Journalistic kibitzer: Alfred Hermida – demos: 30 min Tamara Munzner • start in lab – lab: 45 min – UBC Journalism Department of Computer Science • finish over one week • demo-intensive weeks University of British Columbia • due at start of next class session – Week 1 & Week 4: longer demo from guest lecturer Robert Kosara – some solo, some in groups of 2 JRNL 520M, Special Topics in Contemporary Journalism: Visualization for Journalists • Guest lecturer and significant labs help: Robert Kosara – foundations 60 min, break 15 min, demos 60 min, lab 45 min Week 1: 15 September 2015 • gradual transition from structured to open-ended – Research Scientist, Tableau Software • final assignment: find your own interesting data and design your own visualization for it – previously UNC Charlotte Computer Science http://www.cs.ubc.ca/~tmm/courses/journ15 • draft plan, may change as pilot continues! 2 3 4 Further reading Finding me Topics VAD Ch 1: What’s Vis and Why Do It? • optional textbook for following up on lecture topics • email is the best way to reach me: tmm@cs.ubc.ca • Week 1 • Week 4 – Intro – Arrange Networks – Tamara Munzner. Visualization Analysis and Design. CRC Press, 2014. • office hours by appointment • Why have a human in the decision-making loop? – Marks and Channels – Demo: Tableau II, Kosara • http://www.cs.ubc.ca/~tmm/vadbook/ – X661 (X-Wing of ICICS/CS bldg) • Why have a computer in the loop? – Demo: Tableau I, Kosara – library has multiple ebook copies • Week 5 • Why use an external representation? – to buy yourself, see course page • course page is font of all information – Facet Into Multiple Views • Week 2 • Why depend on vision? • optional papers/books – Reduce Items and Attributes – Task and Data Abstractions – don’t forget to refresh, frequent updates • Why show the data in detail? – links and references posted on course page – Demo: TBD – Arrange Tables – http://www.cs.ubc.ca/~tmm/courses/journ15 – if DL links, use library EZproxy from off campus • Why is the vis idiom design space so huge? – Demo: TBD • Week 6 • Why focus on tasks and effectiveness? – Rules of Thumb • Week 3 • Why are there resource limitations? – Putting It All Together – Color • Why analyze vis? – Demo: TBD – Arrange Spatial Data – Demo: Text Tools & Resources, Brehmer 5 6 7 8 Defining visualization (vis) Why have a human in the loop? Why use an external representation? Why have a computer in the loop? Computer-based visualization systems provide visual representations of datasets Computer-based visualization systems provide visual representations of datasets Computer-based visualization systems provide visual representations of datasets Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. designed to help people carry out tasks more effectively. designed to help people carry out tasks more effectively. designed to help people carry out tasks more effectively. Visualization is suitable when there is a need to augment human capabilities • beyond human patience: scale to large datasets, support interactivity • external representation: replace cognition with perception Why?... rather than replace people with computational decision-making methods. – consider: what aspects of hand-drawn diagrams are important? • don’t need vis when fully automatic solution exists and is trusted • many analysis problems ill-specified – don’t know exactly what questions to ask in advance • possibilities – long-term use for end users (e.g. exploratory analysis of scientific data) – presentation of known results – stepping stone to better understanding of requirements before developing models – help developers of automatic solution refine/debug, determine parameters [Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context. Barsky, Munzner, Gardy, and Kincaid. IEEE – help end users of automatic solutions verify, build trust TVCG (Proc. InfoVis) 14(6):1253-1260, 2008.] 9 10 11 [Cerebral: a Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Barsky, Gardy, Hancock, and Munzner. Bioinformatics 23(8):1040-1042, 2007.] 12 Why depend on vision? Why show the data in detail? Why analyze? Analysis framework: Four levels, three questions domain • summaries lose information • huge design space • domain situation Computer-based visualization systems provide visual representations of datasets abstraction designed to help people carry out tasks more effectively. – who are the target users? – confirm expected and find unexpected patterns – visual encoding: combinatorial explosion of choices idiom • abstraction algorithm – assess validity of statistical model – add interaction: even bigger • human visual system is high-bandwidth channel to brain – add data abstraction transformation: truly enormous – translate from specifics of domain to vocabulary of vis [A Nested Model of Visualization Design and Validation. – overview possible due to background processing Munzner. IEEE TVCG 15(6):921-928, 2009 (Proc. InfoVis 2009). ] • most possibilities ineffective for particular task/data combination • what is shown? data abstraction • subjective experience of seeing everything simultaneously domain • why is the user looking at it? task abstraction • significant processing occurs in parallel and pre-attentively – implication: avoid random walk, be guided by principles Anscombe’s Quartet abstraction • idiom • sound: lower bandwidth and different semantics • analysis framework: scaffold to think systematically about design space Identical statistic tistics • how is it shown? – overview not supported – ensure that consideration space encompasses full scope of possibilities idiom x mean 9 • visual encoding idiom : how to draw • subjective experience of sequential stream – improve chances that selected solution is good not mediocre x variance 10 algorithm • interaction idiom : how to manipulate • touch/haptics: impoverished record/replay capacity y mean 8 – next week’s focus: abstractions and idioms, what-why-how • algorithm y variance 4 – only very low-bandwidth communication thus far [A Multi-Level Typology of Abstract Visualization Tasks – efficient computation x/y correlation 1 Brehmer and Munzner. IEEE TVCG 19(12):2376-2385, 2013 (Proc. InfoVis 2013). ] • taste, smell: no viable record/replay devices 13 14 15 16
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