Visual Explainers Ma Maneesh Agrawala CS 448B: Visualization - - PDF document
Visual Explainers Ma Maneesh Agrawala CS 448B: Visualization Winter 2020 with material from Matthew Conlen and Jessica Hullman 1 Topics 1. Storytelling 2. Design space of narrative visualization 3. Interactive documents 4. Chart sequences
with material from Matthew Conlen and Jessica Hullman
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Going forward I carried wax along the line, and laid it thick on their ears. They tied me up, then, plumb amidships, back to the mast, lashed to the mast, and took themselves again to rowing. Soon, as we came smartly within hailing distance, the two Sirens, noting our fast ship,
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VISUALIZATIONS TELL STORIES
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“… require[s] skills like those familiar to movie directors, beyond a technical expert’s knowledge of computer engineering and science.”
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1995 Obesity Map Vadim Ogievetsky
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2008 Obesity Map Vadim Ogievetsky
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Communication Data exploration
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70% Journalism 20% Business 10% Research
[Segel & Heer 2010]
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Grab attention with image and position Matching on content Visual prominence Reduced visual priority
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Genres for Narrative Visualization (Segel & Heer 2010) 30
Genres Visual Narrative Narrative Structure
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MAGAZINE STYLE ANNOTATED CHART SCIENCE FAIR POSTER FLOWCHART COMICSTRIP SLIDESHOW MOVIE
Details on Demand Highlighting Filtering Selection Timelines Tacit Tutorial Navigation
Attached Article Headlines Interpret Captions Summaries Annotations
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Author Driven strong ordering heavy messaging limited interactivity Reader Driven weak ordering light messaging free interactivity
martini glass interactive slideshow drill-down story STORYTELLING SPEED CLARITY ASK QUESTIONS FIND EXPLORE
Genres + Interactivity + Messaging =
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Remembrance of Things Parsed [Mandler and Johnson 1977]
Story grammars: Models of narrative cognition based on systematic studies of what impacts peoples’ ability to recall parts of a story Reader mentally indexes events by time, space, protagonist, causality, intention [Zwaan 1995] 35
Graph Comics [Bach et al. 2016]
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Graph Comics [Bach et al. 2016]
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New visualization research or data analysis project
I Research: Pose problem, Implement creative solution I Data analysis: Analyze dataset in depth & make a visual explainer
Deliverables
I Research: Implementation of solution I Data analysis/explainer: Article with multiple interactive
visualizations
I 6-8 page paper
Schedule
I Project proposal: Wed 2/19 I Design review and feedback: 3/9 and 3/11 I Final poster presentation: 3/16 (7-9pm) Location: TBD I Final code and writeup: 3/18 11:59pm
Grading
I Groups of up to 3 people, graded individually I Clearly report responsibilities of each member
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NY Times 2014
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define context / goal
Data
visualize select annotate
filter, transform
Can we automatically identify sequences to recommend to a human designer?
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Nodes are Vega-Lite specifications. Edges represent edit operations, weighted by estimated transition costs. [Kim, Wongsuphasawat, Hullman, Heer, 2017]
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Add Size(count) Bin Add Filter Bin
“Too many data points !”
Random Sample Binned Scatter Plot
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Sequence Cost
: 10 : 12 : 13 …
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Previously we’ve discussed approaches for automatic design of a single visualization (e.g. Mackinlay’s APT) GraphScape supports automated design methods for collections of visualizations. Plenty of future work to do here!
[Kim, Wongsuphasawat, Hullman, Heer 2017]
60 Narrative visualizations blend communication via imagery and text with interaction techniques Specific strategies can be identified by studying what expert designers make Automating construction of effective explainers is an active area of Visualization research
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