CIS 47/02-0: Data Visualization Introduction & Data Dr. David Koop
Recap (Administrivia) Course Focus Course Web Site Syllabus - Plagiarism - Accommodations Textbook Assignments Registration - Please email me if you are not currently enrolled if you haven t already 2
Recap (Topics) Relationship to Computer Graphics Meaningful representation of data Why visualization? Types of data How should we create visualizations? Exploration versus explanation 3
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For construction-related service changes on Planned Service Changes in the top menu bar. Little Auburndale Bayside Douglaston Neck On weekends, the Weekender website and app show construction-related scheduled service ch This information is also posted at station entrances and on platform columns of affected lines. 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MTA Fare Data Exploration 5
MTA Fare Data Exploration
MTA Fare Data Exploration 7
MTA Fare Data Exploration 7
MTA Fare Data Exploration 8
MTA Fare Data Exploration 9
MTA Fare Data Exploration A UGUST S U N M O N T U E W E D T H U F R I S A T 2 3 :0 :0 2 0:0 3 8:40 YES YES YES T OR 4 SD DET 8 BOS 25 TB DET 5 CHW 2 LAA 9 TOR 2 TOR DET CHW 3 LAA 20 TOR 27 TOR HOU 7 CHW 4 LAA 2 TOR 28 TOR DET 8 COL 5 LAA 22 TOR 29 TOR SD 9 DET BOS 23 TB 30 BAL SD 0 DET 7 BOS 24 TB 3 BAL 4:0 8:0 8:0 8:0 :0 7:05 :05 YES YES MY9 YES YES YES YES TBA 7:05 7:05 7:05 :05 7:0 4:05 TBA YES YES YES YES MY9 FOX TBA :0 7:05 7:05 :05 7:0 7:0 TBA YES MY9 YES YES MY9 YES :40 7:07 7:07 7:07 7:07 7:05 :05 YES YES YES YES YES YES YES S EPT EMBER S U N M O N T U E W E D T H U F R I S A T :05 2 :05 3 7:05 4 7:05 5 7:05 7:05 7 :05 YES YES YES YES YES YES FOX BAL 8 BOS 5 BOS 22 SF 29 HOU CHW CHW CHW BOS BOS 9 BAL 7:05 0 BAL BAL 2 BAL 3 BOS :0 7 8 9 20 TOR TOR TOR TOR SF 23 :0 YES 24 25 2 27 TB TB TB TB HOU 30 :0 YES 30 27 28 29 ALL GAMES ARE EASTERN TIME. HOU HOU CHW CHW T OR BOS 4 BOS 2 SF 28 HOU 30 T OR TBA 7:05 7:05 7:05 7:0 :05 TBA YES MY9 YES YES MY9 FOX TBA 7:07 2:0 :0 7:07 7:07 7:05 TBA TBA YES MY9 YES YES YES TBA :05 7:05 7:05 7:05 8:0 TBA YES YES MY9 YES YES YES TBA 2 03 R EGULAR SE ASON SCHED ULE 9
Definition Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. T. Munzner 0
Definition Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.
Definition NYC Subway Fare Data Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.
Definition NYC Subway Fare Data Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. Find Interesting NYC Subway Ridership Patterns
Definition Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. 2
Why People? Certain tasks can be totally automated - Statistical computations - Machine learning algorithms - We don t need visualization for these tasks (although perhaps for debugging them ) Analysis problems are often ill-specified - What is the correct question? - Exploit human visual system, pattern detection capabilities - Goal may be an automated solution or a visual analysis system Presentation - It is often easier to show someone something than to tell them a bunch of facts about the data (and let them explore it) 3
Why Computers? [Cerebral, Barsky et al., 2007] 4
Why Computers? [Cerebral, Barsky et al., 2007] 5
Resource Limitations Memory and space constraints How many pixels do I have? Information Density [McGuffin & Robert, 200]
Definition Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively 7
Why Visual? I II III IV x y x y x y x y 0.0 8.04 0.0 9.4 0.0 7.4 8.0.58 8.0.95 8.0 8.4 8.0.77 8.0 5.7 3.0 7.58 3.0 8.74 3.0 2.74 8.0 7.7 9.0 8.8 9.0 8.77 9.0 7. 8.0 8.84.0 8.33.0 9.2.0 7.8 8.0 8.47 4.0 9.9 4.0 8.0 4.0 8.84 8.0 7.04.0 7.24.0.3.0.08 8.0 5.25 4.0 4.2 4.0 3.0 4.0 5.39 9.0 2.50 2.0 0.84 2.0 9.3 2.0 8.5 8.0 5.5 7.0 4.82 7.0 7.2 7.0.42 8.0 7.9 5.0 5.8 5.0 4.74 5.0 5.73 8.0.89 [F. J. Anscombe] 8
Why Visual? I II III IV x y x y x y x y 0.0 8.04 0.0 9.4 0.0 7.4 8.0.58 8.0.95 8.0 8.4 8.0.77 8.0 5.7 3.0 7.58 3.0 8.74 3.0 2.74 8.0 7.7 9.0 8.8 9.0 8.77 9.0 7. 8.0 8.84.0 8.33.0 9.2.0 7.8 8.0 8.47 4.0 9.9 4.0 8.0 4.0 8.84 8.0 7.04.0 7.24.0.3.0.08 8.0 5.25 4.0 4.2 4.0 3.0 4.0 5.39 9.0 2.50 Mean of x 9 Variance of x Mean of y 7.50 Variance of y 4.22 Correlation 0.8 2.0 0.84 2.0 9.3 2.0 8.5 8.0 5.5 7.0 4.82 7.0 7.2 7.0.42 8.0 7.9 5.0 5.8 5.0 4.74 5.0 5.73 8.0.89 [F. J. Anscombe] 8
Why Visual? y 2 0 8 4 y 2 2 0 8 4 4 8 0 2 4 8 4 8 0 2 4 8 x x 2 2 2 y 3 0 8 y 4 0 8 4 4 4 8 0 2 4 8 4 8 0 2 4 8 x 3 x 4 [F. J. Anscombe] CIS 02, Fall 204 9
Visual Pop-out [C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/pp/] 20
Visual Pop-out [C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/pp/] 2
Visual Pop-out [C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/pp/] 22
Visual Perception Limitations [C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/pp/] 23
Visual Perception Limitations [C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/pp/] 24
Other Human Limitations Visual working memory is small Change blindness: Large changes go unnoticed when we are working on something else in our view 25
Definition Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively 2
Why Design? [viz.wtf] 27
Design Iteration http://chartsnthings.tumblr.com/post/2797588/9-sketches-of-quarterback-timelines 28
Design Iteration http://chartsnthings.tumblr.com/post/2797588/9-sketches-of-quarterback-timelines 29
Design Iteration http://chartsnthings.tumblr.com/post/2797588/9-sketches-of-quarterback-timelines 30
Definition Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively 3
Why Effectiveness? It s not just about pretty pictures Any depiction of data requires the designer to make choices about how that data is visually represented - Analogy to photography - Lots of possibilities (see quarterback study) Effectiveness measures how well the visualization helps a person with their tasks - How? insight, engagement, efficiency? - Benchmarks and user studies 32
Analyzing Visualization What? Why? How? What? Why? How? What? Why? How? [Munzner (ill. Maguire), 204] 33