Dr. Christopher Healey - Data Visualization Overview

July 17, 2014 at Science Boot Camp Southeast for Librarians, Raleigh NC

Transcript

1. Data Visualization Science Boot Camp SE
 NCSU Library July 17, 2014 Christopher G. Healey Department of Computer Science North Carolina State University healey@ncsu.edu http://www.csc.ncsu.edu/faculty/healey
 NC STATE UNIVERSITY sic parvis magna
2. Visualization • Harness viewer’s strengths! • human visual system! • pattern recognition capabilities! • domain expertise! • understanding context! • ability to manage ambiguity! • Manage collaboration between
 viewer and computer! • Enhance each participant’s
 individual strengths! • Share initiative to offset
 their weaknesses Painterly visualization of a slice through a simulated! supernova collapse: pressure → luminance,! velocity → hue, flow direction → orientation! ! Data courtesy Dr. Jon Blondin, Astrophysics, NCSU
3. Painterly Visualization Painterly visualization of a slice through a simulated supernova collapse: pressure → luminance, velocity → hue, flow direction → orientation ! Data courtesy Dr. Jon Blondin, Astrophysics, NCSU Tateosian, Healey, Enns. “Engaging Viewers Through Nonphotorealistic Visualizations,” NPAR 2007, 93–102, 2007.
4. Graphs • Traditional method for comparing data attributes! • E.g., line chart, bar chart, scatterplot, histogram
 • Different graphs are used for different tasks:! • tracking changes (line graph)! • correlations, relationships (X-Y plot)! • correlations, clustering (scatterplot)! • category comparison (bar chart)! • distributions (pie chart)! • uniqueness and overlap (Venn diagram)
 • Graph elements can represent multiple attribute values
5. Line Chart U.S. Unemployment Line Chart ! U3: people without employment, actively searching in the last 4 weeks U6: U3 + people not looking + people who gave up + part-time workers who want full-time jobs
6. Line Chart U.S. Unemployment By Recession Line Chart
7. Stacked Line Chart Stacked time series line chart of baby name popularity from 1900–2003 ! Data Courtesy
8. Bar Chart May 2013 Unemployment By State Bar Chart
9. Pie Chart Exploded 3d pie chart of transportation use categorized by transportation type
10. Pie Chart Pie chart of population by U.S. state
11. Histogram Interval histogram (with overlay for comparison) of pixel brightnesses binned by greyscale
12. Scatterplot Petal Length Petal Length Sepal Width class (Number of sepals on a flower is its merosity)
13. Maps • Traditional method for presenting geographic and geospatial data! • E.g., topographic, planimetric, base
 • Different maps are used for different
 tasks:! • choropleth (charting value by region)! • isarithmic (contour lines)! • proportional symbol (point location data)! • dot (presence or absence of a feature)
 • Maps can be extended to visualize
 multidimensional data Topographic map from Indiana Jones and the Last Crusade
14. Choropleth Map 2013 Percentage of U.S. households in poverty by county
15. Choropleth Map Classification of U.S. counties into twelve “groups” ! Data courtesy
16. Isarithmic Map Thickness of the Earth’s crust, 10km intervals ! Data courtesy
17. Proportional Symbol Map Number of venture capital deals in 2012 ! Data courtesy ghost-towns-of-the-new-economy/309460/
18. Dot Map John Snow’s cholera map ! Data courtesy
19. Route Map Henry Beck’s London Underground map circa 1933 ! Data courtesy
20. Route Map London Underground map circa 2012 ! Data courtesy
21. Napolean’s
 March on Russia Charles Joseph minard, 1869, flow map of napolean’s march on russia
22. “Preattentive” Features • Certain basic visual features are detected by our low-level visual system! • detection is rapid, usually in one “glance” of 100–250 msec! • can determine presence or absence, possibly amount! • unique features can capture our focus of attention
 • Initially proposed as an automatic, bottom-up phenomena! • Treisman’s feature map theory, feature hierarchies
 • Combined bottom-up and top-down models also exist! • Wolfe’s guided search, Huang et. al’s boolean maps
23. Visual Interference • Design, conduct, and analyze target detection experiments! • Study colour: luminance, hue; and texture: size, orientation! • Effectiveness of visual features in isolation, pairwise interference 0.06° 0.1225° 0.245° Visual acuity experiment trials — 0.06°, 0.1225°, 0.245° — with novel target detection hue target size target orientation target
24. • Design, conduct, and analyze target detection experiments! • Study colour: luminance, hue; and texture: size, orientation! • Effectiveness of visual features in isolation, pairwise interference Visual Interference 0.06° 0.1225° 0.245° Visual acuity experiment trials — 0.06°, 0.1225°, 0.245° — with novel target detection hue target size target orientation target w/random hue Healey and Sawant. “On the Limits of Resolution and Visual Angle in Visualization,” ACM TAP 9 (4), 2012.
25. Postattentive Amnesia • Does previewing make us faster?! • Intuition suggests it will! • Extract detail! • Access it rapidly on demand! ! • Experiments show that human
 vision does not work this way! • Vision is not a camera! • Detail is only available at the most
 recent focus of attention priming image
26. Postattentive Amnesia • Does previewing make us faster?! • Intuition suggests it will! • Extract detail! • Access it rapidly on demand! ! • Experiments show that human
 vision does not work this way! • Vision is not a camera! • Detail is only available at the most
 recent focus of attention priming image Purple Tilted Wolfe, Klempen, Dahlen, “Post Attentive Vision,” JEP: HPP 26 (2), 2000.
27. Primed Search Green Vertical
28. Primed Search Green Vertical
29. Primed Search
30. Primed Search White Tilted
31. Find Five Differences
32. Find Five Differences eyes tilted up bee’s stripe colours reversed extra leaf patch on knee extra flower
33. Change Blindness Data courtesy Dr. Ron Rensink, Department of Psychology, UBC
34. Change Blindness
35. Change Blindness Rensink. “Seeing, Sensing, and Scrutinizing,” Vision Research 40, (10-12), 2000.
36. Change Blindness Models • Overwriting! • previous image overwritten! • First impression! • initial view abstracted! • Nothing is stored! • scene abstracted with no detail! • Feature combination! • previous and new views
 combined! • Everything is stored, nothing is compared! • details cannot be accessed without external stimulus Main actor changes across movie cut Simons. “Current Approaches to Change Blindness,” Visual Cognition 7 (1-3), 2000.
37. Determine Arm Length
38. Determine Arm Length
39. Inattentional Blindness grey square in frame not detected during attention-demanding task Mack and Rock. Inattentional Blindness. MIT Press, 2000.
40. Inattentional Blindness • Attention-demanding task renders viewers “blind” to change! • Change is easily seen if scene is viewed without any task
 • Limited “visual attention”! • Classic example, counting basketball passes! • Woman wearing a gorilla suit walks through the scene! • More than half of the viewers do
 not notice the gorilla Superimposed video streams Single video stream Woman with umbrella Woman with umbrella Gorilla Gorilla Simons and Chabris, “Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events,” Perception 28 (9), 1999.
41. Nonphotorealism • Visual system operates in two stages! • orientation: focus of attention snaps to a location in an image! • engagement: visual system chooses to linger at the location, taking in detail
 • Local feature differences orient! • Hypothesize that increased visual aesthetic produces engagement! • Measure engagement using memory for detail indication & detail painting style visual complexity painting style
42. Interpretational Complexity (IC) Painterly Styles Indication & Detail (ID) Visual Complexity (VC) < < Tateosian, Healey, Enns. "Engaging Viewers Through Nonphotorealistic Visualizations,” NPAR 2007, 93–102, 2007.
43. Memorization Observe each of the following images, and commit them to memory
44. Memorization Indication & Detail (ID) Painting Style
45. Memorization
46. Memorization Interpretational Complexity (IC) Painting Style
47. Memorization
48. Memorization Visual Complexity (VC) Painting Style
49. Memorization
50. Memorization Traditional Glyph Visualization
52. Recall For each of the following images, determine whether it matches one of the images you memorized ! Answer “Yes” if it does ! Answer “no” if it does not
53. Recall
54. Recall No
55. Recall
56. Recall Yes
57. Recall
58. Recall Yes
59. Recall
60. Recall No
61. Recall
62. Recall No
63. Recall
64. Recall No
65. Memorization
66. Recall Yes
67. Recall
68. Recall Yes
69. Engagement • Perceived aesthetic can be systematically varied within a visualization! ! • Increasing aesthetic improves memory for detail! • Viewers had recall accuracy of 80% or more, even 24 hours after exposure! ! • Accuracy for most aesthetic visualization no better than for traditional visualization! • Traditional visualizations are seen as aesthetic?! • More detailed models of engagement needed?! • Memory for detail is not an appropriate measure of engagement?
70. Contact Information NC STATE UNIVERSITY healey@ncsu.edu http://www.csc.ncsu.edu/faculty/healey ! ! 
 Special Thanks to: John Blondin (Astrophysics, NCSU)
 James Enns (Psychology, UBC)
 Ron Rensink (CS & Psychology, UBC) ! Geniva Liu, Mark Remple,
 Amit Sawant, Laura Tateosian