Joy Mountford at BayCHI: Visualizations of Our Collective Lives

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The lines between art, design, and information are dissolving as we experience new places and objects. Consider, for example, the organic flow of air traffic over North America at daybreak, the bursts of search query memes spreading around the globe, and the pointillist surge of mobile phone usage on New Year's Eve. Using the new techniques of generative data visualization, a new generation of artist/designers/engineer/scientists are creating gorgeous, dynamic experiences driven by massive sets of data about our own lives. Their work comes to life in architectural spaces, on walls of wood and metal and light and shimmering glass clouds suspended overhead. Of course it must be touched to be appreciated and engaged with, simple gestures launch a thousand images and possibilities. Many of these projects have received international recognition. They are primarily 3D applications that can run in real time, but really can only be appreciated by watching them, as movies. These data movies aim to make information easier to understand while being enjoyable to watch. Surprising insights surface through looking at our 'data life' in new ways, and may compel us to design in different, even better ways.

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Joy Mountford at BayCHI: Visualizations of Our Collective Lives

  1. 1. 1
  2. 2. Outline • Background (Joy Mountford) • Data Visualisation (Java Processing movies of working applications) • Ubiquity is here (where is the interface?)
  3. 3. About Joy... • Current: Osher Fellow at Exploratorium • Recent :Yahoo,VP of Design Innovation and UED • Properties: Y! Mail, Messenger, Front Page, My Yahoo, Groups, Photos • 21 years of international University Design Expo: touched 4000 students world-wide
  4. 4. Honeywell - An interface (Dreyfus)
  5. 5. Space Shuttle (1990s)
  6. 6. Featuritis leads to poor consumer use (still!) 6
  7. 7. Apple: QuickTime controller (1988) Designing Interaction Bill Modridge (06)
  8. 8. Lego Music Composer ’03
  9. 9. 9
  10. 10. LinkMark 00 10
  11. 11. Yahoo front page 2005
  12. 12. Personal Information Manager 2006 13
  13. 13. Y! personal information manager 14
  14. 14. Y local offers relevant information 15
  15. 15. Opportunity.... • We can start ‘understanding’ real time data to show what ‘we’ do/are • Opportunity now is huge to actually use information data corporations gather • Uses are both consumer facing, and for internal diagnostics, and to deliver useful content and personalised services
  16. 16. McKinsey report 2009 • In next decades the ability to take data, to be able to understand it, to process it, extract value, to visualise and communication it, is going to be an important skill in next decade. • Not just at professional level, but all levels • We have essentially free and ubiquitous data • Scarce resource is ability to understand and extract value
  17. 17. Statistics and graphs • Long history of graphical communication • “How to lie with statistics” etc.... • Selection of data parameters/styles depends on task and who is asking for the answer • Understanding of data displays is very individual • What is a billion? (US/UK differ)
  18. 18. Data Numbers/Freq Visual Format Data Visual Presentation User Raw Data Streams Style form Task Data Visual View Transformations Encodings Transformations
  19. 19. Accuracy of visual techniques Position Slope Angle Area Volume Colour
  20. 20. Moore’s Law ‘65
  21. 21. Sun position over time 1626
  22. 22. Belousav-Z reaction over time
  23. 23. 1856: Mortality causes: Florence Nightingale
  24. 24. XBox Tableau tool (MS)
  25. 25. Cone Tree (Xerox)
  26. 26. Sense.us work force professions Flouride starts around here? Dentistry 1880 1965 2000
  27. 27. Consumers Q + comment
  28. 28. Group comments collected
  29. 29. Two styles data (Wattenberg) • Voyager - focuses on visualised data. • Actively involved with data to understand • Voyeur - focuses on comment listings • Investigates others explorations
  30. 30. Sense.us: % Dentists and technicians Dentist 1850 1940 2000
  31. 31. New England report • Framingham study data, 1948 • 1948 10% obese • 1985 18% obese • 2009 40% obese • Fast food started and then network took over • Spouse obese increased risk by 37% • Friend obese increased risk by 171%
  32. 32. Understanding Data • Attraction of charts starts initial interest, but aim to increase consumer engagement and exploration • Goal to build user interfaces to support and encourage involvement • Visual sense making can be social - as well as collaborative
  33. 33. Exploratorium SF 36
  34. 34. Slime on peat: diatom 5,000 magnification
  35. 35. Fungal arms: 500 magnification
  36. 36. Book source: debate over techniques
  37. 37. Chris Jordan • 32,000 Barbies 60x80 inch • Number of elective breast augmentation • Performed monthly in US 2006 • Graduation gift
  38. 38. Art can Educate • Different visual style hepls understanding • Different visual styles appeal to folks • Accidental discovery has big impact
  39. 39. Design Innovation Y! team Ben Clemens Aaron Koblin Michael Chang Doug Fritz S. Joy Mountford
  40. 40. Ben Clemens, Aaron Koblin, Michael Chang
  41. 41. Approach for Data Viz • Data can attract • Data can be ‘useful’ • Data can now be real time ‘dynamic’ • Put data around people to change POV • Present in various forms
  42. 42. US flight paths (Koblin)
  43. 43. Y! Answers • Use community to answer Qs • Created vibrant on-line community pulse of ‘what is happening’ • System created for accreditation of quality answers • Potential for bloggers
  44. 44. Y!Answers cloud (Chang)
  45. 45. Traffic • Use to replan your driving routes • Use to dispatch ambulances etc • Use to architect plan cities
  46. 46. Y! Local (Koblin)
  47. 47. J.Yamashita ‘07
  48. 48. Personalized DNA Art
  49. 49. Multi touch Table (MOTO)
  50. 50. FlickR • Built custom multi-user multi-touch surface • Gesture connects us to the data • GeoTagged photos showing what else is happening live
  51. 51. FlickR: multi-user touch table (Chang 07)
  52. 52. SMS traffic New Years (NL) Koblin ’08
  53. 53. Mail activity • 2 hour increments of world-wide market traffic • Shows relative sizes very clearly • Showed Ham and Spam • Located a server data loss from a pulse • Aggregate view shows things otherwise ‘lost’
  54. 54. •Y
  55. 55. NYTE data • Globe encounters show volumes of internet data between NY and world over 24 hours • Real time continuous update • Larger glow implies greater IP flow • Collaboration: Sensible cities MIT, ATT ,Y! • MOMA New York show of live data, Feb 08
  56. 56. Search query bursts • Active key word searches (rate of change) • Use reverse IP look up for geo location-data address locations • Activity plotted as a particle system • Time-lapse queries from geo-locations plotted • Feasible in ‘real time’ for particular users’ interests
  57. 57. Y! search box
  58. 58. Real time editorial ‘clicks’ on
  59. 59. Data information • Search moves to discovery (decision) • Opportunity now to use real time data • Internet business is about personal delivery of useful, relevant content/ads dynamically to consumers
  60. 60. Ambient Interfaces
  61. 61. Internet Archive (Kaehle) • Machine readable versions of all out of print books - free • Tool for librarians, museums and consumers • Bookscape for image browsing • Dynamic resampling of image data inside 1 zoomable space
  62. 62. 3D interactive Internet Archive bookscape browser (Chang)
  63. 63. Multi-user multi-touch found poetry
  64. 64. Personal tools • Tagging of limited use with large info sets • Find all related information • Search anonymously across those with similar habits/interests • Surface want I might want
  65. 65. Clustering on content (D. Fritz ’08)
  66. 66. Technical approach • Tag co-occurrence defines distance algorithm • Hierarchical clustering algorithm defines dendrogram • Recursively search dendrogram to find clusters of optimal cohesion, while selecting a meaningful human size • Hierarchical clustering slower, but gives better results with smaller data sets
  67. 67. Y!Haus lessons from Data Viz • Information can be provided in ambient personal widgets • Displays can be continuous, both in foreground as well as background • Animation gets initial interest, but goal is to encourage understanding
  68. 68. Crowd potential • Crowd sourcing powerful - not directly social • Isolated but part of an anonymous group • Little payment or reinforcement • People want to contribute to the whole: with and without anonymity • Used Mechanical Turk (Amazon)
  69. 69. Draw sheep facing left for .02 cents 40 days for 10000 sheep (Koblin)
  70. 70. 10,000 sheep on poster
  71. 71. 10,000 Cents (Koblin) created unique $100 bills
  72. 72. Consumers • People want to extend their fame • Hunger to understand themselves relative to others • Some happy to participate anonymously, others publically • All of us becoming curators of own presence(s)
  73. 73. What and where is the interface • Ubiquitous computing is here • Interface transparency arrived • Art and maker community is forging ahead • Technologists need to create safe ways of convincing people to allow access to their personal data for benefit • Personal need for selective cloaking
  74. 74. Examples of user ‘inclusion’ • Unaware • Partially shown with no choice • Shown with choice • Unclear effects • Confusion
  75. 75. eCloud San Jose airport (Koblin) Text
  76. 76. 95
  77. 77. Screens at IAC • 6-8 15ft high wall interactive displays • NYU ITP graduate play space, some curated • IAC headquarters, New York • Web properties and media and newspaper taking lead with new media
  78. 78. Moeller San Jose airport ’10: pins on sheets over parking lot 99
  79. 79. 103
  80. 80. 105
  81. 81. 106
  82. 82. Fire Fly Dress (Orth)
  83. 83. Music Perfume bottles (MIT ‘01)
  84. 84. NYU: moving brooch
  85. 85. NYU worn printer
  86. 86. CCA: Light sensors cooked into lollipops
  87. 87. CCA: Real grass moves video: grown in sensor filaments Text
  88. 88. Bar scene
  89. 89. IR light color changing Coke bottles
  90. 90. • Good Guide (mobile) • iPhone app • 70000 products • Health, enviornmental, social • Text message – UPC, Product name, type 117
  91. 91. USC Cinematic Arts: Million story building
  92. 92. BarCode Stories (UCLA) 119
  93. 93. Barcode phone access (UCLA)
  94. 94. EKG Ball games 121
  95. 95. Mattel MindFlex 09 122
  96. 96. 124
  97. 97. Sensors everywhere
  98. 98. User confusion • How do I know what is being sensed? • How do I know what I can use? • How do I know what I am ‘in’ or ‘part of?’ • How do I stop being part of it? sometimes? • Privacy crucial • Cloak/veils to hide
  99. 99. Cloaks or veils? Privacy Partially ‘available’
  100. 100. We are all in the Design • Interfaces are transparent and everywhere in the physical world • We are our own curators and reporters • Data is pervasive, but difficult to know how best to enhance understanding • Issue: how do I know what I am part of, or not part of, and then stop being part of it?
  101. 101. Reality? = Vegas 130

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