Collaborative Data Analysis on Wall Displays

Transcription

1 Collaborative Data Analysis on Wall Displays Challenges for Visualization Petra Isenberg Anastasia Bezerianos

2 2 [source: The Diverse and Exploding Digital Universe, IDC, 2008] [credit: Did You Know; Fisch, McLeod, Brenman]

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4 The value of data depends on our ability to extract meaning and act on it how can we effectively access data? - understand its structure and content? - make comparisons? - make decisions? - communicate to others? 4

5 Need for visualization graphs / hierarchies charts maps

6 Traditional Information Visualization Context work context specific questions / tasks domain-specific PC based single-person use

7 Traditional context no longer sufficient More data to analyze & understand Complex problems Decisions depend on data Frequent requirement: Team work

8 Why Collaborative Work? Go beyond data extraction Discuss, negotiate, argue interpretations of data Reduce individual bias Share task load

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11 Research Space Technique Application Evaluation Theory Representations for small / large displays Interaction techniques Social network analysis Search Biology Contextual overview Methodologies Collaborative data analysis work Perception

12 Research Space Technique Application Evaluation Theory Representations for small / large displays Interaction techniques Social network analysis Search Biology Contextual overview Methodologies Collaborative data analysis work Perception

13 Specific Research Challenges in Collaborative Information Visualization Challenging Aspect Challenge Isenberg et al., Information Visualization Journal, 2011 Socio - Technical

14 Specific Research Challenges in Collaborative Information Visualization Challenging Aspect Users Challenge Multiple backgrounds, work styles, preferences, Isenberg et al., Information Visualization Journal, 2011 Socio - Technical

15 Specific Research Challenges in Collaborative Information Visualization Challenging Aspect Users Tasks Challenge Multiple backgrounds, work styles, preferences, Centered around collaborative work Isenberg et al., Information Visualization Journal, 2011 Socio - Technical

16 Specific Research Challenges in Collaborative Information Visualization Challenging Aspect Users Tasks Cognition Challenge Multiple backgrounds, work styles, preferences, Centered around collaborative work Collaborative foraging & sensemaking Isenberg et al., Information Visualization Journal, 2011 Socio - Technical

17 Specific Research Challenges in Collaborative Information Visualization Challenging Aspect Users Tasks Cognition Analysis Results Challenge Multiple backgrounds, work styles, preferences, Centered around collaborative work Collaborative foraging & sensemaking Consensus, shared insight Isenberg et al., Information Visualization Journal, 2011 Socio - Technical

18 Specific Research Challenges in Collaborative Information Visualization Challenging Aspect Users Tasks Cognition Analysis Results Evaluation Challenge Multiple backgrounds, work styles, preferences, Centered around collaborative work Collaborative foraging & sensemaking Consensus, shared insight Social interaction around data Isenberg et al., Information Visualization Journal, 2011 Socio - Technical

19 Specific Research Challenges in Collaborative Information Visualization Challenging Aspect Users Tasks Cognition Analysis Results Evaluation Interaction Challenge Multiple backgrounds, work styles, preferences, Centered around collaborative work Collaborative foraging & sensemaking Consensus, shared insight Social interaction around data Multiple parallel inputs Isenberg et al., Information Visualization Journal, 2011 Socio - Technical

20 Specific Research Challenges in Collaborative Information Visualization Challenging Aspect Users Tasks Cognition Analysis Results Evaluation Interaction Visual Representations Challenge Multiple backgrounds, work styles, preferences, Centered around collaborative work Collaborative foraging & sensemaking Consensus, shared insight Social interaction around data Multiple parallel inputs Multiple displays, novel display & input technology Isenberg et al., Information Visualization Journal, 2011 Socio - Technical

21 Perception Problems COLLABORATIVE WORK ON WALL- SIZED DISPLAYS

22 INRIA-WILD Large Display Wall size: resolution: 5.5 x 1.8 m (32 LCD screens, 16 machines) x 6400 pixels (130 million px)

23 In Collaboration: Various Viewing Distances and Angles

24 Viewpoints distort perception of visual variables

25 Viewpoints distort perception of visual variables

26 Video

27 Reading visualizations where to put information? task dependent general guidelines 27

28 Reading visualizations where to put information? all areas created equal? viewing angles/distances? what about walking? 28

29 Quantitative Experiment Goal: understand influence of view distortion on perception in elemental graphical tasks Method: magnitude production experiment Results: design recommendations

30 length 2d visualizations and visual variables Series 1 Series 2 Series Category 1 Category 2 Category 3 Category 4 position

31 angle 2d visualizations and visual variables Series 1 Category 1 Category 2 Category 3 Category 4 color hue

32 Perception of Elemental Graphical Tasks William S. Cleveland 1980s

33 Magnitude production Factor elemental graphical tasks: area, length, angle Factor size: 6 different sizes

34 Factor viewing distance and screen location

35 Concept: Visual Angle and Distance Visual Angle decreases from left to right and when stepping back

36 study setup 36

37 static position results Mean AbsError (%) Mean Time (msec) 37

38 static position results (estimation) Close Viewing Distance

39 study 1: modeling data linear model true object size screen horizontal position screen vertical position viewer distance but lower screens different Sig. Correlation Linear Regression, adj R-square >

40 result summary > > unexpected order and behavior previous work > > why low error? why high error? bezels, task orientation

41 moving results Mean AbsError (%) Mean Time (msec) 41

42 study 2 replicated experiment with free movement 9 users 6 magnitudes Time & Estimation Errors (absolute and direction) 42

43 moving results observed 3 strategies not all of them effective Step-back as bad as Close 43

44 Summary new findings avoid comparisons with lower screens careful with full screen visualizations choose robust visual variables inform viewers of distortion use mediators encourage walking, with correct strategy bezels could be beneficial 44

45 example WorldBank, 2011 energy consumption 45

46 example municipal data of employees working and living at different municipalities data: US Bureau of the Census vis: 46

47 example 47

48 conclusion current work test more visual variables and variations model errors for entire visualizations study distortion learning study effect on collaboration 48

49 Collaborative Data Analysis on Wall Displays Challenges for Visualization Petra Isenberg Anastasia Bezerianos