Supporting Asynchronous Collaboration for Interactive Visualization by Jeffrey Michael Heer B.S. (University of California, Berkeley) 2001 M.S. (University of California, Berkeley) 2004 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor Maneesh Agrawala, Chair Doctor Stuart K. Card Professor Marti A. Hearst Professor Joseph M. Hellerstein Fall 2008 The dissertation of Jeffrey Michael Heer is approved: _____________________________________________________ Chair Date _____________________________________________________ Date _____________________________________________________ Date _____________________________________________________ Date University of California, Berkeley Fall 2008 Supporting Asynchronous Collaboration for Interactive Visualization Copyright Fall 2008 by Jeffrey Michael Heer 1 ABSTRACT Supporting Asynchronous Collaboration for Interactive Visualization by Jeffrey Michael Heer Doctor of Philosophy in Computer Science University of California, Berkeley Professor Maneesh Agrawala, Chair Interactive visualizations leverage human visual processing to increase the scale of information with which we can effectively work. However, most visualization research to date relies on a single-user model, overlooking the social nature of visual media. Visualizations are used not only to explore and analyze data, but to communicate findings. People may disagree on how to interpret data and contribute contextual knowledge. Furthermore, some data sets are so large that thorough exploration by a single person is unlikely. Such scenarios arise regularly in scientific collaboration, business intelligence, and public data consumption. This thesis recasts interactive visualizations as not just analytic tools, but social spaces supporting collective data analysis. To this aim, I introduce theoretical design considerations guiding the invention of social visual analysis tools and present the design, implementation, and evaluation of interactive systems based on these principles. The first such system is sense.us, a web site supporting asynchronous collaboration across a variety of visualization types. The site supports view sharing, discussion, graphical annotation, and social navigation and includes novel interaction elements. User studies of the system reveal emergent patterns of social data analysis, cycles of 2 observation and hypothesis, and the complementary roles of social navigation and data-driven exploration. Based on design considerations and lessons learned from sense.us, this dissertation also introduces new techniques to support collaborative interaction around visualizations. The scented widgets system embeds visualizations of social activity in common user interface controls to enhance collective information foraging. A generalized selection framework represents collaborative annotations as declarative queries over visualized data, enabling annotation of dynamic data across multiple visualization views. Interactive query relaxation enables users to further generalize selections along data dimensions of interest. New graphical histories for visualization support analysis and accelerate collaborative sharing of findings, and a framework for animated transitions better communicates the relationship between views in an analysis session. As evidenced in a series of evaluative studies, these components enable teams to collaborate more effectively as they conduct visual data analysis. _____________________________________________________________ Professor Maneesh Agrawala Dissertation Chair i For my parents, Michael and Star Heer ii Table of Contents 1 Introduction 1 1.1 Thesis Problem and Approach 3 1.2 Thesis Contributions 4 1.3 Thesis Outline 5 1.3.1 Principles and Systems for Collaborative Visual Analysis 6 1.3.2 Interface Techniques supporting Social Data Analysis 6 2 Related Work 8 2.1 Information Visualization 8 2.1.1 Graphical Perception 9 2.1.2 Interaction and Sensemaking 10 2.2 Social Software and Collaborative Visualization 11 2.2.1 Collaborative and Distributed Visualization 12 2.2.2 Asynchronous Collaboration with Visualizations 13 3 Design Considerations for Collaborative Visual Analytics 16 3.1 Division and Allocation of Work 17 3.1.1 The Information Visualization Reference Model 18 3.1.2 The Sensemaking Model 19 3.2 Common Ground and Awareness 23 3.2.1 Discussion Models 24 3.2.2 Activity Awareness 25 3.2.3 Social Navigation 25 3.3 Reference and Deixis 26 3.3.1 Graphical Annotation 27 iii 3.3.2 Brushing and Dynamic Queries 27 3.3.3 Ambiguity of Reference 28 3.4 Incentives and Engagement 29 3.4.1 Personal Relevance 29 3.4.2 Social-Psychological Incentives 30 3.4.3 Game Play 30 3.5 Identity, Trust, and Reputation 31 3.5.1 Identity Presentation 31 3.5.2 Reputation Formation 32 3.6 Group Dynamics 33 3.6.1 Group Management 33 3.6.2 Group Size 34 3.6.3 Group Diversity 35 3.7 Consensus and Decision Making 35 3.7.1 Consensus and Discussion 36 3.7.2 Information Distribution 37 3.7.3 Presentation and Story-Telling 37 3.8 Conclusion and Future Directions 38 4 sense.us: A Web Application for Collaborative Visual Analysis 41 4.1 sense.us: A Site for Collaborative Visual Analysis 41 4.1.1 View Sharing 44 4.1.2 Doubly-Linked Discussion 44 4.1.3 Pointing via Graphical Annotation 45 4.1.4 Collecting and Linking Views 46 4.1.5 Awareness and Social Navigation 47 4.1.6 Unobtrusive Collaboration 48 4.2 Implementation Notes 48 4.2.1 Application Bookmarking 48 4.2.2 Doubly-Linked Discussions 49 4.2.3 Graphical Annotation 49 4.3 Usage Observation of sense.us 50 4.3.1 Commentary: Observation, Question, and Hypothesis 52 4.3.2 Graphical Annotation: Pointing and Play 58 4.3.3 Visitation and Navigation: Voyagers and Voyeurs 59 4.3.4 Doubly-Linked Discussions 60 iv 4.3.5 User Experience 61 4.4 Discussion 61 4.4.1 Embedded Social Navigation Cues 62 4.4.2 Enhanced Commentary and Navigation Features 62 4.4.3 Annotations 63 4.4.4 Bookmark Trails and Story-Telling 63 4.5 Summary 64 5 Scented Widgets: Improving Navigation Cues with Embedded Visualizations 65 5.1 Related Work on Navigation Cues 67 5.2 Design Considerations for Scented Widgets 69 5.2.1 Information Scent Metrics 69 5.2.2 Navigation and the Display of Visual Scent 70 5.2.3 Visual Encodings 70 5.2.4 Modifying Widgets 71 5.2.5 Design Guidelines and Feature Requirements 71 5.3 Implementation of Scented Widgets 73 5.3.1 Rendering and Interaction 42 5.3.2 Scent Configuration and Widget Groups 75 5.3.3 Data Management 75 5.3.4 Usage Example 76 5.4 Applications 78 5.4.1 HomeFinder with Histogram Sliders 78 5.4.2 Collaborative Authoring with Activity Indicators 78 5.4.3 Social Data Analysis with Social Navigation 80 5.5 Evaluation of Social Navigation Cues 81 5.5.1 Experiment Design 81 5.5.2 Results: Revisitation 82 5.5.3 Results: Unique Discoveries 83 5.5.4 Results: User Preferences 85 5.5.5 Discussion 86 5.6 Future Work 87 5.7 Conclusion 88 v 6 Generalized Selection via Interactive Query Relaxation 89 6.1 Related Work on Reference and Interactive Querying 91 6.1.1 Selection Techniques and Reference 91 6.1.2 Dynamic Queries, Brushing, and Linking 92 6.1.3 Query Relaxation 93 6.2 Example: Information Visualization 94 6.2.1 Basic Brushing and Selection 96 6.2.2 Selection Reuse 96 6.2.3 Data-Aware Annotation 98 6.2.4 Query Relaxation: Generalizing to Related Selections 98 6.2.5 Alternate Output Modalities 100 6.3 Example: Vector Graphics Editor 100 6.4 Implementation of Generalized Selection 101 6.4.1 Initialization 102 6.4.2 Query Generation 102 6.4.3 Query Visualization 103 6.4.4 Query Relaxation 103 6.4.5 Query Reuse 107 6.5 Evaluation 108 6.5.1 Methods 108 6.5.2 Results 109 6.6 Discussion and Summary 112 7 Graphical Histories for Visual Analysis 114 7.1 Design Space Analysis of Interaction Histories 116 7.1.1 History Models 116 7.1.2 Visual Representation of History 119 7.1.3 Operations on History 120 7.1.4 Design Space Summary 121 7.2 Graphical History in Tableau: A Case Study 122 7.2.1 The Tableau Visual Analysis System 122 7.2.2 A Re-designed History Model 123 7.2.3 Visual Design of the History Interface 124 7.2.4 Navigating and Managing History 127 7.2.5 Operating on History 130 vi 7.3 Using History to Evaluate Visualization Design 131 7.3.1 Analyzing Individual Usage with Behavior Graphs 132 7.3.2 Analyzing Aggregate Usage 134 7.3.3 Findings 134 7.4 Summary and Future Work 136 8 Animated Transitions in Statistical Data Graphics 138 8.1 Animation: A Double-Edged Sword 139 8.1.1 Principles for Animation Design 140 8.1.2 Animation in Information Visualization 141 8.2 Transitions between Statistical Data Graphics 142 8.2.1 A Taxonomy of Transition Types 143 8.2.2 Design Considerations for Animated Transitions 145 8.3 Animated Data Graphics in DynaVis 147 8.3.1 Implementation Notes 150 8.4 Evaluation of Animated Transitions 152 8.4.1 Experiment 1: Object Tracking 152 8.4.2 Experiment 2: Estimating Changing Values 155 8.4.3 Subjective Preferences 158 8.5 Discussion 158 8.5.1 Animation Improves Graphical Perception 158 8.5.2 Trade-Offs Between Design Principles 159 8.5.3 The Case for Staging 160 8.5.4 The Effects of Axis Rescaling: Avoid If Possible 160 8.5.5 The Intricacies of the Donut: Smaller Wedges Are Better? 160 8.6 Summary 161 9 Conclusion 163 9.1 Review of Thesis Contributions 163 9.2 Recent Developments 165 9.3 Limitations and Future Work 166 9.3.1 Synthesizing Collaborative Contributions 167 9.3.2 Pointing, Naming, and Reference 167 9.3.3 Computation as Collaborator 168 9.3.4 Supporting the Information Life-Cycle 168 vii 9.3.5 Evaluating Social Data Analysis 169 9.3.6 Social Data Analysis Applications 170 9.4 Closing Remarks 171 Bibliography 172 viii List of Figures Figure 2.1 Space-time matrix for classifying collaborative applications.