동적 데이터 시각화에 대한 상호 작용 및 애니메이션의 시너지 효과 탐구에 관한 연구 : 정보 전송 및 사용자 경험에 미치는 영향을 중심으로 = Exploring the Synergy of Interaction and Animation for Dynamic Data Visualization : Focusing on the Impact on Information Transmission and User Experience

목차 (Table of Contents)

• Chapter 1 Introduction 2
• 1.1 Research Background 2
• 1.1.1 Evolution and significance of data visualization 2
• 1.1.2 Casual users and data visualization application areas 2
• 1.1.3 Development and definition of dynamic data visualization 4
• 1.2 Research Background 5
• 1.2.1 Research Gap 6
• 1.2.2 Research purpose 6
• 1.3 Research Content and Framework 7
• 1.3.1 Research Content 7
• 1.3.2 Research Framework 8
• Chapter 2 Literature Review 11
• 2.1 Dynamic Data Visualization 11
• 2.1.1 Dynamic data visualization of data availability dimensions 11
• 2.1.2 Dynamic data visualization from temporal dimension 11
• 2.1.3 Consistency principle in dynamic data visualization 13
• 2.2 Casual visualization and casual users 13
• 2.2.1 Needs and opportunities 13
• 2.2.2 Possible challenges and constraints 14
• 2.3 Interaction in visualization 14
• 2.3.1 Interactivity in the context of visualization 15
• 2.3.2 The positive effects of interactivity in visualization 15
• 2.3.3 Empowerment and cognitive challenges for users 16
• 2.4 Animation in visualization 17
• 2.4.1 Animation in the visualization context 17
• 2.4.2 The positive effects of animation in visualization 17
• 2.5 Synergistic effects of interaction and animation in visualization 19
• 2.5.1 Interaction and animation in dynamic data visualization 19
• 2.5.2 The positive impact and application of interaction and animation in dynamic data visualization 21
• 2.5.3 Uncharted territories and emerging challenges 22
• Chapter 3 Redefinition of interaction and animation and data visualization content style categorization 25
• 3.1 Literature retrieval and analysis about animation in data visualization 25
• 3.1.1Literature retrieval procedure 25
• 3.1.2 Content analysis 26
• 3.2 Structured dimensions and process model of animation in data visualization 35
• 3.2.1 The dimensions and values extracted from contents analysis 35
• 3.2.2 Process model of animation in data visualization 42
• 3.3 Redefination about animation and intensity division 44
• 3.3.1 Redefination about animation based on contents relevance and visual richness 44
• 3.3.2 Intensity division of animation in data visualization 45
• 3.4 Redefination about interaction and level 47
• 3.4.1 Redefination about interaction based on contents control 47
• 3.4.2 Interaction level division in data visualization 47
• 3.5 Categoriation of content style for data visualization 49
• 3.5.1 Four data visualization content style types 49
• 3.5.2 Interactions and animations in the four content style categories 51
• Chapter 4 User perceptions and attitudes towards interactivity in dynamic data visualization 56
• 4.1 User perception and cognition in dynamic data visualization 56
• 4.1.1 The Role of Interactivity in Effective Data Visualization 56
• 4.1.2 User perception and cognition related to interactivity 57
• 4.2 Model and Hypothesis Generation 59
• 4.2.1 Related variables and concepts 59
• 4.2.2 Enjoyment 61
• 4.2.3 Engagement 64
• 4.2.4 Enjoyment and Engagement 65
• 4.3 Experimental design and methodology 66
• 4.3.1 Participants 66
• 4.3.2 Task Design and Treatments 67
• 4.3.3 Stimuli 68
• 4.3.4 Measures 70
• 4.4 Path analysis and mediated effects tests 70
• 4.4.1 Manipulation Test 70
• 4.4.2 Measurement Validation 71
• 4.4.3 Hypothesis Test 72
• 4.5 ANOVA analysis and coding analysis 74
• 4.5.1 ANOVA analysis between three groups 74
• 4.5.2 Coding analysis 75
• Chapter 5 Synergistic effects of interaction and animation in dynamic data visualization 80
• 5.1 Variables and method 80
• 5.1.1 Independent variables and research method 80
• 5.1.2 Dependent variables 81
• 5.2 Study design 84
• 5.2.1 Different levels of interaction and animation in visualization 84
• 5.2.2 Stimuli 86
• 5.2.3 Measures 91
• 5.2.4 Pariticipants 92
• 5.3 Experimental results and analysis 92
• 5.3.1 ANOVA analysis between four conditions 92
• 5.3.2 Analysis and insights of four conditions 94
• 5.4 Recommendations for the applicability of the four combinations 100
• Chapter 6 Toolkit and reference framework for four data visualization content style 105
• 6.1 Case analysis 105
• 6.1.1 Ailing Brussels 105
• 6.1.2 Top Movie Directors by Film 107
• 6.1.3 Largest European Cities in History 7500 BC - 2020 109
• 6.1.4 Tracking Heat Across the World 110
• 6.2 Study design Toolkit and template for four content style and conditions 112
• 6.2.1 Toolkit and template for multimedia visual experience 112
• 6.2.2 Toolkit and template for annotated charts 118
• 6.2.3 Toolkit and template for data videos 122
• 6.2.4 Toolkit and template for magazine style 124
• 6.3 Reference framework for future data visualization design 128
• Chapter 7 Conclusion 131
• 7.1 Research Conclusion 131
• 7.1.1 Key Findings 131
• 7.1.2 Implications and contributions 134
• 7.2 Research Limitation and Future Work 136
• 7.2.1 Limitation 136
• 7.2.2 Future Research 138
• - 참고 문헌 - 139
• ▌국문 초록 ▌ 151