Communicating with Motion: A Design Space for Animated Visual Narratives in Data Videos

Shi, Yang; Lan, Xingyu; Li, Jingwen; Li, Zhaorui; Cao, Nan

doi:10.1145/3411764.3445337

Cited by 30 publications

(22 citation statements)

References 29 publications

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“…The first source was the lists from previous studies [4,24,26,34] in narrative visualizations that collected high-quality data videos. The second source was popular video platforms such as YouTube [51], Vimeo [48], Douyin [38], and Tencent Video [42].…”

Section: Methodsmentioning

confidence: 99%

“…By analyzing 70 data videos, Amini et al [4] concluded eight common animation types and found that the setup animation that gradually showed the components (e.g., x-axis, y-axis, visual marks) of a visualization could increase viewers' engagement [3]. Recently, by analyzing 82 data videos, Shi et al [26] proposed a taxonomy of 43 animation techniques that serve for different visual narrative strategies (i.e., Emphasis, Suspense, Comparison, Cohering, Ellipsis, Focalization, Concretization, and Twist). In addition to deepening our knowledge of animation designs in data videos, previous work provided higher-level design guidelines.…”

Section: Narrative Visualization and Data Videosmentioning

confidence: 99%

“…Previous data video research primarily focused on the design of narrative structures [2,34], animations, and transition techniques [26,30,31]. To fill this gap, we draw inspirations from visual expressions and styles from great opening sequences of films and derive design guidance for creating attractive data video openings.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

From `Wow' to `Why': Guidelines for Creating the Opening of a Data Video with Cinematic Styles

Xu,

Yang,

Yip

et al. 2022

Preprint

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Data videos are an increasingly popular storytelling form. The opening of a data video critically influences its success as the opening either attracts the audience to continue watching or bores them to abandon watching. However, little is known about how to create an attractive opening. We draw inspiration from the openings of famous films to facilitate designing data video openings. First, by analyzing over 200 films from several sources, we derived six primary cinematic opening styles adaptable to data videos. Then, we consulted eight experts from the film industry to formulate 28 guidelines. To validate the usability and effectiveness of the guidelines, we asked participants to create data video openings with and without the guidelines, which were then evaluated by experts and the general public. Results showed that the openings designed with the guidelines were perceived to be more attractive, and the guidelines were praised for clarity and inspiration.CCS Concepts: • Human-centered computing → Empirical studies in visualization.

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Section: Methodsmentioning

confidence: 99%

Section: Narrative Visualization and Data Videosmentioning

confidence: 99%

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From `Wow' to `Why': Guidelines for Creating the Opening of a Data Video with Cinematic Styles

Xu,

Yang,

Yip

et al. 2022

Preprint

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“…Visual narratives in data videos are usually performed using animation.Animation can represent temporal changes and enhance the comprehension and user engagement of data stories [109]. Shi et al [114] analyzed 43 animation techniques from a narrative visualization perspective, categorized eight narrative strategies, and constructed a design space that systematically describes these patterns. This design space can be used to reduce barriers to producing data videos and stimulate audience creativity in designing expressive data videos.…”

Section: Data Videomentioning

confidence: 99%

How Does Automation Shape the Process of Narrative Visualization: A Survey on Tools

Chen¹,

Cao²,

Wang³

et al. 2022

Preprint

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In recent years, narrative visualization has gained a lot of attention. Researchers have proposed different design spaces for various narrative visualization types and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, more and more tools have been designed and developed. In this paper, we surveyed 122 papers and tools to study how automation can progressively engage in the visualization design and narrative process. By investigating the narrative strengths and the drawing efforts of various visualizations, we created a two-dimensional coordinate to map different visualization types. Our resulting taxonomy is organized by the seven types of narrative visualization on the +x-axis of the coordinate and the four automation levels (i.e., design space, authoring tool, AI-supported tool, and AI-generator tool) we identified from the collected work. The taxonomy aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.

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“…To facilitate the creation of data stories, during the past decades, theories, techniques, and tools have been extensively studied and developed. For example, a series of design spaces have been proposed from two major aspects: the narrative structures [25,60] and the visual representation methods [18,24,31,40,[44][45][46], while taking communication goals and tasks into consideration. At the same time, to lower the technical barriers, a number of interactive authoring tools that integrate advanced data analysis and visualization functionalities have been developed [8,27,53,55].…”

Section: Introductionmentioning

confidence: 99%

Erato: Cooperative Data Story Editing via Fact Interpolation

Sun

Cai

Cui

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. A data story about natural disasters represented in form of a storyline that was authored by a professional data analyst. The data facts in black (a, c, e, f) were created by the analyst as keyframes of the story, while the facts in red (b, d) were generated based on our interpolation algorithm. The algorithm searches through the fact space to find data facts that best fill the content gap between two keyframes. The story first illustrates an overall situation of global natural disasters (a-c) and gradually focuses on the situation in China (d,e). Finally, it reveals that floods have the most pernicious impact on China (f). The corresponding interpolation process is also shown under the data story. The searching path is marked in red and the nodes with yellow borders are the final selected interpolation results.

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Communicating with Motion: A Design Space for Animated Visual Narratives in Data Videos

Cited by 30 publications

References 29 publications

From `Wow' to `Why': Guidelines for Creating the Opening of a Data Video with Cinematic Styles

From `Wow' to `Why': Guidelines for Creating the Opening of a Data Video with Cinematic Styles

How Does Automation Shape the Process of Narrative Visualization: A Survey on Tools

Erato: Cooperative Data Story Editing via Fact Interpolation

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