Session:「Storytelling and Presentation with Visualization」

Design Patterns for Data Comics

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3173612

論文アブストラクト: Data comics for data-driven storytelling are inspired by the visual language of comics and aim to communicate insights in data through visualizations. While comics are widely known, few examples of data comics exist and there has not been any structured analysis nor guidance for their creation. We introduce data-comic design-patterns, each describing a set of panels with a specific narrative purpose, that allow for rapid storyboarding of data comics while showcasing their expressive potential. Our patterns are derived from i) analyzing common patterns in infographics, datavideos, and existing data comics, ii) our experiences creating data comics for different scenarios. Our patterns demonstrate how data comics allow an author to combine the best of both worlds: spatial layout and overview from infographics as well as linearity and narration from videos and presentations.

日本語のまとめ:

データコミックの絵コンテや表現力と可能性の発展を支援するデザインパターンを実証するためにワークショップを開き、以下の項目の調査を行った。データコミックはコミュニケーションの必要性を教える魅力的な方法であることがわかった。

What's the Difference?: Evaluating Variations of Multi-Series Bar Charts for Visual Comparison Tasks

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3173878

論文アブストラクト: An increasingly common approach to data analysis involves using information dashboards to visually compare changing data. However, layout constraints coupled with varying levels of visualization literacy among dashboard users make facilitating visual comparison in dashboards a challenging task. In this paper, we evaluate variants of bar charts, one of the most prevalent class of charts used in dashboards. We report an online experiment (N = 74) conducted to evaluate four alternative designs: 1) grouped bar chart, 2) grouped bar chart with difference overlays, 3) bar chart with difference overlays, and 4) difference bar chart. Results show that charts with difference overlays facilitate a wider range of comparison tasks while performing comparably to charts without them on individual tasks. Finally, we discuss the implications of our findings, with a focus on supporting visual comparison in dashboards.

日本語のまとめ:

比較作業を容易にする可能性に関して、複数タイプの棒グラフ4つの変異を評価した研究を提示している。個々のタスクで異なるオーバレイを持つチャートが、チャートを持たないチャートと比較してより広い範囲の比較タスクを容易にすることがわかった。

Frames and Slants in Titles of Visualizations on Controversial Topics

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3174012

論文アブストラクト: Slanted framing in news article titles induce bias and influence recall. While recent studies found that viewers focus extensively on titles when reading visualizations, the impact of titles in visualization remains underexplored. We study frames in visualization titles, and how the slanted framing of titles and the viewer's pre-existing attitude impact recall, perception of bias, and change of attitude. When asked to compose visualization titles, people used five existing news frames, an open-ended frame, and a statistics frame. We found that the slant of the title influenced the perceived main message of a visualization, with viewers deriving opposing messages from the same visualization. The results did not show any significant effect on attitude change. We highlight the danger of subtle statistics frames and viewers' unwarranted conviction of the neutrality of visualizations. Finally, we present a design implication for the generation of visualization titles and one for the viewing of titles.

日本語のまとめ:

視覚化されたタイトルの枠組みや、タイトルのフレームと視聴者の既存の考えなどにどのように影響を与えるのか調査した。結果、タイトルのフレームが感じた偏見に影響を与えることなく、視覚化された図の主なメッセージに影響を与えたことを示した。

Investigating the Effect of the Multiple Comparisons Problem in Visual Analysis

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3174053

論文アブストラクト: The goal of a visualization system is to facilitate dataset-driven insight discovery. But what if the insights are spurious? Features or patterns in visualizations can be perceived as relevant insights, even though they may arise from noise. We often compare visualizations to a mental image of what we are interested in: a particular trend, distribution or an unusual pattern. As more visualizations are examined and more comparisons are made, the probability of discovering spurious insights increases. This problem is well-known in Statistics as the multiple comparisons problem (MCP) but overlooked in visual analysis. We present a way to evaluate MCP in visualization tools by measuring the accuracy of user reported insights on synthetic datasets with known ground truth labels. In our experiment, over 60% of user insights were false. We show how a confirmatory analysis approach that accounts for all visual comparisons, insights and non-insights, can achieve similar results as one that requires a validation dataset.

日本語のまとめ:

既知のグラウンドトゥルーラベルを使用して、合成データセットに関するユーザーから報告された洞察の正確さを測定することによって、視覚化ツールでMCPを評価する方法を提示する。検証データセットを使用する統計学的な認知度が類似していることが示された。