https://visualizing.jp/en/tags/%E3%82%A4%E3%83%B3%E3%82%BF%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3/ Recent content in インタラクション on Visualizing.JP Hugo -- gohugo.io en-US Yuichi Yazaki Fri, 30 Oct 2020 00:00:00 +0900 https://visualizing.jp/en/infovis-interaction-taxonomy/ Fri, 30 Oct 2020 00:00:00 +0900 https://visualizing.jp/en/infovis-interaction-taxonomy/ <img src="https://visualizing.jp/infovis-interaction-taxonomy/images/thumb_ph_vizjp.png" alt="Featured image of post A Taxonomy of Interaction Techniques in Information Visualization" /><p>This article examines A Taxonomy of Interaction Techniques in Information Visualization as a case study in data visualization, information design, or visual culture.</p> <p>The article is useful as a case study in how data, design choices, and context shape interpretation.</p> <h2 id="what-it-shows">What It Shows </h2><p>The main point is not only the finished visual form, but also the reasoning behind it: what was selected, emphasized, simplified, or compared. Those decisions determine what readers can notice.</p> <h2 id="design-and-context-notes">Design and Context Notes </h2><ul> <li>Identify the data, audience, and communication goal behind the work.</li> <li>Notice how visual form, annotation, and context shape interpretation.</li> <li>Distinguish the core idea from details that belong to a specific medium or moment.</li> <li>Treat the example as a prompt for design judgment rather than a universal rule.</li> </ul> <h2 id="summary">Summary </h2><p>A Taxonomy of Interaction Techniques in Information Visualization shows how visualization works as both analysis and communication. Reading it carefully means looking at the data, the visual encoding, and the cultural or practical context around the work.</p> https://visualizing.jp/en/visual-analytics-interaction/ Fri, 30 Oct 2020 00:00:00 +0900 https://visualizing.jp/en/visual-analytics-interaction/ <img src="https://visualizing.jp/visual-analytics-interaction/images/thumb_ph_vizjp.png" alt="Featured image of post Organizing Interaction in Visual Analytics" /><p>This article organizes interaction techniques used in visual analytics, where users explore data through filtering, selection, navigation, and view coordination.</p> <p>The focus is on how the visual form supports comparison, pattern recognition, and explanation.</p> <h2 id="how-to-read-it">How to Read It </h2><p>Start by identifying the data units, the visual encodings, and the direction of comparison. Then read the overall pattern before moving to individual values. This helps separate the main structure from small local variation.</p> <h2 id="when-to-use-it">When to Use It </h2><p>Use this approach when the audience needs to understand a relationship that would be hard to see in a table. It is most effective when the chart type matches the question: comparison, distribution, hierarchy, flow, geography, or change over time.</p> <h2 id="design-notes">Design Notes </h2><ul> <li>Clarify what each visual channel represents before interpreting the graphic.</li> <li>Use labels, legends, and units so readers can distinguish pattern from measurement.</li> <li>Choose the method only when its visual structure matches the analytical question.</li> <li>Avoid unnecessary decoration that makes comparison harder.</li> </ul> <h2 id="summary">Summary </h2><p>Organizing Interaction in Visual Analytics is useful when its structure fits the data and the reading task. As with any visualization method, the key is to make the encoding explicit and avoid asking the form to do more than it can support.</p>