Raccoon: Supporting Risk Communicators in Visualizing Health Data for the Public
| dc.contributor.author | Kleinau, Anna | en_US |
| dc.contributor.author | Preim, Bernhard | en_US |
| dc.contributor.author | Meuschke, Monique | en_US |
| dc.contributor.editor | Linsen, Lars | en_US |
| dc.contributor.editor | Thies, Justus | en_US |
| dc.date.accessioned | 2024-09-09T05:26:49Z | |
| dc.date.available | 2024-09-09T05:26:49Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The urgent need to improve health communication is highlighted by the millions of premature deaths worldwide each year due to lifestyle choices and behavioral risks. These losses reveal that researching and understanding these risks is not sufficient; we must also communicate them effectively to the public. In this paper, we discuss how we can assist experts in creating data-based risk visualizations for the general public. Our tool, RACCOON, is able to identify and suggest the most important risk factors in a data set, visualizing them in a way that allows seamless exploration of the data set. Then, we use the latest research in risk communication, narrative visualization, and affective visualization to generate engaging visualizations for the general public. Extensive customization options allow the expert to integrate their domain knowledge, and tailor the visualizations to their data story and communicative intent. We evaluated RACCOON with domain experts, as well as our visualizations with the general public. The findings highlight RACCOON's effectiveness in providing intuitive and engaging visualizations that appeal to a broad audience. They also provide first insights into the interplay of visualization design and communicative intent. By fusing the research fields of risk communication, narrative visualization, and affective visualization in one visualization generation tool, we provide a novel approach to support domain experts in communicating risks and risk factors to the general public. | en_US |
| dc.description.sectionheaders | Information Visualization | |
| dc.description.seriesinformation | Vision, Modeling, and Visualization | |
| dc.identifier.doi | 10.2312/vmv.20241200 | |
| dc.identifier.isbn | 978-3-03868-247-9 | |
| dc.identifier.pages | 8 pages | |
| dc.identifier.uri | https://doi.org/10.2312/vmv.20241200 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/vmv20241200 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Human-centered computing → Visualization systems and tools; Information visualization; Visual analytics | |
| dc.subject | Human centered computing → Visualization systems and tools | |
| dc.subject | Information visualization | |
| dc.subject | Visual analytics | |
| dc.title | Raccoon: Supporting Risk Communicators in Visualizing Health Data for the Public | en_US |