An increasing volume of data, from scientific research to pop culture, is being presented online through interactive graphics. These tools empower general audiences to explore data with a granularity that was previously unavailable, but with variable success. The value of powerful visualization tools is lost when audiences are overwhelmed by a confusing graphical interface, even if the display is technically appropriate. As datasets become larger and require more powerful data visualizations, it is crucial that audiences are intrigued (not intimidated) by interactive visualizations. In this talk, I present a framework and specific techniques for using animation and storytelling to introduce complex visual systems to broad audiences.
When working with visualizations for big data, there are a number of factors which engender confusion. These include:
While the solution to these challenges depends on the intended audience and content, there are generalizable techniques for simplifying visualizations. In this talk, I present a framework for using animation and storytelling to build charts through multiple stages. By separately introducing graphical characteristics, each property of a visualization can be communicated to users. The success of this approach is only fully realized when the information is presented as a narrative story, rather than laborious instructions. For example, this graphic informs the user that each line is a person by first showing a single line with a name rather than directly stating each line represents a person. Specific methods presented in this framework include:
Using these and other techniques, visualization designers can build captivating interactive graphics. Participants in this presentation will be provided with a conceptual framework for weaving together interface instruction and storytelling. This art of articulating graphical properties through data narrative will continue to be a key driver of the impact of powerful visual tools.
Michael Freeman is a lecturer in the Information School at the University of Washington, where he teaches courses on data science, data visualization, and web development. With a background in public health, Michael works alongside research teams to design and build interactive data visualizations to explore and communicate complex relationships in large datasets. Previously, he was a data visualization specialist and research fellow at the Institute for Health Metrics and Evaluation, where he performed quantitative global health research and built a variety of interactive visualization systems to help researchers and the public explore global health trends. Michael is interested in applications of data visualization to social change. He holds a master’s degree in public health from the University of Washington. You can take a look at samples from his projects on his webiste.
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