OpenVisConf 2017 Recap: A Guide for DataVis Researchers
OpenVisConf 2017, like the years past, brought a mix of high quality talks from visualization practitioners and researchers. The talks are now online.
All are worth a look, but there are a few I’d like to amplify because they connect with emerging themes in the visualization research community. As researchers continue to tackle problems in visualization, we must remember to engage with the broader, larger visualization community that we want to impact through our experiments and writing.
Why Does Data Vis Need a Style Guide?
Think of a large organization, with multiple people making multiple visualizations over time, using multiple tools. Inevitably, people will end up making visualizations that look very different.
Amy Cesal introduces style guides for visualizations in organizations. Style guides enable users to create visualizations that look related through documentation and example visualizations.
In the research community, visualization style remains a topic we don’t know much about. There are a few exceptions, though. Michelle Borkin’s studies on visualization memorability showed us that style elements, like human recognizable objects, help people remember a visualization. Cesal’s talk suggests that future studies might examine the effects of consistent representations and color schemes on how people comprehend and remember visualizations.
Designing Visualization Tools for Learners
Catherine D’Ignazio and Rahul Bhargava are researchers studying how people learn data analysis and visualization tools. They have surveyed existing visualization tools and frameworks, finding that few are explicitly aimed towards beginners. To fill this gap for beginners, they recently released databasic.io.
Visualization literacy is receiving a lot of attention lately. Jeremy Boy has developed a test-based method for gauging a person’s visualization literacy. Bum Chul Kwon has a few papers on literacy, including the Visualization Literacy Assessment Test (VLAT) and another study comparing methods of introducing novices to new visualizations.
One takeaway from Rahul and Ignazio’s talk is that visualization literacy is a problem with more dimensions than we’re currently acknowledging. Future studies should bear in mind that multiple communities are chipping away at the literacy problem. Even better: there should be common workshops and symposia on visualization and data literacy.
Hacking your health with JavaScript
The spread of medical sensors, actuators, and mobile devices bring new ways for people to understand and shape their health. Alan McLean’s talk shares a personal perspective on health visualization. He tells the story of how he arrived at developing his own alternative to diabetes monitoring interfaces, using graphic pictures from Google Images to nudge himself towards healthy behavior.
Research at the intersection of health and data visualization is pretty well established, even approaches which focus on patient understanding and decision-making. Researchers like Luana Micallef and Alvitta Ottley have studied how people interpret Bayesian statistics related to medical tests, for example.
What’s different about McLean’s talk, however, is that he explicitly considers the emotional gravity of health decisions in his designs. The need for emotional alignment is something I experienced first hand when interviewing prostate cancer survivors with Anzu Hakone. Getting face to face with the people you’re designing for is crucial, especially in health, and Alan’s talk captures the outcome of this dialogue wonderfully.
What I love about OpenVisConf is that it reminds me that researchers and practitioners are ultimately working on a lot of the same problems. This is just a sample of the connections between communities. All OpenVisConf talks are worth your time, and next year’s talks will no doubt be just as insightful and inspiring. Hope to see you there.
