Embedding science in decision support tools and representing it in public communications has long been a challenge. This is partly because most scientific information is infused with multiple trends or patterns and often contains significant scientific uncertainty. For public communication, this problem is compounded by a lack of understanding of which trend or pattern should be predominantly displayed. As a result, multiple trends are often shown, leading to complicated scientific graphics being reproduced for public use.
Over the past few years, my team has been investigating these problems for climate and environmental information. Specifically, we have used the (1) US Global Research Change Program (USGCRP) indicator suite and 3rd National Climate Assessment graphics and (2) temperature and precipitation outlooks produced by the NOAA Climate Prediction Center (CPC) to test the extent to which visualization design affects how well users and the general public understand scientific information. Tackling this problem requires integration of visualization science, decision science, and design theory. Using control/treatment testing, we can test whether understanding is improved by applying general design principles to modify existing visualizations and testing those modified graphics through focus groups and online surveys.
Results/Conclusions
Overall, we have found that diagnosing understandability challenges and then applying simple, generally-accepted design principles can significantly improve how well users understand scientific information. Among the most important findings is that simple visualizations are easier to understand. As a result, attention must be paid to what trend or patterns are high priority messages. Low priority trends should be excluded or visually de-emphasized so users can easily focus on key messages. We have found that when these principles are used a two-fold positive effect occurs. In addition to increases in overall understandability, well-designed visualizations appear to reduce how much users interpret information through their pre-existing beliefs about climate change. While preliminary, this result points to the efficacy of good design in communicating climate information to diverse audiences. The implications and applications extend beyond these studies to new work my team is conducting focused on ecological forecasts and dynamic water resources decision support.
Thus, when designing climate graphics, it is important to (1) know your audience’s goals, (2) design to inform not to explore, (3) graphics should be as simple as possible, but no simpler, and (4) iteratively design graphics with users to improve understanding for complicated key messages or nationally important graphics.