Want your science to be used by planners or decision makers? Interactive data visualizations are a great way to make your information more accessible and usable. Providing users with easy “soundbites” to take away from your tool helps too.
Scaling climate projections to local, policy-relevant scales is difficult. Trying to take these results andpackage them in an accessible way for decision makers can be even more challenging. By leveraging the expertise of communication experts and scientists at the Climate Impacts Group (CIG) as well as data visualization experts at Tableau, I created a set of data visualizations for the new sea level rise projections that were produced in 2018 for coastal Washington state. These visualizations will allow planners and decision makers around the state to more easily access the sea level rise projections they need to make decisions around coastal resilience planning.
While I have known that I wanted to be an oceanographer since the age of 10, I had not given much thought to how the climate-induced changes to the ocean would affect humans (I was way too preoccupied with the loss of the beautiful coral reefs that had drawn me to studying the ocean in the first place!). Throughout my time at UW, I have been heavily involved with the Program on Climate Change and have learned that there is a big communication gap between the climate scientists conducting cutting-edge research and the decision makers who are actually in positions to use this science to address climate change. I hoped to use my Graduate Certificate in Climate Science (GCeCS) capstone project to help learn and practice some of the skills needed to bridge this communication gap to get the best available science into the hands of decision makers.
I partnered with Dr. Heidi Roop at CIG to work on a strategy for better communicating the updated sea level rise (SLR) projections for Washington state that were released in late 2018 as part of the Washington Coastal Resilience Project (WCRP) (Miller et al., 2018). These new projections include a number of important updates from previous work. They are localized to 171 locations along Washington’s coast, incorporate the varying rates of vertical land movement across the state, and are probabilistic (e.g., instead of giving just one estimate of the “most likely” sea level change for 2050, the amounts of change that have a 1%, 50%, or 99% chance of occurring are all given). These updates allow users to make SLR planning decisions at smaller scales, determine the relative, not absolute, SLR their regions will experience, and explore how likely different magnitudes of sea level change are over time. Originally, these projections were released as 171 site-specific separate Excel spreadsheets, making comparisons between different sites and between different scenarios for individual sites hard to understand and visualize quickly. Based on feedback Dr. Roop and other members of CIG received during a number of workshops around the state that were held to present the new SLR projections to local and regional stakeholders, it seemed that these users would be much more likely to incorporate these projections into their work if they were translated into a more user-friendly format, especially one that allowed them to visually explore the different probabilities. This is where my project came in.
We decided to display the SLR projections in a set of interactive data visualizations using Tableau. CIG, who has an ongoing partnership with Tableau, has found the data visualization and analytics software to be extremely helpful for increasing access to the climate impacts data and resources they create (e.g. their NW Climate Trends tool).
Initially, we put the SLR projections into Tableau and did our best to visualize the data in a way that we thought would be most helpful for stakeholders based on feedback from pre-project workshop sessions and using visual design principles contributed by our partners at Tableau. We then conducted three phases of interviews with local and regional stakeholders from the CIG and WCRP networks, interviewing sixteen people total and updating the visualizations with the feedback we received between each phase. During the interviews, we took notes on what the users liked and did not like about the visualizations and also asked a series of pre- and post-interview questions to better understand the possible use cases for these data and to assess whether the visualizations were successful in their goals to facilitate understanding and use of these projections.
Overall, the feedback from the interviews was extremely positive. All but one of the interviewees “strongly preferred” the data visualizations over the pre-existing spreadsheets, with the one user in disagreement asserting that the Excel tables and the visualizations are both extremely useful, just for different purposes, which is a fair point. Unfortunately, our interviewees’ confidence in their ability to incorporate these data into their own work, represented on a Likert scale (here, Very confident | Somewhat confident | Neutral | Somewhat unconfident | Very unconfident) generally did not change over the course of the interview. Given the short nature of the interviews (30–45 minutes), this was not too surprising but several interviewees did follow up with us after the interviews to express their increased confidence in understanding and using these data after having more time to explore the visualizations on their own. Additionally, many of the interviewees discussed specific communication or usage barriers within their organization or with external stakeholders that they would now be able to effectively address using the visualizations. Many users specifically cited how useful it was to be able to visualize the projections for the two greenhouse gas scenarios (i.e., representative concentration pathways – RCPs) together to see when the two sets of projections begin to diverge. Another feature users found very helpful were the tooltips that appear when users hover over points on the visualizations and contain summary statements for each point such as “Under the High (RCP 8.5) greenhouse gas scenario, there is a 56% chance the amount of relative sea level rise will meet or exceed 2.5 feet by 2120 at this location.” Our interviewees said that these statements allowed them to better understand the projections being shown and gave them an easy way to export the main point of the data for their own usage or communications.
As we wrapped up my capstone project, I had the opportunity to present at a number of meetings to show off the visualizations and gather further feedback. These included a Tableau Research team meeting, the Washington Coastal Resilience Project wrap-up meeting, a Department of Ecology Toxics Cleanup Program workshop, and the Northwest Climate Conference. A number of people at these meetings found our visualizations very useful and exciting. The Toxics Cleanup Program has incorporated our visualizations into a GIS-based hazards mapping tool that is in development and Seattle Public Utilities is planning to share a King County-specific version of the visualizations on their website.
Through this project I have been able to develop and practice my skills for distilling and presenting science for local and regional decision makers. I also became very proficient in Tableau which I had never used before this work! This project gave me invaluable insight on how decisions are made around coastal resilience in Washington and the role of science in informing these decisions. My biggest takeaway from this project is that while there are certain political barriers to addressing climate change that are out of the hands of scientists, there is still a lot of work we can, and should, do to make our scientific results more accessible and usable for stakeholders and other science end-users.
Paige Lavin is a PhD candidate in the School of Oceanography. She studies the circulation and mixing of the deep ocean as well as its contribution to ocean heat storage and sea level rise. Recently, much of her work has involved translating methods from machine learning and data visualization into the oceanography community. Occasional tweets @thesciencepaige.