Visualizing the Nuclear Fuel Cycle: Effects of Graphical Characteristics on Comprehension

Sunday, 15 February 2015
Exhibit Hall (San Jose Convention Center)
Nan Li, University of Wisconsin-Madison, Madison, WI
Background: Uranium used for civilian nuclear energy is a finite resource. Advanced nuclear fuel cycles (NFC) involving reprocessing of spent fuel are therefore an important policy issue. While reprocessing raises concerns about nuclear proliferation, it provides additional source of nuclear grade uranium and reduces the volume of high-level waste. The comparison of different NFC options has therefore become an integral policy analysis tool with respect to nuclear energy. When being called to choose an appropriate NFC option, many non-technical decision-makers (i.e. those who lack specialized training in science or engineering) are prompted to use scientific data to inform their choices. However, the way in which the data is visually represented can shape how non-technical viewers process and understand it. In this study, we examined the effects of two types of visual characteristics, namely graph format and interactivity, on audiences’ comprehension of NFC-related data. We also examined how the appearance of a visual indicator of data source influences audiences’ confidence in the data shown in a graph, and how that influence might vary across people with distinct level of trusts in different sources. Methods: 517 undergraduate students majoring in humanities and science in a large midwestern university participated in the study. In a computer-assisted experiment, participants were randomly assigned to one of sixteen treatment groups, viewing a graph showing either the costs of waste disposal or the mass of waste streams associated with three different NFCs. The experimental stimuli followed a 2 (chart vs. infographic) * 2 (static vs. dynamic) * 2 (MIT vs. DOE) design. We tested the effects of these manifested characteristics on viewers’ graph comprehension (i.e., how accurately participants can identify and compare the data points manifested in a stimulus) and their confidence in data quality (i.e., the extent to what participants perceive the data as trustworthy, objective and accurate). Results: Participants made more accurate data-based assessments when viewing infographics than when viewing traditional charts. Dynamic visuals allowed the viewers to retrieve quantitative information more accurately than the static ones. Noticeably, the advantages of dynamic infographics over static charts were more salient when showing the cost data than when showing the waste data. Additionally, the appearance of a logo of the data source influenced how people perceived the data quality, depending on the level of trust they have in university scientists and federal agencies respectively. Conclusion: Traditional static charts are not the best ways to convey complex information to non-technical audiences. Infographics with some dynamic features that allow viewers to interact with the data are shown to be more effective in terms of enhancing comprehension. What’s more, attributing the data shown in a graph to an academic source can boost viewers’ confidence in it in spite of how much they trust different sources.