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"Being Simple on Complex Issues" -- Accounts on Visual Data Communication about Climate Change (2211.10254v3)

Published 18 Nov 2022 in cs.HC

Abstract: Data visualizations play a critical role in both communicating scientific evidence about climate change and in stimulating engagement and action. To investigate how visualizations can be better utilized to communicate the complexities of climate change to different audiences, we conducted interviews with 17 experts in the fields of climate change, data visualization, and science communication, as well as with 12 laypersons. Besides questions about climate change communication and various aspects of data visualizations, we also asked participants to share what they think is the main takeaway message for two exemplary climate change data visualizations. Through a thematic analysis, we observe differences regarding the included contents, the length and abstraction of messages, and the sensemaking process between and among the participant groups. On average, experts formulated shorter and more abstract messages, often referring to higher-level conclusions rather than specific details. We use our findings to reflect on design decisions for creating more effective visualizations, particularly in news media sources geared toward lay audiences. We hereby discuss the adaption of contents according to the needs of the audience, the trade-off between simplification and accuracy, as well as techniques to make a visualization attractive.

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References (85)
  1. R. Bayes, T. Bolsen, and J. N. Druckman, “A research agenda for climate change communication and public opinion: The role of scientific consensus messaging and beyond,” Environmental Communication, vol. 17, no. 1, pp. 16–34, 2023.
  2. Pew Research Center, “In response to climate change, citizens in advanced economies are willing to alter how they live and work,” https://www.pewresearch.org/global/2021/09/14/in-response-to-climate-change-citizens-in-advanced-economies-are-willing-to-alter-how-they-live-and-work/ (accessed July 22, 2023), 2021.
  3. T. Brosch, “Affect and emotions as drivers of climate change perception and action: A review,” Current Opinion in Behavioral Sciences, vol. 42, pp. 15–21, 2021.
  4. A. Markman, “Why people aren’t motivated to address climate change,” https://hbr.org/2018/10/why-people-arent-motivated-to-address-climate-change (accessed July 22, 2023), 2018.
  5. S. Joslyn and R. Demnitz, “Explaining how long CO2 stays in the atmosphere: Does it change attitudes toward climate change?” Journal of Experimental Psychology: Applied, vol. 27, no. 3, pp. 473–484, 2021.
  6. B. W. Head, “Wicked problems in public policy,” Public Policy, vol. 3, no. 2, pp. 101–118, 2008.
  7. J. Johansson, T.-S. S. Neset, and B.-O. Linnér, “Evaluating climate visualization: An information visualization approach,” in 14th International Conference on Information Visualisation, 2010, London, UK, E. Banissi, S. Bertschi, R. A. Burkhard et al., Eds.   IEEE Computer Society, 2010, pp. 156–161.
  8. S. J. O’Neill and N. Smith, “Climate change and visual imagery,” WIREs Climate Change, vol. 5, no. 1, pp. 73–87, 2014.
  9. B. Schneider, “Climate model simulation visualization from a visual studies perspective,” WIREs Climate Change, vol. 3, no. 2, pp. 185–193, 2012.
  10. IPCC, “Summary for Policymakers,” in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, V. MassonDelmotte, P. Zhai, A. Pirani et al., Eds.   Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 2021.
  11. M. Böttinger, H.-N. Kostis, M. Velez-Rojas, P. Rheingans, and A. Ynnerman, “Reflections on visualization for broad audiences,” in Foundations of Data Visualization, M. Chen, H. Hauser, P. Rheingans, and G. Scheuermann, Eds.   Cham: Springer International Publishing, 2020, pp. 297–305.
  12. B. Lee, E. K. Choe, P. Isenberg, K. Marriott, and J. Stasko, “Reaching broader audiences with data visualization,” IEEE Computer Graphics and Applications, vol. 40, no. 2, pp. 82–90, 2020.
  13. E. M. Peck, S. E. Ayuso, and O. El-Etr, “Data is personal: Attitudes and perceptions of data visualization in rural Pennsylvania,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, S. A. Brewster, G. Fitzpatrick, A. L. Cox, and V. Kostakos, Eds.   ACM, 2019.
  14. A. Burns, C. Lee, T. On, C. Xiong, E. Peck, and N. Mahyar, “From invisible to visible: Impacts of metadata in communicative data visualization,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–16, 2022.
  15. J. H. Park, A. Kaufman, and K. Mueller, “Graphoto: Aesthetically pleasing charts for casual information visualization,” IEEE Computer Graphics and Applications, vol. 38, no. 6, pp. 67–82, 2018.
  16. H. Kennedy and R. L. Hill, “The feeling of numbers: Emotions in everyday engagements with data and their visualisation,” Sociology, vol. 52, no. 4, pp. 830–848, 2018.
  17. D. Sprague and M. Tory, “Exploring how and why people use visualizations in casual contexts: Modeling user goals and regulated motivations,” Information Visualization, vol. 11, no. 2, pp. 106–123, 2012.
  18. Z. Pousman, J. T. Stasko, and M. Mateas, “Casual Information Visualization: Depictions of Data in Everyday Life,” IEEE Trans. Vis. Comput. Graph., vol. 13, no. 6, pp. 1145–1152, 2007.
  19. S. Lee, S.-H. Kim, and B. C. Kwon, “VLAT: Development of a visualization literacy assessment test,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 551–560, 2017.
  20. J. Boy, R. A. Rensink, E. Bertini, and J.-D. Fekete, “A principled way of assessing visualization literacy,” IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1963–1972, 2014.
  21. E. Adar and E. Lee, “Communicative visualizations as a learning problem,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 946–956, 2021.
  22. S. L. Franconeri, L. M. Padilla, P. Shah, J. M. Zacks, and J. Hullman, “The science of visual data communication: What works,” Psychological Science in the Public Interest, vol. 22, no. 3, pp. 110–161, 2021.
  23. E. Lee-Robbins and E. Adar, “Affective learning objectives for communicative visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 1–11, 2023.
  24. E. Holder and C. X. Bearfield, “Polarizing political polls: How visualization design choices can shape public opinion and increase political polarization,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2023.
  25. H. A. He, J. Walny, S. Thoma, S. Carpendale, and W. Willett, “Enthusiastic and grounded, avoidant and cautious: Understanding public receptivity to data and visualizations,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2023.
  26. F. Morini, A. Eschenbacher, J. Hartmann, and M. Dork, “From shock to shift: Data visualization for constructive climate journalism,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2023.
  27. A. Quispel, A. Maes, and J. Schilperoord, “Graph and chart aesthetics for experts and laymen in design: The role of familiarity and perceived ease of use,” Information Visualization, vol. 15, no. 3, pp. 238–252, 2016.
  28. S. Lee, “Investigation of visualization literacy: A visualization sensemaking model, a visualization literacy assessment test, and the effects of cognitive characteristics,” Ph.D. dissertation, Purdue University, 2017.
  29. K. Giuseffi, B. Sievert, B. M. Wells, and F. Westfall, “Storytelling and sensemaking through data visualization,” in The Cambridge Handbook of Technology and Employee Behavior, R. N. Landers, Ed.   Cambridge University Press, 2019, pp. 836–846.
  30. A. Burns, C. Xiong, S. Franconeri, A. Cairo, and N. Mahyar, “Designing with pictographs: Envision topics without sacrificing understanding,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 12, pp. 4515–4530, 2022.
  31. C. Xiong, V. Setlur, B. Bach, K. Lin, E. Koh, and S. Franconeri, “Visual arrangements of bar charts influence comparisons in viewer takeaways,” IEEE Transactions on Visualization and Computer Graphics, 2021.
  32. S. Malakis and T. Kontogiannis, “A sensemaking perspective on framing the mental picture of air traffic controllers,” Applied Ergonomics, vol. 44, no. 2, pp. 327–339, 2013.
  33. G. Klein, J. Phillips, E. Rall, and D. Peluso, “A data-frame theory of sensemaking,” Expertise out of Context: Proceedings of the Sixth International Conference on Naturalistic Decision Making, pp. 113–155, 2007.
  34. D. M. Russell, M. J. Stefik, P. Pirolli, and S. K. Card, “The cost structure of sensemaking,” in Proceedings of the INTERACT ’93 and CHI ’93 Conference on Human Factors in Computing Systems, ser. CHI ’93.   New York, NY, USA: Association for Computing Machinery, 1993, pp. 269–276.
  35. A. Burns, “Communicative information visualizations: How to make data more understandable by the general public,” Ph.D. dissertation, University of Massachusetts Amherst, 2022.
  36. S. Bateman, R. L. Mandryk, C. Gutwin, A. Genest, D. McDine, and C. Brooks, “Useful junk? the effects of visual embellishment on comprehension and memorability of charts,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.   Association for Computing Machinery, 2010, p. 2573–2582.
  37. A. G. Ballantyne, V. Wibeck, and T.-S. Neset, “Images of climate change – A pilot study of young people’s perceptions of ICT-based climate visualization,” Climatic Change, vol. 134, no. 1, pp. 73–85, 2016.
  38. IPCC, “About the IPCC,” https://www.ipcc.ch/about/ (accessed July 22, 2023).
  39. A. Corner, C. Shaw, and J. Clarke, “Principles for effective communication and public engagement on climate change: A handbook for IPCC authors,” 2018, Oxford: Climate Outreach.
  40. R. Pidcock, K. Heath, L. Messling, S. Wang, A. Pirani, S. Connors, A. Corner, C. Shaw, and M. Gomis, “Evaluating effective public engagement: Local stories from a global network of IPCC scientists,” Climatic Change, vol. 168, no. 3, 2021.
  41. A. Wozniak, “Stakeholders’ visual representations of climate change,” in Research Handbook on Communicating Climate Change.   Edward Elgar Publishing, 2020, pp. 131–142.
  42. J. Harold, I. Lorenzoni, T. F. Shipley, and K. R. Coventry, “Communication of IPCC visuals: IPCC authors’ views and assessments of visual complexity,” Climatic Change, vol. 158, no. 2, pp. 255–270, 2020.
  43. H. Fischer, S. Schütte, A. Depoux, D. Amelung, and R. Sauerborn, “How well do COP22 attendees understand graphs on climate change health impacts from the Fifth IPCC Assessment Report?” International Journal of Environmental Research and Public Health, vol. 15, no. 5, 2018.
  44. H. Fischer, K. L. van den Broek, K. Ramisch, and Y. Okan, “When IPCC graphs can foster or bias understanding: Evidence among decision-makers from governmental and non-governmental institutions,” Environmental Research Letters, vol. 15, no. 11, 2020.
  45. R. McMahon, M. Stauffacher, and R. Knutti, “The unseen uncertainties in climate change: Reviewing comprehension of an IPCC scenario graph,” Climatic Change, vol. 133, no. 2, pp. 141–154, 2015.
  46. J. Harold, I. Lorenzoni, K. Coventry, and A. Minns, “Enhancing the accessibility of climate change data visuals: Recommendations to the IPCC and guidance for researchers,” https://tyndall.ac.uk/wp-content/uploads/2021/09/Data_Visuals_Guidance_Full_Report_0.pdf (accessed July 22, 2023), 2017.
  47. T. Gaulkin, “Why the bad news in the IPCC report is good news for visual learners,” https://thebulletin.org/2021/08/why-the-bad-news-in-the-ipcc-report-is-good-news-for-visual-learners/ (accessed July 22, 2023), 2021.
  48. J. Harold, I. Lorenzoni, T. F. Shipley, and K. R. Coventry, “Cognitive and psychological science insights to improve climate change data visualization,” Nature Climate Change, vol. 6, no. 12, pp. 1080–1089, 2016.
  49. S. O’Neill, “More than meets the eye: A longitudinal analysis of climate change imagery in the print media,” Climatic Change, vol. 163, no. 1, pp. 9–26, 2020.
  50. J. E. Hopke and L. E. Hestres, “Visualizing the Paris Climate Talks on Twitter: Media and climate stakeholder visual social media during COP21,” Social Media + Society, vol. 4, no. 3, 2018.
  51. H. Wessler, A. Wozniak, L. Hofer, and J. Lück, “Global multimodal news frames on climate change: A comparison of five democracies around the world,” The International Journal of Press/Politics, vol. 21, no. 4, pp. 423–445, 2016.
  52. S. Wang, A. Corner, D. Chapman, and E. Markowitz, “Public engagement with climate imagery in a changing digital landscape,” Wiley Interdisciplinary Reviews: Climate Change, vol. 9, no. 2, 2018.
  53. L. Feldman and P. S. Hart, “Is there any hope? How climate change news imagery and text influence audience emotions and support for climate mitigation policies,” Risk Analysis, vol. 38, no. 3, pp. 585–602, 2018.
  54. J. Metag, M. S. Schäfer, T. Füchslin, T. Barsuhn, and K. Kleinen-von Königslöw, “Perceptions of climate change imagery: Evoked salience and self-efficacy in Germany, Switzerland, and Austria,” Science Communication, vol. 38, no. 2, pp. 197–227, 2016.
  55. F. Windhager, G. Schreder, and E. Mayr, “On inconvenient images: Exploring the design space of engaging climate change visualizations for public audiences,” in 7th Workshop on Visualisation in Environmental Sciences, EnvirVis@EuroVis 2019, Porto, Portugal, June 3, 2019, R. Bujack, K. Feige, K. Rink et al., Eds.   Eurographics Association, 2019, pp. 1–8.
  56. K. Dudman and S. de Wit, “An IPCC that listens: Introducing reciprocity to climate change communication,” Climatic Change, vol. 168, no. 1, 2021.
  57. M. McGrath, “Climate change: Five things we have learned from the IPCC report,” https://www.bbc.com/news/science-environment-58138714 (accessed July 22, 2023), 2021, BBC News at bbc.co.uk/news.
  58. Guardian, “As a verdict on the climate crimes of humanity, the new Intergovernmental Panel on Climate Change report could not be clearer: We’re guilty as hell.” https://www.instagram.com/p/CSXHuKIqCs1/ (accessed July 22, 2023), 2021, Slide 4.
  59. R. Schuster, L. Koesten, K. Gregory, and T. Möller, “‘The main message is that sustainability would help’ – Reflections on takeaway messages of climate change data visualizations,” https://doi.org/10.48550/arXiv.2305.04030 (accessed Jan 3, 2024), 2023.
  60. C. Robson and K. McCartan, “Real world research, 4th edition,” 2017.
  61. J. Kekeya, “Analysing qualitative data using an iterative process,” Contemporary PNG Studies, vol. 24, pp. 86–94, 2020.
  62. K. A. LaDonna, A. R. Artino, Jr, and D. F. Balmer, “Beyond the guise of saturation: Rigor and qualitative interview data,” Journal of Graduate Medical Education, vol. 13, no. 5, pp. 607–611, 2021.
  63. IPCC, “AR6 WGI SPM Basic slide deck with figures,” https://www.ipcc.ch/report/ar6/wg1/downloads/outreach/IPCC_AR6_WGI_SPM_Basic_Slide_Deck_Figures.pdf (accessed Mar. 29, 2021), 2021.
  64. @quarks | Instagram, “So hat sich die globale Temperatur der Erde entwickelt,” https://www.instagram.com/p/CbnFrMIK9fl/ (accessed July 22, 2023), 2022, Author: Lena Bültena, Editor: Andreas Sträter, Graphic Artist: Elica Petrova-Wallek.
  65. E. M. Peck, S. E. Ayuso, and O. El-Etr, “Data is personal: Attitudes and perceptions of data visualization in rural pennsylvania,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, ser. CHI ’19.   Association for Computing Machinery, 2019, p. 1–12.
  66. J. Hullman and N. Diakopoulos, “Visualization rhetoric: Framing effects in narrative visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 2231–2240, 2011.
  67. M. A. Borkin, Z. Bylinskii, N. W. Kim, C. M. Bainbridge, C. S. Yeh, D. Borkin, H. Pfister, and A. Oliva, “Beyond memorability: Visualization recognition and recall,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 519–528, 2016.
  68. H.-K. Kong, Z. Liu, and K. Karahalios, “Frames and slants in titles of visualizations on controversial topics,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ser. CHI ’18.   Association for Computing Machinery, 2018, p. 1–12.
  69. A. Kause, W. Bruine de Bruin, F. Fung et al., “Visualizations of projected rainfall change in the United Kingdom: An interview study about user perceptions,” Sustainability, vol. 12, 2020.
  70. R. McMahon, M. Stauffacher, and R. Knutti, “The scientific veneer of IPCC visuals,” Climatic Change, vol. 138, no. 3, pp. 369–381, 2016.
  71. M. Ferreira, M. Coelho, V. Nisi, and N. Jardim Nunes, “Climate change communication in HCI: A visual analysis of the past decade,” in Creativity and Cognition.   ACM, 2021, pp. 1–16.
  72. A. Burns, C. Xiong, S. Franconeri, A. Cairo, and N. Mahyar, “How to evaluate data visualizations across different levels of understanding,” in 2020 IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization (BELIV), 2020.
  73. K. Rothermich, E. K. Johnson, R. M. Griffith, and M. M. Beingolea, “The influence of personality traits on attitudes towards climate change – An exploratory study,” Personality and Individual Differences, vol. 168, 2021.
  74. S. Deng and V. Sloutsky, “Selective attention, diffused attention, and the development of categorization,” Cognitive Psychology, vol. 91, 2016.
  75. S. Lee, S.-H. Kim, Y.-H. Hung, H. Lam, Y.-A. Kang, and J. S. Yi, “How do people make sense of unfamiliar visualizations?: A grounded model of novice’s information visualization sensemaking,” IEEE Trans. Vis. Comput. Graph., vol. 22, no. 1, pp. 499–508, 2016.
  76. C. Fish, “Storytelling for making cartographic design decisions for climate change communication in the United States,” Cartographica: The International Journal for Geographic Information and Geovisualization, vol. 55, no. 2, pp. 69–84, 2020, publisher: University of Toronto Press.
  77. P. J. Hinds, M. Patterson, and J. Pfeffer, “Bothered by abstraction: The effect of expertise on knowledge transfer and subsequent novice performance,” The Journal of Applied Psychology, vol. 86, no. 6, pp. 1232–1243, 2001.
  78. A. Lundgard and A. Satyanarayan, “Accessible visualization via natural language descriptions: A four-level model of semantic content,” IEEE Transactions on Visualization and Computer Graphics, 2021.
  79. A. Morelli, T. G. Johansen, R. Pidcock, J. Harold, A. Pirani, M. Gomis, I. Lorenzoni, E. Haughey, and K. Coventry, “Co-designing engaging and accessible data visualisations: A case study of the IPCC reports,” Climatic Change, vol. 168, no. 3, p. 26, 2021.
  80. B. Fischhoff, “The sciences of science communication,” Proceedings of the National Academy of Sciences, vol. 110, 2013.
  81. W. Samuelson and R. Zeckhauser, “Status quo bias in decision making,” Journal of Risk and Uncertainty, vol. 1, no. 1, pp. 7–59, 1988.
  82. A. Patt and R. Zeckhauser, “Action bias and environmental decisions,” Journal of Risk and Uncertainty, vol. 21, no. 1, pp. 45–72, 2000.
  83. S. Boehm, K. Lebling, K. Levin et al., “State of climate action 2021: Systems transformations required to limit global warming to 1.5°C,” 2021.
  84. C. S. Fish, “Cartographic content analysis of compelling climate change communication,” Cartography and Geographic Information Science, vol. 47, no. 6, pp. 492–507, 2020.
  85. A. Burns, C. Lee, R. Chawla, E. Peck, and N. Mahyar, “Who do we mean when we talk about visualization novices?” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.   Association for Computing Machinery, 2023.
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Authors (4)
  1. Regina Schuster (5 papers)
  2. Kathleen Gregory (14 papers)
  3. Torsten Möller (29 papers)
  4. Laura Koesten (21 papers)
Citations (3)
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