Document Open Access Logo

How Do Texture and Color Communicate Uncertainty in Climate Change Map Displays? (Short Paper)

Authors Irene M. Johannsen, Sara Irina Fabrikant , Mariele Evers

PDF
Thumbnail PDF

File

LIPIcs.GISCIENCE.2018.37.pdf
  • Filesize: 399 kB
  • 6 pages

Document Identifiers

Author Details

Irene M. Johannsen
  • Department of Geography, University of Bonn, Meckenheimer Allee 172, D-53115 Bonn, Germany
Sara Irina Fabrikant
  • Department of Geography, University of Zurich, Winterthurerstr. 180, CH-8057 Zurich, Switzerland
Mariele Evers
  • Department of Geography, University of Bonn, Meckenheimer Allee 166, D-53115 Bonn, Germany

Funding

This work is based on the unpublished MSc thesis by the first author advised by the subsequent authors. It was partially supported by the Canton of Zurich, Switzerland.

Cite AsGet BibTex

Irene M. Johannsen, Sara Irina Fabrikant, and Mariele Evers. How Do Texture and Color Communicate Uncertainty in Climate Change Map Displays? (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 37:1-37:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.37

Abstract

We report on an empirical study with over hundred online participants where we investigated how texture and color value, two popular visual variables used to convey uncertainty in maps, are understood by non-domain-experts. Participants intuit denser dot textures to mean greater attribute certainty; irrespective of whether the dot pattern is labeled certain or uncertain. With this additional empirical evidence, we hope to further improve our understanding of how non-domain experts interpret uncertainty information depicted in map displays. This in turn will allow us to more clearly and legibly communicate uncertainty information in climate change maps, so that these displays can be unmistakably understood by decision-makers and the general public.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • Human-centered computing → User centered design
  • Human-centered computing → Contextual design
  • Human-centered computing → Empirical studies in visualization
  • Human-centered computing → Visualization design and evaluation methods
Keywords
  • uncertainty visualization
  • empirical study
  • visual variables
  • climate change

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. J. Bertin. Sémiologie Graphique: Les Diagrammes – les Réseaux – les Cartes, Mouton, Paris (English translation of the second French edition by William J. Berg, 1983). Editions de l'Ecole des Hautes Etudes en Sciences Sociales, 1967. Google Scholar
  2. N. Boukhelifa, A. Bezerianos, T. Isenberg, and J. D. Fekete. Evaluating sketchiness as a visual variable for the depiction of qualitative uncertainty. IEEE Transactions on Visualization and Computer Graphics, 18(12):2769-2778, Dec 2012. URL: http://dx.doi.org/10.1109/TVCG.2012.220.
  3. L. Cheong, S. Bleisch, A. Kealy, K. Tolhurst, T. Wilkening, and M. Duckham. Evaluating the Impact of Visualization of Wildfire Hazard upon Decision-making under Uncertainty. International Journal of Geographical Information Science, 30(7):1377-1404, 2016. URL: http://dx.doi.org/10.1080/13658816.2015.1131829.
  4. R. B. Ekstrom, J. W. French, H. H. Harman, and D. Diran. Manual for Kit of Factor-Referenced Cognitive Tests. Educational Testing Service, Princeton, N.J, 1976. Google Scholar
  5. IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland, 2014. Google Scholar
  6. L. Itti, C. Koch, and E. Niebur. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254-1259, 1998. Google Scholar
  7. B. Jenny and Nathaniel V. Kelso. Designing Maps for the Colour-vision Impaired. Bulletin of the Society of Cartographers, 40(1):9-12, 2007. Google Scholar
  8. C. Kinkeldey, A. M. MacEachren, and J. Schiewe. How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies. The Cartographic Journal, 51(4):372-386, 2014. URL: http://dx.doi.org/10.1179/1743277414Y.0000000099.
  9. M. Kunz, A. Grêt-Regamey, and L. Hurni. Visualization of Uncertainty in Natural Hazards Assessments Using an Interactive Cartographic Information System. Natural Hazards, 59(3):1735-1751, 2011. URL: http://dx.doi.org/10.1007/s11069-011-9864-y.
  10. M. Leitner and B. P. Buttenfield. Guidelines for the Display of Attribute Certainty. Cartography and Geographic Information Science, 27(1):3-14, 2000. URL: http://dx.doi.org/10.1559/152304000783548037.
  11. A. M MacEachren. Visualizing Uncertain Information. Cartographic Perspective, 13:10-19, 1992. URL: http://dx.doi.org/10.1.1.62.285.
  12. A. M. MacEachren, R. E. Roth, J. O'Brien, B. Li, D. Swingley, and M. Gahegan. Visual Semiotics & Uncertainty Visualization: An Empirical Study. IEEE Transactions on Visualization and Computer Graphics, 18(12):2496-2505, 2012. URL: http://dx.doi.org/10.1109/TVCG.2012.279.
  13. J. Mason, A. Klippel, S. Bleisch, A. Slingsby, and S. Deitrick. Special issue introduction: Approaching Spatial Uncertainty Visualization to Support Reasoning and Decision Making. Spatial Cognition and Computation, 16(2):97-105, 2016. URL: http://dx.doi.org/10.1080/13875868.2016.1138117.
  14. J.M. Melillo, T.C. Richmond, and G. W. Yohe. Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Government Printing Office, Washington D.C., USA, 2014. Google Scholar
  15. I. Neverla and M. S. Schäfer. Einleitung: Der Klimawandel und das Medien-Klima. In I. Neverla and M.S. Schäfer, editors, Das Medienklima: Fragen und Befunde der Kommunikationswissenschaftlichen Klimaforschung, pages 9-25. Springer, Wiesbaden, Germany, 2012. URL: http://dx.doi.org/10.1007/978-3-531-94217-9.
  16. D. P. Retchless and C. A. Brewer. Guidance for Representing Uncertainty on Global Temperature Change Maps. International Journal of Climatology, 36(3):1143-1159, 2016. URL: http://dx.doi.org/10.1002/joc.4408.
  17. A.H. Robinson. Elements of Cartography. John Wiley & Sons, New York, NY, 1995. Google Scholar
  18. J. O. Wobbrock, L. Findlater, D. Gergle, and J. J. Higgins. The Aligned Rank Transform for Nonparametric Factorial Analyses Using only ANOVA Procedures. In Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11, pages 143-146, New York, NY, 2011. ACM. URL: http://dx.doi.org/10.1145/1978942.1978963.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact