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How does the visualization of uncertainty influence decision making with hazard prediction maps?
Department of Geography, University of Zürich.
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0001-5629-0981
Department of Geography, University of Zürich.ORCID iD: 0000-0003-1263-8792
2017 (English)Other (Other academic)
Resource type
Text
Abstract [en]

A wealth of design strategies has been proposed by an interdisciplinary scientific community to visually communicate data uncertainty in maps, with the aim to support spatio-temporal decision-making under uncertainty (MacEachren et al., 2012). However, very few researchers have looked at whether and how uncertainty depictions might influence people’s reasoning processes and decision making outcomes, especially in problem contexts for which uncertainty truly matters, i.e., in life-threatening situations, or for dilemmatic decisions. We report on a map-based multi-criteria decision making study where participants (N=35) were asked to imagine purchasing a house shown on map stimuli inspired by Swiss National hazard prediction maps (SFOEN, 2016). These area-classed maps show the probability and intensity of natural disasters occurring in areas with varying danger levels in a pre-defined color scheme (i.e., red=high, blue=moderate, and yellow=low danger). Current hazard prediction maps do not depict prediction uncertainties, even though suggestions have been proposed in the cartographic literature (Kunz and Hurni, 2011). However, because there are uncertainties associated with the areal extent of the classed danger zones, we modified the zonal boundaries to show this locational uncertainty using the visual variables color value, focus, and texture, as suggested by prior empirical research (MacEachren, 2012). In a within-subject design, participants were repeatedly asked to decide which house they wished to buy, given varying house location characteristics, and respective purchase price information. The houses were depicted on a series of hazard prediction maps showing an area unknown to participants, with/without data uncertainty depicted. The maps showing uncertainty varied in the visual variables (i.e., color value|focus|texture) used to convey the locational uncertainty of the zonal boundaries. We recorded participants’ house selections, response times, and eye movements during the experiment. The task asked for participants’ preferences; there were no right or wrong answers. As hypothesized, our results show that participants’ decision making outcomes were indeed influenced by the depicted uncertainty information. Participants decided to buy different houses, as they weighted selection criteria differently, depending on whether uncertainty was shown on the map or not. We thus provide rare evidence on how uncertainty and the type of uncertainty visualization (i.e., varying color value, focus, or texture) can influence people’s reasoning to arrive at a complex, multi-criteria-based decision. We also find that participants’ individual differences with respect to their risk taking behavior tested with a standardised questionnaire influences their decision making. Risk takers underestimate the dangers of natural hazards when prediction uncertainties are depicted. With this unique study we are able to shed additional light on how people use visualized uncertainty information to make complex map-based decisions. Echoing Hegarty et al.'s (2016) findings, we again demonstrate that not only display design characteristics are relevant for map-based reasoning and decision making outcomes, but also the decision makers’ individual background, and the map-based decision-making task and context. References: Hegarty, M., Friedman, A., Boone, A.P., Barrett, T.J. (2016). Where Are You? The Effect of Uncertainty and Its Visual Representation on Location Judgments in GPS-Like Displays. Journal of Experimental Psychology, Applied, DOI: 10.1037/xap0000103. Kunz, M. and Hurni, L. (2011). How to Enhance Cartographic Visualisations of Natural Hazards Assessment Results. The Cartographic Journal, 48(1): 60-71. MacEachren, A. M., Roth, R. E., O'Brien, J., Li, B., Swingley, D., Gahegan, M. (2012). Visual Semiotics & Uncertainty Visualization: An Empirical Study. IEEE Transactions on Visualization and Computer Graphics, 18(12): 2496-2505. Swiss Federal Office for the Environment (SFOEN). Gefahrenkarten, Intensitätskarten und Gefahrenhinweiskarten. (Natural Hazard Maps), http://www.bafu.admin.ch/naturgefahren/14186/14801/15746/ (not available in English, accessed Oct. 2016).

Place, publisher, year, pages
Washington, DC, 2017.
Keyword [en]
cartography, uncertainty, eye tracking, decision making
National Category
Geosciences, Multidisciplinary
Identifiers
URN: urn:nbn:se:umu:diva-137772OAI: oai:DiVA.org:umu-137772DiVA: diva2:1127557
Available from: 2017-07-17 Created: 2017-07-17 Last updated: 2017-07-17

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Richter, Kai-FlorianFabrikant, Sara Irina
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CiteExportLink to record
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