Stephanie Deitrick.
Evaluating Implicit Visualization of Uncertainty for Public Policy Decision Support.
In Proceedings AutoCarto 2012, pp. available online, 2012.

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Abstract:

Decision makers increasingly rely on science to inform public policy decisionmaking. Although this integration of science and policy offers the potential to support more informed decisions, scientific results are often not provided in a manner usable to decision makers. When faced with highly uncertain conditions, such as climate change, communicating science in a usable manner becomes even more important. In decision support settings, visualization of geographic information offers a powerful means to communicate uncertain science to decision makers. However, building believable representations does not provide a complete understanding of the potential consequences of decisions. Developing uncertainty representations to reflect the processes of decision-making under uncertainty offers a means to provide insight into the relationships between decisions, uncertainty, and outcomes (consequences of policy decisions). Yet, visualizations often avoid the explicit inclusion of contextual information, such as explanations of risk and uncertainty. This research makes a distinction between explicit and implicit uncertainty for visualization in decision support. In explicit visualization, uncertainty is conceived of, and evaluated as, unique information, related to, but not the same as, the underlying data. Implicit visualizations embed uncertainty information into the representation, instead of expressing uncertainty as separate or additional data. When reframing uncertainty in this way, the relationship between uncertainty, outcomes and decisions is emphasized over explicit representation frameworks that dissociate the method from the user. This paper presents an implicit method for visualizing the impact of climate change uncertainty on policy outcomes in a water model for a hypothetical metropolitan area. The effectiveness of this method for visualizing the relationship between uncertainty and policy impacts was evaluated through a human subject test. The paper reports on the results of the pilot study and how this method compares to methods for explicitly visualizing uncertainty.

Bibtex:

@InProceedings{  deitrick:2012:EIVU,
  author = 	 {Stephanie Deitrick},
  title = 	 {Evaluating Implicit Visualization of Uncertainty for Public Policy Decision 
Support},
  booktitle =    {Proceedings AutoCarto 2012},
  pages = 	 {available online},
  OPTyear = 	 {2012},
  OPTmonth = 	 {September},
}

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References:

Abbasi, D. R. (2005) Closing the Gap Between Science and Action: A Synthesis of Insights and Recommendations from the 2005 Yale F&ES Conference on Climate Change. New Haven: Yale.
Aerts, J. C. J. H., K. C. Clarke, and A. D. Keuper. (2003) Testing popular visualization techniques for representing model uncertainty. Cartography and Geographic Information Sciences 30 (3):249-261.
Bertin, J. (1983) Semiology of Graphics: Diagrams, Network, Maps. Madison, WI: University of Wisconsin Press.
Boykoff, M. T., and J. M. Boykoff. (2007) Climate change and journalistic norms: A case-study of US mass-media coverage. Geoforum 38 (6):1190-1204.
Brugnach, M., A. Tagg, F. Keil & W. J. d. Lange. (2007) Uncertainty Matters: Computer Models at the Science-Policy Interface. Water Resource Management, 21, 1075-1090.
Brugnach, M., A. Dewulf, C. Pahl-Wostl & T. Taillieu (2008) Toward a Relational Concept of Uncertainty: about Knowing Too Little, Knowing Too Differently, and Accepting Not to Know. Ecology and Society, 13, 30
Carvalha, A., and J. Burgess. (2005) Cultural Circuits of Climate Change in U.K. Broadsheet Newspapers, 1985-2003. Risk Analysis 25 (6):1457-1469.
Cliburn, D. C., J. J. Feddema, J. R. Miller, and T. A. Slocum. (2002) Design and evaluation of a decision support system in a water balance application. Computers & Graphics 26 (6):931-949.
Corbett, J. B., and J. L. Durfee. (2004) Testing Public (Un)Certainty of Science: Media Representations of Global Warming. Science Communication 26 (2):129-151.
Devillers, R., and R. Jeansoulin, eds. (2006) Fundamentals of Spatial Data Quality. Newport Beach: ISTE, Ltd.
Ehlschlaeger, C. R., A. M. Shortridge, and M. F. Goodchild. (1997) Visualizing Spatial Data Uncertainty Using Animation. Compuers & Geosciences 23 (4):387-395.
Fisher, P. (1994) Randomization and sound for the visualization of uncertain information. In Visualization in geographic information systems, eds. D. Unwin and H. Hearnshaw, 181-185. London, UK: John Wiley & Sons.
Gershon, N. (1998) Short Note: Visualization of an Imperfect World. IEEE ComputerGraphics and Applications 18 (4):43-45.
Goodchild, M. F. (1994) GIS error models and visualization techniques for spatial variability in soils. . Proceedings, 15th International Congress of Soil Science, Acapulco, 683-698.
Goodchild, M. F. (2006) Foreword. Fundamentals of Spatial Data Quality. R. Devillers and R. Jeansoulin. Newport Beach, ISTE, Ltd.: 13-16.
Goovaerts, P. (2006) Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation. International Journal of Health Geographics 5 (1):7.
Heuvelink, G. B. M. (2005) Propagation of error in spatial data modelling in GIS. In Geographic Information Systems: Principles, Techniques, Management and Applications, eds. P. A. Longley, M. F. Goodchild, D. J. Maguire and D. W. Rhind, 85-95. Hoboken, New Jersey: John Wiley & Sons, Inc.
Isendahl, N., A. Dewulf, M. Brugnach, G. Francois, S. Mollenkamp & C. Pahl-Wostl (2009) Assessing Framing of Uncertainties in Water Management Practice. Water Resource Management, 23, 3191-3205.
Leitner, M. and Buttenfield, B. P., (2000) Guidelines for display of attribute certainty. Cartography and Geographic Information Sciences, 27, 3-14.
Lempert, R. J., S. W. Popper & S. C. Bankes. (2003) Shaping the Next One Hundred Years: New Methods for Quantitative Long-Term Policy Analysis. Santa Monica: RAND.
MacEachren, A. M. (1992) Visualizing Uncertain Information. Cartographic Perspectives 13:10-19.
MacEachren, A. M., C. A. Brewer, and L. W. Pickle. (1998) Visualizing georeferenced data: representing reliability of health statistics. Environment and Planning A 30:1547-1561.
Milly, P. C. D., J. Betancourt, M. Falkenmark, R. Hirsch, M., Z. W. Kundzewicz, D. P. Lettenmaier, and R. J. Stouffer. (2008) Stationarity is Dead: Wither Water Management. Science 319:573-574.
Newman, T., and W. Lee. (2004) On visualizing uncertainty in volumetric data: techniques and their evaluation. Journal of Visual Languages & Computing 15:463-491.
Slocum, T. A., R. B. McMaster, F. C. Kessler, and H. H. Howard. (2004) Thematic Cartography and Geographic Visualization, 2nd ed. Upper Saddle River, NJ: Prentice Hall.
Smith, J. (2005) Dangerous News: Media Decision Making about Climate Change Risk. Risk Analysis 25 (6):1471-1482.
Tversky, A., and C. R. Fox. (1995) Weighing Risk and Uncertainty. Psychological Review 102 (2):269-283.
Tversky, A., and D. Kahneman. (1974) Judgment under Uncertainty: Heuristics and Biases. Science 185 (4157):1124-1131.
---. (1981) The Framing of Decisions and the Psychology of Choice. Science 211 (4481):453-458.