Abstract
How does it arise in data
Surveys being conducted
ConclusionsConclusions
Abstract
The work being done
A visual example:
Symbols fall into two categories
Symbols will be placed at point locations
Currently, there is no clear conclusion as to how data quality should be depicted, and this has been recognized as an important challenge to the visualization field (Wittenbrink et al. 1996). There have been numerous suggestions, but there has been little testing of these proposed methods (MacEachren 1997). In order to address this need, fifteen sets of symbols have been designed that aim to communicate a data value as well as its corresponding degree of certainty. These symbols were developed based upon ideas posed in the cartographic literature from the authors MacEachren, Schweizer and Goodchild, Leitner and Buttenfield, Drecki, Wittenbrink, Pang, and Lodha, Deitrick and Edsall, and Cliburn et al..
Intrinsic symbols modify a characteristic of the symbol such as opacity, shape, or texture to portray uncertainty, while extrinsic symbols use additional geometry.
Moderately Dry,High Certainty
Moderately Dry, Low Certainty
Moderately Dry,Medium Certainty
Extremely Dry,High Certainty
Extremely Dry, Low Certainty
Extremely Dry,Medium Certainty
Moderately Wet,High Certainty
Moderately Wet, Low Certainty
Moderately Wet,Medium Certainty
Informed Decisions
Predictable Outcomes
Understanding
Background informationSubject
Actions
Uncertainty
Data reliability
Enhanced Deci-sion Making
DataMaps
+
Decisions
Decisions
Decisions
Decisions
Uncertainty occurs when you lack a complete understanding and background information about a subject. It makes it difficult to make informed decisions and judge outcomes.
What is uncertainty?
E F
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E F
1/2
Why communicate it?Why communicate it?
Intuitive abilities of symbols
All of the symbol sets (left) and symbols (right) that appeared in the
survey
Intuitive abilities of symbol sets
Feedback regarding participant’s perceived effectiveness of the symbols
Maybe the inaccuracy was not disclosed to you Maybe it was and you prefer just to ignore it
Uncertainty info is disclosed and you take it into accountYour fueling habits are altered
You borrow a car with an inaccurate gas gauge. Its true to within a quarter tank of the needle.
Dipping below a quarter tank is risky business. Are you keen on running out of gas?
Knowing about the uncertainty leads to informed decisionsand helps you avoid running out of gas.
Information always carries some degree of uncertainty. Therefore, if a decision support systems uses a map to communicate, should it not convey the data’s reliability to allow for optimal decisions?
An example:
Decision Support ToolsDecision Makers
Aid in decision making
BUT... Experience difficulty communicating data uncertainties
Individuals participating in various forms of management seek information to guide their learning, understanding, and decision-making. Web based decision support tools facilitate this, but often fail to provide any measure of the presented data’s reliability. Therefore, decision support systems (D'S) put managers in contact with data of varying degrees of reliability, as uncertainty is unavoidable and inherent in information.
Decision support systems (DSS’s) increasingly communicate through information visuals such as maps. When viewing a map, it is often assumed that all the data presented is truthful and accurate. This is never quite the case, however, as maps are just simplified representations of reality. Cartography faces the challenge of communicating data’s reliability in order to enhance decision making.
Interpolation Example
Natural Phenomenon
Synthesized Understanding of
Natural Phenomenon
Collection
Examination
Presentation
Communicating Data Certainty on Maps cisaPoster by Jay Fowler
Special thanks to Dr. Sarah Battersby for all of the insight, knowledge and encouragement
Data that is being analyzed varies in reliability. This may be due to human error such as incorrectly measuring a phenomenon, or due to instrument error if a certain tool is not working correctly.
Data is often manipulated introducing error. Examples of this are interpolation and extrapolation, two methods producing results that are not completely accurate.
It is impossible to perfectly capture and represent the complexities of reality on a map.
As data is collected, examined, and presented, uncertainties compound.
A cluster of data points with medium level certainty
Two clusters of data points with low to medium level certainty
Data Certainty shown Through Symbol Opacity
High Certainty
Low Certainty
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The second survey will place the most successful symbol sets from study one on maps for re-evaluation. Generating results from a testing environment similar to what decision-makers actually experience is desired (Hope and Hunter 2007).
Two separate human-subject surveys are being conducted evaluating symbol performance. The first is a comprehensive evaluation of all fifteen sets of symbols. It examines:
Study Two
High Certainty
Low Certainty
LegendExtremely Dry
Moderately Dry
Regular
Moderately Wet
Extremely Wet
Characterize the drought within the circle
Characterize the related data certainty within the circle
Extremely Dry
Moderately Dry
Regular
Moderately Wet
Extremely Wet
High Certainty
Medium Certainty
Low Certainty
Communicating uncertainty is important, but there is limited knowledge and studies examining comprehensively the best way to do this on a map. The proposed effectiveness testing will provide valuable information to the uncertainty visualization community and allow for better communication with individuals using decision support tools and maps.
Cliburn, D. C., J. J. Feddema, et al. (2002). "Design and evaluation of a decision support system in a water balance application." Computers & Graphics-Uk 26(6): 931-949.Deitrick, S. and R. Edsall (2008). "Making Uncertainty Usable: Approaches for Visualizing Uncertainty Information." Geographic Visualization: Concepts, Tools and Applications.Drecki, I. (2002). "Visualization of Uncertainty in Geographical Data." Spatial Data Quality(W. Shi, P. Fisher, and M. Goodchild (eds)): 140-159.Hope, S. and G. J. Hunter (2007). "Testing the effects of positional uncertainty on spatial decision-making." International Journal of Geographical Information Science 21(6): 645-665.Leitner, M. and B. P. Buttenfield (2000). "Guidelines for the display of attribute certainty." Cartography and Geographic Information Science 27(1): 3-14.MacEachren, A. M. and M. J. Kraak (1997). "Exploratory cartographic visualization: Advancing the agenda." Computers & Geosciences 23(4): 335-343.Pang, A. T., C. M. Wittenbrink, et al. (1997). "Approaches to uncertainty visualization." Visual Computer 13(8): 370-390.Penrod, J. (2001). "Refinement of the concept of uncertainty." Journal of Advanced Nursing 34(2): 238-245.Schweizer, D. M. and M. F. Goodchild (1992). "Data quality and choropleth maps: An experiment with the use of color." Proceedings, GIS/LIS '92, San Jose, California. ACSM and ASPRS, Washington, D.C. : 686-699.Wittenbrink, C. M., A. T. Pang, et al. (1996). "Glyphs for visualizing uncertainty in vector fields." Ieee Transactions on Visualization and Computer Graphics 2(3): 266-279.