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

1/2

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.