A typology for visualizing uncertainty

Judi Thomson, Elizabeth Hetzler, Alan Maceachren, Mark Gahegan, Misha Pavel

Research output: Contribution to journalConference article

103 Citations (Scopus)

Abstract

Information analysts must rapidly assess information to determine its usefulness in supporting and informing decision makers. In addition to assessing the content, the analyst must be confident about the quality and veracity of the information. Visualizations can concisely represent vast quantities of information, thus aiding the analyst to examine larger quantities of material; however, visualization programs are challenged to incorporate a notion of confidence or certainty because the factors that influence the certainty or uncertainty of information vary with the type of information and the type of decisions being made. For example, the assessment of potentially subjective human-reported data leads to a large set of uncertainty concerns in fields such as national security, law enforcement (witness reports), and even scientific analysis where data is collected from a variety of individual observers. What's needed is a formal model or framework for describing uncertainty as it relates to information analysis, to provide a consistent basis for constructing visualizations of uncertainty. This paper proposes an expanded typology for uncertainty, drawing from past frameworks targeted at scientific computing. The typology provides general categories for analytic uncertainty, a framework for creating task-specific refinements to those categories, and examples drawn from the national security field.

Original languageEnglish (US)
Article number16
Pages (from-to)146-157
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5669
DOIs
StatePublished - Jul 20 2005
EventProceedings of SPIE-IS and T Electronic Imaging - Visualization and Data Analysis 2005 - San Jose, CA, United States
Duration: Jan 17 2005Jan 18 2005

Fingerprint

Uncertainty
Visualization
National security
Drawing (graphics)
information analysis
Natural sciences computing
Information analysis
Law enforcement
Law Enforcement
Scientific Computing
Formal Model
confidence
Large Set
Confidence
Observer
Refinement
Vary
Framework

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Thomson, Judi ; Hetzler, Elizabeth ; Maceachren, Alan ; Gahegan, Mark ; Pavel, Misha. / A typology for visualizing uncertainty. In: Proceedings of SPIE - The International Society for Optical Engineering. 2005 ; Vol. 5669. pp. 146-157.
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A typology for visualizing uncertainty. / Thomson, Judi; Hetzler, Elizabeth; Maceachren, Alan; Gahegan, Mark; Pavel, Misha.

In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 5669, 16, 20.07.2005, p. 146-157.

Research output: Contribution to journalConference article

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