Making Uncertainty Usable: Approaches for Visualizing Uncertainty Information - Geographic Visualization: Concepts, Tools and Applications - page 279

Table 14.1 Concepts of uncertainty (source: compiled from Duckham et al ., 2000;
Goodchild, 2000; Zhang and Goodchild, 2002; Molden and Higgins, 2004; Fisher, 2005;
MacEachren et al ., 2005; Veregin, 2005; Devillers and Jeansoulin, 2006)

Components Definition

Data quality:
Lineage

History of data, including sources, data processing and
transformations

Completeness Extent to which data is comprehensive

Logical consistency Extent to which objects within the dataset agree, topology

Currency Temporal gaps between occurrence, information collection and use

Credibility Reliability of information source

Subjectivity Amount of interpretation or judgment included

Accessibility Format, availability, documentation of access or use restrictions

External quality How well data meets the needs or specifications of the user

Internal quality How closely data reflect or represent the actual phenomenon

Accuracy (positional,
attribute and temporal)

Closeness of measured values, observations or estimates to the true
value

Precision (statistical and
numerical)

Number of significant digits of a measurement or observation
(numerical precision)
Conformity of repeated measurements to the reported value
(statistical precision)

Fitness for use Suitability of data for a particular use and user, subjective quality

Error Difference between a measured or reported value and the true value,

encompassing both precision and accuracy

Random error Random derivation from the true value; 'noise': inconsistent effect

across values (some results may be low, others may be high) and
influences the variance of a measurement or sample

Systematic error Systematic deviation from the true value; 'bias': consistent effect

across values (errors occur in one direction, either low or high),
influences the average of a measurement or sample

Vagueness Poor definition of an object or class of objects

Ambiguity Doubt in classification of an object; differing perceptions of the

phenomenon

An important distinction should be made between the possibly diverging concerns of the
producers of data and those of the users of the data, particularly with respect to uncertainty.
Data producers are primarily concerned with internal quality , which corresponds to the
similarity between the data and the actual phenomenon, which itself represents a 'perfect'
dataset. Users, on the other hand, are typically more concerned with data's external quality ,
or its fitness for use in a given situation. For users, a data set's currency, scope and credibility
are given high priority. Clearly, quality is a context-dependent concept, dependent on both
the individual and the situation of the data's creation and use.

14.2.1 Uncertainty representation research in GIScience

The GIScience community has long identified uncertainty as an important research theme.
Although uncertainty has been the focus of much research (Hunter and Goodchild, 1995;