GEOGRAPHICAL INFORMATION UNCERTAINTY: THE CONCEPT AND REPRESENTATIONAL CHALLENGES

I. Drecki

School of Geography, Geology and Environmental Science, The University of Auckland, Auckland, New Zealand

i.drecki@auckland.ac.nz

 

Understanding and representing geographical information uncertainty poses a significant challenge in geographical information science (GIScience) research. The concept of information uncertainty is not well defined and has different interpretations across many disciplines of knowledge. In regards to uncertainty associated with geographical information, the situation is equally complex. Buttenfield (1993) identifies ambiguous terminology used in uncertainty characterisation, as one of the impediments to effective representation of information uncertainty. Terms such as accuracy, error, data quality or reliability are often used interchangeably, each containing a degree of ambiguity when applied to describing uncertainty.

 

The development of representation tools to assist researchers in understanding and dealing with geographical information uncertainty has been underway for over a decade. However, these efforts lack comprehensiveness in their approach to representing information uncertainty by not considering all known or desirable factors that influence visualisation of information uncertainty for a particular purpose. Furthermore, little is known about the parameters that help to create successful uncertainty representations. Consequently, geographical information analysis is often not well supported by appropriate and comprehensive statements on information uncertainty.

 

This paper examines the nature of geographical information uncertainty by discussing the concept of uncertainty and its relevance in GIScience. Although not specifically defined, uncertainty covers a broad idea of vagueness or doubt, and includes accuracy as a key component. The challenge of representing geographical information uncertainty in a comprehensive way is identified and a strategy that involves considering all known or desirable parameters that influence representation of uncertainty for a particular purpose is proposed.