A practical taxonomy of methods and literature for managing uncertain spatial data in geographic information systems

Madjid Tavana, Weiru Liu, Paul Elmore, Frederick E. Petry, Brian S. Bourgeois

Research output: Contribution to journalArticle

5 Citations (Scopus)
240 Downloads (Pure)

Abstract

Perfect information is seldom available to man or machines due to uncertainties inherent in real world problems. Uncertainties in geographic information systems (GIS) stem from either vague/ambiguous or imprecise/inaccurate/incomplete information and it is necessary for GIS to develop tools and techniques to manage these uncertainties. There is a widespread agreement in the GIS community that although GIS has the potential to support a wide range of spatial data analysis problems, this potential is often hindered by the lack of consistency and uniformity. Uncertainties come in many shapes and forms, and processing uncertain spatial data requires a practical taxonomy to aid decision makers in choosing the most suitable data modeling and analysis method. In this paper, we: (1) review important developments in handling uncertainties when working with spatial data and GIS applications; (2) propose a taxonomy of models for dealing with uncertainties in GIS; and (3) identify current challenges and future research directions in spatial data analysis and GIS for managing uncertainties.
Original languageEnglish
Pages (from-to)123-162
Number of pages40
JournalMeasurement
Volume81
Early online date14 Dec 2015
DOIs
Publication statusPublished - Mar 2016

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