A practical taxonomy of methods and literature for managing uncertain spatial data in geographic information systems. (March 2016)

• Record Type: Journal Article
• Authors: Tavana, Madjid; Liu, Weiru; Elmore, Paul; Petry, Frederick E.; Bourgeois, Brian S.
• Is Part Of: Measurement. Volume 81(2016:Mar.)
• Page Start: 123
• Page End: 162
• Publication Date: 2016-03
• Subjects: Uncertainty -- Spatial data -- Geographic information systems -- Taxonomy -- Literature review; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2015.12.007
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: Perfect information is seldom available to man or machines due to uncertainties inherent in real world problems. Uncertainties in GIS stem from either vague/ambiguous or imprecise/inaccurate/incomplete information. We review important developments in handling uncertainties when working with spatial data and GIS applications. We propose a taxonomy of models for dealing with uncertainties in GIS. We identify current challenges and future research directions in spatial data analysis and GIS. 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.