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 language | English |
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Pages (from-to) | 123-162 |
Number of pages | 40 |
Journal | Measurement |
Volume | 81 |
Early online date | 14 Dec 2015 |
DOIs | |
Publication status | Published - Mar 2016 |