Fuzzy Set Approaches to Model Uncertainty in Spatial Data and Geographic Information Systems

Over the years database management systems have evolved to include spatially referenced data. Because spatial data are complex and have a number of unique constraints~i.e., spatial components and uncertain properties!, spatial database systems can be effective only if the spatial data are properly handled at the physical level. Therefore, it is important to develop an effective spatial and aspatial indexing technique to facilitate flexible spatial and/or aspatial querying for such databases. For this purpose we introduce an indexing approach to use~fuzzy! spatial and fuzzy! aspatial data. We use a number of spatial index structures, such as Multilevel Grid Filẽ MLGF!, G-tree, R-tree, and R * -tree, for fuzzy spatial databases and compare the performances of these structures for various flexible queries.

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A Framework for Modeling Uncertainty in Spatial Databases

Morris

2003

Transactions in GIS

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Geographic Information Systems and spatial databases are inherently suited for fuzziness, because of the uncertainty inherent in the assimilation, storage, and representation of spatial data. These objects may also have naturally occurring imprecise boundaries. It is difficult to store and represent these objects while continuing to demonstrate the uncertainty inherent in the objects. This paper describes a fuzzy object-oriented framework to model spatial objects with either precise or uncertain boundaries that will also provide for fuzzy querying of these objects. A prototype system, FOOSBALL, which implements this framework is also discussed.

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Fuzzy Set Approaches to Model Uncertainty in Spatial Data and Geographic Information Systems

Cited by 3 publications

References 12 publications

Uncertainty in Spatial Databases

Uncertainty in Spatial Databases

Design and implementation of index structures for fuzzy spatial databases

A Framework for Modeling Uncertainty in Spatial Databases

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