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Current data models for representing geospatial data are decades old and well developed, but suffer from two major flaws. First, they employ a onesize-fits-all approach, in which no connection is made between the characteristics of data and the specific applications that employ the data. Second, they fail to convey adequate information about the gap between the data and the phenomena they represent. All spatial data are approximations of reality, and the errors they contain may have serious implications for geoprocessing activities that employ them. As a consequence of this lack of information, users of spatial data generally have a limited understanding of how errors in data affect their particular applications. This paper reviews extensive work on spatial data uncertainty propagation. It then proposes development of a data producer focused ontologically-driven GIS to implement the Monte Carlo based uncertainty propagation paradigm. We contend that this model offers tremendous advantages to the developers and users of spatial information by encapsulating with data appropriate uncertainty models for specific users and applications. [ABSTRACT FROM AUTHOR]

Reviews several books. 'Elsevier's Dictionary of Geographical Information Systems: In English, German, French and Russian,' by B. Deluska; 'Creating Location Services for the Wireless Web: Professional Developer's Guide,' by Johan Hjelm; 'Classification Methods for Remotely Sensed Data,' by Brandt Tso and Paul Mather; 'Spatial Databases: A Tour,' by Shashi Shekhar and Sanjay Chawla; 'Spatial Databases With Application to GIS,' by P. Rigaux, M. Scholl and A. Voisard.