Abstract
Imprecision in spatial data arises from the granularity or resolution at which observations of phenomena are made, and from the limitations imposed by computational representations, processing and presentational media. Precision is an important component of spatial data quality, and a key to appropriate integration of collections of data sets. Previous work of the author provides a theoretical foundation for imprecision of spatial data resulting from finite granularities, and gives the beginnings of an approach to reasoning with such data using methods similar to rough set theory. This paper develops the theory further, and extends the work to a model that includes both spatial and semantic components. Notions such as observation, schema, frame of discernment and vagueness are examined and formalised.
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