Spatial data has become more important everyday in decision-making and planning processes. As such, it needs to be stored and retrieved in information systems that often require high performance due to the voluminous nature of spatial data. Typically this is not much of a problem unless one considers the effect of spatial extent as a function of time in information retrieval. Taxonomies of spatial objects can be useful in suggesting a storage model that addresses spatio-temporal queries. This research develops such a taxonomy and then proposes how the taxonomy might lend itself to a high performance binary tree model for query and storage of spatial data that considers the relationship of time on the shape of objects in storage. The approach has the potential to retrieve data for certain types of queries much more quickly than a linear search of the same types of spatial objects. Comparative evaluation will be the subject of future work.
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