Automated spatial and thematic generalization using a context transformation model : integrating steering parameters, classification and aggregation hierarchies, reduction factors, and topological structures for multiple abstractions

This dissertation presents a model for spatial and thematic digital generalization. To do so, the development of digital generalization over the last thirty years is first reviewed

The approach to generalization taken in this research differs from other existing works as it tackles the task from a database perspective rather than a representation perspective. Accordingly, a context transformation model is presented that can automatically decrease representation density (the density of objects and object classes appearing on maps) through the application of steering parameters, classification and aggregation hierarchies, and topological data structures.

The underlying philosophy in the development has been to provide a user environment with as much flexibility as possible.