GIS and Automated Generalization
As GIS gained prevalence in the late 20th century and the demand for producing maps automatically increased automated generalization became an important issue for National Mapping Agencies (NMAs) and other data providers. Thereby automated generalization is the automated extraction of data (becoming then information) regarding purpose and scale. Different researchers invented conceptual models for automated generalization:
- Gruenreich model
- Brassel & Weibel model
- McMaster & Shea model
Besides these established models, different views on automated generalization have been established: the representation-oriented view and the process-oriented view. The first view focuses on the representation of data on different scales, which is related to the field of Multi-Representation Databases (MRDB). The latter view focuses on the process of generalization.
In the context of creating databases on different scales, additionally it can be distinguished between the ladder and the star-approach. The ladder-approach is a stepwise generalization, in which each derived dataset is based on the other database of the next larger scale. The star-approach is the derived data on all scales is based on a single (large-scale) data base.
Read more about this topic: Cartographic Generalization
Famous quotes containing the word automated:
“Nature is a self-made machine, more perfectly automated than any automated machine. To create something in the image of nature is to create a machine, and it was by learning the inner working of nature that man became a builder of machines.”
—Eric Hoffer (1902–1983)