Optimization approaches for generalization and data abstraction

Abstract

The availability of methods for abstracting and generalizing spatial data is vital for understanding and communicating spatial information. Spatial analysis using maps at different scales is a good example of this. Such methods are needed not only for analogue spatial data sets but even more so for digital data. In order to automate the process of generating different levels of detail of a spatial data set, generalization operations are used. The paper first gives an overview on current approaches for the automation of generalization and data abstraction, and then presents solutions for three generalization problems based on optimization techniques. Least‐Squares Adjustment is used for displacement and shape simplification (here, building groundplans), and Self‐Organizing Maps, a Neural Network technique, is applied for typification, i.e. a density preserving reduction of objects. The methods are validated with several examples and evaluated according to their advantages and disadvantages. Finally, a scenario describes how these methods can be combined to automatically yield a satisfying result for integrating two data sets of different scales.

Keywords:

• Optimization
• Generalization
• Data abstraction
• Spatial data

Acknowledgements

The ideas on typification were developed during a stay at the IGN, France. I thank Sebastièn Mustière and Jean‐Francois Hangouët, for their valuable discussions that triggered the idea of using Kohonen Feature nets for typification. Claus Brenner's help applying it was very important. The comments of the reviewers are highly appreciated.