Details
| Original language | English |
|---|---|
| Pages (from-to) | 871-897 |
| Number of pages | 27 |
| Journal | International Journal of Geographical Information Science |
| Volume | 19 |
| Issue number | 8-9 |
| Publication status | Published - 1 Sept 2005 |
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
- Data abstraction, Generalization, Optimization, Spatial data
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
- Social Sciences(all)
- Library and Information Sciences
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In: International Journal of Geographical Information Science, Vol. 19, No. 8-9, 01.09.2005, p. 871-897.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Optimization approaches for generalization and data abstraction
AU - Sester, Monika
PY - 2005/9/1
Y1 - 2005/9/1
N2 - 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.
AB - 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.
KW - Data abstraction
KW - Generalization
KW - Optimization
KW - Spatial data
UR - http://www.scopus.com/inward/record.url?scp=26644453573&partnerID=8YFLogxK
U2 - 10.1080/13658810500161179
DO - 10.1080/13658810500161179
M3 - Article
AN - SCOPUS:26644453573
VL - 19
SP - 871
EP - 897
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
SN - 1365-8816
IS - 8-9
ER -