5 Model Generalization
105
is an important asset. In addition, because the faces and edges have a fine-grained
‘importance lifetime,’ smooth zooming (in small steps) is very well possible.
Crucial for the quality of the GAP-tree generalization is how to establish the appropriate importance ranges for the feature objects. An taxonomy of feature classes
could be used, on which the compatibility functions between two different feature
classes can be based (to find ‘the most compatible neighbor’). More research is
needed in this area to automatically obtain good generalization results for real-world
data.
In this chapter the focus was on two generalization operations: area aggregation
and line simplification, but the basic idea of the tGAP approach (assigning importance values to objects during ‘offline’ model generalization, which are then used
during query and visualization for ‘on-the-fly’ refinement/generalization) could be
extended to other generalization operations. This will also be a subject for future
research.
Acknowledgments
This publication is the result of the research program ‘Sustainable Urban Areas’
(SUA) carried out by Delft University of Technology. Special thanks to Martijn Meijers for supplying test data and discussing first results of his MSc thesis project.
References
1. Ai T, Van Oosterom P (2002) GAP-tree Extensions Based on Skeletons. In: Richardson D,
Van Oosterom P (eds) Advances in Spatial Data Handling, 10th International Symposium
on Spatial Data Handling. Springer, Berlin
2. Bertolotto M, Egenhofer MJ (2001) Progressive Transmission of Vector Map Data over
the World Wide Web. GeoInformatica 5(4):345–373
3. Burghardt D, Neun M, Weibel R (2005) Generalization Services on the Web – A Classification and an Initial Prototype Implementation. In: Proc. of Auto-Carto 2005, Las Vegas
(USA)
4. Buttenfield BP (2002) Transmitting Vector Geospatial Data across the Internet. In: Egenhofer MJ, Mark DM (eds) Proc. of GIScience 2002, Berlin: Springer Verlag, Lecture
Notes in Computer Science Vol 2478, 51–64
5. Cecconi A (2003) Integration of Cartographic Generalization and Multi-Scale Databases
for Enhanced Web Mapping. PhD Thesis, University of Zurich, Zurich
6. Douglas DH, Peucker TK (1973) Algorithms for the Reduction of the Number of Points
Required to Represent a Line or Its Caricature. The Canadian Cartographer 10(2):
112–122
7. Hampe M, Sester M, Harrie L (2004) Multiple Representation Databases to Support Visualisation on Mobile Devices. 10th ISPRS Congress – Commission IV, WG IV/2, Istanbul,
Turkey.
8. Hildebrandt J, Owen M, Hollamby R (2000) CLUSTER RAPTOR: Dynamic Geospatial
Imagery Visualisation using Backend Repositories. In: Proc. 5th International Command
and Control Research and Technology Symposium (ICCRTS)
105
is an important asset. In addition, because the faces and edges have a fine-grained
‘importance lifetime,’ smooth zooming (in small steps) is very well possible.
Crucial for the quality of the GAP-tree generalization is how to establish the appropriate importance ranges for the feature objects. An taxonomy of feature classes
could be used, on which the compatibility functions between two different feature
classes can be based (to find ‘the most compatible neighbor’). More research is
needed in this area to automatically obtain good generalization results for real-world
data.
In this chapter the focus was on two generalization operations: area aggregation
and line simplification, but the basic idea of the tGAP approach (assigning importance values to objects during ‘offline’ model generalization, which are then used
during query and visualization for ‘on-the-fly’ refinement/generalization) could be
extended to other generalization operations. This will also be a subject for future
research.
Acknowledgments
This publication is the result of the research program ‘Sustainable Urban Areas’
(SUA) carried out by Delft University of Technology. Special thanks to Martijn Meijers for supplying test data and discussing first results of his MSc thesis project.
References
1. Ai T, Van Oosterom P (2002) GAP-tree Extensions Based on Skeletons. In: Richardson D,
Van Oosterom P (eds) Advances in Spatial Data Handling, 10th International Symposium
on Spatial Data Handling. Springer, Berlin
2. Bertolotto M, Egenhofer MJ (2001) Progressive Transmission of Vector Map Data over
the World Wide Web. GeoInformatica 5(4):345–373
3. Burghardt D, Neun M, Weibel R (2005) Generalization Services on the Web – A Classification and an Initial Prototype Implementation. In: Proc. of Auto-Carto 2005, Las Vegas
(USA)
4. Buttenfield BP (2002) Transmitting Vector Geospatial Data across the Internet. In: Egenhofer MJ, Mark DM (eds) Proc. of GIScience 2002, Berlin: Springer Verlag, Lecture
Notes in Computer Science Vol 2478, 51–64
5. Cecconi A (2003) Integration of Cartographic Generalization and Multi-Scale Databases
for Enhanced Web Mapping. PhD Thesis, University of Zurich, Zurich
6. Douglas DH, Peucker TK (1973) Algorithms for the Reduction of the Number of Points
Required to Represent a Line or Its Caricature. The Canadian Cartographer 10(2):
112–122
7. Hampe M, Sester M, Harrie L (2004) Multiple Representation Databases to Support Visualisation on Mobile Devices. 10th ISPRS Congress – Commission IV, WG IV/2, Istanbul,
Turkey.
8. Hildebrandt J, Owen M, Hollamby R (2000) CLUSTER RAPTOR: Dynamic Geospatial
Imagery Visualisation using Backend Repositories. In: Proc. 5th International Command
and Control Research and Technology Symposium (ICCRTS)