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COMPUTER - ASSISTED CARTOGRAPHIC GENERALIZATION 
AND ITS PRACTICAL APPLICATION WITH PHOCUS 
Philipp Willkomm, Carl Zeiss, Germany 
ABSTRACT 
Research in computer-assisted cartographic generalization has entered a phase that affords practical implementation. 
This paper gives a short overview of the ideas and concepts in this field and of their relevance in the age of geographical information systems. 
The second part of the paper deals with the solution of computer-assisted cartographic generalization as integrated in PHOCUS, the universal 
photogrammetric and cartographic system from Carl Zeiss. This solution for large- and medium-scale applications enables nearly automatic 
generalization of buildings and traffic routes. A semi-automatic solution is provided for the displacement problem. 
Special emphasis is placed on the practicability of the used method also by means of exemplary results. Apart from purely geometric 
generalization, conceptual generalization, i.e. generalization of the attribute data, is also dealt with. In this context, the relational data structure for 
attribute data in PHOCUS is described. 
KEY WORDS  Cartographic, Data Base, GIS/LIS, Mapping, Photogrammetry 
1. INTRODUCTION e Simplification 
e Enlargement 
Computer-assisted cartographic generalization has been a research * Displacement as a result of enlargement 
topic for more than two decades. A large number of publications * Combination 
appeared in the seventies which, due to hardware and software * Selection and elimination 
restrictions, were of a rather theoretical nature. With the elimination e Classification and typification 
of these restrictions and the possibility to make practical tests, it * Evaluation and emphasizing 
became obvious that this complex problem had to be solved in 
stages. These may again be broken down into 
Late in the eighties, a large number of partial solutions with some- 
times noteworthy results existed. The object was and still is to inte- * geometrical and 
grate these partial solutions in a generalization package and to pro- * geometrical-conceptual methods, 
gress on the road from research and development to practical 
application. with a clear distinction sometimes being impossible. 
Carl Zeiss now introduces the modular CHANGE system, a complex 
generalization package for practical use. CHANGE has been de- Practical experience with automatic methods has shown that it 
veloped by the Cartographic Institute of the University of Hannover makes sense to use different generalization methods for different 
and is fully integrated in PHOCUS, the univeral photogrammetric and object types. For example, the methods used for natural objects can, 
cartographic system from Carl Zeiss. in general, not be used for artificial objects like buildings. 
In addition, a distinction has to be made in computer-assisted car- 
tographic generalization according to the used data types. This dis- 
2. COMPUTER-ASSISTED CARTOGRAPHIC GENERALIZATION tinction is used here in the following. Precise information on the 
methods is given in the literature (e.g.: /2/, /5/). Only a short list of 
2.1 Relevance major ideas is given here that is not intended to be complete. 
The use of space-related information is currently subject to funda- 2.2.1 Vector data approach 
mental change. Today the classical map is just one of many possible 
forms of presenting such information. This is why the question arises Advantage: Precise geometric calculations possible. 
what relevance a cartographic method like generalization might Examples: - Building generalization through the elimination 
have. Is it still required in the age of geographical information of small sides 
Systems? - Upon closer inspection, the answer quickly becomes - Line generalization by matching splines or 
obvious: The need for automated generalization methods is con- polynomials, by running averages or by filtering 
Siderably increased by these new technologies because: methods, 
- Displacement by linearly decreasing displace- 
* Graphical presentation of varied space-related information ment amounts starting from a zone of maximum 
requires very diverse representations of the basic spatial displacement 
data (from detailed to very generalized). - Center-axis determination of area or double-line 
* The useful life of the data is much longer than that of the traffic arteries. 
hardware and software. This means that the data base has 
to be kept as flexible as possible. 2.2.2 Raster data approach 
* The cost and the time required for scale-dependent data 
acquisition and editing and for the management of spatial Advantage: Elegant handling of area problems 
data bases with different scales can be minimized by the Examples: - Line simplification by filtering and skeletoning 
use of computer-assisted cartographic generalization. methods 
- Object symbolization with pattern recognition 
Furthermore it is rather anachronistic to derive digital successor methods. 
maps form digital basic maps by means of manual methods, i.e. the 
higher the degree of digitization in cartography, the more urgent the 2.2.3 Hybrid approach 
need for the methods described here. 
Offers the advantages of both data types 
2.2 Exemplary approaches Example: - Recognition of object overlaps after enlargement 
in the raster data format followed by displacement 
The theory of cartographic generalization is characterized by dif- in the vector data format. 
ferent approaches. Initially the following major procedural steps can 
be distinguished /1/: 
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