Conflict resolution in cartographic name placement
Computer-Aided Design - Digital cartography
Artificial intelligence (3rd ed.)
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ACM Transactions on Graphics (TOG)
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Cartographic Name Placement with Prolog
IEEE Computer Graphics and Applications
A map generalization model based on algebra mapping transformation
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Proceedings of the 10th ACM international symposium on Advances in geographic information systems
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Geoinformatica
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GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
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SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
A genetic algorithm approach to cartographic map generalisation
Computers in Industry - Special issue: Soft computing in industrial applications
Automatic generation of tourist maps
ACM SIGGRAPH 2008 papers
Constrained set-up of the tGAP structure for progressive vector data transfer
Computers & Geosciences
Automated schematization for web service applications
W2GIS'07 Proceedings of the 7th international conference on Web and wireless geographical information systems
Multi-criteria optimization in GIS: continuous k-nearest neighbor search in mobile navigation
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part I
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Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Map data are usually derived from a source that is based on a particular scale of representation and hence are subject to a particular degree of map generalization. Attempts to display data at scales smaller than the source can result in spatial conflict, whereby map symbols become too close or overlap. Several map generalization operators may be applied to resolve the problem, including displacement. In this paper we address the problem of displacing multiple map objects in order to resolve graphic conflict. Each of n objects is assigned k candidate positions into which it can possibly move, resulting in a total of k^{n} map realizations. The assumption is that some of these realizations will contain a reduced level of conflict. Generating and evaluating all realizations is however not practical, even for relatively small values of n and k. We present two iterative improvement algorithms, which limit the number of realizations processed. The first algorithm adopts a steepest gradient descent approach; the second uses simulated annealing. They are tested on a number of data sets and while both are successful in reducing conflict while limiting the number of realizations that are examined, the simulated annealing approach is superior with regard to the degree of conflict reduction. The approach adopted is regarded as generic, in the context of map generalization, in that it appears possible in principle to employ several map generalization operators combined with more sophisticated evaluation functions.