Conflict Reduction in Map Generalization Using Iterative Improvement

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 con ̄ict, 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 con ̄ict. Each of n objects is assigned k candidate positions into which it can possibly move, resulting in a total of k map realizations. The assumption is that some of these realizations will contain a reduced level of con ̄ict. 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 ®rst 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 con ̄ict while limiting the number of realizations that are examined, the simulated annealing approach is superior with regard to the degree of con ̄ict 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.