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Fast spatial clustering with different metrics and in the presence of obstacles

In many GIS settings, the Euclidean metric is not applicable as the model for distance between points. Other geometric models are needed in many practical scenarios, for which urban geography is a common example. Recently, Estivill-Castro and Lee [8] proposed an effective and efficient boundary-based clustering method overcoming drawbacks of traditional spatial clustering, but has a geometric focus. By factoring out the topological aspects of the method we obtain a generic boundary-based clustering that robustly generalizes for arbitrary Minkowski distances and is capable of handling obstacles. We illustrate this with the Manhattan distance and the Dominance distance. Experiments demonstrate that our method consistently finds various types of high-quality clusters within subquadratic time.