Computing some distance functions between polygons
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A dissimilarity function for clustering geospatial polygons
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Polygons can serve an important role in the analysis of geo-referenced data as they provide a natural representation for particular types of spatial objects and in that they can be used as models for spatial clusters. This paper claims that polygon analysis is particularly useful for mining related, spatial datasets. A novel methodology for clustering polygons that have been extracted from different spatial datasets is proposed which consists of a meta clustering module that clusters polygons and a summary generation module that creates a final clustering from a polygonal meta clustering based on user preferences. Moreover, a density-based polygon clustering algorithm is introduced. Our methodology is evaluated in a real-world case study involving ozone pollution in Texas; it was able to reveal interesting relationships between different ozone hotspots and interesting associations between ozone hotspots and other meteorological variables.