Abstract
Detecting changes in spatial datasets is important for many fields. In this paper, we introduce a methodology for change analysis in spatial datasets that combines contouring algorithms with supervised density estimation techniques. The methodology allows users to define their own criteria for features of interest and to identify changes in those features between two datasets. Change analysis is performed by comparing interesting regions that have been derived using contour clustering. A novel clustering algorithm called DCONTOUR is introduced for this purpose that computes contour polygons that describe the boundary of a supervised density function at a given density threshold. Relationships between old and new data are analyzed relying on polygon operations. We evaluate our methodology in a case study that analyzes changes in earthquake patterns.
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© 2009 Springer-Verlag Berlin Heidelberg
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Chen, C.S., Rinsurongkawong, V., Eick, C.F., Twa, M.D. (2009). Change Analysis in Spatial Data by Combining Contouring Algorithms with Supervised Density Functions. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_95
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DOI: https://doi.org/10.1007/978-3-642-01307-2_95
Publisher Name: Springer, Berlin, Heidelberg
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