Abstract
Multiresolution decomposition is commonly understood as a procedure to capture scale-dependent features in random signals. Such methods were first established for image processing andtypically rely on raster or regularly gridded data. In this article, we extend a particular multiresolutiondecomposition procedure to areal count data, i.e. discrete irregularly gridded data. More specifically,we incorporate in a new model concept and distributions from the so-called Besag–York–Mollié modelto include a priori demographical knowledge. These adaptions and subsequent changes in the com-putation schemes are carefully outlined below, whereas the main idea of the original multiresolutiondecomposition remains. Finally, we show the extension’s feasibility by applying it to oral cavity cancercounts in Germany.