Multiresolution Decomposition of Areal Count Data

Multiresolution decomposition is commonly understood as a procedure to capture scaledependent features in random signals. Such methods were first established for image processing and typically rely on raster or regularly gridded data. In this article, we extend a particular multiresolution decomposition 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 model to include a priori demographical knowledge. These adaptions and subsequent changes in the computation schemes are carefully outlined below, whereas the main idea of the original multiresolution decomposition remains. Finally, we show the extension’s feasibility by applying it to oral cavity cancer counts in Germany.