Data classification for highlighting polygons with local extreme values in choropleth maps

Following the general demand for task-orientation in map design, one specific task will be examined here: the preservation and highlighting of local extreme values in choropleth maps. Extreme value polygons are ones that show a larger (local maximum) or smaller (local minimum) attribute value compared to all directly neighboring polygons. For a visual identification in a classified choropleth map, such a polygon must belong to a class other than the surrounding polygons. However, data classification methods that are commonly used in the process of generating choropleth maps are data-driven, i.e., the intervals are determined solely on the basis of the present frequency distribution of the original values. With such a division along the number line, the spatial context of the underlying data is completely neglected and with that the desired categorization for local extreme values is not guaranteed. As a consequence, a new method (called PLEX) is presented for this purpose. The application and the effectiveness of this method will be demonstrated using real-world examples.