Task-oriented data classification and design of choropleth maps (aChor)

点击翻译按钮获取中文摘要

Choropleth maps are the type of thematic maps that are probably most frequently used. To obtain a better overview and faster readability, the displayed attribute values are often classified in advance.The classification methods commonly used and implemented in software packages (such as equidistance, Quantile, Jenks, etc.) are data-driven, i.e., the intervals are determined solely on the basis of the present frequency distribution of the original values. The spatial context of the underlying data, which is relevant for many applications, is completely neglected when using such a division along the number line. With that information about spatial relationships or patterns (or even desired "messages" of a map) might be lost; in practice (and in a best case), a manual-subjective selection out of several classification options is performed.Therefore, in this project a task-oriented approach is advocated. Although it is not possible to assign a specific task or "message" to each choropleth in advance, there are enough applications in absolute numbers to justify the following developments.The project will develop and test methods that produce optimal data classifications for synoptic tasks which can be performed by using choropleth maps. These tasks include the detection of values differences between polygons, hot and cold spots, global or local extreme values as well as cluster regions.After the creation of classes those have to be visualized by a suitable color scheme, that adequately preserves (or even stresses) the original attribute value differences between adjacent polygons or groups of polygons. A special focus will be laid on the transformation of value differences into (proportional) color differences. So far, the selection and evaluation of a color scheme taking account of specific use purposes have been treated only in a few publications.Finally, the question remains whether the potential added value of a task-oriented data classification and a corresponding visualization is actually perceived by the user - and can thus lead to good decisions. For this purpose, comprehensive empirical user studies are carried out, that also take into account various parameters of the data classification step.