Assessing the effect of attribute uncertainty on the robustness of choropleth map classification

Choropleth maps are often used to visualize the spatial distribution of information collected for enumeration units. Such maps, however, are normally produced without considering the effect of uncertainty associated with data, which can contribute to incorrect interpretation. The purpose of this paper is to develop a method that can be used to evaluate the classification robustness of choropleth maps when the attribute uncertainty associated with the data is known or can be estimated. We first develop a measure to indicate the robustness of classification schemes. We then design a set of experiments to examine the robustness of different choropleth map classifications under various levels and types of uncertainty. Our experiments suggest that the robustness of a choropleth classification scheme is a function of uncertainty and the number of classes used. Increases in data uncertainty will decrease map robustness. However, it is possible to increase map robustness by choosing a smaller number of classes. We also discuss a visualization approach that can be used to display the classification robustness of each enumeration unit within a choropleth map.