Choropleth map

Abstract: Challenge and response in cartographic design geography and cartographic design reconstructing the relevancy of design in postmodernism automated cartography and the human paradigm a pilot study into empirical knowledge about cartographic design cartographic complementary objectives, strategies and examples tactile mapping design and the visually impaired user designing maps for the young elementary school child gender differences in map reading abilities design issues to be considered when mapping time re-examining the cartographic depiction of topography cartographic symbolization requirements by microcomputer-based geographic information systems an experiment with choropleth maps on a monochrome LCD panel an evaluation of multivariate quantitative point symbols for maps feature matching and the similarity of maps examination of effects of task type and map complexity on sequenced and static choropleth maps closing address.

Abstract: Visualization is an effective means for exploring and analyzing complex data. Color coding is a fundamental technique for mapping data to visual representations. Although color coding is widely used in a large variety of visualizations, it is often provided in a limited way only or it is not used effectively. Therefore, we describe in this paper how appropriate (automatic) color coding can enhance the visual exploration of spatial-temporal data. We demonstrate our techniques with a system for visualizing human health data by means of choropleth maps. Furthermore, we focus on how to use color coding for facilitating comparison tasks in visualization.

Abstract: Despite conceptual and technology advancements in cartography over the decades, choropleth map design and classification fail to address a fundamental issue: estimates that are statistically indifferent may be assigned to different classes on maps or vice versa. Recently, the class separability concept was introduced as a map classification criterion to evaluate the likelihood that estimates in two classes are statistical different. Unfortunately, choropleth maps created according to the separability criterion usually have highly unbalanced classes. To produce reasonably separable but more balanced classes, we propose a heuristic classification approach to consider not just the class separability criterion but also other classification criteria such as evenness and intra-class variability. A geovisual-analytic package was developed to support the heuristic mapping process to evaluate the trade-off between relevant criteria and to select the most preferable classification. Class break values can be...

Abstract: 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.