Sun Min, Wong David, Kronenfeld Barry
Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA.
Department of Geology/Geography, Eastern Illinois University, Charleston, IL, USA.
Cartogr Geogr Inf Sci. 2017;44(3):246-258. doi: 10.1080/15230406.2016.1145072. Epub 2016 Feb 25.
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 adjusted to improve the performance of a classification.
尽管数十年来制图学在概念和技术方面取得了进步，但分级统计图的设计和分类未能解决一个基本问题：在统计学上无差异的估计值可能会在地图上被分配到不同的类别，反之亦然。最近，类别可分离性概念被引入作为一种地图分类标准，以评估两个类别中的估计值在统计上不同的可能性。不幸的是，根据可分离性标准创建的分级统计图通常具有高度不平衡的类别。为了生成合理可分离但更平衡的类别，我们提出了一种启发式分类方法，不仅要考虑类别可分离性标准，还要考虑其他分类标准，如均匀性和类内变异性。开发了一个地理可视化分析软件包来支持启发式映射过程，以评估相关标准之间的权衡并选择最优选的分类。可以调整分类断点值以提高分类的性能。
