A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping.
A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping.
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DOI：
10.1080/15230406.2016.1145072
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发表时间：
2017
影响因子：
2.5
通讯作者：
Kronenfeld B
中科院分区：
文献类型：
--
作者：
Sun M;Wong D;Kronenfeld B
尽管几十年来制图学的概念和技术取得了进步，但分区统计图的设计和分类未能解决一个基本问题：统计上无关的估计可能会被分配到地图上的不同类别，反之亦然。最近，引入了类可分离性概念作为地图分类标准，以评估两个类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区统计图通常具有高度不平衡的类别。为了产生合理可分离但更平衡的类，我们提出了一种启发式分类方法，不仅考虑类可分离性标准，还考虑其他分类标准，例如均匀性和类内变异性。开发了地理视觉分析包来支持启发式绘图过程，以评估相关标准之间的权衡并选择最优选的分类。可以调整分类间隔值以提高分类的性能。
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.