A concentration-based approach to data classification for choropleth mapping

The choropleth map is a device used for the display of socioeconomic data associated with an areal partition of geographic space. Cartographers emphasize the need to standardize any raw count data by an area-based total before displaying the data in a choropleth map. The standardization process converts the raw data from an absolute measure into a relative measure. However, there is recognition that the standardizing process does not enable the map reader to distinguish between low–low and high–high numerator/denominator differences. This research uses concentration-based classification schemes using Lorenz curves to address some of these issues. A test data set of nonwhite birth rate by county in North Carolina is used to demonstrate how this approach differs from traditional mean–variance-based systems such as the Jenks’ optimal classification scheme.

等值区域图（choropleth map）是一类用于呈现地理空间区域划分所关联的社会经济数据的可视化工具。制图学界普遍强调，在通过等值区域图展示数据前，需先以基于面积的总量对所有原始计数数据进行标准化处理。该标准化流程可将原始数据从绝对量测形式转换为相对量测形式。不过，学界已意识到，该标准化流程无法帮助地图读者区分分子与分母的低-低组合和高-高组合差异。本研究采用基于洛伦兹曲线（Lorenz curves）的集中度分类方案，以解决上述部分问题。本研究以美国北卡罗来纳州各郡的非白人出生率测试数据集为例，展示了该方法与詹克斯最优分类法（Jenks’ optimal classification scheme）这类传统均值-方差分类体系的差异。