地理信息数据分级评价的相对指数熵模型

提出了一种基于相对指数熵的地理信息数据分级评价模型，构建级内相对指数熵与级间指数熵指标，分别量化分级数据级别内集聚水平和级别间的离散水平，并利用这两个指标构建了地理信息数据分级的相对指数熵评价指标。在Python中实现地理信息数据分级以及分级的相对指数熵计算。试验中，应用5种常用的分级方法对5种典型分布的6个数据集以及1个人口普查数据集进行分级，并分别计算分级结果的相对指数熵指标。试验结果表明，在面向不同分布的数据集时，相对指数熵指标能够很好地指示出最优分级方法，并且反映出不同分级方法的细小差异，对于地理信息数据分级的评价是有效的。

Relative exponential entropy model on classification evaluation of geographic information data

In this paper, we propose an evaluation model of geographic information data classification based on relative exponential entropy. The internal relative exponential entropy of class and external exponential entropy among classes are designed to respectively quantify the agglomeration level of the data within each class and the discrete level among classes. With these two indexes, the relative exponential entropy of geographic information data classification is constructed. We implement the classification of the geographic information data and relative exponential entropy calculation of the classification in the Python platform, based on which the experiments are conducted. Six datasets of 5 classical distributions and a census dataset are classified with 5 frequently-used classification methods and the relative exponential entropies of the classifications are calculated. The experimental results demonstrate that when facing different distributed data the relative exponential entropy index could well indicate the optimal classification method. Meanwhile, the relative exponential entropy could reflect the tiny differences of different classification methods. The relative exponential entropy proves to be valid for the evaluation of geographic information data classification.