Relative exponential entropy model on classification evaluation of geographic information data
Xiaodong Jia, TIAN Qin, Zongyi He · DOAJ (DOAJ: Directory of Open Access Journals) · 2020
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.