地理信息数据分级评价的相对指数熵模型

doi:10.11947/j.AGCS.2020.20190528

测绘学报 ›› 2020, Vol. 49 ›› Issue (11): 1497-1505.doi: 10.11947/j.AGCS.2020.20190528

地理信息数据分级评价的相对指数熵模型

肖佳^1,2, 田沁^3,4, 何宗宜⁵

1. 华中师范大学城市与环境科学学院, 湖北武汉 430079;
2. 华中师范大学地理过程分析与模拟湖北省重点实验室, 湖北武汉 430079;
3. 深圳市数字城市工程研究中心, 广东深圳 518034;
4. 自然资源部城市国土资源监测与仿真重点实验室, 广东深圳 518034;
5. 武汉大学资源与环境科学学院, 湖北武汉 430079

收稿日期:2019-07-04 修回日期:2019-12-12 发布日期:2020-11-25
作者简介:肖佳(1987-),男,讲师,研究方向为地理空间语义、地图综合和深度学习。E-mail:jiaxiao@mail.ccnu.edu.cn
基金资助:
华中师范大学中央高校基本科研业务费（CCNU30106190127）

Relative exponential entropy model on classification evaluation of geographic information data

XIAO Jia^1,2, TIAN Qin^3,4, HE Zongyi⁵

1. School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China;
2. Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China;
3. Shenzhen Research Center of Digital City Engineering, Shenzhen 518034, China;
4. Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China;
5. School of Resource and Environment Sciences, Wuhan University, Wuhan 430079, China

Received:2019-07-04 Revised:2019-12-12 Published:2020-11-25
Supported by:
The Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE (No. CCNU30106190127)

摘要/Abstract

摘要： 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。

关键词: 相对指数熵, 地理信息数据分级, 分级评价模型, 人口普查数据

Abstract: 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.

Key words: relative exponential entropy, geographic information data classification, classification evaluation model, census data

中图分类号:

P208

肖佳, 田沁, 何宗宜. 地理信息数据分级评价的相对指数熵模型[J]. 测绘学报, 2020, 49(11): 1497-1505.

XIAO Jia, TIAN Qin, HE Zongyi. Relative exponential entropy model on classification evaluation of geographic information data[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(11): 1497-1505.

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地理信息数据分级评价的相对指数熵模型

Relative exponential entropy model on classification evaluation of geographic information data

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