标题：Grid pattern recognition in road networks using the C4.5 algorithm

作者：Tian, Jing; Song, Zihan; Gao, Fei; Zhao, Feng

来源出版物：CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE 卷：43 期：3 页码：266-282 DOI: 10.1080/15230406.2015.1062425 出版年：MAY 26 2016

摘要：Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh's individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.

入藏号: WOS:000372039200006

文献类型：Review

语种：English

作者关键词：Road network, grid pattern, supervised learning, C4.5 algorithm, classification

扩展关键词：DATABASES

通讯作者地址：Song, ZH,Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430072, Peoples R China.

电子邮件地址：songzh1993@163.com

地址：

[Tian, Jing; Song, Zihan; Gao, Fei; Zhao, Feng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan *430072*, Peoples R China.

[Tian, Jing] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430072, Peoples R China.

研究方向：Geography

ISSN：1523-0406

eISSN: 1545-0465

影响因子（2014）：0.944