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Analysis of co-occurrence toponyms in web pages based on complex networks

Author

Listed:
  • Zhong, Xiang
  • Liu, Jiajun
  • Gao, Yong
  • Wu, Lun

Abstract

A large number of geographical toponyms exist in web pages and other documents, providing abundant geographical resources for GIS. It is very common for toponyms to co-occur in the same documents. To investigate these relations associated with geographic entities, a novel complex network model for co-occurrence toponyms is proposed. Then, 12 toponym co-occurrence networks are constructed from the toponym sets extracted from the People’s Daily Paper documents of 2010. It is found that two toponyms have a high co-occurrence probability if they are at the same administrative level or if they possess a part-whole relationship. By applying complex network analysis methods to toponym co-occurrence networks, we find the following characteristics. (1) The navigation vertices of the co-occurrence networks can be found by degree centrality analysis. (2) The networks express strong cluster characteristics, and it takes only several steps to reach one vertex from another one, implying that the networks are small-world graphs. (3) The degree distribution satisfies the power law with an exponent of 1.7, so the networks are free-scale. (4) The networks are disassortative and have similar assortative modes, with assortative exponents of approximately 0.18 and assortative indexes less than 0. (5) The frequency of toponym co-occurrence is weakly negatively correlated with geographic distance, but more strongly negatively correlated with administrative hierarchical distance. Considering the toponym frequencies and co-occurrence relationships, a novel method based on link analysis is presented to extract the core toponyms from web pages. This method is suitable and effective for geographical information retrieval.

Suggested Citation

  • Zhong, Xiang & Liu, Jiajun & Gao, Yong & Wu, Lun, 2017. "Analysis of co-occurrence toponyms in web pages based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 462-475.
    • Handle: RePEc:eee:phsmap:v:466:y:2017:i:c:p:462-475
    DOI: 10.1016/j.physa.2016.09.024
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    References listed on IDEAS

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    Cited by:

    1. Shakibian, Hadi & Charkari, Nasrollah Moghadam, 2018. "Statistical similarity measures for link prediction in heterogeneous complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 248-263.
    2. Ma, Jun-Chao & Wang, Li & Jiang, Zhi-Qiang & Yan, Wanfeng & Zhou, Wei-Xing, 2021. "City logistics networks based on online freight orders in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).

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