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Trends in disaster safety research in Korea: Focusing on the journal papers of the departments related to disaster prevention and safety engineering

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(10), pp.43-57
  • DOI : 10.9708/jksci.2022.27.10.043
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 15, 2022
  • Accepted : October 13, 2022
  • Published : October 31, 2022

Byungkyu Kim 1,  Beom-Jong You 1,  Shim, Hyoung-Seop 1

1한국과학기술정보연구원

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ABSTRACT

In this paper, we propose a method of analyzing research papers published by researchers belonging to university departments in the field of disaster & safety for the scientometric analysis of the research status in the field of disaster safety. In order to conduct analysis research, the dataset constructed in previous studies was newly improved and utilized. In detail, for research papers of authors belonging to the disaster prevention and safety engineering type department of domestic universities, institution identification, cited journal identification of references, department type classification, disaster safety type classification, researcher major information, KSIC(Korean Standard Industrial Classification) mapping information was reflected in the experimental data. The proposed method has a difference from previous studies in the field of disaster & safety and data set based on related keyword searches. As a result of the analysis, the type and regional distribution of organizations belonging to the department of disaster prevention and safety engineering, the composition of co-authored department types, the researchers' majors, the status of disaster safety types and standard industry classification, the status of citations in academic journals, and major keywords were identified in detail. In addition, various co-occurrence networks were created and visualized for each analysis unit to identify key connections. The research results will be used to identify and recommend major organizations and information by disaster type for the establishment of an intelligent crisis warning system. In order to provide comprehensive and constant analysis information in the future, it is necessary to expand the analysis scope and automate the identification and classification process for data set construction.

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This paper was written with support from the National Research Foundation of Korea.
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