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A study on improving the fire fighting system according to the change of national job
Journal of the Korean Data & Information Science Society 2021;32:605-18
Published online May 31, 2021;  https://doi.org/10.7465/jkdi.2021.32.3.605
© 2021 Korean Data and Information Science Society.

Jihoo Yoon1 · Kyeongjun Lee2

1Department of Business Administration, Pusan University
2Division of Mathematics and Big Data Science, Daegu University
Correspondence to: 1 Ph. D., Department of Business Administration, Pusan National University, Busan 46241, Korea.
2 Corresponding author: Assistant professor, Division of Mathematics and Big Data Science, Daegu University, Gyeongbuk 38453, Korea. E-mail: aindra_74@naver.com
Received April 28, 2021; Revised May 12, 2021; Accepted May 20, 2021.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This article is that data of dispatch of 119 fire safety centers were analyzed using social network analysis and cluster analysis to improve the firefighting system following the transition to national jobs. First of all, through social network analysis, the relationship between 119 safety centers is analyzed. Fire departments with high Outdegree centrality suggested a relative allocation of human and material resources, and fire departments with high In-degree centrality suggested strategies for disaster prevention. Second, as a result of analyzing PAM (partitioning around medoids) clusters based on the relationship between Ambulance dispatches between 119 safety centers, new clusters (17, 9) were proposed. The proposed cluster was verified with data from Ambulance dispatch in 2018 and fire dispatch from 2015 to 2018. Based on this, a new cluster that is advantageous in terms of training, command, decision making, and communication was proposed.
Keywords : Clustering analysis, firefighting system, partitioning around medoids, social network analysis.