Spatial-temporal big data analysis of ship avoidance patterns during typhoon approaches
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Title
- Spatial-temporal big data analysis of ship avoidance patterns during typhoon approaches
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Author(s)
- Lee, Jeongseok; Kim, Minkyeong; Kim, Bo Ram; Kim, Tae Kyun; Lee, Chol Young; Park, Yong Gil
- KIOST Author(s)
- Lee, Jeongseok(이정석); Kim, Bo Ram(김보람); Kim, Tae Kyun(김태균); Lee, Chol Young(이철용); Park, Yong Gil(박용길)
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Alternative Author(s)
- 이정석; 김민경; 김보람; 김태균; 이철용; 박용길
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Publication Year
- 2025-03
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Abstract
- Typhoons pose a significant threat to maritime safety, affecting numerous ships and ports in South Korea annually. This study analyzes ship avoidance patterns during Typhoons SOULIK and KONG-REY using Automatic Identification System data. Through K-means clustering, anchorage and drifting patterns both inside and outside territorial waters are identified. A spatial-temporal analysis, utilizing minimum bounding geometry and centroid comparison, assesses the positions of ship clusters relative to the typhoon's path. The analysis recommends maintaining a minimum distance of 300 km from the typhoon's path for safe avoidance. Statistically significant differences in ship patterns under non-typhoon conditions were also identified using Kuiper's test. These findings emphasize the importance of data-driven decisions for enhancing maritime safety and developing typhoon avoidance strategies.
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ISSN
- 0029-8018
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URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/46626
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DOI
- 10.1016/j.oceaneng.2025.120316
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Bibliographic Citation
- Ocean Engineering, v.320, 2025
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Publisher
- Pergamon Press Ltd.
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Keywords
- Typhoon; Big AIS data; K-means; Spatial-temporal analysis; Avoidance pattern
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Type
- Article
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Language
- English
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