Journal of Geo-information Science ›› 2022, Vol. 24 ›› Issue (9): 1676-1687.doi: 10.12082/dqxxkx.2022.210455

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Geographical Spatial Distribution and Risk Prediction of Maritime Traffic Accidents in Port of Xiamen

YANG Yang1(), SHAO Zheping1,2, ZHAO Qiang1,2, PAN Jiacai1,2, HU Yu4, MEI Qiang1,2,3,*()   

  1. 1. Navigation College, Jimei University, Xiamen 361021, China
    2. National-Local Joint Engineering Research Center for Aids to Navigation, Xiamen 361021, China
    3. Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
    4. Xiamen Data Intelligence Academy of Chinese Academy Sciences, ICT, Xiamen 361021, China
  • Received:2021-08-04 Revised:2021-12-14 Online:2022-09-25 Published:2022-11-25
  • Contact: MEI Qiang E-mail:1042280675@qq.com;meiqiang@jmu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(71804059);Natural Science Foundation of Fujian Province(2021J01821);Science and Technology Project of Education Department of Fujian Province(JAT200265);Open Foundation of National-Local Joint Engineering Research Center for Aids to Navigation(JMCBZD202002)

Abstract:

Safety analysis based on the spatial distribution of maritime traffic accidents is of great significance for maritime traffic safety management. In this paper, we obtained the potential risk areas of maritime traffic accidents in Xiamen waters through extraction, analysis, and prediction of spatial distribution of maritime traffic accident data in Xiamen Port from 2008 to 2020. The original accident data were firstly used for spatial orientation in GIS software to form a visualized spatial distribution map of accident points. Then, the areas where maritime traffic accidents frequently occur were identified using the kernel density analysis. In addition, the spatial autocorrelation analysis method was employed to obtain the spatial distribution characteristics and specific gathering points of accidents in this region. Finally, we performed standardized grid segmentation on the data in the target waters based on the spatial distribution characteristics and predicted potential risk areas of maritime traffic accidents using a machine learning algorithm (i.e., random forest). The kernel density analysis results showed that Xiamen Bay and Western Sea were high risk areas in terms of accident frequency. Moreover, the spatial autocorrelation analysis results indicated that in terms of spatial distribution characteristics, the spatial distribution of Xiamen Port showed aggregation characteristics and was positively correlated with time. For specific spatial gathering points of accidents, Xiamen Bay and Western Sea were still the centers with a high incidence of accidents. Furthermore, the risk prediction model of Xiamen Bay and its surrounding waters demonstrated that potential risk areas of maritime traffic accidents were mostly located at the intersection of coastal and estuary areas, because there were frequent crossings of vessels and fishing boats. The results of this study show that based on the distribution characteristics extracted and grid analysis of geospatial data, our proposed method has a good effect in predicting maritime traffic accidents by use of machine learning methods.

Key words: maritime traffic accidents, spatial distribution characteristics, kernel density analysis, spatial auto-correlation analysis, accident forecast, random forest, Xiamen port, risk