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A Data Cleaning Method for the Identification of Outliers in Fishing Vessel Trajectories Based on a Geocoding Algorithm

文献类型： 外文期刊

作者： Zhang, Li 1 ; Zhou, Weifeng 1 ;

作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Shanghai 200090, Peoples R China

2.Zhejiang Ocean Univ, Coll Informat Engn, Zhoushan 316022, Peoples R China

关键词： Geohash; fishing vessel; trajectory data; outliers; data cleaning; data mining

期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.8； 五年影响因子：2.8 ）

ISSN：

年卷期： 2025 年 13 卷 5 期

页码：

收录情况： SCI

摘要： In modern fishery management, fishing vessel trajectory data are used to monitor and analyze fishing vessel activities. However, trajectory data are often of low quality, probably due to environmental factors, equipment failures, signal loss and operation errors, leading to numerous outliers in these data. These outliers not only undermine the credibility of the data but also negatively affect the subsequent data mining and decision-making. In this study, a data cleaning method for the identification of outlier points in fishing vessel trajectories based on the Geohash geocoding algorithm is given, which involves several key steps: obtaining and preprocessing the raw trajectory data; generating the corresponding Geohash codes for each ship position based on its latitude and longitude; calculating the reachable distance considering the time interval between the current point and the following points and their speeds; querying the neighborhood of the current point based on the reachable distance; and obtaining all Geohash codes of the reachable areas of the fishing vessels within the time interval as the reachable range grid set of the current position. The reachable range grid set of the current position is compared with the reachable range grid sets of the previous point identified as normal and the next point in the fishing vessel trajectory. If there is no intersection, it is determined that the current fishing vessel position is an outlier, and this point will be excluded. The method proposed in this study is able to effectively identify outliers in trajectory data, achieving efficient and effective trajectory data cleaning and improving the accuracy and reliability of the data.

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