Iphar Clément, Le Berre Iwan, Sahuquet Manuel, Napoli Aldo, Foulquier Éric
LETG-Brest GEOMER, UMR 6554 CNRS, IUEM-Université de Bretagne Occidentale, Rue Dumont D'Urville, F-29280 Plouzané, France.
CRC, Mines Paris - PSL, Rue Claude Daunesse, Sophia Antipolis, France.
Data Brief. 2024 Jun 10;55:110617. doi: 10.1016/j.dib.2024.110617. eCollection 2024 Aug.
With the growth in maritime traffic comes an increased need for precise modelling, analysis, and visualisation to enhance the monitoring capabilities of maritime authorities. To address this need, a range of sensing technologies have been developed to track vessel movements worldwide. Among these, the Automatic Identification System (AIS) is particularly significant, offering high-frequency transmission of both location and identification data. This makes AIS an invaluable tool in the intricate process of modelling maritime traffic that we use in this study. Our study presents a comprehensive dataset for the Caribbean in 2019, including port calls, quay geometries, vessel trajectories, daily locations, a seven-class vessel classification, port statistics, and United Nations reference data for comparison. Beneficial for geomatics, geography, and economics, the dataset provides a versatile tool for visualising data, assessing maritime impact on coastal areas, and enhancing maritime trade analysis. The methodology extracts 1.5 million port calls from 642 million AIS messages, offering detailed data tables and reusable processes. Its granularity down to the single quay allows for flexible data analysis, facilitating in-depth understanding of port and inter-port maritime activities.
随着海上交通量的增长,对精确建模、分析和可视化的需求也日益增加,以增强海事当局的监测能力。为满足这一需求,已开发出一系列传感技术来跟踪全球范围内的船舶移动。其中,自动识别系统(AIS)尤为重要,它能高频传输位置和识别数据。这使得AIS在我们本研究中用于海上交通建模的复杂过程中成为一项极有价值的工具。我们的研究展示了2019年加勒比地区的一个综合数据集,包括港口停靠、码头几何形状、船舶轨迹、每日位置、七类船舶分类、港口统计数据以及用于比较的联合国参考数据。该数据集对地理信息学、地理学和经济学有益,为数据可视化、评估海洋对沿海地区的影响以及加强海上贸易分析提供了一个多功能工具。该方法从6.42亿条AIS消息中提取了150万个港口停靠信息,提供了详细的数据表和可重复使用的流程。其细化到单个码头的粒度允许进行灵活的数据分析,便于深入了解港口和港口间的海上活动。