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How big data enriches maritime research - a critical review of Automatic Identification System (AIS) data applications

机译：数据如何丰富海事研究 - 对自动识别系统（AIS）数据应用的关键综述

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The information-rich vessel movement data provided by the Automatic Identification System (AIS) has gained much popularity over the past decade, during which the employment of satellite-based receivers has enabled wide coverage and improved data quality. The application of AIS data has developed from simply navigation-oriented research to now include trade flow estimation, emission accounting, and vessel performance monitoring. The AIS now provides high frequency, real-time positioning and sailing patterns for almost the whole world's commercial fleet, and therefore, in combination with supplementary databases and analyses, AIS data has arguably kickstarted the era of digitisation in the shipping industry. In this study, we conduct a comprehensive review of the literature regarding AIS applications by dividing it into three development stages, namely, basic application, extended application, and advanced application. Each stage contains two to three application fields, and in total we identified seven application fields, including (1) AIS data mining, (2) navigation safety, (3) ship behaviour analysis, (4) environmental evaluation, (5) trade analysis, (6) ship and port performance, and (7) Arctic shipping. We found that the original application of AIS data to navigation safety has, with the improvement of data accessibility, evolved into diverse applications in various directions. Moreover, we summarised the major methodologies in the literature into four categories, these being (1) data processing and mining, (2) index measurement, (3) causality analysis, and (4) operational research. Undoubtedly, the applications of AIS data will be further expanded in the foreseeable future. This will not only provide a more comprehensive understanding of voyage performance and allow researchers to examine shipping market dynamics from the micro level, but also the abundance of AIS data may also open up the rather opaque aspect of how shipping companies release information to external authorities, including the International Maritime Organization, port states, scientists and researchers. It is expected that more multi-disciplinary AIS studies will emerge in the coming years. We believe that this study will shed further light on the future development of AIS studies.
机译：通过自动识别系统（AIS）提供的信息丰富的船舶运动数据在过去十年中获得了很多人气，在此期间，卫星的接收器的就业使得能够广泛覆盖和提高数据质量。 AIS数据的应用已从简单的导航导航的研究开发，现在包括交易流程估算，排放会计和船舶性能监测。 AIS现在为几乎全世界的商业车队提供高频，实时定位和帆船模式，因此，与补充数据库和分析相结合，AIS数据可以说是在航运业中的数字化的时代。在这项研究中，我们通过将其划分为三个开发阶段，即基本应用，扩展申请和高级应用程序来全面审查关于AIS应用的文献。每个阶段包含两到三个应用领域，总共确定了七个应用领域，包括（1）AIS数据挖掘，（2）导航安全，（3）船舶行为分析，（4）环境评估，（5）贸易分析，（6）船舶和港口性能，（7）北极航运。我们发现，随着数据可访问性的提高，AIS数据的原始应用程序具有在各种方向上演变为不同的应用。此外，我们将文献中的主要方法总结为四类，这些是（1）数据处理和采矿，（2）指标测量，（3）因果区分析，（4）运行研究。毫无疑问，AIS数据的应用将在可预见的未来进一步扩大。这不仅可以提供更全面的对航程性能的理解，并允许研究人员从微观层面检查航运市场动态，但也可以对AIS数据的丰富也可能开辟了如何向外部当局发布信息的相当不透明的方面，包括国际海运组织，港口国家，科学家和研究人员。预计未来几年将出现更多多学科的AIS研究。我们认为，这项研究将进一步轻松阐明AIS研究的未来发展。

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