To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local vessel density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June 2020, when the most severe restrictions were in force. We quantify a variation of mobility between −5.62 and −13.77% for container ships, between +2.28 and −3.32% for dry bulk, between −0.22 and −9.27% for wet bulk, and between −19.57 and −42.77% for passenger traffic. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50,000 ships, a figure that closely parallels the documented size of the world merchant fleet.

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Therapeutic level IV. See instructions for authors for a complete description of levels of evidence.

Several modern day problems need to deal with large amounts of spatio-temporal data. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. This work is motivated by the question of which of those data storage systems is better suited to address the needs of industrial applications. In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e. MongoDB and PostgreSQL. The evaluation is based upon real, business scenarios and their subsequent queries as well as their underlying infrastructures and concludes in confirming the superiority of PostgreSQL in almost all cases with the exception of the polygon intersection queries. Furthermore, the average response time is radically reduced with the use of indexes, especially in the case of MongoDB.

To prevent the outbreak of the Coronavirus disease (COVID-19), numerous countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data, collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and the containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We introduce the notion of a “maritime mobility index”, a synthetic composite index, to quantitatively assess ship mobility in a given unit of time. The mobility index calculation used in this study, has a worldwide extent and is based on the computation of Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. The reduced activity is observable from March to June, when the most severe restrictions were in force, producing a variation of mobility quantified between -5.62% and -13.77% for container ships, between 2.28% and -3.32% for dry bulk, between -0:22% and -9.27% for wet bulk, and between -19.57% and -42.77% for passenger shipping. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50,000 ships, a figure that closely parallels the documented size of the world merchant fleet.

In this work we propose a novel spatial knowledge discovery pipeline capable of automatically unravelling the ''roads of the sea'' and maritime traffic patterns by analysing voluminous vessel tracking data, as collected through the Automatic Identification System (AIS). We present a computationally efficient and highly accurate solution, based on a MapReduce approach and unsupervised learning methods, capable of identifying the spatiotemporal dynamics of ship routes and most crucially their characteristics, thus deriving maritime ''patterns of life'' at a global scale, without the reliance on any additional information sources or a priori expert knowledge. Experimental results confirm high accuracy of results and superior performance in comparison to other methods, with the entire processing duration completing in less than 3 hours for more than a terabyte of non-uniform spatial data. Finally, to clearly demonstrate the applicability and impact of our proposed method, we evaluate its ability to detect real world ''anomalies'', such as maritime incidents reported in the European Marine Casualty Information Platform. Numerical results show the advantages of our scheme in terms of accuracy, with an achieved anomaly detection accuracy of higher than 93%, by detecting 313 out of 335 relevant maritime incidents. INDEX TERMS AIS, anomaly detection, data driven maritime traffic, patterns of life, routes.

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