A Big Data Driven Approach to Extracting Global Trade Patterns

Spiliopoulos, Giannis; Zissis, Dimitrios; Chatzikokolakis, Konstantinos

doi:10.1007/978-3-319-73521-4_7

Cited by 14 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AIS data has been used to extract information on vessel behaviour and maritime traffic for a number of years. Previous studies mainly focussed on vessel anomaly detection and risk assessment of ship collision (see Spiliopoulos et al (2018) for references of related work). They extract shipping routes using the Origin-Destination method (by counting departure ports and destinations), grid-based methods (by dividing a study area into grids and counting the frequency of ships in each grid) combined with cluster analysis to group vessels by group and a density-based approach (the denser the ship distribution, the higher the possibility that a major route exists at that location).…”

Section: Past Research Using Ais Datamentioning

confidence: 99%

An ocean of data

2024

OECD Statistics Working Papers

View full text Add to dashboard Cite

The OECD Statistics Working Paper Series -managed by the OECD Statistics and Data Directorate -is designed to make available in a timely fashion and to a wider readership selected studies prepared by OECD staff or by outside consultants working on OECD projects. The papers included are of a technical, methodological or statistical policy nature and relate to statistical work relevant to the Organisation. The Working Papers are generally available only in their original language -English or French -with a summary in the other.OECD Working Papers should not be reported as representing the official views of the OECD or of its member countries. The opinions expressed and arguments employed are those of the authors.Working Papers describe preliminary results or research in progress by the authors and are published to stimulate discussion on a broad range of issues on which the OECD works. Comments on Working Papers are welcomed and may be sent to the Statistics and Data Directorate, OECD, 2 rue André Pascal,

show abstract

Section: Past Research Using Ais Datamentioning

confidence: 99%

An ocean of data

2024

OECD Statistics Working Papers

View full text Add to dashboard Cite

show abstract

“…These are bulk carriers width deadweight tonnage (DWT) over 75,000 metric tons, oil/chemical tankers with DWT over 50,000 metric tons, and crude oil tankers with DWT over 100,000 metric tons. In both the mooring and anchored cases, the 0.5-knot threshold is used solely so as to filter out clearly erroneous navigational status reports Spiliopoulos et al (2018),. for example, use this same threshold.…”

mentioning

confidence: 99%

World Seaborne Trade in Real Time: A Proof of Concept for Building AIS-based Nowcasts from Scratch

Cerdeiro¹,

Komaromi²,

Liu³

et al. 2020

IMF Working Papers

View full text Add to dashboard Cite

Maritime data from the Automatic Identification System (AIS) have emerged as a potential source for real time information on trade activity. However, no globally applicable end-to-end solution has been published to transform raw AIS messages into economically meaningful, policy-relevant indicators of international trade. Our paper proposes and tests a set of algorithms to fill this gap. We build indicators of world seaborne trade using raw data from the radio signals that the global vessel fleet emits for navigational safety purposes. We leverage different machine-learning techniques to identify port boundaries, construct port-to-port voyages, and estimate trade volumes at the world, bilateral and within-country levels. Our methodology achieves a good fit with official trade statistics for many countries and for the world in aggregate. We also show the usefulness of our approach for sectoral analyses of crude oil trade, and for event studies such as Hurricane Maria and the effect of measures taken to contain the spread of the novel coronavirus. Going forward, ongoing refinements of our algorithms, additional data on vessel characteristics, and country-specific knowledge should help improve the performance of our general approach for several country cases. JEL Classification Numbers: F14, F47, E66.

show abstract