IMF Working Papers

Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time

By Serkan Arslanalp, Marco Marini, Patrizia Tumbarello

December 13, 2019

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Serkan Arslanalp, Marco Marini, and Patrizia Tumbarello. Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time, (USA: International Monetary Fund, 2019) accessed October 10, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

Vessel traffic data based on the Automatic Identification System (AIS) is a big data source for nowcasting trade activity in real time. Using Malta as a benchmark, we develop indicators of trade and maritime activity based on AIS-based port calls. We test the quality of these indicators by comparing them with official statistics on trade and maritime statistics. If the challenges associated with port call data are overcome through appropriate filtering techniques, we show that these emerging “big data” on vessel traffic could allow statistical agencies to complement existing data sources on trade and introduce new statistics that are more timely (real time), offering an innovative way to measure trade activity. That, in turn, could facilitate faster detection of turning points in economic activity. The approach could be extended to create a real-time worldwide indicator of global trade activity.

Subject: Big data, Exports, Imports, International trade, Technology, Trade balance, Trade in goods

Keywords: AIS data, Big data, Cargo load indicator, Europe, Exports, Global, Imports, Port call data, Trade activity, Trade balance, Trade data, Trade in goods, Trade statistics, WP

Publication Details

  • Pages:

    34

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2019/275

  • Stock No:

    WPIEA2019275

  • ISBN:

    9781513521121

  • ISSN:

    1018-5941