Big data of the past: Analysis of historical freight shipping corridor data in the period 1662–1855

Bart Wiegmans, Patrick Witte, Milan Janic, Tom de Jong

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

This paper examines the use of big data and data analytics in international transport networks from the perspective of historical big data, focusing on shipping logs from the British, Dutch, Spanish and French fleets in between 1662 and 1855. Based on a large-scale database containing mainly meteorological data collected in the CLIWOC project (2003), we computed travel distances and analyzed historical global maritime networks. This paper focuses on route choice, and consequently the time, distance, speed and reliability of the ships, covering different time periods, seasonal patterns and trade flows. The results reveal a clear picture of the main routes per nationality that is also indicative of the linguistical, cultural and economic colonial heritage that remains in the ‘host’ countries up to this day. The average daily distances covered vary over the countries involved, over the seasons and over different time periods. Also the trip characteristics vary notably over the different countries. Zooming in on the main trade flows, the corridor from the Netherlands to Indonesia stands out, but also considerable differences in average speed and stopover times were found along this route. Related to the complexity of using big data in studying international transport networks, our conclusion is that the degree of permutations and interactions with the dataset is not necessarily less for analyzing historical shipping records. It seems that big data of the past still can inspire future explorations of our historical transport networks on the world's oceans.

Original languageEnglish
Article number100459
Number of pages13
JournalResearch in Transportation Business and Management
Volume34
DOIs
Publication statusPublished - 2020

Keywords

  • CLIWOC
  • Corridors
  • Data analysis
  • Historical freight data
  • Reliability
  • Time

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