Spatiotemporal Data Analytics for the Maritime Industry

Patrick Schmitt (), Marcin Lukasz Bartosiak () and Torbjörn Rydbergh ()
Additional contact information
Patrick Schmitt: BearingPoint GmbH
Marcin Lukasz Bartosiak: Università di Pavia
Torbjörn Rydbergh: Marine Benchmark

A chapter in Maritime Informatics, 2021, pp 335-353 from Springer

Abstract: Abstract Having the right information at the right time is a key ingredient to creating value for any business, and the maritime industry is no different. From optimising vessels routes and preparing ports for efficient operations to reducing pollution and saving the environment, data analytics makes a difference for various maritime industry stakeholders. With the streams of digital data coming from pervasive sensors and transmitters, it is possible to track the position of almost any object in space and time, thus, supporting situational awareness to improve efficiency of sea operations. Spatiotemporal data merges both geospatial and temporal data points, which make it particularly useful in the maritime context but also challenging from the analytical point of view. This chapter presents the state of the art in spatiotemporal analytics and provides an overview of its practical applications in the maritime industry. The described scenarios are (1) long-term route planning, (2) environment preservation, (3) collision avoidance, (4) cargo tracking and (5) port call optimisation.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-030-50892-0_20

Ordering information: This item can be ordered from
http://www.springer.com/9783030508920

DOI: 10.1007/978-3-030-50892-0_20

Access Statistics for this chapter

More chapters in Progress in IS from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().