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Geospatial mining of massive AIS data for strategic management of maritime navigation safety: Methodological and policy considerations

Geospatial mining of massive AIS data for strategic management of maritime navigation safety: Methodological and policy considerations
Geospatial mining of massive AIS data for strategic management of maritime navigation safety: Methodological and policy considerations
A spatial model of maritime risk would be useful to navigational authorities in accident prevention and waterway management. Mapping maritime incident rates, the frequency of accidents per vessel movement within an area could provide such an evidence base. However, aggregating spatial data is subject to challenges associated with the Modifiable Areal Unit Problem that can influence the validity of any outputs and has not been well explored within the context of navigation safety. This paper provides a framework for calculating incident rates from massive vessel traffic and accident databases and contrasts the impact of different spatial data structures and resolutions on the results. The results show that the distribution, correlations and the underlying incident rates varies significantly depending on which spatial data structure is utilised. This paper provides a discussion of these challenges, highlighting their possible impacts on maritime risk analysis, and providing some possible solutions to address them.
Rawson, Andrew, David
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Brito, Mario
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Sabeur, Zoheir
e2c5b8cc-713d-435b-bbd2-6a53542369ca
Rawson, Andrew, David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Sabeur, Zoheir
e2c5b8cc-713d-435b-bbd2-6a53542369ca

Rawson, Andrew, David, Brito, Mario and Sabeur, Zoheir (2021) Geospatial mining of massive AIS data for strategic management of maritime navigation safety: Methodological and policy considerations. Royal Institute of Navigation Conference 2021, Virtual. 15 - 18 Nov 2021. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

A spatial model of maritime risk would be useful to navigational authorities in accident prevention and waterway management. Mapping maritime incident rates, the frequency of accidents per vessel movement within an area could provide such an evidence base. However, aggregating spatial data is subject to challenges associated with the Modifiable Areal Unit Problem that can influence the validity of any outputs and has not been well explored within the context of navigation safety. This paper provides a framework for calculating incident rates from massive vessel traffic and accident databases and contrasts the impact of different spatial data structures and resolutions on the results. The results show that the distribution, correlations and the underlying incident rates varies significantly depending on which spatial data structure is utilised. This paper provides a discussion of these challenges, highlighting their possible impacts on maritime risk analysis, and providing some possible solutions to address them.

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More information

Accepted/In Press date: 29 October 2021
Venue - Dates: Royal Institute of Navigation Conference 2021, Virtual, 2021-11-15 - 2021-11-18

Identifiers

Local EPrints ID: 452357
URI: http://eprints.soton.ac.uk/id/eprint/452357
PURE UUID: 537f4009-403b-4d6a-996c-d3b37a48924f
ORCID for Andrew, David Rawson: ORCID iD orcid.org/0000-0002-8774-2415
ORCID for Mario Brito: ORCID iD orcid.org/0000-0002-1779-4535

Catalogue record

Date deposited: 08 Dec 2021 18:47
Last modified: 13 Dec 2021 03:30

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Contributors

Author: Andrew, David Rawson ORCID iD
Author: Mario Brito ORCID iD
Author: Zoheir Sabeur

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