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Geospatial data analysis for global maritime risk assessment using the discrete global grid system

Geospatial data analysis for global maritime risk assessment using the discrete global grid system
Geospatial data analysis for global maritime risk assessment using the discrete global grid system
The effective management of the safety of navigation by coastguards is challenged by the complexity in quantifying and describing the relative risk of accidents occurrence. The discovery of patterns in observation data is reliant on the collection and analysis of significant volumes of relevant heterogenous spatial datasets. Conventional approaches of risk mapping which aggregate vessel traffic and incident data into Cartesian grids can result in misrepresentation due to inherent inadequacies in this spatial data format. In this paper, we explore how the Discrete Global Grid System (DGGS) overcomes these limitations through the development of global maps of incident rates at multiple resolutions. The results demonstrate hot spots of relative high risk across different regions and clearly show that DGGS is more suited to global analysis than conventional grids. This work contributes to a greater understanding of both the disposition of maritime risk and the advantages of adopting DGGS in supporting big data analysis.
Rawson, Andrew, David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Sabeur, Zoheir
e6e98155-eadb-4b0f-ba88-ba5c313f0e24
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Rawson, Andrew, David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Sabeur, Zoheir
e6e98155-eadb-4b0f-ba88-ba5c313f0e24
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c

Rawson, Andrew, David, Sabeur, Zoheir and Brito, Mario (2021) Geospatial data analysis for global maritime risk assessment using the discrete global grid system. In International Geoscience and Remote Sensing Symposium IGARSS 2021. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

The effective management of the safety of navigation by coastguards is challenged by the complexity in quantifying and describing the relative risk of accidents occurrence. The discovery of patterns in observation data is reliant on the collection and analysis of significant volumes of relevant heterogenous spatial datasets. Conventional approaches of risk mapping which aggregate vessel traffic and incident data into Cartesian grids can result in misrepresentation due to inherent inadequacies in this spatial data format. In this paper, we explore how the Discrete Global Grid System (DGGS) overcomes these limitations through the development of global maps of incident rates at multiple resolutions. The results demonstrate hot spots of relative high risk across different regions and clearly show that DGGS is more suited to global analysis than conventional grids. This work contributes to a greater understanding of both the disposition of maritime risk and the advantages of adopting DGGS in supporting big data analysis.

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Accepted/In Press date: 13 July 2021

Identifiers

Local EPrints ID: 450422
URI: http://eprints.soton.ac.uk/id/eprint/450422
PURE UUID: 031f3522-b69c-4417-a544-68a3b079a28a
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: 28 Jul 2021 16:30
Last modified: 13 Dec 2021 03:30

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Contributors

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

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