The University of Southampton
Email:
A.Rawson@soton.ac.uk

Andrew Rawson BA (Hons), FRGS, MIMarEST, AMNI

2018 - Present: Doctoral Researcher at University of Southampton

2011 - 2018: Senior Consultant at Marico Marine

 

Andrew Rawson is a doctoral researcher and maritime consultant specialising in risk analysis and quantitative methods for maritime risk assessment. He has more than a decade experience as project manager and lead analyst of delivering navigation risk studies to ports, offshore developers and governments around the world. In 2018, Andrew commenced doctoral study at the University of Southampton with his thesis entitled “Intelligent Geospatial Analytics for Maritime Risk Assessment”. His principal research interests include the development and application of risk models and machine learning to predict the likelihood of navigation accidents. He has a First-Class degree in Geography from the University of Nottingham. He is a Fellow of the Royal Geographical Society, Member of IMAREST and Associate Member of the Nautical Institute.

Research

Research interests

Shipping is an essential component of the global economy, but every year accidents result in significant loss of life and environmental pollution. Navigating vessels might collide with one another, run aground or capsize amongst a multitude of challenges to operating at sea. As the number and sizes of vessels have increased, novel or autonomous technologies are adopted and new environments such as the Arctic are exploited, these risks are likely to increase.

Coastal states, ports and developers have a responsibility to assess these risks, and where the risk is intolerably high, implement mitigation measures to reduce them. To support this, significant research has developed a field of maritime risk analysis, attempting to employ rigorous scientific study to quantifying the risk of maritime accidents. Such methods are diverse, yet have received criticism for their lack of methodological rigour, narrow scope and one-dimensional rather than spatial approach to risk. More broadly, there is a recognition that by combining different datasets together, novel techniques might lead to more robust and practicable risk analysis tools.

Andrew's research interest contributes to this purpose. It argues that by integrating massive and heterogenous datasets related to vessel navigation, machine learning algorithms can be used to predict the relative likelihood of accident occurrence. Whilst such an approach has been adopted in other disciplines this remains relatively unexplored in maritime risk assessment. To achieve this, four aspects are under investigation. Firstly, to enable fast and efficient integration of different spatial datasets, the Discrete Global Grid System has been trialled as the underlying spatial data structure. Such an approach is shown to have numerous advantageous qualities, particular relevant to large scale spatial analysis, that addresses some of the limitations of the Modifiable Areal Unit Problem. Secondly, a comparison is made of various conventional and machine methodologies, identifying that whilst the latter are often more complex, they address some failings in conventional methods. Thirdly, to overcome the infrequency of accidents, near-miss modelling has been undertaken. Finally, machine learning methods are used to develop high-resolution and reliable strategic and real-time risk analysis.

The results demonstrate the potential of these methods as a novel form of maritime risk analysis, supporting decision makers and contributing to improving the safety of vessels and the protection of the marine environment.

My supervision team is spread across both Electronics and Computer Science and the Business school and I am guided by Dr Zoheir Sabeur (Bournemouth University), Dr Long Tran-Thahn (University of Warwick) and Dr Mario Brito (University of Southampton). This research is supported by the Southampton Marine and Maritime Institute (SMMI) and will be conducted in conjunction with the Horizon 2020 SEDNA project.

Publications

Rawson, Andrew, David (2017) An analysis of vessel traffic flow before and after the grounding of the MV Rena, 2011. 12th International Conference on Marine Navigation and Safety of Sea Transportation, Poland. 21 - 23 Jun 2017. (doi:10.1201/9781315099132).

Rawson, Andrew David and Rogers, Edward (2015) Assessing the impacts to vessel traffic from offshore wind farms in the Thames Estuary. Scientific Journals of the Maritime University of Szczecin, 43 (115), 99-107.

Rawson, Andrew David, Rogers, Ed, Foster, David and Phillips, David (2014) Practical application of domain analysis: port of London case study. Journal of Navigation, 67 (2), 193-209. (doi:10.1017/S0373463313000684).

Rawson, Andrew David and Riding, John (2014) Improving movement management using GIS technology: modern tools for the harbour master. International Harbour Masters Congress, Belgium. 26 - 30 May 2014.

Rawson, Andrew, David, Rogers, Edward and Towens, Mark (2016) Determination of vessel traffic capacity in Central London. International Harbour Masters Congress, Canada. 30 May - 03 Jun 2016.

Rawson, Andrew, David, Sabeur, Zoheir and Correndo, Gianluca (2019) Spatial challenges of maritime risk analysis using big data. Soares, C. Guedes (ed.) In Proceedings of the 8th International Conference on Collision and Grounding of Ships and Offshore Structures (ICCGS 2019). vol. 4, CRC Press / Balkema. pp. 275-283 .

Rawson, Andrew David and Brito, Mario (2020) Modelling of ship navigation in extreme weather events using machine learning. In Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment And Management Conference. Research Publlishing. 8 pp .

Rawson, Andrew David and Brito, Mario (2021) A critique of the use of domain analysis for spatial collision risk assessment. Ocean Engineering, 219, [108259]. (doi:10.1016/j.oceaneng.2020.108259).

Rawson, Andrew David and Brito, Mario (2021) Developing contextually aware ship domains using machine learning. Journal of Navigation. (doi:10.1017/S0373463321000047).

Rawson, Andrew David, Brito, Mario, Sabeur, Zoheir and Tran-Thanh, Long (2021) A machine learning approach for monitoring ship safety in extreme weather events. Safety Science, 141, [105336]. (doi:10.1016/j.ssci.2021.105336).

Rawson, Andrew, David, Brito, Mario, Sabeur, Zoheir and Tran-Thanh, Long (2021) From Conventional to Machine Learning Methods for Maritime Risk Assessment. In Proceedings of the 14th International Conference On Marine Navigation And Safety of Sea Transportation. (In Press)

(2021) Aggregated Maritime Risk Datasets for the United States at DGGS7. Dataverse doi:10.7910/dvn/zzhbfd [Dataset]

Rawson, Andrew, David (2021) Application of quantitative route modelling of navigation safety impacts of offshore wind farms. 8TH PRIMARE (Partnership for Research in Marine Renewable Energy ) CONFERENCE 2021, Virtual. 29 - 30 Jun 2021.

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)

Rawson, Andrew, David and Brito, Mario (2021) Empirical Analysis of Ship Anchor Drag Incidents for Cable Burial Risk Assessments. 31st European Safety and Reliability Conference, Angers, France, Angers, France. 20 - 23 Sep 2021. (doi:10.3850/978-981-18-2016-8_053-cd).

Rawson, Andrew, David, Sabeur, Zoheir and Brito, Mario (2021) Intelligent geospatial maritime risk analytics using the discrete global grid system. Big Earth Data, 1-29. (doi:10.1080/20964471.2021.1965370).

Rawson, Andrew, David (2021) Mapping the safety of navigation in UK waters. Royal Geographical Society Annual Conference, Virtual, London, United Kingdom. 31 Aug - 03 Sep 2021.

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