Application of Geographic information system to calculate the probability of piracy occurrence

This paper shows a successful application of Geographic information system to maritime safety. Pirate is a main threat to maritime safety. It is a critical problem to calculate the probability of encountering pirates for a trip. This paper proposes a GIS based method to estimate the probability using historical data from International Maritime Bureau (IMB) and the United Nations Conference on Trade and Development (UNCTAD). At first, it is assumed that the probability is equal everywhere in the sea. The average probability of piracy is calculated. Then, an adjusting coefficient is proposed to revise the probability of piracy in each trip. The adjusting coefficient of each trip is related to the average number of piracy in every grid which is calculated in Geographic Information System (GIS) and come across by the trip. Finally, a case study of a trip from Shanghai to the Republic of Kenya is used to illustrate the application of the proposed method. The results show the average probability of encountering pirates in 2012 is 0.0216% when a ship travels a thousand miles. The adjusted probability of encountering pirates for the case is estimated to be 1.62%.

[1]  Adolf K.Y. Ng,et al.  The impacts of maritime piracy on global economic development: the case of Somalia , 2010 .

[2]  Hao Hu,et al.  Developing an Effective Fuzzy Logic Model for Managing Risks in Marine Oil Transport , 2013 .

[3]  Ian Taylor,et al.  United Nations Conference on Trade and Development (Unctad) , 2007 .

[4]  Anja Shortland,et al.  Contemporary Maritime Piracy: Five Obstacles to Ending Somali Piracy , 2013 .

[5]  David Ríos Insua,et al.  Adversarial Risk Analysis: The Somali Pirates Case , 2012, Decis. Anal..

[6]  Karl Sörenson,et al.  Wrong Hands on Deck? : Combatting Piracy and Building Maritime Security in Eastern Africa , 2011 .

[7]  Franck Guarnieri,et al.  The Contribution of Bayesian Networks to Manage Risks of Maritime Piracy against Oil Offshore Fields , 2012, DASFAA Workshops.

[8]  Franck Guarnieri,et al.  Integration of a Bayesian network for response planning in a maritime piracy risk management system , 2012, 2012 7th International Conference on System of Systems Engineering (SoSE).

[9]  Qiang Meng,et al.  Analyses and Implications of Accidents in Singapore Strait , 2012 .

[10]  Hao Hu,et al.  Spatial Analysis of Maritime Accidents Using the Geographic Information System , 2013 .

[11]  James P. Dobbins,et al.  Geographic Information Systems for Estimating Coastal Maritime Risk , 2011 .

[12]  Ken Menkhaus,et al.  The Context of Contemporary Piracy The Case of Somalia , 2012 .

[13]  Bridget L Coggins,et al.  Global patterns of maritime piracy, 2000–09 , 2012 .

[14]  Robert Beckman,et al.  Piracy and Armed Robbery Against Ships , 2014 .

[15]  Jonas W. Ringsberg,et al.  Quantitative risk analysis - Ship security analysis for effective risk control options , 2013 .

[16]  Yi-Zhou Li,et al.  Application of Fuzzy Logic to Safety Risk Assessment of China's Maritime Passages , 2012 .

[17]  Christian Bueger The Global Fight against Piracy , 2013 .

[18]  Yi-Zhou Li,et al.  Dynamic Fuzzy Logic Model for Risk Assessment of Marine Crude Oil Transportation , 2012 .

[19]  Paul J. Sanchez,et al.  Simulating pirate behavior to exploit environmental information , 2010, Proceedings of the 2010 Winter Simulation Conference.

[20]  A. Bowden The Economic Cost of Maritime Piracy , 2010 .