TITLE:
A Models Approach to Analyzing and Forecasting Oil and Piracy in the Gulf of Guinea
AUTHORS:
Nana Raymond Lawrence Ofosu-Boateng
KEYWORDS:
Bayesian Network Predictor, Forecasting, Gulf of Guinea, Maritime Security, Maritime Transportation, Ordinal Logistic Regression, Series Hazard Model
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.5 No.3,
March
17,
2017
ABSTRACT: Piracy at sea is a great detriment to the transportation of goods through our seaways. Seaborne trade accounts for about 80% of global trade. To effectively monitor their territorial waters and Exclusive Economic Zones is problematic for many countries in West and Central Africa. To figure out the modus operandi of the pirates and be aware and proactive enough to create effective countermeasures remains a crucial endeavour to arrest this menace. The Gulf of Guinea (GoG) has been an active arena for piratical activities. This paper utilizes systematic analysis to determine and examine pirate incidents that occured from 2006 to 2015 with data from the International Maritime Bureau (IMB). Analysis of the data was done using RealStats resouce add-on for Excel and predictive modelling was used to test the forecasting ability of various models empirically. Three key models namely, Ordinal Logistic Regression, Bayesian Network Predictor, and Series Hazard Models are used to compute the probable increase in pirate attacks with a forecast into the next fifteen years. This is essential to the trend of the new wave of pirate attacks and its current and potential impact on Maritime Transportation and Maritime Security. While there is an international presence in the Gulf of Aden, there is no such dedicated force in the GoG. The findings from the statistical results are to assist policymakers in their decision making when using their scarce law enforcement apparatus to combat Maritime Piracy and their Maritime Transportation and Security challenges.