open access publication

Article, Early Access, 2024

PiracyAnalyzer: Spatial temporal patterns analysis of global piracy incidents

RELIABILITY ENGINEERING & SYSTEM SAFETY, ISSN 0951-8320, 0951-8320, Volume 243, 10.1016/j.ress.2023.109877

Contributors

Liang, Maohan 0000-0001-7470-3313 [1] [2] Li, Huanhuan 0000-0002-4293-4763 [3] Li, Yun Rose 0000-0002-8077-4975 [2] Lam, Jasmine Siu Lee (Corresponding author) [4] Qin, Pei 0000-0003-1385-493X [3]

Affiliations

  1. [1] Natl Univ Singapore, Dept Civil & Environm Engn, Singapore, Singapore
  2. [NORA names: Singapore; Asia, South];
  3. [2] Wuhan Univ Technol, Sch Nav, Hubei Key Lab Inland Shipping Technol, Wuhan, Peoples R China
  4. [NORA names: China; Asia, East];
  5. [3] Liverpool John Moores Univ, Sch Engn Technol & Maritime Operat, Liverpool, England
  6. [NORA names: United Kingdom; Europe, Non-EU; OECD];
  7. [4] Tech Univ Denmark, Dept Technol Management & Econ, Lyngby, Denmark
  8. [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

Maritime piracy incidents present significant threats to maritime security, resulting in material damages and jeopardizing the safety of crews. Despite the scope of the issue, existing research has not adequately explored the diverse risks and theoretical implications involved. To fill that gap, this paper aims to develop a comprehensive framework for analyzing global piracy incidents. The framework assesses risk levels and identifies patterns from spatial, temporal, and spatio-temporal dimensions, which facilitates the development of informed anti-piracy policy decisions. Firstly, the paper introduces a novel risk assessment mechanism for piracy incidents and constructs a dataset encompassing 3,716 recorded incidents from 2010 to 2021. Secondly, this study has developed a visualization and analysis framework capable of examining piracy incidents through the identification of clusters, outliers, and hot spots. Thirdly, a number of experiments are conducted on the constructed dataset to scrutinize current spatial-temporal patterns of piracy accidents. In experiments, we analyze the current trends in piracy incidents on temporal, spatial, and spatio-temporal dimensions to provide a detailed examination of piracy incidents. The paper contributes new understandings of piracy distribution and patterns, thereby enhancing the effectiveness of anti-piracy measures.

Keywords

Data visualization, Hot spots, Maritime security, Piracy incidents, Spatial -temporal patterns

Data Provider: Clarivate