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• A novel database of maritime piracy incidents capturing the characteristics of risk factors. • A holistic framework of spatio-temporal pattern mining and analysis of maritime accidents. • A novel time-series measurement method for time-based and spatio-temporal pattern analysis. • Advanced data-driven clustering and classification analysis of maritime piracy incidents. • Piracy spatial pattern analysis, findings, and implications against influential risk factors. Since the new century, we have witnessed the fast evolution of pirate attack modes in terms of locations, time, used weapons, and targeted ships. It reveals that the current understanding of pirate attack spatio-temporal patterns is fading, requiring new technologies of big data analysis to master the hidden rules of piracy-related risk spatio-temporal patterns and rationalize the development of relevant anti-piracy measures and policies. This paper aims to develop a new framework of spatio-temporal pattern mining to realize the visualization and analysis of maritime piracy incidents from different standpoints using a new piracy incident database generated from three datasets. Time-based, space-based, and spatial-temporal pattern mining of piracy incidents are systematically investigated to dissect the influence of different risk factors and mine the characteristics of the incidents. Moreover, a novel Fast Adaptive Dynamic Time Warping (FADTW) method is proposed to uncover the hidden temporal and spatial-temporal patterns of piracy incidents. Furthermore, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to extract the spatial distribution patterns and discover the high-risk areas. Finally, risk factors-based classification exploration has uncovered different spatial patterns. The findings, showing the global and local features of piracy incidents, have made significant contributions to rationalizing anti-pirate measures for safe navigation. [ABSTRACT FROM AUTHOR]

• Develop a new data-driven collision risk analysis model from a global perspective. • Generate new findings in terms of risk factor ranking for collision accident prevention. • Investigate the hierarchical significance of diverse ship types to provide valuable insights. • Conduct an in-depth investigation into the roots of collision accident factors. • Provide a reliable prediction model for future collision accident prevention. Ship collision accidents are one of the most frequent accident types in global maritime transportation. Nevertheless, conducting an in-depth analysis of collision prevention poses a formidable challenge due to the constraints of limited Risk Influential Factors (RIFs) and available datasets. This paper aims to incorporate a global perspective into a new data-driven risk model, scrutinize the root causes of collision accidents, and advance measures for their mitigation. Additionally, it seeks to analyze the spatial distribution and conduct a comprehensive comparative study on collision characteristics for both pre- and post-COVID-19, utilizing the real accident dataset collected from two reputable organizations: Global Integrated Shipping Information System (GISIS) and Lloyd's Register Fairplay (LRF). The research findings and implications encompass several crucial aspects: 1) the constructed model demonstrates its reliability and accuracy in predicting collision accidents, as evident from its prediction performance and various scenario analysis; 2) the most hazardous voyage segment for collision accidents is identified to provide valuable guidance to different stakeholders; and 3) the hierarchical significance of various ship types in the context of collision accident is highlighted regarding the most probable scenario for collision occurrences; 4) During the pandemic, the rise in collision probabilities, particularly involving older vessels and bulk carriers, implies heightened operational challenges or maintenance issues for these ship types; (5) The prominence of favorable and adverse sea conditions in collision reports underscores the significant influence of weather on accidents during the pandemic. These findings and implications help enhance safety protocols, ultimately reducing the frequency of collision accidents in the global maritime domain. [ABSTRACT FROM AUTHOR]

• Generate a novel maritime accident database capturing the characteristics of all the Risk Influencing Factors (RIFs). • Develop a data-driven BN-based risk analysis model for maritime casualty analysis. • Incorporate new RIFs and states to improve the accuracy of maritime risk analysis. • Analyse the interdependency amongst the RIFs and the causes of accident severity from a global perspective. • Generate new managerial implications for maritime risk management and accident prevention. Maritime casualty analysis needs to be addressed given the increasing safety demand in the field due to the accidents' low-frequency and high-consequence features. This paper aims to delve deeper into the factors that affect maritime accident casualties by establishing a new database and conducting an accident casualty evolution analysis. Based on the refined dataset, a pure data-driven Bayesian Network (BN) model is developed to conduct the casualty analysis of maritime accidents that occurred under different ship operational conditions. Methodologically, it introduces new risk factors to improve maritime casualty analysis accuracy through the enriched updated maritime accident database. Furthermore, the new database is categorised into five new datasets based on temporal development trends to better analyse the evolution of the casualty. Five risk analysis models are individually constructed based on different timeframes to illustrate the dynamics of the casualties and compared by seven evaluation indexes to demonstrate the effectiveness of the proposed data-driven BN model. It, for the first time, investigates the changing roles of different risk factors on maritime casualties with time. The insights gained from this model are invaluable, contributing to improved risk prediction and maritime safety strategies by acknowledging the changing patterns of maritime accidents. [ABSTRACT FROM AUTHOR]

• A framework for assessing piracy risk levels and patterns. • Constructs a dataset of global piracy incidents. • Analyses and identifies clusters, outliers, and hot spots. • Dry bulk carriers and oil tankers are primary targets of piracy attacks. • Piracy incidents are concentrated along the 0 latitude line. 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. [ABSTRACT FROM AUTHOR]