A Geographic Information System (GIS)-Based Investigation of Spatiotemporal Characteristics of Pirate Attacks in the Maritime Industry
Bibliographic record
Abstract
Maritime transportation is vital for the movement of cargo between different continents and distant locations but can be disrupted by the frequent occurrence of pirate attacks. Based on the pirate attacks from July 1994 to December 2019, a spatial analysis of pirate attacks using a Geographic Information System (GIS) was conducted in the present study using the data available for tankers, dry bulk carriers, container vessels, general cargo vessels, and tugs. The adoption of the kernel density analysis was intended to identify the spatial pattern of global pirate attacks. The research results demonstrated that the pirate attacks showed a clustering pattern and were mostly associated with areas experiencing economic depression, a high unemployment rate, and social unrest. Accordingly, spatiotemporal hot spot analysis was carried out to recognize the changing directions of cold spots and hot spots over a period of time. The waters off Somalia, the Strait of Malacca, the Philippines, the Bay of Bengal, the Gulf of Guinea, and the northwest of South America were found to be the common locations of pirate attacks. The cold and hot spots of pirate attacks on the three key vessel types, including tankers, dry bulk carriers, and container vessels, were found to be similar. When considering the same area, the trends of cold and hot spots of different vessel types being attacked were substantially different. This study can provide a useful guideline for the International Maritime Organization and other relevant organizations in the world to design and implement targeted strategies to combat and mitigate pirate attacks. Additionally, the introduction of a GIS may help to envision the spatial and temporal distribution of pirate attacks and to explore the characteristics of pirate behaviors at sea and the patterns of piracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".