---

name: marine-safety-incidents description: Collect, analyze, and report marine safety incident data from 7 global maritime authorities. Use for incident scraping, safety trend analysis, risk assessment, geographic hotspot identification, and marine safety reporting.

Marine Safety Incidents Skill

Collect, analyze, and report marine safety incident data from global maritime authorities including USCG, NTSB, BSEE, IMO, and more.

When to Use

• Marine safety incident data collection and scraping
• Safety trend analysis and risk assessment
• Geographic hotspot identification
• Incident type classification and severity analysis
• Environmental impact assessment from marine incidents
• Regulatory compliance reporting
• Root cause analysis

Prerequisites

• Python environment with worldenergydata package installed
• Database connection (PostgreSQL recommended)
• API keys for relevant data sources (if applicable)

Analysis Types

1. Incident Data Collection

Scrape and import incident data from multiple sources.

Copymarine_safety:
  collection:
    flag: true
    sources:
      - uscg       # US Coast Guard
      - ntsb       # National Transportation Safety Board
      - bsee       # Bureau of Safety and Environmental Enforcement
      - imo        # International Maritime Organization
      - maib       # UK Marine Accident Investigation Branch
      - atsb       # Australian Transport Safety Bureau
      - tsb        # Canadian Transportation Safety Board
    date_range:
      start: "2020-01-01"
      end: "2024-12-31"
    output:
      database: "marine_safety_db"
      format: "normalized"

2. Trend Analysis

Analyze incident trends over time.

Copymarine_safety:
  trend_analysis:
    flag: true
    grouping:
      - by_year
      - by_month
      - by_incident_type
      - by_severity
    metrics:
      - incident_count
      - fatality_rate
      - injury_rate
      - environmental_impact_score
    output:
      report_file: "results/safety_trends.html"
      data_file: "results/trend_data.csv"

3. Geographic Analysis

Identify incident hotspots and high-risk areas.

Copymarine_safety:
  geographic_analysis:
    flag: true
    regions:
      - gulf_of_mexico
      - north_sea
      - asia_pacific
    clustering:
      method: "dbscan"
      eps: 50  # km
    output:
      map_file: "results/incident_hotspots.html"
      summary: "results/geographic_summary.json"

4. Risk Assessment

Calculate risk scores for vessel types and operations.

Copymarine_safety:
  risk_assessment:
    flag: true
    vessel_types:
      - tanker
      - cargo
      - offshore_platform
      - drilling_rig
    factors:
      - historical_incidents
      - environmental_conditions
      - operational_complexity
    output:
      risk_matrix: "results/risk_matrix.csv"
      recommendations: "results/risk_recommendations.md"

Python API

Data Collection

Copyfrom worldenergydata.modules.marine_safety.scrapers import MarineSafetyScraper
from worldenergydata.modules.marine_safety.database import IncidentDatabase

# Initialize scraper
scraper = MarineSafetyScraper()

# Scrape from specific source
incidents = scraper.scrape(
    source="uscg",
    start_date="2023-01-01",
    end_date="2023-12-31"
)

# Store in database
db = IncidentDatabase()
db.insert_incidents(incidents)
print(f"Imported {len(incidents)} incidents")

Incident Analysis

Copyfrom worldenergydata.modules.marine_safety.analysis import IncidentAnalyzer

# Initialize analyzer
analyzer = IncidentAnalyzer(database_url="postgresql://...")

# Get trend summary
trends = analyzer.get_trends(
    start_date="2020-01-01",
    end_date="2024-12-31",
    grouping="monthly"
)

# Analyze by incident type
type_summary = analyzer.analyze_by_type(
    incident_types=["collision", "grounding", "fire", "explosion"]
)

# Get severity distribution
severity = analyzer.severity_distribution()

Geographic Hotspot Detection

Copyfrom worldenergydata.modules.marine_safety.analysis import GeographicAnalyzer

# Initialize geographic analyzer
geo = GeographicAnalyzer()

# Find hotspots
hotspots = geo.detect_hotspots(
    region="gulf_of_mexico",
    method="dbscan",
    min_incidents=5
)

# Generate interactive map
geo.generate_map(
    hotspots=hotspots,
    output_file="results/hotspot_map.html"
)

Risk Scoring

Copyfrom worldenergydata.modules.marine_safety.analysis import RiskAssessor

# Initialize risk assessor
risk = RiskAssessor()

# Calculate risk scores
scores = risk.calculate_risk(
    vessel_type="offshore_platform",
    region="north_sea",
    factors=["weather", "traffic_density", "historical_incidents"]
)

print(f"Risk Score: {scores['overall']:.2f}")
print(f"Risk Level: {scores['level']}")  # LOW, MEDIUM, HIGH, CRITICAL

Reporting

Copyfrom worldenergydata.modules.marine_safety.visualization import SafetyReportGenerator

# Initialize report generator
reporter = SafetyReportGenerator()

# Generate comprehensive report
report = reporter.generate_report(
    start_date="2023-01-01",
    end_date="2023-12-31",
    sections=[
        "executive_summary",
        "trend_analysis",
        "geographic_distribution",
        "vessel_type_breakdown",
        "recommendations"
    ],
    output_file="results/safety_report.html"
)

CLI Usage

Copy# Scrape incident data
python -m worldenergydata.modules.marine_safety.cli scrape --source uscg --year 2023

# Analyze trends
python -m worldenergydata.modules.marine_safety.cli analyze --type trends --output trends.html

# Generate risk report
python -m worldenergydata.modules.marine_safety.cli report --format html --output safety_report.html

# Export data
python -m worldenergydata.modules.marine_safety.cli export --format csv --output incidents.csv

Key Classes

Data Sources

Output Formats

Incident CSV

Copyincident_id,date,location_lat,location_lon,vessel_type,incident_type,severity,fatalities,injuries,source
INC001,2023-05-15,28.5,-88.2,tanker,collision,high,0,3,uscg
INC002,2023-06-20,29.1,-94.5,platform,fire,critical,2,5,bsee

Risk Assessment JSON

Copy{
  "assessment_date": "2024-01-15",
  "vessel_type": "offshore_platform",
  "region": "gulf_of_mexico",
  "overall_risk_score": 7.2,
  "risk_level": "HIGH",
  "factors": {
    "historical_incidents": 8.5,
    "weather_exposure": 6.0,
    "traffic_density": 7.0
  },
  "recommendations": [
    "Increase safety inspections",
    "Enhanced weather monitoring"
  ]
}

Best Practices

1. Rate limiting - Respect source rate limits when scraping
2. Data validation - Validate and deduplicate incoming data
3. Incremental updates - Use incremental scraping for efficiency
4. Geographic accuracy - Verify coordinates for hotspot analysis
5. Source attribution - Always track data provenance

Related Skills

• bsee-data-extractor - BSEE-specific extraction
• field-analyzer - Field-level analysis
• energy-data-visualizer - Visualization

References

• USCG Marine Safety Information Portal
• BSEE Incident Statistics
• IMO GISIS Maritime Casualties Database
• DNV Maritime Safety Standards