Marine Safety Incidents

Scrape and import incident data from multiple sources.

marine_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.

marine_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.

marine_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.

marine_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

from worldenergydata.marine_safety.scrapers import MarineSafetyScraper
from worldenergydata.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

from worldenergydata.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

from worldenergydata.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

from worldenergydata.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

from worldenergydata.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

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

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

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

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

Key Classes

Data Sources

Output Formats

Incident CSV

incident_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

{
  "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