North America Fire Analysis v1.4.3

Comprehensive Fire Detection Analysis System for USA & Canada

🔥 Overview

北米地域（アメリカ合衆国・カナダ）における火災検知データの包括的クラスタリング分析システム。NASA FIRMS衛星データを活用し、機械学習による高度な分析を実施します。

Key Features

• Real-time Data: NASA FIRMS VIIRS_SNPP_NRT satellite data
• Geographic Coverage: USA & Canada (25°N-70°N, 170°W-50°W)
• Advanced ML: FAISS k-means clustering with sentence-transformers
• Comprehensive Analysis: Geographic, temporal, intensity, and regional analysis
• Professional Reports: Automated Markdown report generation
• Rich Visualizations: t-SNE plots, geographic distribution, temporal patterns

🌍 Geographic Coverage

Coverage Area: North America
- Latitude: 25°N to 70°N
- Longitude: 170°W to 50°W

Regions Included:
├── Alaska
├── Western Canada (British Columbia, Alberta)
├── Central Canada (Saskatchewan, Manitoba, Ontario)
├── Eastern Canada (Quebec, Atlantic Provinces)
├── Western USA (California, Oregon, Washington, etc.)
├── Midwest USA (Great Plains, Great Lakes)
├── Southern USA (Texas, Florida, etc.)
├── Eastern USA (Northeast, Southeast)
└── Hawaii

📊 Analysis Capabilities

1. Geographic Analysis

• Cluster centroids and spatial distribution
• Geographic density analysis
• Regional fire pattern identification
• Multi-region cluster detection

2. Temporal Analysis

• Hourly fire activity patterns
• Daily/weekly trend analysis
• Peak activity time identification
• Fire duration analysis

3. Intensity Analysis

• Fire brightness (temperature) analysis
• Confidence level distribution
• Fire intensity categorization
• High-risk fire identification

4. Regional Characteristics

• 9 detailed North American regions
• Cross-regional fire pattern analysis
• Regional diversity scoring
• Dominant region identification

🚀 Quick Start

Prerequisites

Python 3.8+
pip install -r requirements.txt

Installation

git clone https://github.com/yourusername/north-america-fire-analysis-v1-4-3.git
cd north-america-fire-analysis-v1-4-3
pip install -r requirements.txt

Basic Usage

python north_america_firms_pipeline_v143.py

Advanced Configuration

# Edit config/config_north_america_firms.json
{
  "nasa_firms": {
    "days_back": 10,
    "confidence_threshold": 60,
    "area_params": {
      "north": 70,
      "south": 25,
      "east": -50,
      "west": -170
    }
  }
}

📁 Project Structure

north-america-fire-analysis-v1-4-3/
├── north_america_firms_pipeline_v143.py  # Main pipeline
├── config/
│   └── config_north_america_firms.json   # Configuration
├── scripts/
│   ├── data_collector.py                 # NASA FIRMS data collection
│   ├── model_loader.py                   # ML model loading
│   ├── embedding_generator.py            # Text embeddings
│   ├── clustering.py                     # Clustering algorithms
│   ├── visualization.py                  # Visualizations
│   ├── adaptive_clustering_selector.py   # Adaptive clustering
│   ├── hdbscan_clustering.py            # HDBSCAN implementation
│   ├── cluster_feature_analyzer.py       # Feature analysis
│   └── fire_analysis_report_generator.py # Report generation
├── docs/                                 # Documentation
├── results/                             # Analysis outputs
└── README.md                            # This file

🛠️ Core Components

1. Data Collection (data_collector.py)

• NASA FIRMS API integration
• Automatic data filtering and validation
• Geographic boundary enforcement
• Confidence threshold application

2. Machine Learning Pipeline

• Embeddings: sentence-transformers/all-MiniLM-L6-v2 (384 dimensions)
• Clustering: Adaptive FAISS k-means + HDBSCAN
• Quality Metrics: Silhouette score, Calinski-Harabasz index, Davies-Bouldin index

3. Analysis Engine (cluster_feature_analyzer.py)

• Geographic distribution analysis
• Temporal pattern extraction
• Fire intensity categorization
• Regional classification system

4. Visualization (visualization.py)

• t-SNE 2D cluster visualization
• Geographic distribution maps
• Temporal pattern charts
• Intensity distribution plots

5. Report Generation (fire_analysis_report_generator.py)

• Comprehensive Markdown reports
• Executive summary generation
• Statistical analysis tables
• Visualization integration

📈 Sample Output

Analysis Statistics

Total Fire Detections: 20,000+
Clusters Identified: 15
Quality Score: 0.672
Noise Ratio: 0.0%
Processing Time: ~122 seconds

Generated Files

results/
├── nasa_firms_data.csv
├── tsne_plot.png
├── score_distribution.png
├── cluster_geographic_distribution.png
├── cluster_regional_analysis.png
├── cluster_intensity_analysis.png
├── cluster_temporal_patterns.png
└── comprehensive_fire_analysis_report.md

🔧 Configuration Options

NASA FIRMS Settings

{
  "nasa_firms": {
    "api_url": "https://firms.modaps.eosdis.nasa.gov/api/area/csv",
    "map_key": "your_api_key",
    "satellite": "VIIRS_SNPP_NRT",
    "days_back": 10,
    "confidence_threshold": 60
  }
}

ML Model Settings

{
  "embedding": {
    "model_name": "sentence-transformers/all-MiniLM-L6-v2",
    "device": "cpu"
  },
  "clustering": {
    "random_state": 42,
    "min_cluster_size": 10
  }
}

🌎 Regional Classification System

The system identifies 9 distinct North American regions:

1. Alaska: Arctic and subarctic regions
2. Western Canada: British Columbia, Alberta
3. Central Canada: Saskatchewan, Manitoba, Ontario
4. Eastern Canada: Quebec, Atlantic provinces
5. Western USA: Pacific Coast, Mountain West
6. Midwest USA: Great Plains, Great Lakes
7. Southern USA: Texas, Southeast, Gulf Coast
8. Eastern USA: Northeast, Mid-Atlantic
9. Hawaii: Pacific islands

📊 Analysis Methodology

1. Data Preprocessing

• Geographic filtering (North America bounds)
• Confidence threshold application (≥60%)
• Data validation and cleaning

2. Feature Engineering

• Text description generation from fire attributes
• 384-dimensional embedding generation
• Spatial and temporal feature extraction

3. Clustering Analysis

• Adaptive algorithm selection (k-means/HDBSCAN)
• Quality-based optimization
• Noise detection and filtering

4. Feature Analysis

• Geographic centroids and spread
• Temporal activity patterns
• Intensity distribution analysis
• Regional characteristic extraction

🎯 Use Cases

1. Emergency Response

• Real-time fire cluster identification
• High-intensity fire prioritization
• Geographic resource allocation

2. Research & Analysis

• Fire pattern trend analysis
• Climate change impact assessment
• Regional fire behavior studies

3. Policy & Planning

• Fire prevention strategy development
• Cross-border coordination (USA-Canada)
• Resource allocation optimization

4. Environmental Monitoring

• Ecosystem impact assessment
• Air quality correlation analysis
• Biodiversity conservation planning

📋 Requirements

System Requirements

• Python 3.8 or higher
• 8GB+ RAM recommended
• Internet connection for NASA FIRMS API

Python Dependencies

pandas>=1.3.0
numpy>=1.21.0
scikit-learn>=1.0.0
matplotlib>=3.4.0
seaborn>=0.11.0
requests>=2.25.0
sentence-transformers>=2.0.0
faiss-cpu>=1.7.0
hdbscan>=0.8.0
plotly>=5.0.0

🚨 API Key Setup

1. Register at NASA FIRMS
2. Obtain your API key
3. Update config/config_north_america_firms.json:

{
  "nasa_firms": {
    "map_key": "YOUR_API_KEY_HERE"
  }
}

📝 Output Interpretation

Cluster Quality Score

• 0.7-1.0: Excellent clustering
• 0.5-0.7: Good clustering
• 0.3-0.5: Fair clustering
• <0.3: Poor clustering

Fire Intensity Categories

• Very High (350K+, 80%+ confidence): Emergency response required
• High (320K+, 70%+ confidence): Close monitoring needed
• Medium (310K+, 60%+ confidence): Standard monitoring
• Low (<310K): Routine observation

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• NASA FIRMS: Fire data provision
• Sentence Transformers: Text embedding technology
• FAISS: Efficient similarity search
• scikit-learn: Machine learning algorithms

📞 Support

For questions or issues:

• Create an issue on GitHub
• https://www.linkedin.com/in/yasunotkt/

🔮 Future Enhancements

• Real-time processing pipeline
• Web dashboard interface
• Mobile app integration
• Weather data correlation
• Predictive modeling
• International expansion

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Generated by North America Fire Analysis System v1.4.3
Advancing fire safety through data science and machine learning