🚴‍♂️ UnfallAtlasBicycleAccidentAnalyzer

A comprehensive analysis tool for bicycle accidents in Germany using official Unfallatlas data (2016-2024). Features interactive web interface, temporal trend analysis, and geographic visualization.

✨ Features

🗺️ Multiple Interfaces

• Streamlit Web App: Modern, interactive web interface (recommended)
• GUI Application: Traditional desktop interface with Tkinter
• Command Line: Scriptable interface for automation

📊 Comprehensive Analysis

• Load and process traffic accident data from German Unfallatlas (2016-2024)
• Advanced filtering by accident type, severity, time, and lighting conditions
• Interactive maps with color-coded severity indicators
• Temporal trend analysis showing changes over 9 years
• Seasonal and day-of-week pattern analysis
• Statistical dashboards with exportable data

🎯 Smart Filtering

• Geographic: Select analysis areas with GPS coordinates and dimensions
• Temporal: Filter by year range, hour of day, month, day of week
• Severity: Filter by accident outcomes (fatal, serious injury, light injury)
• Accident Type: Filter by collision types (pedestrian, oncoming traffic, etc.)
• Environmental: Filter by lighting conditions (daylight, twilight, darkness)

🚀 Quick Start

Prerequisites

• Python 3.8+
• Internet connection (for map tiles)

Installation

1. Clone the repository

git clone https://github.com/yourusername/UnfallAtlasBicycleAccidentAnalyzer.git
cd UnfallAtlasBicycleAccidentAnalyzer

2. Install dependencies

pip install -r requirements.txt

3. Download data (place in data/ folder)

• Download German Unfallatlas data from Statistische Ämter des Bundes und der Länder
• Place zip files in data/ folder (see Data Requirements)

Usage

🌐 Streamlit Web App (Recommended)

streamlit run streamlit_app.py

• Open browser to http://localhost:8501
• Select location and filters in sidebar
• Explore interactive maps and trend analysis

🖥️ Desktop GUI

python accident_analyzer.py

1. Click "Load Accident Data"
2. Enter coordinates and area dimensions
3. Click "Analyze and Plot"

⌨️ Command Line

python accident_analyzer.py --cli

Follow prompts for coordinates and area dimensions.

📊 Analysis Capabilities

🗺️ Interactive Mapping

• Color-coded markers by accident severity (🔴 fatal, 🟠 serious, 🟡 light)
• Hover details with accident information
• Multiple map styles (OpenStreetMap, Satellite imagery)
• Real-time filtering with instant map updates

📈 Trend Analysis

• 9-year temporal trends (2016-2024)
• Seasonal patterns and monthly distributions
• Day-of-week analysis for targeted safety campaigns
• Before/after comparisons for policy impact assessment

📋 Data Export

• CSV export of filtered datasets
• Interactive charts with Plotly
• Statistical summaries for reports

📁 Data Requirements

Place these files in the data/ folder:

data/
├── Unfallorte2016_EPSG25832_CSV.zip
├── Unfallorte2017_EPSG25832_CSV.zip
├── Unfallorte2018_EPSG25832_CSV.zip
├── Unfallorte2019_EPSG25832_CSV.zip
├── Unfallorte2020_EPSG25832_CSV.zip
├── Unfallorte2021_EPSG25832_CSV.zip
├── Unfallorte2022_EPSG25832_CSV.zip
├── Unfallorte2023_EPSG25832_CSV.zip
└── Unfallorte2024_EPSG25832_CSV.zip

Data Source: German Unfallatlas

Note: The tool handles format inconsistencies between years automatically (2016-2019 use .txt files in csv/ folders, 2020-2024 use direct .csv files).

🏙️ Example Locations

Pre-configured coordinates for major German cities:

• Munich: 48.1351, 11.5820
• Augsburg: 48.3661, 10.8985
• Berlin: 52.5200, 13.4050
• Hamburg: 53.5511, 9.9937

⚙️ Technical Details

Architecture

• Core: AccidentAnalyzer class for data processing
• Frontend: Streamlit web app with Plotly visualizations
• Legacy: Tkinter GUI and CLI interfaces

Data Processing

• Coordinate Systems: WGS84 ↔ UTM Zone 32N (EPSG:25832)
• Format Handling: Automatic detection of CSV/TXT formats
• Performance: Optimized for datasets with 300K+ records

Dependencies

• Core: pandas, numpy, pyproj, plotly
• Mapping: geopandas, contextily (legacy), streamlit-folium (web)
• UI: streamlit, tkinter (built-in)

🚀 Performance Notes

• Large datasets: 300K+ bicycle accidents across 9 years
• Memory usage: ~500MB RAM for full dataset
• Rendering: Interactive maps handle 10K+ points efficiently
• Network: Internet required for map tiles

🤝 Contributing

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

📄 License

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

🙏 Acknowledgments

• Data: Statistische Ämter des Bundes und der Länder
• Maps: OpenStreetMap contributors
• Libraries: Streamlit, Plotly, GeoPandas communities

📧 Support

For questions or issues:

• Open an issue
• Check existing documentation
• Review the data format guide