An interactive intelligence dashboard and automated reporting tool built to analyze road safety data across West Yorkshire. This project transforms raw government datasets into actionable insights using a modern Python stack.

See the full gallery here:

This project is divided into two main components to balance real-time interaction with deep-dive analysis:

The "Frontend" of the project. It provides a real-time interface for users to explore the data.

• Dynamic Geospatial Mapping: Visualizes accident hotspots across Leeds, Bradford, Wakefield, Kirklees, and Calderdale.
• Instant Filtering: Filter by Severity (Fatal, Serious, Slight), Year, Weather, and Road Type.
• Key Metrics: High-level KPIs that update instantly based on user selection.

The "Analytical Backend." This script handles the heavy lifting of data visualization and document generation.

• 16 Custom Charts: Generates a comprehensive suite of visualizations (Trend lines, Hourly heatmaps, Vehicle type distributions).
• Automated PDF Generation: Compiles all 16 charts into a professional forensic report (West_Yorkshire_Report.pdf) for offline review.

• app.py: Entry point for the Streamlit web application.
• main.py: Logic for chart generation and PDF reporting.
• src/: Modularized helper scripts (load_data.py, filters.py, map_utils.py).
• data/: Regionalized West Yorkshire datasets (Accidents, Vehicles, Casualties).
• output_charts/: Destination folder for generated PDF forensic analyses.
• tests/ Pytest/Hypothesis suite.

• Python 3.10 (Development Environment)
• Streamlit: For the web interface.
• Pandas: For high-performance data manipulation.
• Folium/Leaflet: For interactive geospatial mapping.
• Matplotlib/Seaborn: For forensic chart generation.
• FPDF/ReportLab: For automated PDF document creation.
• reverse-geocoder: For high-speed, offline reverse geocoding of incident coordinates into human-readable locations.

To run this project locally:

1. Clone the repo: git clone https://github.com/reory/west_yorkshire_traffic_analysis.git
2. Install dependencies: pip install -r requirements.txt
3. Launch the app: streamlit run app.py

• Data Source: UK Department for Transport (DfT) Open Data.
• Community: Thanks to the Streamlit and Python communities for the robust library support.
• Testing: Special thanks to my family for "Quality Assurance" and bug reporting! 🍻

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

Built By Roy Peters Click here for contact details