West Yorkshire Traffic Analysis

🚦 West Yorkshire Traffic Analysis & Forensic Reporting

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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.

🚀 Live Demo

Check out the live dashboard here: [https://westyorkshiretrafficanalysis-s4uloec7gumf65rz2thpl3.streamlit.app/]

📸 Screenshots

See the full gallery here: screenshots/

🛠️ Project Architecture

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

1. Interactive Dashboard (`app.py`)

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.

2. Forensic Reporting Engine (`main.py`)

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.

📁 File Structure

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.

🧰 Tech Stack

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.

⚙️ Installation & Local Usage

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

🙏 Acknowledgments

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! 🍻

⚖️ License

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

Built By Roy Peters Click here for contact details