A collection of scripts and tools that tracks the availability of Hotspot 2.0, Passpoint, and OpenRoaming networks in the wild.

Pulls from the Wigle.net dataset.

Note: Information here may not be entirely accurate or complete, but it's the best semi-public dataset available for tracking this kind of information. The stats are dynamic, we only pull year to date stats. But ultimately we'll update it to do devices mapped in the last 365 days.

Note:: All of the categorizations and identifiers were discovered from public documentation and research that is repeatable with google searches.

• Track OpenRoaming Passpoint
  • OpenRoaming and Hotspot 2.0 Stats Table
  • Most Common RCOI Enabled SSIDs
  • Unique RCOIs
  • OpenRoaming and Hotspot 2.0 Table Mapped
  • Table of Contents
  • Introduction
  • Installation
  • Usage
    • Scripts
      • map_wigle_devices.py
      • filter_sort_wigle_results.py
      • generate_map_html.py
      • generate_map_png.py
      • update_readme_stats.py
      • classify_locations.py
    • Running the Scripts
  • Automated Workflow
    • Workflow: .github/workflows/update_statistics.yml
  • GitHub Pages
  • Contributing
  • License

This project aims to track the availability of Hotspot 2.0, Passpoint, and OpenRoaming networks using data collected from WiGLE and other sources. The collected data is processed and visualized using various scripts.

1. Clone the repository:

   git clone https://github.com/yourusername/track-openroaming-passpoint.git
   cd track-openroaming-passpoint

2. Set up a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Create a .env file in the root directory with your API credentials:

   API_NAME=your_api_name
   API_TOKEN=your_api_token
   AUTH_HEADER=your_auth_header

Fetches data from the WiGLE API and saves it to a CSV file.

Filters and sorts the fetched WiGLE results and processes them for further analysis.

Generates an interactive HTML map using Folium, displaying WiGLE data points. The HTML map files are saved to the Output directory and an archive subdirectory with the current year appended.

Generates a static PNG map using Matplotlib and Basemap, displaying WiGLE data points. The PNG map files are saved to the Output directory and an archive subdirectory with the current year appended.

Updates the README.md file with statistics about the WiGLE data.

Classifies SSIDs from the WiGLE data as Residential, Business, or Public based on heuristics and performs reverse geocoding to add location information. The results are saved to a new CSV file.

1. Fetch data from WiGLE:

   python scripts/map_wigle_devices.py

2. Filter and sort the results:

   python scripts/filter_sort_wigle_results.py

3. Generate the HTML map:

   python scripts/generate_map_html.py

4. Generate the PNG map:

   python scripts/generate_map_png.py

5. Update the README with statistics:

   python scripts/update_readme_stats.py

6. Classify SSIDs and perform reverse geocoding:

   python scripts/classify_locations.py

The project includes a GitHub Actions workflow that runs the scripts automatically every 24 hours and updates the repository.

• Installs the pip requirements.
• Runs the scripts in order:
  • map_wigle_devices.py
  • generate_map_html.py
  • generate_map_png.py
  • update_readme_stats.py
• Commits the changes back to the repository.

The HTML maps are made available through GitHub Pages. The workflow ensures the HTML files are copied to the docs folder, which is configured for GitHub Pages.

Contributions are welcome! Please open an issue or submit a pull request.

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