Rapid Street Assessment (RSAs) are designed to provide quick, comprehensive analysis of street and land use data - using USRNs (Unique Street Reference Numbers).
It consists of a Python backend using Robyn framework.
- Fetches and analyses street network data and special designations
- Provides detailed information about:
- Street characteristics
- Special designations and restrictions
- Engineering difficulties
- Traffic sensitivity
- Street Manager Aggregated Stats
- Uses chat gpt-4o mini to generate human-readable analysis of the technical data
- Retrieves and processes land use and building data
- Provides insights about:
- Property types and distributions
- Land use categories
- Total area statistics
- Building characteristics
- Uses chat gpt-4o mini to generate human-readable analysis of the technical data
- Retrieves and processes street manager data, street information and land use data and combines them into a single object,
- Merges insight from land use and street informatioon to provide a recommendation for collaborative street works
- Uses chat gpt-4o mini to generate human-readable analysis of the technical data
- RESTful API endpoints:
/street-infoand/street-info-llm: Summary of network and RAMI data as well as street manager stats/land-use-infoand/land-use-info-llm: Summary of Land use and building information/collaborative-street-works-llm: Collaborative street works recommendation endpoint
- Asynchronous processing of multiple OS NGD API calls
- Intelligent data filtering and data aggregation
- Integration with OpenAI's chat gpt-4o mini for data interpretation
- Python ≥3.11
- Robyn (API framework)
- LangChain (AI processing)
- MotherDuck (data storage)
- Ordnance Survey National Geographic Database (NGD)
- Supports multiple OS data collections:
- RAMI (Routing and Asset Management Information)
- Network data
- Land use data
- Street Manager data