Welcome! 👋 This repository explores how Auckland's bus routes operate and quantifies spatial accessibility at popular sites, including the CBD and suburbs like Mount Eden. 🚍
As an undergraudate summer research project, we’ve developed an interactive Shiny app to visualise and understand the dynamics of accessibility across the city. Whether you're a transport planner, urban analyst, or just curious about Auckland's public transport system, this project offers valuable insights! 🌏
🧐 Why This Matters Public transport accessibility is a critical factor in urban mobility. By analyzing bus routes and accessibility in Auckland, this project aims to:
- Highlight gaps in the public transport network.
- Provide insights for improved urban planning and policy-making.
- Dynamic Visualisation
- Explore accessibility changes in real time using the Shiny app.
- Dive into metrics that highlight disparities in accessibility.
- Python for data processing and analysis.
- Shiny in Python for interactive visualization.
- GIS tools for spatial analysis.
- ShinyApp/: Contains the Shiny app code.
- ShinyApp/data/: Preprocessed datasets used in the analysis.
- DataProcessing/: Python scripts for cleaning and processing raw data to be used by the Shiny app.
- DataProcessing/Data/: Raw data sourced from Auckland Transport and Stats NZ
- Both data folders can be found for download at: https://uoa-my.sharepoint.com/:f:/g/personal/hshi103_uoa_auckland_ac_nz/EkJpKkNH7xlFtMg4sWBgh-0BWUKdaXt0rRnYjrLKdokGUw?e=RIwn1C
- Execute Python scripts in DataProcessing/ in sequence:
- daily_busses.py
- add_routes_to_busstops.py
- process_routes.py
- ShinyApp_data_processing.py (customise center coordinates and study radius)
- Copy generated GeoJSON files from DataProcessing/outputs/geojson to ShinyApp/data
To run the app locally:
pip install shiny
python -m shiny run ShinyApp/app.pyInstructions for hosting can be found at: https://shiny.posit.co/py/docs/deploy.html
We welcome contributions! If you have ideas or suggestions, feel free to reach out!