STIB-Hach-My-Ride

Geospatial analysis of Brussels public transport (STIB) data — a hackathon or academic project exploring mobility patterns and route analysis.

The Problem

• Public transport usage data contains rich spatial and temporal patterns that are not visible from raw trip records alone.
• Analysing STIB (Brussels transit network) data requires handling geospatial formats, coordinate projections, and large tabular datasets efficiently.

The Approach

Inputs: STIB transit data files in data/, including geospatial shapefiles or CSVs.

Processing: Jupyter notebooks in notebooks/ perform data loading, spatial calculations (haversine distance via helpers.py), and exploratory analysis. pyshp handles shapefile parsing; pyproj handles coordinate projection.

Outputs: Spatial visualisations and analysis of Brussels transit patterns.

Value Delivered

• Demonstrates geospatial data analysis skills using real public transport data.
• Haversine distance calculations enable stop-to-stop travel time and distance analysis.
• Useful as a reference for transit data analysis in Python.

Scope & Status

• Project type: Hackathon / learning project
• Current state: Archived
• Known limitations:
  • Dataset is specific to Brussels STIB network; not generalised.
  • No README or setup docs existed prior to this file.
  • Notebooks reflect exploratory work, not production code.

Tech Stack

• Language: Python 3.9
• Libraries: Pandas, NumPy, Matplotlib, scikit-learn, pyshp, pyproj, tqdm
• Format: Jupyter Notebooks

Who This Is For

Developers and analysts interested in geospatial transit data analysis in Python.

Getting Started

pip install pipenv
pipenv install
pipenv run jupyter notebook

License

Not specified.