Participant catchment maps (postcode-based)

A Jupyter notebook that demonstrates a few ways to visualise counts per Australian postcode (e.g., study participants per postcode):

• Option A (centroids): point markers and proportional circles on a Folium (Leaflet) map

• Option B (polygons): choropleth using ABS Postal Areas (POA 2021) boundaries (GeoPandas + Matplotlib, and an interactive Folium version)

The notebook is written so you can swap the public example counts for your own cohort’s participant counts.

What’s in this repo

.
├── PlotParticipantCatchment.ipynb
├── example_data.csv 
├── australian_postcodes.csv
└── README.md

Data

The notebook expects three inputs:

1. example_data.csv
   A small CSV with one row per postcode (recommended) with at least:

• postcode
• total_licences (or your cohort count column)

If you have participant-level rows, aggregate first (e.g., count participants per postcode) before merging with polygons.

2. australian_postcodes.csv
   A postcode lookup with centroid lat/long (used for centroid maps). The notebook uses the dataset from the matthewproctor/australianpostcodes repo.

3. ABS POA 2021 boundary files (for polygon maps)
   Download ASGS Edition 3 – POA 2021 boundary files (Shapefile) from the ABS, unzip, and place the folder as:

POA_2021_AUST_GDA94_SHP/POA_2021_AUST_GDA94.shp (plus its companion files)

Running the notebook with your cohort data

• Replace example_data.csv with your dataset (or keep the example and change the filename).
• Set COUNT_COL to your count column (e.g., n_participants).
• Ensure postcode is present and formatted as 4-digit strings (the notebook includes a cleaning function).

Outputs

• Interactive maps are shown inline in Jupyter.
• You can optionally export a standalone HTML file (see the “save as HTML” cell) and put it into outputs/.

Attribution & licences

If you use the NSW open data example and/or ABS boundaries, keep their attribution statements in your derived outputs and repo notes.