Overview

Last updated: 2026-04-04 14:27:17 NZDT

This dashboard summarises the latest availability for selected Auckland carparks and trends over time. Data is collected hourly by an automated pipeline and rendered here.

Note (Downtown carpark): Downtown is no longer operated by Auckland Transport (now operated by Precinct Properties). AT availability data for Downtown may be inaccurate. Historic data is retained for reference only.

Carparks tracked: 4
Available spaces (now): 1,924
Mean occupancy (where capacity known): 32%

Latest snapshot

Downtown data is archived (updates discontinued); historic values are shown for reference.

Occupancy trend

How full each carpark is over time? This view uses daily averages plus a smoothed curve to reveal longer-term patterns. 📊

Which days are busiest vs quietest? This weekday view reveals recurring patterns🚗.

Insights and Patterns

🏆 Popularity ranking (overall)

🗓️ Average occupancy by day of week

⏰ Hourly occupancy pattern

🔥 Heatmap: weekday × hour

📈 Weekly occupancy trend over time

🎓 Credits and Reflections

This dashboard was inspired by my STATS 220 (University of Auckland, Semester 1 2025) course, where I learnt R and R Markdown for data collection, wrangling, and visualisation.

In the course, Anna Fergusson introduced an Auckland carpark availability endpoint during a lecture. I found it really interesting, so I extended the idea into this small tool: an automated pipeline that collects the data hourly and produces this report.

Credit: Anna Fergusson 🙌

🔍 What I found interesting

Even with a small dataset, you can spot real patterns, for example:

  • Some carparks are consistently busier than others (likely influenced by location and nearby activities).
  • Day-of-week patterns could reflect work-from-home behaviour.
  • Civic sometimes shows an occupancy lift around 7–8pm, which might align with nearby events / movies and evening visits.

🚀 Next steps

There are still lots of improvements I could make (e.g., better annotations, handling missing capacity, and adding richer context like events or weather), but this is a milestone for me — and I’m happy to share it with anyone who’s curious. Thanks to everyone who’s taken the time to look at it. Happy coding! 💻🎉