Background
Smukfest has an app where guests mark the artists they want to see. Those data had not yet been used to understand movement between stages.
I lead data and analytics at Smukfest, and I wanted to see the movement show by show and hour by hour. If 12,000 people walk from Shawn Mendes to Nik & Jay, security and logistics need advance warning of the pressure on each area.
What I built
SmukFlow takes more than 500,000 favourites from nearly 25,000 guests and calculates movement patterns between 194 shows across 7 stages over 8 days.
Tech stack
- SQLite for data with optimized indexes
- Python for computation and resolving time conflicts
- D3.js for interactive visualization of flows between stages
The algorithm builds a “path” for each guest based on their favorites, resolves overlaps via popularity, and aggregates all paths into crowd flows. The entire calculation runs in under 10 seconds.
The result is a map of the festival showing exactly which corridors get congested, when peak times hit, and how many people move between which stages.
Results
SmukFlow was built as a proof of concept on 2025 data for use in 2026 planning. It gives security, logistics and festival leadership a shared data basis for discussing staffing, capacity and simultaneous headline shows.