A few years back I did a bit of dance music related data visualization over at Lazily Evaluated. My favourite was an analysis of clubs and their lineups using Resident Advisor / RA data, I called it Clubster Analysis. I always wanted to dig into the technical aspects of gathering the data, analyzing it and building the charts and graphs to tell a story and give people insight. With this blog I now have the right venue for that kind of tech talk, so here goes.

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A technical walkthrough of building a data visualization project that analyzed nightclub lineups from Resident Advisor. The project involved web scraping with Python and Beautiful Soup, data analysis using Pandas and NetworkX to calculate club similarities via Jaccard index, community detection for clustering, and interactive visualization with D3 and React. Analysis of 131 clubs revealed 9,405 unique artists across 8,502 events in 2019, with only 1% average overlap between venues and fewer resident bookings than expected.

How I analyzed the lineups at the world's most popular nightclubs