I love the idea behind it and the visualisation indeed looks awesome, but the RA data behind it is messy to say the least.
For example, 3/7 "nightclubs" it lists in Croatia are not nightclubs. I would also group Kalypso and Zrće Beach together as Kalypso is one out of 4 nightclubs located on Zrće Beach, but the beach itself is not a venue. Similarly, Barbarella's is kind of an afterparty destination for festivals organised at the Garden Resort. They only match 6%, but I wouldn't call them completely separate. This is the kind of "lore" that's impossible to catch from raw data.
I like the Essential Mix visualisation even more! It's such a shame most of the other ones appear dead due to API changes.
In excitement over the cool-looking visualizations, we lose the sight of its usefulness. The entire article hardly has any text talking about how these visualization would be useful and to whom.
Even if this is an ask from a top-level managers hoping that it would help their decision-making, let me tell you that, this will be binned after everyone agrees that the visualizations are very cool. Decisions are driven by other factors.
Don't confuse this list with actually very popular djs. Many clubs in this list host mostly resident and local djs (like Berghain as the club name/culture is the main reason people go there, not the lineup) instead of popular touring djs who have a big fan base of their own (and are more expensive to book).
Am I missing something or is the author using dots for both breaking up large numbers and also for decimal points?
Including San Francisco and not Chicago? Criminal.
This is pretty cool, interesting to think about what causes more or less resident DJs.
As a side note, on the RA events sometimes artists are listed in the description but not linked in the description so there could be some artists missing. In my experience it tends to vary on the nightclub.
This is really cool but in my experience, RA is typically used for less popular “underground” (in very large quotes) events, warehouse parties, etc. It might be different in Europe or other cities here in the states. The data for such events is interesting in its own right, but it’s probably not quite representative of the most popular clubs and/or artists.
Perhaps doing a similar thing with Ticketmaster/tixr/all the other ticket apps and combining them together could produce more representative data, of course you’d still have the problem of having to filter out actual clubs.
on an unrelated note, what in gods name is that color scheme on the visualizations? xD
I think this analysis is cool, and the comments are too critical. The author put something out there, documented it, and I learned something by reading it. Thank you.
Maybe the title needs reframing to soften the reader’s expectations, and I definitely agree that RA as a data source has important limitations that should be mentioned.
This was a great read. I used to work at RA as a dev, I always look back and wish I did more with the data I had access too!
Looks like I'm in the minority here but I was not impressed by the visualizations or the color scheme or anything. Oh well...
Ah I miss De School. It always feels a bit uncanny seeing Tillatec wearing the hand me downs, even though they have their own thing going.
Might have been nice to know where the clubs are? San Francisco? South Africa? Belgium? And what demographic the clubs serve, i.e are any of the clubs LGBTQ+, or are the all hetero? They don't generally play the same material.
I love finding new music. Looks like now I can go find some far away clubs and listen to their djs while doing some coding.
The technical details of a visualisation built around the lineups and programming at the world's most popular nightclubs.
I wonder if you’d get a higher percent of overlap if you only focused on Friday/Saturday bookings
Was surprised to not see Space (the Ibiza club) and it turns it closed back in 2016.
Data raw material is meh, as are the results (clubs are clustered by geography and music style? Shocker), but the post isn't about collection or survey methodology.
The hard work here is in the analysis and presentation, which are nice. That will still be valuable if applied to better-curated datasets. I don't blame the author for working with a mediocre dataset that happened to reflect his interests, he's not making big claims about what this means for the entertainment industry or the future of culture.
Btw, SF night clubs actively refuse to let “tech nerds” in. I witnessed whole groups of people who attended the huggingface “Woodstock of AI” meetup circa 2023 and the local clubs would refuse to let anyone in who wore glasses or wore Patagonia.
Thanks John Hughes films!
What's with using . for both decimal and thousands?
I kinda understand swapping witch is which, but why have no distinction.
I just came to the comments section to say "blablabla flawed blabla incorrect data blablabla representation failure blablabla... but I just saw others came first.
Yeah, humans.
I actually want to go to Haunted House more than I want to go to Aqua.
As an introvert, what are you even supposed to do at "clubs"?
Besides having your eardrums damaged
I'm just gonna say that some aspect of the data collection here seems flawed: among the SF clubs listed are DNA Lounge and Public Works – which are great clubs, don't get me wrong – but they are very much on the smaller side. And, Phonobar? That is a bar/restaurant, not a nightclub at all. Meanwhile, The Warfield and 1015 Folsom are left out – how does that make any sense?