I'm not sure this data is very good. The Lexington Club is marked as having 7% more male checkins. I live right next door to the Lex, and it's a lesbian bar. Women outnumber men there at least 10-to-1.
Can we correctly label datasets please?<p>Visualization: Guy-to-girl ratio of foursquare/gowalla/facebook checkins for every bar, restaurant and other random location in SF/NYC frequented by foursquare/gowalla/facebook places users who remember to check in.<p>There are so many baises in there about the only solid fact you can take away from any of this is the relative popularity of locations with people who feel the need to broadcast their location.
Is there a known guy/gal differential on Foursquare in these cities? If so, it might be nice to have the ability scale these scores to reflect that.<p>In other words, if 2/3 of SF's Foursquare users are female and 2/3 of a particular SF bar's Foursquare users are female:<p>1) It's true that the guy/gal ratio is 2:1, but<p>2) It's also true that there is no enrichment of women at this bar relative to the null expectation.<p><i>Edit</i>: A collection of presumably neutral places (e.g., Bay Bridge) might help achieve such an estimate.
Do they normalize for the strong possibility that in general, guys check in more than girls? I'm assuming this is the case.<p>Or for that guys are more likely to want to check in and advertise that they're at a bar rather than a beauty shop, or even a coffeehouse? And vice versa?
If the "Bay Bridge" is 24% more girls to guys, then I think there is a problem here with reporting information that's not statistically significant, because that seems very unlikely to me.
(Late to the game, but I assume zain has been working to fix the kinks.)<p>Right now, the #1 highest female ratio is showing up as... 1887%. Without making a dumb joke about how your math may be off (duh), I will say this: from a business perspective, if you're going to err on one side or the other, it may as well be this one. Haha.
Not serious statistics, but very cool visual nonetheless.<p>Now, so long as everyone realizes the 'woman at the bar' scene in A Beautiful Mind isn't actual game theory, feel free to go nuts.
yet another site that fills the stereotype of "hyperlocal" and "location-based service". Both of these seem to be a euphemism for "NYC and San Francisco"
I'd like to see this same data, but normalized for the total number of checkins per group. Right now all it tells me is that of the people going to bars, guys are more likely to check in than girls, not necessarily that there are more guys at the bar.
There may be problems, as others have pointed out, but it still seems like a valuable tool for me. After all, I don't need it to be perfectly accurate.. just ballpark accurate in order to take advantage of it.<p>Please do Austin. :)
The filtering might be a bit off on that data set...Sephora is a beauty retail outlet, which hardly qualifies as a bar or restaurant and its the #1 place for women in NYC.
How did the authors of the visualization get the checkin data for every venue in SF and NYC? I have been trying to do the same for another project, and it is not supported by foursquare..
On behalf of my single friends that I forwarded this to: "Thank you".<p>While I can't use this myself it's definitely helping a friend make plans for the near future.
To me the surprising thing is that ANY women check in to venues.... I can't think of any of my mates that do. But that might be a Manchester, UK thing.
Pretty cool, but I'm a fan of the more macro version of the idea, as based on census data: <a href="http://www.xoxosoma.com/singles/" rel="nofollow">http://www.xoxosoma.com/singles/</a> (also a really awesome design)
I understand that the statistics isn't completely accurate, but it still should give a rough idea. Think we could get the data graphed for more cities? (I just moved to Columbus, Ohio:)
Wow, this is one of those "why didn't I think of that" ideas. I could see this getting very popular over the next few weeks. Even if the data is not reliable, it's a very fun idea.