Our research group also had a similar idea to this back in 2006, though looking at laptops (since this was before smartphones really took off). This was also before MAC randomization, as many other posters have pointed out. Here's our research paper if you're interested:
http://cmuchimps.org/uploads/publication/paper/86/putting_pe...
Two ideas to consider for next steps, if you're interested. One is to crowdsource the data, to build out a map of places and how busy those places are. You would need to add in a lot of privacy mechanisms though, e.g. only sharing data of mostly public places vs homes. You could also build out a map of interesting places based on this (e.g. we used foursquare data in our past research to build out clusters of places, see http://livehoods.org/)
Another is to estimate how busy a place really is based on traffic, in terms of #people, and #seats or #tables available. This could be especially useful for campuses (where is a good place to study?) or cafes. You might need some crowd-based approach to label ground truth, and it's unclear what the incentive would be.
We did consider commercialization back in the day, but never came up with a plausible business model. It's not clear why business owners would want this, and they might even have an incentive to cheat. Though I would definitely say that cities would be interested in this data, e.g. urban planners or depts of public works. They have so little data about what is actually going on in a city. For example, we spoke with people who wanted to know the effects of closing a bridge or closing off a street.
My university still has people count by hand twice a shift, a dozen floors of tables. It's hard to beat the benefits of how cheap it is to hand a minimum wage undergrad a $5 mechanical counter and have them get a more precise count in the 20 minutes it takes to do a lap of the building, especially when said undergrad would just be idling at the front desk, on their phone, and still on the clock for those 20 minutes anyway.
> Another is to estimate how busy a place really is based on traffic, in terms of #people, and #seats or #tables available. This could be especially useful for campuses (where is a good place to study?) or cafes. You might need some crowd-based approach to label ground truth, and it's unclear what the incentive would be.
Google Maps does that. Search for a popular cafe on your area and you should see the "Popular times" graph with live data.
"Project Beacon is designed to improve the performance of location-related features in Google products for your venue, such as popular times, reviews etc. The beacon itself is configured for just this purpose, and isn't re-configurable by the user. If you would like to obtain a beacon to use with our developer platform, you can find a list of manufacturers at g.co/beacons"
I assume they do that by tracking how many Androids are at a given location; so that doesn't count those who are using iOS. Given Android's dominance though, not counting iOS devices might not be even skew the results significantly.
The pervasiveness of Android would mitigate random errors. It alone can not, however, mitigate systematic errors. Apple Stores (and, employing stereotypes for maximum effect, art galleries and organic food stores) would be undercounted.
But, wait: Google only gives you relative data over time, where that error is irrelevant. Never mind.
I'd bet that any iOS device running Google Maps is probably listening for those beacons too and calling home to tell them. (And Google Maps is likely snitching on you via your own GPS as well, in the absence of Bluetooth beacons.)
Two ideas to consider for next steps, if you're interested. One is to crowdsource the data, to build out a map of places and how busy those places are. You would need to add in a lot of privacy mechanisms though, e.g. only sharing data of mostly public places vs homes. You could also build out a map of interesting places based on this (e.g. we used foursquare data in our past research to build out clusters of places, see http://livehoods.org/)
Another is to estimate how busy a place really is based on traffic, in terms of #people, and #seats or #tables available. This could be especially useful for campuses (where is a good place to study?) or cafes. You might need some crowd-based approach to label ground truth, and it's unclear what the incentive would be.
We did consider commercialization back in the day, but never came up with a plausible business model. It's not clear why business owners would want this, and they might even have an incentive to cheat. Though I would definitely say that cities would be interested in this data, e.g. urban planners or depts of public works. They have so little data about what is actually going on in a city. For example, we spoke with people who wanted to know the effects of closing a bridge or closing off a street.