Data Stories: Interview with Data Scientist Blake Shaw of Foursquare

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At Gnip, we believe the value of social data is unlimited. Data Stories is how we bring this belief to life by showcasing how social data is used. This week we’re interviewing data scientist Blake Shaw of Foursquare about how data science is not only shaping Foursquare and its recommendations, but how Foursquare can be a “microscope for cities.” You can follow Blake on Twitter at @metablake and check out Foursquare’s blog for more data science. 

Data Scientist Blake Shaw of Foursquare

1. Your team has found a correlation between warm days and ice cream consumption in NYC. At some point, do you envision Foursquare being able to trigger offers based on different correlations your data science has found?

Yes!  In fact, we currently trigger recommendations (which often contain deals and offers) based on a ton of different contextual signals that the team here has identified as useful.  These signals include where you are, the places you like to go, the time of the day, the preferences of your friends, and what is popular around you. Mapping all of these signals to good recommendations requires finding correlations in massive amounts of data.  Some of these correlations are simple like when it’s the morning people like to get coffee, and some correlations are more complex like when it’s cold out in New York, people are more likely to go to ramen and noodle shops.

2. One of my favorite features of the Explore feature is that Foursquare lets you know when you check into a city locations where both locals and out-of-towners like to go. How does data science and product work together to make recommendations such as these?

Tourist recommendations is definitely one of my favorite features of Explore as well. In general, there is a healthy mix of product-driven and data-driven development at Foursquare. We will often work together to brainstorm not only what would be best to build from a product perspective but also what data we should be investigating further. Tourist recommendations came from the data; we realized that it would be easy to identify places that had a statistically high proportion of tourists and surface them to Explore users who find themselves in unfamiliar areas.  The results are fantastic — it’s like having millions of people creating a travel guide, just by walking around a city and checking in.

3. Foursquare got its start in NYC. What are interesting observations you’ve seen on how people use Foursquare in smaller cities such as Boulder and Denver?

I feel like Foursquare is more of a necessity in big cities like New York, where new places are opening all the time and it’s hard to keep track of them all.  That said, we see strong usage in places like Boulder and Denver as well. As expected, users in smaller cities such as these are more interested in old favorites rather than exploring new places.

4. What signals does Foursquare use to recommend places to people?

I can’t reveal all of the signals we use to rank places, but we believe that place recommendation should be highly personalized, so we heavily weight signals about your tastes and the tastes of your friends.  We also think that from all of this data about where people are going we can discern which are the best places.  Imagine being able to ask everyone who has been to a restaurant if they would go back. We believe that by measuring signals about places such as loyalty, expertise, and sentiment we can tease out the best places. This is the idea behind our recently launched Foursquare ratings.  People are voting with their feet in the real world, not simply leaving a star or a like on a website.

5. Do you see a correlation between Foursquare sharing check-ins and badges on other social sites and increased usage of Foursquare? For example, if someone chooses to share a checkin on Twitter or Facebook, does that increase the likelihood of other people checking in?

Yes we do. Roughly a quarter of all check-ins are shared to wider audiences on Twitter and Facebook.  These in turn help spread awareness and adoption of Foursquare.

6. Foursquare recently showed a visualization of how check-ins in NYC were affected by hurricane Sandy. How else do you see check-in data being useful other than for powering your recommendation engine?

Visualization of Foursquare Checkins Before and After Hurricane Sandy

One of my favorite aspects of working at Foursquare is getting to study this data from a larger sociological perspective. We are capturing this amazing signal about what millions of people are doing in the real world at every moment of the day in cities all around the globe. We have seen that when we aggregate check-in patterns across many individuals, we can measure features of cities at a higher resolution than was ever possible before.  I think this data can act almost like a “microscope for cities.”  If you look at how the storm affected NYC, you can see how this incredibly powerful force disrupted the natural rhythm of the city. It’s striking how predictable these patterns are, and how precisely we can identify unusual events. For example, in this plot we see how check-ins at grocery stores went up more than 200% in the days before the storm.  I see this real-time pulse or “EKG” of a city being a valuable resource in the future for understanding cities, giving us a larger view of the collective movement patterns of millions of people.

A big thanks to Blake for participating in the interview. If you have any suggestions on future Data Stories, please let us know in the comments. 

Past Data Stories:

Hilary Mason, Chief Data Scientist of bitly

Simon Rogers, data journalist at The Guardian 

Lada Adamic of Michigan on information networks

Mel Hogan of CU Boulder on digital archiving 

Liv Buli of Next Big Sound, the world’s first music data journalist

Sherry Emery of UIC, studying social data and smoking cessation

Annicka Campbell of SapientNitro on the Digital Love Project 

Seth Grimes of the Sentiment Symposium on sentiment analysis

Gabriel Banos of ZauberLabs on predicting the election with social data