Fast Food Data: Where User-Generated Content Works and Where It Does Not

As big urban data usage expands in the social sciences, there remain real concerns about fidelity to on the ground conditions. In this paper, we examine the correspondence between Phoenix metro area restaurants identified by a social media source (yelp.com) and those from an administrative source (Maricopa Association of Governments [MAG]). We find that they capture largely disjoint subsets of Phoenix restaurants, with only about one-third of restaurants in each data set present in the other. Point pattern analyses indicate that the Yelp data is significantly clustered relative to the MAG data. Specifically, restaurants in Yelp are concentrated in certain parts of metro Phoenix, most notably the downtowns of Phoenix, Scottsdale, and Tempe. Further analysis indicates that areas with more Yelp than MAG restaurants tend to have more college-educated workers and workers employed in the Arts, Entertainment, and Recreation sector. Our comparison highlights the strengths and weaknesses of each data source: Yelp data is far more detailed and comprehensive in certain locations, while MAG data is more consistent across the entire region due to its systematic construction. When combined, administrative and user generated databases seem to provide a more holistic and comprehensive picture of the world than either would provide by itself.