  The rise in popularity of major social media platforms have enabled people to
share photos and textual information about their daily life. One of the popular
topics about which information is shared is food. Since a lot of media about
food are attributed to particular locations and restaurants, information like
spatio-temporal popularity of various cuisines can be analyzed. Tracking the
popularity of food types and retail locations across space and time can also be
useful for business owners and restaurant investors. In this work, we present
an approach using off-the shelf machine learning techniques to identify trends
and popularity of cuisine types in an area using geo-tagged data from social
media, Google images and Yelp. After adjusting for time, we use the Kernel
Density Estimation to get hot spots across the location and model the
dependencies among food cuisines popularity using Bayesian Networks. We
consider the Manhattan borough of New York City as the location for our
analyses but the approach can be used for any area with social media data and
information about retail businesses.
