  In this paper we present the first population-level, city-scale analysis of
application usage on smartphones. Using deep packet inspection at the network
operator level, we obtained a geo-tagged dataset with more than 6 million
unique devices that launched more than 10,000 unique applications across the
city of Shanghai over one week. We develop a technique that leverages transfer
learning to predict which applications are most popular and estimate the whole
usage distribution based on the Point of Interest (POI) information of that
particular location. We demonstrate that our technique has an 83.0% hitrate in
successfully identifying the top five popular applications, and a 0.15 RMSE
when estimating usage with just 10% sampled sparse data. It outperforms by
about 25.7% over the existing state-of-the-art approaches. Our findings pave
the way for predicting which apps are relevant to a user given their current
location, and which applications are popular where. The implications of our
findings are broad: it enables a range of systems to benefit from such timely
predictions, including operating systems, network operators, appstores,
advertisers, and service providers.
