In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems to pinpoint problematic locations because the origin of such measurements (i.e., user location) is usually not registered. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this work, a data-driven model is proposed to deduce the statistical distribution of connections, exploiting the knowledge of network layout and population density in the sceario. Due to the absence of GPS measurements, the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from their traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged messages from two social networks (Twitter and Flickr).
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