People in motion: Spatio-temporal analytics on Call Detail Records

Abstract

The data about how people move in a city can be potentially used by various enterprises and government organizations to strategically optimize their operations and maximize their revenue. However, fine-grained and real-time data is currently unavailable to the enterprises. We believe that Cellular Network operators can deliver such data and insights to enterprises. Call records collected in the networks embed a wealth of information about where, when and how a large fraction of the city moves. However, this information is untapped; a majority of the cellular operators are not deriving spatio-temporal insights or monetizing the data that is already available. In this paper, we demonstrate 'People in Motion': an end-to-end Hadoop-based system with a library of spatio-temporal algorithms that operates on the call record data to derive business insights. We identify the hangouts and trajectories of users with different interests. Finally, we demonstrate a visual analytics tool that facilitates business users to compute, compare and contrast the importance of spatial regions at different times for different categories of users. © 2014 IEEE.