Mobility Genome™- A Framework for Mobility Intelligence from Large-Scale Spatio-Temporal Data

Massive amount of spatio-temporal data is generated as a result of user movement and data access activities, from both smart mobile devices and network infrastructure. We aim to be able to derive mobility intelligence from spatiotemporal data at scale with efficiency and yet meet the needs of diverse applications. In this paper, we propose Mobility GenomeTM, a computational framework that enables efficient and extensible discovery of mobility intelligence from large-scale spatio-temporal data. The framework is organised as multiple layers composed of fundamental and extensible computational units. We describe several algorithms and models, such as Human Daily Activity to demonstrate the derivation of mobility intelligence from these units, together with validation results. The framework has been integrated into our Mobility Intelligence platform, processing hundreds of millions of records per day in our production environment. We also show the possibility of building more advanced applications on top of the framework such as detection of home and work location, origin-destination trips and footfall analysis. With its deployment, the framework helps us avoid duplicated efforts on repeating common data processing and algorithmic tasks while serving a diverse set of mobility intelligence applications.