Origin-Destination Matrix Using Mobile Network Data with Spark - Databricks

Origin-Destination Matrix Using Mobile Network Data with Spark

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We live in a digital era where mobile communications play a major role in our daily lives. At Teralytics, the analysis of billions of telecom events around the globe is allowing us to derive unprecedented insights into human mobility behavior in cities and across cities. The acquisition, standardization, processing, and analysis of these data is carried out performantly using Spark.
In this talk, we’ll describe a) how we compute origin-destination (OD) matrices to understand the flow of mobile users through countries and b) how we determine their mode of transportation using a classification algorithm. OD matrices are a common tool in transport forecasting models to provide better transportation services, to determine carbon emissions in cities, and to plan new roads and railways.

About Javiera Guedes

Javiera is a data scientist at Teralytics, a big data analytics company that uses cutting-edge technology to understand human mobility patterns. Formerly, she obtained a double bachelor's degrees in Physics and Astrophysics, and a PhD in computational astrophysics at the University of California. She was awarded top prizes such as the Hubble and Einstein Fellowships for her contributions in the fields of planet formation, black hole dynamics, and galaxy evolution using high-resolution cosmological simulations. Currently, she works with Scala and Spark to derive mobility insights from billions of telecom events.