Abstract: 

Mobile and smartphone penetration rates have been steadily increasing globally. Smartphone penetration at 33.3% of global populations currently and projected to increase to 37% by 2020. Better technological coverage and rapid advances in positioning technology has important implications in modeling dynamic human mobility patterns. With data from global positioning systems, we are not only able to work towards a remote-sensing method of creating a population count, but these counts be used to create a spatiotemporally dynamic, real-time representation of mobility.

In this talk, we walk through our process for using cell phone GPS data to model 24 hour population foot traffic data, including origin-destination flows and transit modes, at scale. We first present some of the methodological challenges of mode detection using passive GPS data, model validation using unlabelled data, and scaling our models to create urban data extracts that are flexible across time and space. These have implications on our ability to better understand and represent a range of scenarios such as transportation planning, site selection, and policy impact analysis.

We will demonstrate a use case of this data through creating a real-time mobility survey of New York City and analyzing the impact on infrastructure congestion. We use this survey to understand the potential impact of road closures.

Bio: 

Wenfei is Spatial Data Scientist at CARTO and a PhD student in Urban Planning at Columbia University. She has a background in urban planning, economics, and design and combines those skills in her work and research. Previously, Wenfei worked the Civic Data Design Lab and Senseable Cities Lab at MIT, where she researched vacancies or underuse in residential developments in China through social media and crowd-sourced data, the impact of street infrastructure on ethnic minority communities in Los Angeles, citizen-driven pollution documentation in China, and informal transit in Nairobi.