Urban phenology

Toward a real-time census of the city using Wi-Fi data

+ Constantine Kontokosta, Nicholas Johnson


Urban phenology: Toward a real-time census of the city using Wi-Fi data

Computers, Environment and Urban Systems

Volume 64, July 2017, Pages 144-153

New streams of data are being generated by a range of in-situ instrumentation, mobile sensing, and social media that can be integrated and analyzed to better understand urban activity and mobility patterns. While several studies have focused on understanding flows of people throughout a city, these data can also be used to create a more spatially and temporally granular picture of local population, and to forecast localized population given some exogenous environmental or physical conditions. Effectively modeling population dynamics at high spatial and temporal resolutions would have significant implications for city operations and policy, strategic long-term planning processes, emergency response and management, and public health.

This paper develops a real-time census of the city using Wi-Fi data to explore urban phenology as a function of localized population dynamics. Using Wi-Fi probe and connection data accounting for more than 20,000,000 data points for the year 2015 from New York City's Lower Manhattan neighborhood – combined with correlative data from the U.S. Census American Community Survey, the Longitudinal Employer-Household Dynamics survey, and New York City administrative records – we present a model to create real-time population estimates classified by residents, workers, and visitors/tourists in a given neighborhood and localized to a block or geolocation proximate to a Wi-Fi access point. The results indicate that the approach has merit: we estimate intra-day, hourly worker and resident population counts within 5% of survey validation data. Our building-level test case demonstrates similar accuracy, estimating worker population to within 1% of the reported building occupancy.


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Constantine E. Kontokosta, Ph.D., is an Associate Professor of Urban Science and Planning and Director of the Civic Analytics program at the NYU Marron Institute of Urban Management. He also directs the Urban Intelligence Lab and holds cross-appointments at the Center for Urban Science and Progress (CUSP) and the Department of Civil and Urban Engineering at the NYU Tandon School of Engineering, and is affiliated faculty at the NYU Wagner School of Public Service.

Nicholas E. Johnson is a Postdoctoral Associate in Dr. Constantine Kontokosta’s Civic Analytics Program at the NYU Marron Institute of Urban Management.

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