@inproceedings{e1dc6d79961c449fbc1d06eb4677ea33,
title = "Towards sensing urban-scale COVID-19 policy compliance in new york city",
abstract = "Big data on the urban scale can enable many applications for improving city life and provide a more holistic understanding of urban life to researchers. While there are approaches to sense and model urban occupant behaviors using sound, radio frequency, and vision, how such behaviors are altered due to city governance and policies in response to emergencies such as a natural disaster or a public health crisis has been less explored. In this paper, we present a computer vision-based approach to capture patterns and interference in the urban life of New York City dwellers from March 2020 to August 2020. Using ∼1 million images gathered with cameras mounted on ride-sharing vehicles throughout the city, we approximated the social proximity of pedestrians to understand policy compliance on the street. Our analysis reveals a correlation between policy violation and virus transmission. We believe that such big data-driven city-scale citizen modeling can inform policy design and crisis management schemes for urban scale smart infrastructure.",
keywords = "COVID-19, people detection, public policy, urban sensing",
author = "Tahiya Chowdhury and Ansh Bhatti and Ilan Mandel and Taqiya Ehsan and Wendy Ju and Jorge Ortiz",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021 ; Conference date: 17-11-2021 Through 18-11-2021",
year = "2021",
month = nov,
day = "17",
doi = "10.1145/3486611.3491123",
language = "English (US)",
series = "BuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments",
publisher = "Association for Computing Machinery, Inc",
pages = "353--356",
booktitle = "BuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments",
}