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Towards sensing urban-scale COVID-19 policy compliance in new york city

Published: 17 November 2021 Publication History

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.

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Cited By

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  • (2023)Detecting disparities in police deployments using dashcam dataProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594020(534-544)Online publication date: 12-Jun-2023
  • (2022)Bird's-eye View Social Distancing Analysis System2022 IEEE International Conference on Communications Workshops (ICC Workshops)10.1109/ICCWorkshops53468.2022.9814627(427-432)Online publication date: 16-May-2022

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  1. Towards sensing urban-scale COVID-19 policy compliance in new york city

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      cover image ACM Conferences
      BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
      November 2021
      388 pages
      ISBN:9781450391146
      DOI:10.1145/3486611
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 17 November 2021

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      Author Tags

      1. COVID-19
      2. people detection
      3. public policy
      4. urban sensing

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      BuildSys '21 Paper Acceptance Rate 28 of 107 submissions, 26%;
      Overall Acceptance Rate 148 of 500 submissions, 30%

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      View all
      • (2023)Detecting disparities in police deployments using dashcam dataProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594020(534-544)Online publication date: 12-Jun-2023
      • (2022)Bird's-eye View Social Distancing Analysis System2022 IEEE International Conference on Communications Workshops (ICC Workshops)10.1109/ICCWorkshops53468.2022.9814627(427-432)Online publication date: 16-May-2022

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