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Spatialising urban health vulnerability: An analysis of NYC’s critical infrastructure during COVID-19

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  • Gayatri Kawlra

    (Columbia University, USA)

  • Kazuki Sakamoto

    (Columbia University, USA)

Abstract

This paper examines how fragmentation of critical infrastructure impacts the spread of the coronavirus outbreak in New York City at the neighbourhood level. The location of transportation hubs, grocery stores, pharmacies, hospitals and parks plays an important role in shaping spatial disparities in virus spread. Using supervised machine learning and spatial regression modelling we examine how the geography of COVID-19 case rates is influenced by the spatial arrangement of four critical sectors of the built environment during the public health emergency in New York City: health care facilities, mobility networks, food and nutrition and open space. Our models suggest that an analysis of urban health vulnerability is incomplete without the inclusion of critical infrastructure metrics in dense urban geographies. Our findings show that COVID-19 risk at the zip code level is influenced by (1) socio-demographic vulnerability, (2) epidemiological risk, and (3) availability and access to critical infrastructure.

Suggested Citation

  • Gayatri Kawlra & Kazuki Sakamoto, 2023. "Spatialising urban health vulnerability: An analysis of NYC’s critical infrastructure during COVID-19," Urban Studies, Urban Studies Journal Limited, vol. 60(9), pages 1629-1649, July.
    • Handle: RePEc:sae:urbstu:v:60:y:2023:i:9:p:1629-1649
    DOI: 10.1177/00420980211044304
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    References listed on IDEAS

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    1. Helen F. Ladd, 1998. "Evidence on Discrimination in Mortgage Lending," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 41-62, Spring.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Jean-Paul D. Addie & Michael R. Glass & Jen Nelles, 2020. "Regionalizing the infrastructure turn: a research agenda," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 7(1), pages 10-26, January.
    4. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    5. Shima Hamidi & Sadegh Sabouri & Reid Ewing, 2020. "Does Density Aggravate the COVID-19 Pandemic?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 86(4), pages 495-509, October.
    6. Patricia A. McCoy, 2007. "The Home Mortgage Disclosure Act: A Synopsis and Recent Legislative History," Journal of Real Estate Research, American Real Estate Society, vol. 29(4), pages 391-398.
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    Cited by:

    1. Yingling Fan & Scott Orford & Philip Hubbard, 2023. "Urban public health emergencies and the COVID-19 pandemic. Part 2: Infrastructures, urban governance and civil society," Urban Studies, Urban Studies Journal Limited, vol. 60(9), pages 1535-1547, July.
    2. Noel A Manzano Gómez, 2023. "Planning for social distancing: How the legacy of historical epidemics shaped COVID-19's spread in Madrid," Urban Studies, Urban Studies Journal Limited, vol. 60(9), pages 1570-1587, July.

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