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Socio-spatial inequality and the effects of density on COVID-19 transmission in US cities

Nature Cities volume 1, pages 83–93 (2024)Cite this article

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Abstract

Cities are often associated with the rapid spread of infectious diseases, driven by the perceived risks of urban density and overcrowding. However, transmission risk can vary considerably within urbanized areas as a function of socio-spatial disparities and the adoption of mitigating behaviors across communities. Here we examine the effect of density on coronavirus disease 2019 (COVID-19) infection rates at the neighborhood scale, within and across US cities. We integrate high-spatial resolution measures of land use and residential population density, mobility, infection rates and social determinants of health to evaluate the impact of neighborhood context on infection risk, while controlling for the potential mitigating effects of social distancing behavior. We are particularly focused on disparities among marginalized and vulnerable neighborhoods, and the generalizability of the results across political, socioeconomic, regional and built environment contexts. Our findings demonstrate a nonlinear relationship between urban density and infection rates, with higher-density neighborhoods more likely to adopt mitigating behaviors to reduce transmission. However, low-income and minority communities, facing cascading health challenges, are found to be least able to modify mobility behavior and therefore experienced a disproportionate burden of COVID-19 infection risk during the first wave of the pandemic.

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Fig. 1: Census tract-level exposure density changes between the prepandemic period and the period after the stay-at-home order (left), and between the stay-at-home period and the period after phase 1 reopening (right).
Fig. 2: Neighborhood distribution of exposure density change across the studied cities.
Fig. 3: The results of the neighborhood clustering algorithm based on density and land use, with Austin, TX (left), Chicago, IL (middle) and New York City, NY (right) as examples. The number of census tract neighborhoods assigned to each clustered group are shown in the legend.
Fig. 4: Scatter plots of residential population density and exposure density change for each cluster group.
Fig. 5: Relationship between residential population density and COVID-19 infection rate.

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Data availability

The data used to conduct the analyses described here are summarized in Supplementary Tables 6–8. Processed, aggregate data derived from publicly available, open data sources that support the findings of this study are available via the dedicated data repositories. The rasterized land use classification data for selected US cities is available in New York University’s UltraViolet repository with the identifier of https://doi.org/10.58153/t7m6v-37090 (ref. 50). The dataset of zip code-level COVID-19 case rates used in this study is also available in the NYU’s UltraViolet platform with the identifier of https://doi.org/10.58153/avp10-a8h86 (ref. 51). The primary mobility data that support the findings of this study are available from VenPath, Inc., but restrictions apply based on a data sharing agreement. The aggregated neighborhood-level exposure density metrics along with the corresponding code are available in the dedicated GitHub repository.

Code availability

Code and associated materials are available in a dedicated GitHub repository available at https://github.com/UrbanIntelligenceLab/socio-spatial-inequality-and-the-effects-of-density-on-covid19-transmission-in-us-cities under MIT License.

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Acknowledgements

This work was supported, in part, by National Science Foundation awards no. 2028687 (C.E.K.) and no. 2040898 (C.E.K.). The study was approved by NYU Institutional Review Board IRB-FY2018-1645, as updated. We thank L. Thorpe and A. Gupta for their feedback on preliminary versions of this methodology, and thank A. Gupta for sharing access to VenPath, Inc. data. All errors remain our own.

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Authors and Affiliations

  1. Marron Institute of Urban Management, New York University, New York, NY, USA

    Constantine E. Kontokosta, Boyeong Hong & Bartosz J. Bonczak

  2. Center for Urban Science and Progress, New York University, Brooklyn, NY, USA

    Constantine E. Kontokosta & Bartosz J. Bonczak

  3. Tandon School of Engineering, New York University, Brooklyn, NY, USA

    Constantine E. Kontokosta & Bartosz J. Bonczak

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  1. Constantine E. Kontokosta
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Contributions

C.E.K. conceived the study. C.E.K. obtained funding. B.H. and B.J.B. analyzed the data. C.E.K., B.H. and B.J.B. interpreted the results. C.E.K. and B.H. prepared the preliminary manuscript. All authors revised and approved the final manuscript.

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Correspondence to Constantine E. Kontokosta.

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Nature Cities thanks Mehdi Alidadi, Creighton Connolly and Laura F. White for their contribution to the peer review of this work.

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Kontokosta, C.E., Hong, B. & Bonczak, B.J. Socio-spatial inequality and the effects of density on COVID-19 transmission in US cities. Nat Cities 1, 83–93 (2024). https://doi.org/10.1038/s44284-023-00008-2

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