References
- Altmann, A., L. Toloşi, O. Sander, and T. Lengauer. 2010. Permutation importance: A corrected feature importance measure. Bioinformatics 26 (10):1340–47. doi: https://doi.org/10.1093/bioinformatics/btq134.
- American Community Survey (ACS). 2020. American Community Survey Data. Accessed October 23, 2020. https://www.census.gov/programs-surveys/acs/data.html.
- Baek, C., P. B. McCrory, T. Messer, and P. Mui. 2020. Unemployment effects of stay-at-home orders: Evidence from high frequency claims data. Review of Economics and Statistics. doi:https://doi.org/10.1162/rest_a_00996.
- Bao, Y., and Z. Liu. 2006. A fast grid search method in support vector regression forecasting time series. In International Conference on Intelligent Data Engineering and Automated Learning, ed. E. Corchado, H. Yin, V. Botti, and C. Fyfe, 504–11. Berlin: Springer.
- Barnett-Howell, Z., and A. M. Mobarak. 2020. The benefits and costs of social distancing in rich and poor countries. arXiv Preprint https://arxiv.org/abs/2004.04867.
- Barrios, J. M., and Y. Hochberg. 2020. Risk perception through the lens of politics in the time of the COVID-19 pandemic. Working Paper 27008. Cambridge, MA: National Bureau of Economic Research.
- Bonaccorsi, G., F. Pierri, M. Cinelli, A. Flori, A. Galeazzi, F. Porcelli, A. L. Schmidt, C. M. Valensise, A. Scala, W. Quattrociocchi, et al. 2020. Economic and social consequences of human mobility restrictions under COVID-19. Proceedings of the National Academy of Sciences of the United States of America 117 (27):15530–35. doi: https://doi.org/10.1073/pnas.2007658117.
- Briscese, G., N. Lacetera, M. Macis, and M. Tonin. 2020. Compliance with COVID-19 social-distancing measures in Italy: The role of expectations and duration. Working Paper 26916. Cambridge, MA: National Bureau of Economic Research. doi: https://doi.org/10.3386/w26916.
- Brodeur, A., I. Grigoryeva, and L. Kattan. 2020. Stay-at-home orders, social distancing and trust. GLO Discussion Paper No. 553, Global Labor Organization (GLO), Essen, Germany.
- Brunsdon, C., A. S. Fotheringham, and M. E. Charlton. 2010. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis 28 (4):281–98. doi: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x.
- Chen, S. L. S., A. M. F. Yen, C. C. Lai, C. Y. Hsu, C. C. Chan, and T. H. H. Chen. 2020. An index for lifting social distancing during the COVID-19 pandemic: Algorithm recommendation for lifting social distancing. Journal of Medical Internet Research 22 (9):e22469. doi: https://doi.org/10.2196/22469.
- Chiou, L., and C. Tucker. 2020. Social distancing, internet access and inequality. Working Paper 26982. Cambridge, MA: National Bureau of Economic Research. doi: https://doi.org/10.3386/w26982.
- Craig, L., and B. Churchill. 2021. Dual-earner parent couples’ work and care during COVID-19. Gender, Work & Organization 28 (Suppl. 1):66–79. doi: https://doi.org/10.1111/gwao.12497.
- Cunningham, T. J., J. B. Croft, Y. Liu, H. Lu, P. I. Eke, and W. H. Giles. 2017. Vital signs: Racial disparities in age-specific mortality among Blacks or African Americans—United States, 1999–2015. MMWR: Morbidity and Mortality Weekly Report 66 (17):444–56. doi: https://doi.org/10.15585/mmwr.mm6617e1.
- Czeisler, M. É., M. A. Tynan, M. E. Howard, S. Honeycutt, E. B. Fulmer, D. P. Kidder, R. Robbins, L. K. Barger, E. R. Facer-Childs, G. Baldwin, et al. 2020. Public attitudes, behaviors, and beliefs related to COVID-19, stay-at-home orders, nonessential business closures, and public health guidance—United States, New York City, and Los Angeles, May 5–12, 2020. MMWR: Morbidity and Mortality Weekly Report 69 (24):751–58. doi: https://doi.org/10.15585/mmwr.mm6924e1.
- Dasgupta, N., M. J. Funk, A. Lazard, B. E. White, and S. W. Marshall. 2020. Quantifying the social distancing privilege gap: A longitudinal study of smartphone movement. medRxiv. doi: https://doi.org/10.1101/2020.05.03.20084624.
- Donohue, J. M., and E. Miller. 2020. COVID-19 and school closures. JAMA 324 (9):845–47. doi: https://doi.org/10.1001/jama.2020.13092.
- Farber, S., and A. Páez. 2009. My car, my friends, and me: A preliminary analysis of automobility and social activity participation. Journal of Transport Geography 17 (3):216–25. doi: https://doi.org/10.1016/j.jtrangeo.2008.07.008.
- Friedman, J. H. 2001. Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29 (5):1189–232. doi: https://doi.org/10.1214/aos/1013203451.
- Gao, S., J. Rao, Y. Kang, Y. Liang, J. Kruse, D. Dopfer, A. Sethi, J. Reyes, B. Yandell, and J. A. Patz. 2020. Association of mobile phone location data indications of travel and stay-at-home mandates with COVID-19 infection rates in the U.S. JAMA Network Open 3 (9):e2020485. doi: https://doi.org/10.1001/jamanetworkopen.2020.20485.
- Garbe, A., U. Ogurlu, N. Logan, and P. Cook. 2020. Parents’ experiences with remote education during COVID-19 school closures. American Journal of Qualitative Research 4 (3):45–65.
- Gayer, T. 2000. Neighborhood demographics and the distribution of hazardous waste risks: An instrumental variables estimation. Journal of Regulatory Economics 17 (2):131–55. doi: https://doi.org/10.1023/A:1008139927218.
- Godley, P. A., A. P. Schenck, M. A. Amamoo, V. J. Schoenbach, S. Peacock, M. Manning, M. Symons, and J. A. Talcott. 2003. Racial differences in mortality among Medicare recipients after treatment for localized prostate cancer. Journal of the National Cancer Institute 95 (22):1702–10. doi: https://doi.org/10.1093/jnci/djg094.
- Gray, D. M., A. Anyane-Yeboa, S. Balzora, R. B. Issaka, and F. P. May. 2020. COVID-19 and the other pandemic: Populations made vulnerable by systemic inequity. Nature Reviews: Gastroenterology & Hepatology 17 (9):520–22. doi: https://doi.org/10.1038/s41575-020-0330-8.
- Gregorutti, B., B. Michel, and P. Saint-Pierre. 2017. Correlation and variable importance in random forests. Statistics and Computing 27 (3):659–78. doi: https://doi.org/10.1007/s11222-016-9646-1.
- Huang, Q., and D. W. Wong. 2016. Activity patterns, socioeconomic status and urban spatial structure: What can social media data tell us? International Journal of Geographical Information Science 30 (9):1873–98. doi: https://doi.org/10.1080/13658816.2016.1145225.
- Huang, X., Z. Li, Y. Jiang, X. Li, and D. Porter. 2020. Twitter reveals human mobility dynamics during the COVID-19 pandemic. PLoS ONE 15 (11):e0241957. doi: https://doi.org/10.1371/journal.pone.0241957.
- Huang, X., Z. Li, Y. Jiang, X. Ye, C. Deng, J. Zhang, and X. Li. 2020. The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic. International Journal of Digital Earth 14:424–42. doi: https://doi.org/10.1080/17538947.2021.1886358.
- Huang, X., Z. Li, J. Lu, S. Wang, H. Wei, and B. Chen. 2020. Time-series clustering for home dwell time during COVID-19: What can we learn from it? ISPRS International Journal of Geo-Information 9 (11):675. doi: https://doi.org/10.3390/ijgi9110675.
- Jones, B. 2020. Urban residents in states hit hard by COVID-19 most likely to see it as a threat to daily life. Accessed October 21, 2020. https://www.pewresearch.org/fact-tank/2020/03/20/urban-residents-in-states-hit-hard-by-covid-19-most-likely-to-see-it-as-a-threat-to-daily-life/.
- Kong, E., and D. Prinz. 2020. Disentangling policy effects using proxy data: Which shutdown policies affected unemployment during the COVID-19 pandemic? Journal of Public Economics 189:104257. doi: https://doi.org/10.1016/j.jpubeco.2020.104257.
- Kraemer, M. U. G., C.-H. Yang, B. Gutierrez, C.-H. Wu, B. Klein, D. M. Pigott, L. Du Plessis, N. R. Faria, R. Li, W. P. Hanage, et al. 2020. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368 (6490):493–97. doi: https://doi.org/10.1126/science.abb4218.
- Kuppam, A. R., and R. M. Pendyala. 2001. A structural equations analysis of commuters’ activity and travel patterns. Transportation 28 (1):33–54. doi: https://doi.org/10.1023/A:1005253813277.
- Lahut, J. 2020. Fauci says the coronavirus is “shining a bright light” on “unacceptable” health disparities for African Americans. Business Insider, April 7. https://www.businessinsider.com/fauci-covid-19-shows-unacceptable-disparities-for-african-americans-2020-4.
- Leal, C., K. Bean, F. Thomas, and B. Chaix. 2012. Multicollinearity in associations between multiple environmental features and body weight and abdominal fat: Using matching techniques to assess whether the associations are separable. American Journal of Epidemiology 175 (11):1152–62. doi: https://doi.org/10.1093/aje/kwr434.
- Lekfuangfu, W. N., S. Piyapromdee, P. Porapakkarm, and N. Wasi. 2020. On Covid-19: New implications of job task requirements and spouse’s occupational sorting. doi: https://doi.org/10.2139/ssrn.3583954.
- Liaw, A., and M. Wiener. 2002. Classification and regression by randomForest. R News 2 (3):18–22.
- Mendelson, L. 2020. Stay on top of “Stay at home” – A list of statewide orders. Accessed October 10, 2020. https://www.littler.com/publication-press/publication/stay-top-stay-home-list-statewide.
- Morency, C., A. Paez, M. J. Roorda, R. Mercado, and S. Farber. 2011. Distance traveled in three Canadian cities: Spatial analysis from the perspective of vulnerable population segments. Journal of Transport Geography 19 (1):39–50. doi: https://doi.org/10.1016/j.jtrangeo.2009.09.013.
- Nasri, A., and L. Zhang. 2012. Impact of metropolitan-level built environment on travel behavior. Transportation Research Record: Journal of the Transportation Research Board 2323 (1):75–79. doi: https://doi.org/10.3141/2323-09.
- Oyedotun, T. D. T., and S. Moonsammy. 2020. Spatiotemporal variation of COVID-19 and its spread in South America: A rapid assessment. Annals of the American Association of Geographers. doi: https://doi.org/10.1080/24694452.2020.1830024.
- Painter, M., and T. Qiu. 2020. Political beliefs affect compliance with COVID-19 social distancing orders. doi: https://doi.org/10.2139/ssrn.3569098.
- Parr, T., J. D. Wilson, and J. Hamrick. 2020. Nonparametric feature impact and importance. arXiv Preprint arXiv:2006.04750.
- Remuzzi, A., and G. Remuzzi. 2020. COVID-19 and Italy: What next? The Lancet 395 (10231):1225–28. doi: https://doi.org/10.1016/S0140-6736(20)30627-9.
- Ross, C. E., and C. L. Wu. 1995. The links between education and health. American Sociological Review 60 (5):719–45. doi: https://doi.org/10.2307/2096319.
- SafeGraph. 2019. What about bias in the SafeGraph dataset? Accessed November 8, 2020. https://www.safegraph.com/blog/what-about-bias-in-the-safegraph-dataset.
- SafeGraph. 2020. Social distancing metrics. Accessed October 23, 2020. https://docs.safegraph.com/docs/social-distancing-metrics.
- See reopening plans and mask mandates for all 50 states. 2020. The New York Times. Accessed October 10, 2020. https://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html.
- Shim, E., A. Tariq, W. Choi, Y. Lee, and G. Chowell. 2020. Transmission potential and severity of COVID-19 in South Korea. International Journal of Infectious Diseases 93:339–44. doi: https://doi.org/10.1016/j.ijid.2020.03.031.
- Stoecklin, S. B., P. Rolland, Y. Silue, A. Mailles, C. Campese, A. Simondon, M. Mechain, L. Meurice, M. Nguyen, C. Bassi., et al. 2020. First cases of coronavirus disease 2019 (COVID-19) in France: Surveillance, investigations and control measures. Eurosurveillance 25 (6):2000094.
- Tai, D. B. G., A. Shah, C. A. Doubeni, I. G. Sia, and M. L. Wieland. 2021. The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States. Clinical Infectious Diseases 72 (4):703–6. doi: https://doi.org/10.1093/cid/ciaa815.
- U.S. Census Bureau. 2020a. Metropolitan and micropolitan. Accessed October 23, 2020. https://www.census.gov/programs-surveys/metro-micro/about.html.
- U.S. Census Bureau. 2020b. Metropolitan and micropolitan statistical areas population totals and components of change: 2010–2019. Accessed October 22, 2020. https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html.
- Waidmann, T. A., and S. Rajan. 2000. Race and ethnic disparities in health care access and utilization: An examination of state variation. Medical Care Research and Review 57 (Suppl. 1):55–84. doi: https://doi.org/10.1177/1077558700574004.
- Weill, J. A., M. Stigler, O. Deschenes, and M. R. Springborn. 2020. Social distancing responses to COVID-19 emergency declarations strongly differentiated by income. Proceedings of the National Academy of Sciences of the United States of America 117 (33):19658–60. doi: https://doi.org/10.1073/pnas.2009412117.
- Wen, M., C. R. Browning, and K. A. Cagney. 2003. Poverty, affluence, and income inequality: Neighborhood economic structure and its implications for health. Social Science & Medicine 57 (5):843–60. doi: https://doi.org/10.1016/S0277-9536(02)00457-4.
- Wilder-Smith, A., and D. O. Freedman. 2020. Isolation, quarantine, social distancing and community containment: Pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine 27 (2):taaa020. doi: https://doi.org/10.1093/jtm/taaa020.
- World Health Organization. 2020. COVID-19 weekly epidemiological update. World Health Organization. Accessed October 20, 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20201020-weekly-epi-update-10.pdf.
- Wu, Q., and Y. Xu. 2020. Parenting stress and risk of child maltreatment during the COVID-19 pandemic: A family stress theory-informed perspective. Developmental Child Welfare 2 (3):180–96. doi: https://doi.org/10.1177/2516103220967937.