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The social correlates of flood risk: variation along the US rural–urban continuum

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Abstract

Compositional and contextual characteristics of a place capture the collective financial, physical, human, and social capital of an area and its ability to prevent, plan for, and recover from severe weather events. Research that examines the compositional and contextual characteristics of places with elevated flood risk is largely limited to urban-centric analyses and case studies. However, rural areas of the USA are not immune to flooding. In this paper, we integrate social and physical data to identify the social correlates of flood risk and determine if and how they vary across the rural–urban continuum for all census tracts in the coterminous USA. Our results show that risk of flooding is higher in rural tracts, in tracts with larger relative shares of socioeconomically vulnerable populations, and in tracts reliant on flood-vulnerable industries. We also show that compositional social correlates of flooding are not consistent across rural–urban areas. This work widens the scope of discourse on flooding to attend to the heterogeneity of social correlates and the implications for policy and future research.

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Availability of data and material

Data is available through the First Street Foundation.

Code availability

Code available upon request to the corresponding author.

Notes

  1. This value only includes flooding events that accrued more than $1 billion in costs. Therefore, it is likely a gross underestimate of the total costs associated with flooding during this time period.

  2. The lambda of the standardized Box-Cox transformation for percent of properties at risk is 0.011, which means that the natural logarithm is the best transformation (Glen, 2015). Thus, we used the natural logarithm to transform percent of properties at risk. Because percent of properties at risk contains a 0 value, we added 1 to percent of properties at risk before the natural logarithm transformation. The lambda of the standardized Box-Cox transformation for average risk score was − 1.000, which means that the reciprocal is the best transformation (Glen, 2015). However, the reciprocal reverses the magnitude of the average risk score, which might skew the associations. Thus, we still used the natural logarithm to transform the average risk score.

  3. The authors used percent racial/ethnic minority (i.e., percent not non-Hispanic white) because percent non-Hispanic Black and percent Hispanic were too highly correlated. While different racial/ethnic groups have different histories and experiences in the USA, tracts with larger relative shares of minorities are at greater risk of disenfranchisement and marginalization from decision-making processes related to flood mitigation and adaptation.

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Acknowledgements

Dr. Rhubart wishes to acknowledge that the majority of the work for this manuscript was conducted while she was a postdoctoral fellow at the Lerner Center for Public Health Promotion at Syracuse University. The authors also wish to thank Dr. Shannon Monnat for her feedback on an early draft of this paper. In addition, the authors acknowledge the First Street Foundation Flood Lab for providing access to the county-level data.

Funding

This study received support from the National Institute on Aging (NIA) Interdisciplinary Network on Rural Population Health and Aging (R24 AG065159) and the USDA Agricultural Experiment Station Multistate Research Project W4001, Social, Economic and Environmental Causes and Consequences of Demographic Change in Rural America.

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Correspondence to Danielle Rhubart Ph.D..

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Appendix

Appendix

Table 4 Linear regression model results examining social correlates of flood risk using original versions of the dependent variables and with state-level fixed effects
Table 5 Fixed-effects linear regression model results examining social correlates of flood risk by rural–urban status using original dependent variables and with state-level fixed effects

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Rhubart, D., Sun, Y. The social correlates of flood risk: variation along the US rural–urban continuum. Popul Environ 43, 232–256 (2021). https://doi.org/10.1007/s11111-021-00388-4

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