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This paper first explores spatial distributions and patterns of COVID-19 case rates (cases/100,000 people) and mortality rates (deaths/100,000 people) and their disparities between urban and rural counties in the contiguous US. A county-level social vulnerability index was created using principal component analysis. Social vulnerability components were regressed against both county case and mortality rates. Results suggest that hotspots of case and mortality rates are clustered in Midwest and Upper-Midwest US. We found substantial disparities in case and mortality rates between urban and rural counties. County social vulnerability was positively correlated with both case and mortality rates suggesting counties with higher social vulnerability had higher case and mortality rates. Relationships between social vulnerability components and case and mortality rates vary across the conterminous US. Additionally, counties with increased racial and ethnic minorities, higher percentages of minors, and lower median household income are associated with higher COVID-19 case and mortality rates. [ABSTRACT FROM AUTHOR]

Tidal flats are widely recognized as sentinels of coastal environment change, and are also the guardians for beachfront communities. As a result of urban expansion, tidal flats have increasingly received environmental pressures and the surrounding ecosystem has been functionally downgraded. However, the existing studies could not provide an effective method to identify and quantify the interactions between urban areas and tidal flats, which is essential work particularly for the coastal preservations in the United States. Aiming at this environmental crisis, we proposed an approach which quantifies the change patterns from a spatiotemporal perspective. To justify the rationality and feasibility of this approach, this study selected three highly urbanized coastal counties in the southeastern United States as the study area. We analyzed the annual dynamics during 1985~2015, and the generated spatiotemporal regularities were used to identify and quantify the correlations between urban expansion and tidal flat dynamics. This study not only justified that the coastal urban expansion could considerably damage the environment of tidal flats, but also verified an effective approach to investigate the correlations between urban expansion and tidal flat loss on a large spatiotemporal scale. [ABSTRACT FROM AUTHOR]

In recent years, building resilient communities to disasters has become one of the core objectives in the field of disaster management globally. Despite being frequently targeted and severely impacted by disasters, the geographical extent in studying disaster resilience of the coastal communities of the United States (US) has been limited. In this study, we developed a composite community disaster resilience index (CCDRI) for the coastal communities of the conterminous US that considers different dimensions of disaster resilience. The resilience variables used to construct the CCDRI were justified by examining their influence on disaster losses using ordinary least squares (OLS) and geographically weighted regression (GWR) models. Results suggest that the CCDRI score ranges from −12.73 (least resilient) to 8.69 (most resilient), and northeastern communities are comparatively more resilient than southeastern communities in the study area. Additionally, resilience components used in this study have statistically significant impact on minimizing disaster losses. The GWR model performs much better in explaining the variances while regressing the disaster property damage against the resilience components (explains 72% variance) than the OLS (explains 32% variance) suggesting that spatial variations of resilience components should be accounted for an effective disaster management program. Moreover, findings from this study could provide local emergency managers and decision-makers with unique insights for enhancing overall community resilience to disasters and minimizing disaster impacts in the study area. [ABSTRACT FROM AUTHOR]