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.

Urban expansion is one of the most dramatic forms of land transformation in the world and it is one of the greatest challenges in achieving sustainable development in the 21st century. Previous studies analyzed urbanization patterns in areas with rapid urban expansion while urban areas with low to moderate expansion have been overlooked, especially in developed countries. In this study, we examined the spatiotemporal dynamics of urban expansion patterns in South Florida, United States (US) over the last 25 years (1992–2016) using Remote Sensing and GIS techniques. The main goal of this paper was to investigate the degree and spatiotemporal patterns of urban expansion at different administrative level in the study area and how spatiotemporal variance in different explanatory factors influence urban expansion in this region. More specifically, this research quantifies the rates, types, intensity, and landscape metrics of urban expansion in Miami-Fort Lauderdale-Palm Beach, Florida Metropolitan Statistical Area (Miami MSA) which is the 7th largest MSA and 4th largest urbanized area in the US using remote sensing (satellite imageries) data from National Land Cover Datasets (NLCD) and Coastal Change Analysis Program (C-CAP) at 30 m spatial resolution. We further investigated the urban growth patterns at the county and city areas that are located within this MSA to portray the local ‘picture’ of urban growth in this region. Urban expansion in this region can be divided into two time periods: pre-2001 and post-2001 where the former experienced rapid urban expansion and the later had comparatively slow urban expansion. Results suggest that infilling was the dominant type of urban expansion followed by edge-expansion and outlying. Results from landscape metrics represent that newly developed urban lands became more aggregated and simplified in form as the time progressed in the study region. Also, new urban lands were generated away from the east coast and historic cities which eventually created new urban cores. We also used correlation analysis and multiple linear stepwise regression to address major explanatory factors of spatiotemporal change in urban expansion during the study period. Although the influence of factors on urban expansion varied temporally, Population and Distance to Coast were the strongest variables followed by Distance to Roads and Median Income that influence overall urban expansion in the study area.

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.

The Gulf and southeast coastal communities in the United States are particularly vulnerable to tropical cyclones. Coastal areas generally receive the greatest economic losses from tropical cyclones; however, research suggests that losses in the inland zone can occasionally be higher than the coastal zone. Previous research assessing the inland impacts from tropical cyclones was limited to the areas that are adjacent to the coastal zone only, where losses are usually higher. In this study, we assessed the spatial distribution of inland property damage caused by tropical cyclones. We included all the inland counties that fall within the inland zone in the states of Louisiana, Mississippi, and Alabama. Additionally, different factors, including meteorological storm characteristics (tropical cyclone wind and rain), elevation, and county social-economic vulnerability (county social vulnerability index and GDP) were assessed to measure their influence on property damage, using both ordinary least squares (OLS) and geographically weighted regression (GWR) models. GWR performs better than the OLS, signifying the importance of considering spatial variations in the explanation of inland property damage. Results from the tristate region suggest that wind was the strongest predictor of property damage in OLS and one of the major contributing factors of property damage in the GWR model. These results could be beneficial for emergency managers and policymakers when considering the inland impacts of tropical cyclones.