Coastal communities faced with multiple hazards have shown uneven responses and behaviors. These responses and behaviors could be better understood by analyzing real-time social media data through categorizing them into the three phases of the emergency management: preparedness, response, and recovery. This study analyzes the spatial–temporal patterns of Twitter activities during Hurricane Sandy, which struck the U.S. Northeast on 29 October 2012. The study area includes 126 counties affected by Hurricane Sandy. The objectives are threefold: (1) to derive a set of common indexes from Twitter data so that they can be used for emergency management and resilience analysis; (2) to examine whether there are significant geographical and social disparities in disaster-related Twitter use; and (3) to test whether Twitter data can improve postdisaster damage estimation. Three corresponding hypotheses were tested. Results show that common indexes derived from Twitter data, including ratio, normalized ratio, and sentiment, could enable comparison across regions and events and should be documented. Social and geographical disparities in Twitter use existed in the Hurricane Sandy event, with higher disaster-related Twitter use communities generally being communities of higher socioeconomic status. Finally, adding Twitter indexes into a damage estimation model improved the adjusted R² from 0.46 to 0.56, indicating that social media data could help improve postdisaster damage estimation, but other environmental and socioeconomic variables influencing the capacity to reducing damage might need to be included. The knowledge gained from this study could provide valuable insights into strategies for utilizing social media data to increase resilience to disasters.

This paper presents an assessment of community resilience to coastal hazards in the Lower Mississippi River Basin (LMRB) region in southeastern Louisiana. The assessment was conducted at the census block group scale. The specific purpose of this study was to provide a quantitative method to assess and validate the community resilience to coastal hazards, and to identify the relationships between a set of socio-environmental indicators and community resilience. The Resilience Inference Measurement (RIM) model was applied to assess the resilience of the block groups. The resilience index derived was empirically validated through two statistical procedures: K-means cluster analysis of exposure, damage, and recovery variables to derive the resilience groups, and discriminant analysis to identify the key indicators of resilience. The discriminant analysis yielded a classification accuracy of 73.1%. The results show that block groups with higher resilience were concentrated generally in the northern part of the study area, including those located north of Lake Pontchartrain and in East Baton Rouge, West Baton Rouge, and Lafayette parishes. The lower-resilience communities were located mostly along the coastline and lower elevation area including block groups in southern Plaquemines Parish and Terrebonne Parish. Regression analysis between the resilience scores and the indicators extracted from the discriminant analysis suggests that community resilience was significantly linked to multicomponent capacities. The findings could help develop adaptation strategies to reduce vulnerability, increase resilience, and improve long-term sustainability for the coastal region.

Assessing the vulnerability and resilience to coastal hazards is a critical worldwide issue, especially for hurricane-prone coastal regions such as the Caribbean. However, the development of a useful metric for vulnerability and resilience assessment has a lot of challenges. Cartography and GIS analysis can contribute effectively to the solution of the issue by integrating natural and human data layers for assessment, mapping, and visualization. This paper uses the new Resilience Inference Measurement (RIM) model to assess the resilience of 25 countries in the Caribbean region to hurricanes. The RIM indices of the countries were computed using three variables representing three dimensions: exposure, damage, and recovery, and eight variables representing social-environmental capacity. The RIM resilience indices were mapped and compared with the vulnerability indices computed in a previous study. The results show that Turks & Caicos Islands had the highest resilience, whereas Montserrat had the lowest. This paper contributes to the hazard literature by demonstrating new vulnerability and resilience assessment methodologies that include validation and enable inference. The paper also contributes to the cartography and GIS literature by demonstrating the need to integrate data and perspectives from multiple disciplines and regions, as well as the ability of geospatial technology, in producing useful decision-making tools for a very pressing societal problem.

This article conducts a national, county-based assessment of the changes in population and urban areas in high-risk flood zones from 2001 to 2011 in the contiguous United States. The U.S. Federal Emergency Management Agency's (FEMA) 100-year flood maps, land cover data, and census data were used to extract the proportion of developed (urban) land in flood zones by county at the two time points, and indexes of difference were calculated. Local Moran's I statistic was applied to identify hot spots of increase in urban area in flood zones, and geographically weighted regression was used to estimate the population in flood zones from the land cover data. Results show that in 2011, an estimate of about 25.3 million people (8.3 percent of the total population) lived in high-risk flood zones. Nationally, the ratio of urban development in flood zones is less than the ratio of land in flood zones, implying that Americans were responsive to flood hazards by avoiding development in flood zones. This trend varied from place to place, however, with coastal counties having less urban development in flood zones than the inland counties. Furthermore, the contrast between coastal and inland counties increased between 2001 and 2011. Finally, several exceptions from the trend (hot spots) were detected, most notably in New York City and Miami, where significant increases in urban development in flood zones were found. This assessment provides important baseline information on the spatial patterns of flood exposure and their changes from 2001 to 2011. The study pinpoints regions that might need further investigations and better policy to reduce the overall flood risks.