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.

The abundant research examining aspects of social-ecological resilience, vulnerability, and hazards and risk assessment has yielded insights into these concepts and suggested the importance of quantifying them. Quantifying resilience is complicated by several factors including the varying definitions of the term applied in the research, difficulties involved in selecting and aggregating indicators of resilience, and the lack of empirical validation for the indices derived. This paper applies a new model, called the resilience inference measurement (RIM) model, to quantify resilience to climate-related hazards for 52 U.S. counties along the northern Gulf of Mexico. The RIM model uses three elements (exposure, damage, and recovery indicators) to denote two relationships (vulnerability and adaptability), and employs both K-means clustering and discriminant analysis to derive the resilience rankings, thus enabling validation and inference. The results yielded a classification accuracy of 94.2% with 28 predictor variables. The approach is theoretically sound and can be applied to derive resilience indices for other study areas at different spatial and temporal scales.

Social media such as Twitter is increasingly being used as an effective platform to observe human behaviors in disastrous events. However, uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster resilience. This paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and Louisiana. We seek to answer a fundamental question: did social-geographical disparities of Twitter use exist during the three phases of emergency management (preparedness, response, recovery)? We employed a Twitter data mining framework to process the data and calculate two indexes: Ratio and Sentiment. Regression analyses between the Ratio indexes and the social-geographical characteristics of the counties at the three phrases reveal significant social and geographical disparities in Twitter use during Hurricane Harvey. Communities with higher disaster-related Twitter use in Harvey generally were communities having better social and geographical conditions. These results of Twitter use patterns can be used to compare with future similar studies to see whether the Twitter use disparities have increased or decreased. Future research is also needed to examine the effects of Twitter use disparities on disaster resilience and to test whether Twitter use can predict community resilience.

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.

Despite the increasingly prominent role of social media in disaster events, studies analyzing its use in rescue operations remain scanty. Hurricane Harvey hit Texas with unprecedented rainfall and flooding in 2017 and was marked by widespread use of social media for rescue requests. We conducted a survey of 195 Twitter users in Houston and surrounding communities who had requested for rescue during Harvey. The objective was to investigate our targeted group's socioeconomic and flood exposure characteristics, report the effectiveness of Twitter, and highlight lessons learnt and suggestions made for its use in future rescue missions. Survey revealed that those requesting rescue on Twitter were better educated, employed (80%), and homeowners (81%). Majority of them were flooded (87%), but remained satisfied with current location and did not consider moving. Calling relatives and friends for rescue was most responsive and yielded higher assistance-provided rate than using Twitter. Our respondents found Twitter helpful, but identified issues such as not knowing when volunteers received their requests or whether they would send help. They suggested promoting Twitter accounts and hashtags that accept emergency requests. This study provides baseline information and actionable suggestions for first responders, community managers, and resilience practitioners to improve future rescue missions.

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.