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1. Financial Preparedness for Emergencies: Age Patterns and Multilevel Vulnerabilities

   https://doi.org/10.1177/01640275211034471  

   Cong, Zhen; Feng, Guanggang (March 2022, Research on Aging)

   Guided mainly by the social vulnerability perspective and life course perspective, this study examined age patterns of financial preparedness for emergencies and how they were contextualized by vulnerabilities at the individual and community levels. We matched data from the Federal Emergency Management Agency 2018 National Household Survey and 15 indicators of the Social Vulnerability Index at the county level. Two-level logistic regressions were conducted with the working sample, which included 4,623 respondents from 958 counties. The results showed that adults aged 18 to 44 were more likely than those aged 65 to 74 to set aside money for emergencies among Hispanics, those with minor children in the household, and in communities with higher levels of poverty, higher percentages of minorities, and higher percentages of no vehicles, but less likely to do so among the White and those with insurance. The findings were discussed within a multilevel layered vulnerability framework. 

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2. Predicting groundwater contamination to protect the storm-exposed vulnerable

   https://doi.org/10.1016/j.crm.2023.100499  

   Hochard, Jacob; Abashidze, Nino; Bawa, Ranjit; Carr, Grace; Kirkland, Bailey; Li, Yuanhao; Matlock, Kayla; Siu, Wai Yan (March 2023, Climate risk management)

   Domestic wells provide drinking water to 44 million people nationwide. Many of these wells, which remain federally unregulated and rarely tested for pollutants, serve rural populations clustered near surface-contaminated sites (e.g., hazardous waste sites, animal agriculture operations, coal ash ponds, etc.). The potential for natural disasters to deteriorate drinking water quality is well documented. Less understood is whether opportunistic post-disaster sampling might underrepresent vulnerable populations. When disaster strikes, well water sampling campaigns offer a glimpse into the quality of water for exposed residents. We examined over 8,000 well water samples from 2016 and 2017 to measure Hurricane Matthew’s impact on the presence of indicator bacteria. Bacteria presence was predicted at the household level following Hurricane Matthew’s landfall. The residential addresses associated with birth records as well as clinically estimated dates of conception and birth dates were used to predict the likelihood of indicator bacteria in drinking water sources that were unsampled but likely to have served pregnant women. We estimate that opportunistic well water sampling captures the average predicted contamination rates among households with pregnant women. Our approach documents a distribution of contamination risk where 2.7% of the vulnerable sample (670 unsampled households) have a 75% likelihood of total coliform presence. The predicted likelihood of indicator bacteria is elevated for a small share of households nearby swine lagoons that experienced the most torrential rainfall. However, the gap between sampled and unsampled households cannot otherwise be explained by the storm event or proximity to surface-contaminated sites. Findings suggest that sophisticated and holistic water quality prediction models may support post-disaster sampling campaigns by targeting individual households within vulnerable groups that are likely to experience higher risks from groundwater contamination. 

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3. Barriers to Preparing for Disasters: Age Differences and Caregiving Responsibilities

   https://doi.org/https://doi.org/10.1016/j.ijdrr.2021.102338  

   Cong, Z.; Chen, Z.; Liang, D. (July 2021, International journal of disaster risk reduction)

   null (Ed.)

   This study examined age differences in barriers to preparing for disasters and how caregiving responsibilities are associated with these barriers among different age groups. Using a sample of 1142 individuals from the 2017 Federal Emergency Management Agency National Household Survey, binary and multinomial logistic regressions were conducted to investigate the likelihood of encountering any or one of the two types of barriers, namely, barriers related to coping appraisal (i.e., capacity) and those related to threat appraisal (i.e., risk perception). Age was the key predictor and was categorized into five groups: 18–34, 35–49, 50–64, 65–74, and 75+. The results showed that the 18–34, 35–49, and 75+ age groups were more likely to have coping appraisal barriers than those aged between 65 and 74. In addition, being a caregiver increased the likelihood of having coping appraisal barriers. Interestingly, relative to the 65–74 age group, being a caregiver in the 18–34, 35–49, and 50–64 age groups would be more likely to have coping appraisal barriers. Our findings highlighted age patterns and heterogeneity among older adults. This study also directed attention to how disaster preparation behaviors were shaped by life course experiences. 

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4. Supporting Disaster Resilience Spatial Thinking with Serious GeoGames: Project Lily Pad

   https://doi.org/10.3390/ijgi9060405  

   Tomaszewski, Brian; Walker, Amy; Gawlik, Emily; Lane, Casey; Williams, Scott; Orieta, Deborah; McDaniel, Claudia; Plummer, Matthew; Nair, Anushka; San Jose, Nicolas; et al (June 2020, ISPRS International Journal of Geo-Information)

   The need for improvement of societal disaster resilience and response efforts was evident after the destruction caused by the 2017 Atlantic hurricane season. We present a novel conceptual framework for improving disaster resilience through the combination of serious games, geographic information systems (GIS), spatial thinking, and disaster resilience. Our framework is implemented via Project Lily Pad, a serious geogame based on our conceptual framework, serious game case studies, interviews and real-life experiences from 2017 Hurricane Harvey survivors in Dickinson, TX, and an immersive hurricane-induced flooding scenario. The game teaches a four-fold set of skills relevant to spatial thinking and disaster resilience, including reading a map, navigating an environment, coding verbal instructions, and determining best practices in a disaster situation. Results of evaluation of the four skills via Project Lily Pad through a “think aloud” study conducted by both emergency management novices and professionals revealed that the game encouraged players to think spatially, can help build awareness for disaster response scenarios, and has potential for real-life use by emergency management professionals. It can be concluded from our results that the combination of serious games, geographic information systems (GIS), spatial thinking, and disaster resilience, as implemented via Project Lily Pad and our evaluation results, demonstrated the wide range of possibilities for using serious geogames to improve disaster resilience spatial thinking and potentially save lives when disasters occur. 

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5. Tracking Disaster Footprints with Social Streaming Data

   https://doi.org/10.1609/aaai.v34i01.5372  

   Cheng, Lu; Li, Jundong; Candan, K. Selcuk; Liu, Huan (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Social media has become an indispensable tool in the face of natural disasters due to its broad appeal and ability to quickly disseminate information. For instance, Twitter is an important source for disaster responders to search for (1) topics that have been identified as being of particular interest over time, i.e., common topics such as “disaster rescue”; (2) new emerging themes of disaster-related discussions that are fast gathering in social media streams (Saha and Sindhwani 2012), i.e., distinct topics such as “the latest tsunami destruction”. To understand the status quo and allocate limited resources to most urgent areas, emergency managers need to quickly sift through relevant topics generated over time and investigate their commonness and distinctiveness. A major obstacle to the effective usage of social media, however, is its massive amount of noisy and undesired data. Hence, a naive method, such as set intersection/difference to find common/distinct topics, is often not practical. To address this challenge, this paper studies a new topic tracking problem that seeks to effectively identify the common and distinct topics with social streaming data. The problem is important as it presents a promising new way to efficiently search for accurate information during emergency response. This is achieved by an online Nonnegative Matrix Factorization (NMF) scheme that conducts a faster update of latent factors, and a joint NMF technique that seeks the balance between the reconstruction error of topic identification and the losses induced by discovering common and distinct topics. Extensive experimental results on real-world datasets collected during Hurricane Harvey and Florence reveal the effectiveness of our framework. 

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