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1. Food Insecurity in the Post-Hurricane Harvey Setting: Risks and Resources in the Midst of Uncertainty

   https://doi.org/10.3390/ijerph17228424  

   Fitzpatrick, Kevin M.; Willis, Don E.; Spialek, Matthew L.; English, Emily (November 2020, International Journal of Environmental Research and Public Health)

   null (Ed.)

   Food insecurity is of heightened concern during and after natural disasters; higher prevalence is typically reported in post-disaster settings. The current study examines food insecurity prevalence and specific risk/resource variables that may act as barriers or advantages in accessing food in such a setting. Using a modified quota sample (n = 316), Hurricane Harvey survivors participated in face-to-face interviews and/or online surveys that assessed health, social and household factors, and sociodemographic characteristics. Using logistic regression analyses we find that social vulnerabilities, circumstantial risk, and social and psychological resources are important in determining the odds of food insecurity. Hispanic and/or Nonwhite survivors, renters, and those persons displaced during the natural disaster have higher food insecurity odds. Survivors with stronger social ties, higher levels of mastery, and a greater sense of connectedness to their community are found to have lower food insecurity odds. A more nuanced analysis of circumstantial risk finds that while the independent effects of displacement and home ownership are important, so too is the intersection of these two factors, with displaced-renters experiencing significantly higher odds than any other residence and displacement combinations, and particularly those who are homeowners not displaced during the disaster. Strategies for addressing differential risks, as well as practical approaches for implementation and education programming related to disaster recovery, are discussed. 

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2. Equitable community resilience: The case of Winter Storm Uri in Texas

   Ali Nejat Laura Solitare Edward Pettitt Hamed Mohsenian-Rad (July 2022, International journal of disaster risk reduction)

   Community resilience in the face of natural hazards involves both a community's sensitivity to disaster and its potential to bounce back. A failure to integrate equity into resilience considerations results in unequal recovery and disproportionate impacts on vulnerable populations, which has long been a concern in the United States. This research investigated aspects of equity related to community resilience in the aftermath of Winter Storm Uri in Texas which led to extended power outages for more than 4 million households. County-level outage/recovery data was analyzed to explore potential significant links between various county attributes and their share of the outages during the recovery/restoration phase. Next, satellite imagery was used to examine data at a much higher geographical resolution focusing on census tracts in the city of Houston. The goal was to use computer vision to extract the extent of outages within census tracts and investigate their linkages to census tracts attributes. Results from various statistical procedures revealed statistically significant negative associations between counties' percentage of non-Hispanic whites and median household income with the ratio of outages. Additionally, at census tract level, variables including percentages of linguistically isolated population and public transport users exhibited positive associations with the group of census tracts that were affected by the outage as detected by computer vision analysis. Informed by these results, engineering solutions such as the applicability of grid modernization technologies, together with distributed and renewable energy resources, when controlled for the region's topographical characteristics, are proposed to enhance equitable power grid resiliency in the face of natural hazards. 

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3. Modes of climate mobility under sea-level rise

   https://doi.org/10.1088/1748-9326/acfe22  

   Seeteram, Nadia A.; Ash, Kevin; Sanders, Brett F.; Schubert, Jochen E.; Mach, Katharine J. (October 2023, Environmental Research Letters)

   Abstract Exposure to sea-level rise (SLR) and flooding will make some areas uninhabitable, and the increased demand for housing in safer areas may cause displacement through economic pressures. Anticipating such direct and indirect impacts of SLR is important for equitable adaptation policies. Here we build upon recent advances in flood exposure modeling and social vulnerability assessment to demonstrate a framework for estimating the direct and indirect impacts of SLR on mobility. Using two spatially distributed indicators of vulnerability and exposure, four specific modes of climate mobility are characterized: (1) minimally exposed to SLR (Stable), (2) directly exposed to SLR with capacity to relocate (Migrating), (3) indirectly exposed to SLR through economic pressures (Displaced), and (4) directly exposed to SLR without capacity to relocate (Trapped). We explore these dynamics within Miami-Dade County, USA, a metropolitan region with substantial social inequality and SLR exposure. Social vulnerability is estimated by cluster analysis using 13 social indicators at the census tract scale. Exposure is estimated under increasing SLR using a 1.5 m resolution compound flood hazard model accounting for inundation from high tides and rising groundwater and flooding from extreme precipitation and storm surge. Social vulnerability and exposure are intersected at the scale of residential buildings where exposed population is estimated by dasymetric methods. Under 1 m SLR, 56% of residents in areas of low flood hazard may experience displacement, whereas 26% of the population risks being trapped (19%) in or migrating (7%) from areas of high flood hazard, and concerns of depopulation and fiscal stress increase within at least 9 municipalities where 50% or more of their total population is exposed to flooding. As SLR increases from 1 to 2 m, the dominant flood driver shifts from precipitation to inundation, with population exposed to inundation rising from 2.8% to 54.7%. Understanding shifting geographies of flood risks and the potential for different modes of climate mobility can enable adaptation planning across household-to-regional scales. 

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4. Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale

   https://doi.org/10.1371/journal.pone.0275916  

   Thompson, Amy E.; Walden, John P.; Chase, Adrian S.; Hutson, Scott R.; Marken, Damien B.; Cap, Bernadette; Fries, Eric C.; Guzman Piedrasanta, M. Rodrigo; Hare, Timothy S.; Horn, Sherman W.; et al (November 2022, PLOS ONE)

   Hart, John P. (Ed.)

   Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization. 

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5. How Valid Are Social Vulnerability Models?

   Rufat, S.; Tate, E.; Emrich, C.; and Antolini, F. (March 2019, Annals of the American Association of Geographers)

   Social vulnerability models are becoming increasingly important for hazard mitigation and recovery planning,but it remains unclear how well they explain disaster outcomes. Most studies using indicators and indexes employ them to either describe vulnerability patterns or compare newly devised measures to existing ones. The focus of this article is construct validation, in which we investigate the empirical validity of a range of models of social vulnerability using outcomes from Hurricane Sandy. Using spatial regression, relative measures of assistance applicants, affected renters, housing damage, and property loss were regressed on four social vulnerability models and their constituent pillars while controlling for flood exposure. The indexes best explained housing assistance applicants, whereas they poorly explained property loss. At the pillar level,themes related to access and functional needs, age, transportation, and housing were the most explanatory.Overall, social vulnerability models with weighted and profile configurations demonstrated higher construct validity than the prevailing social vulnerability indexes. The findings highlight the need to expand the number and breadth of empirical validation studies to better understand relationships among social vulnerability models and disaster outcomes. 

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