Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States

Abstract

The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks.

A network-based analysis of critical resource accessibility during floods

“…The Gini coefficient is a measure of deviation from the perfectly equitable society (Gini = 0), where a value of -1 and 1 are perfect inequity, favoring the bottom and top net worth of households respectively. Lorenz curves are gaining a renewed interest in measuring inequality and have recently been applied to other flood risk studies (Sanders et al, 2022;Yarveysi et al, 2023). For this application, the x-axis of the Lorenz curve is the social vulnerability (or social/economic status) index percentile and the y-axis is the specific variable being measured for each metric.…”

“…The misalignment between recent advancements in flood inundation research and these needs has led to the slower adoption of knowledge by flood management stakeholders, generating a push across disciplines to move beyond the "hot spots and hot moments" framework to better understand the temporal and spatial characteristics of events as they develop, occur, and unfold (Bernhardt et al, 2017;Coles et al, 2017). Additionally, there are established needs for flood impact models to consider transportation infrastructure disruptions (Yarveysi et al, 2023), as well as for models with high speeds and flexibility in order for multiple stakeholders to adopt new methods in existing flood risk management systems (Leskens et al, 2014).…”

“…For example, a hazard could affect every individual equally (G = 0), but this would be a less desirable solution than if only a handful of individuals were impacted (G = 0) (Logan et al, 2021). While a Gini coefficient between 0.3-0.4 is typically considered a reasonable gap when strictly using income data, evidence suggests that the threshold is less when examining inequalities in flood risk and vulnerability (Sanders et al, 2022;Yarveysi et al, 2023). Our peak Gini coefficients for household redundancy (SVI at peak flooding, G = 0.32, Q4 = 47%) and household reliability (SS 4 h post peak flooding, G = 0.30, Q4 = 45%) are two examples representing steep disproportionalities in the distribution of impacts.…”