Social vulnerability and the natural and built environment: a model of flood casualties in Texas

Abstract

Studies on the impacts of hurricanes, tropical storms, and tornados indicate that poor communities of colour suffer disproportionately in human death and injury.(2) Few quantitative studies have been conducted on the degree to which flood events affect socially vulnerable populations. We address this research void by analysing 832 countywide flood events in Texas from 1997-2001. Specifically, we examine whether geographic localities characterised by high percentages of socially vulnerable populations experience significantly more casualties due to flood events, adjusting for characteristics of the natural and built environment. Zero-inflated negative binomial regression models indicate that the odds of a flood casualty increase with the level of precipitation on the day of a flood event, flood duration, property damage caused by the flood, population density, and the presence of socially vulnerable populations. Odds decrease with the number of dams, the level of precipitation on the day before a recorded flood event, and the extent to which localities have enacted flood mitigation strategies. The study concludes with comments on hazard-resilient communities and protection of casualty-prone populations.

Linking local vulnerability assessments to climatic hazard losses for river basin management

“…With the exception of a significantly positive relationship between employment rates and casualty numbers, and between social dependence counts and disaster losses in the Gaoping watershed, the other socioeconomic factors had a weak relationship with casualty and loss distributions. These results do not fully conform with the results of existing studies, which claim that the relationship between social vulnerability factors and disaster losses (or risk) is functional (Zahran et al, 2008;Hung and Chen 2013 For all of the regression models, increasing urban or agricultural development significantly increased casualty counts and typhoon losses, although increasing agricultural uses strongly decreased casualty distributions in the Gaoping watershed.…”

“…The second type focuses on bottom-up and data-based analysis, which often uses historical or survey data to characterize the disaster damage (Zahran et al, 2008;Bhattarai et al, 2015). This approach concentrates more on mapping the disaster damage distribution at national or regional levels.…”

“…The casualties were a non-negative integer that exhibit significant over-dispersion with a disproportionate number of zero counts. The data were thus investigated using a ZINB (zero-inflated negative binomial) or ZIP (zero-inflated Posisson) regression model, which allows us to estimate the net effects of independent vulnerability factors on casualties (Cameron and Trivedi, 1998;Zahran et al, 2008). To more comprehensively scrutinize the effect of disaster losses, the integrated typhoon loss index (ITLI) was estimated and acts as a proxy for combined losses due to the typhoon:…”

“…The areas where there is greater proximity to rivers and/or higher levels of 20 elevations are both more vulnerable and more sensitive to disaster damage (Ni et al, 2010 (Zahran et al, 2008). In contrast, populations and social dependence are expected to have a positive relationship with disaster damage (Hung et al, 2016).…”

Integrating epidemiological and engineering approaches in the assessment of human casualties in earthquakes

“…Evidence from other natural events such as floods suggests that communities with socially vulnerable populations experience more casualties (Cutter et al 2003;Zahran et al 2008). In light of recent events such as the 2010 Haiti earthquake, this finding should be investigated in the specific context of earthquake loss estimation and casualty modeling.…”