A review of multivariate social vulnerability methodologies: a case study of the River Parrett catchment, UK

I Willis,J Fitton

doi:10.5194/nhess-16-1387-2016

Abstract

Abstract. In the field of disaster risk reduction (DRR), there exists a proliferation of research into different ways to measure, represent, and ultimately quantify a population's differential social vulnerability to natural hazards. Empirical decisions such as the choice of source data, variable selection, and weighting methodology can lead to large differences in the classification and understanding of the "at risk" population. This study demonstrates how three different quantitative methodologies (based on Cutter et al., 2003; Rygel et al., 2006; Willis et al., 2010) applied to the same England and Wales 2011 census data variables in the geographical setting of the 2013/2014 floods of the River Parrett catchment, UK, lead to notable differences in vulnerability classification. Both the quantification of multivariate census data and resultant spatial patterns of vulnerability are shown to be highly sensitive to the weighting techniques employed in each method. The findings of such research highlight the complexity of quantifying social vulnerability to natural hazards as well as the large uncertainty around communicating such findings to stakeholders in flood risk management and DRR practitioners.

Highlights

The impacts of a natural hazard event upon a population vary considerably depending on the socioeconomic attributes of the people exposed to the hazard (O’Keefe et al, 1976; Yoon, 2012; Zakour and Gillespie, 2013)
The aim of this paper is to compare the social vulnerability indices produced when using three published methodologies: a method based on Cutter et al (2003), a method using Pareto ranking based on Rygel et al (2006), and a method with Gini coefficient weighting based on Willis et al (2010)
The social vulnerability scores from Cutter et al (2003) and Willis et al (2010) show a relationship close to linear with a strong correlation evident (R2 = 0.8975)

Summary

Introduction

The impacts of a natural hazard event upon a population vary considerably depending on the socioeconomic attributes of the people exposed to the hazard (O’Keefe et al, 1976; Yoon, 2012; Zakour and Gillespie, 2013) This concept can be termed social vulnerability, but the exact definition of this term, and other associated concepts such as resilience and adaptive capacity, is contested within the literature (Brooks, 2003; Fuchs, 2009; Kuhlicke et al, 2011). An individual’s level of social vulnerability is multifaceted and determined by a number of spatially and temporally distant political, economic, and social “root causes” (Birkmann et al, 2013; Watts and Bohle, 1993) These processes manifest at a local scale into a range of “unsafe conditions”: e.g. living in dangerous locations, low income (see the Pressure and Release Model (PAR) developed by Wisner et al, 2004).

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