A review of multivariate social vulnerability methodologies: a case study of the River Parrett catchment, UK
I.Willis1 and J.Fitton2
1Birkbeck, University of London, London, UK
2University of Glasgow, Glasgow, UK
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Willis et al. (2010) analysed Italian census areas around Mount Vesuvius using Mosaic Italy 2007 geodemographic index scores (Table 1). Instead of using PCA extraction scores, it was proposed that an additive model was applied, whereby social vulnerability variables were weighted according to their economic Gini coefficient value to provide a composite score. The concept of this approach being that the Gini coefficient provides a precise measure of variable discrimination and therefore an appropriate weighting tool to assign some vulnerability variables with higher loadings than others.
https://www.nat-hazards-earth-syst-sci.net/16/1387/2016/nhes...

METHODOLOGICAL APPROACH CONSIDERING DIFFERENT FACTORS INFLUENCING VULNERABILITY – PAN-EUROPEAN SCALE
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Annex 2 Vulnerability factors and variables (extended table)
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Table 5-1 Vulnerability factors and variable
http://www.eu-drought.org/media/default.aspx/emma/org/108599...

ASSESSMENT OF AGRICULTURAL VULNERABILITY DUE TO CLIMATE CHANGE THROUGH EXPOSURE, SENSITIVITY AND ADAPTIVE CAPACITY BASED ON COMPOSITE INDEX: A CASE STUDY OF KENYA’S AGRO ECOLOGICAL ZONES
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 The second option, assigns different weights to the vulnerability variables considering that they are diverse.
http://meteorology.uonbi.ac.ke/sites/default/files/cbps/sps/...

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Note added at 52 mins (2017-07-31 03:28:00 GMT)
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Examining the Relationship Between Physical Vulnerability and Public Perceptions of Global Climate Change in the United States
Samuel D. Brody
Texas A&M University
Sammy Zahran
Colorado State University
Arnold Vedlitz
Himanshu Grover
Texas A&M University
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First,we conduct bivariate correlation analysis using a range of physical vulnerability variables possibly affecting risk perceptions grouped into four categories: proximity, weather, natural hazards, and anthropogenic hazards. Second, we analyze these variables using multiple regression analysis to test their overall statistical significance and more effectively isolate the effect of specific physical vulnerability variables on climate change risk perception
http://www.theurbanclimatologist.com/uploads/4/4/2/5/4425040...

In this study, the social vulnerability variable is constructed from 17 variables, i.e. 12 independent variables which represent the socio-economic concepts and 5 dependent variables which represent the damage and losses due to Merapi eruption in 2010.
http://adsabs.harvard.edu/abs/2015AGUFMNH13B1923L