Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceSocial Vulnerability is an area of growing interest among researchers and decision makers. As disaster losses mount, it emerged the understanding that disasters are not just a product of Hazards characteristics and Exposure, but also a Social construct that creates differentiate levels of ability to cope with, resist to and recover from extreme events.
The assessment of a multidimensional and intangible phenomenon like Social Vulnerability is extremely complex and over the years a number of indexes have emerged as an attempt to reduce the phenomenon to a simple metric, temporal and spatially comparable.
Social Vulnerability Index (SOVI) is a particularly robust and widely used index. A recent version of this algorithm, the Social Vulnerability to Natural and Technological Hazards Index (SOVI_NTH) addressed the caveat of having in the same SOVI Components variables regarding the socioeconomic attributes that make people vulnerable and the support structures and facilities that help them to resist and recover. Both indexes were implemented using the Hazards-of-Place model, that combines Social Vulnerability and Hazards Susceptibility to pinpoint areas where both have high scores.
In this research we compared the results and the statistical performance of both indexes to determine their consistency. Additionally, we analysed the sensitivity to data aggregation in order to determine whether it is possible to use very small spatial statistical units to highlight asymmetries and niches of particularly high Social Vulnerability
