Social vulnerability assessment using spatial multi-criteria analysis (SEVI model) and the Social Vulnerability Index (SoVI model) - a case study for Bucharest, Romania

Type Journal Article - Natural Hazards and Earth System Science
Title Social vulnerability assessment using spatial multi-criteria analysis (SEVI model) and the Social Vulnerability Index (SoVI model) - a case study for Bucharest, Romania
Author(s)
Volume 13
Issue 6
Publication (Day/Month/Year) 2013
Page numbers 1481-1499
URL http://www.nat-hazards-earth-syst-sci.net/13/1481/2013/nhess-13-1481-2013.pdf
Abstract
In recent decades, the development of vulnerability
frameworks has enlarged the research in the natural hazards
field. Despite progress in developing the vulnerability
studies, there is more to investigate regarding the quantitative
approach and clarification of the conceptual explanation
of the social component. At the same time, some disasterprone
areas register limited attention. Among these, Romania’s
capital city, Bucharest, is the most earthquake-prone
capital in Europe and the tenth in the world. The location
is used to assess two multi-criteria methods for aggregating
complex indicators: the social vulnerability index (SoVI
model) and the spatial multi-criteria social vulnerability index
(SEVI model). Using the data of the 2002 census we
reduce the indicators through a factor analytical approach to
create the indices and examine if they bear any resemblance
to the known vulnerability of Bucharest city through an exploratory
spatial data analysis (ESDA). This is a critical issue
that may provide better understanding of the social vulnerability
in the city and appropriate information for authorities
and stakeholders to consider in their decision making. The
study emphasizes that social vulnerability is an urban process
that increased in a post-communist Bucharest, raising
the concern that the population at risk lacks the capacity to
cope with disasters. The assessment of the indices indicates
a significant and similar clustering pattern of the census administrative
units, with an overlap between the clustering areas
affected by high social vulnerability. Our proposed SEVI
model suggests adjustment sensitivity, useful in the expertopinion
accuracy.

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