In the field of disaster risk analysis various approaches using indicators for vulnerability assessment can be found (Cardona, 2004, Cutter et al 2003, Dilley et al., 2005, Peduzzi, 2006, Balica et al, 2009). All these approaches aim at assessing risk and vulnerability quantitatively by means of indicators to compare different regions or communities (Birkmann, 2007, Schmidtlein et al., 2008).
Trying to calculate vulnerability assessment, indicators represent an operational representation of a characteristic or a quality of a system able to provide useful information regarding the exposure, the susceptibility, and the resilience of a the chosen to be studied system to an impact of a disaster (Birkmann, 2006). Different factors reflecting the special characteristics of a system determine in general its vulnerability. Therefore, the vulnerability couldn’t be calculated or assessed by using just one single indicator. Instead multi-dimensional concepts, such as composite indices, are required to assess the vulnerability of a system.
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Composite indices are formed when individual indicators are compiled to a single index based on an underlying theoretical vulnerability framework (Nardo et al., 2005). As described above, in risk perceptions the understanding of vulnerability is very broad and current literature encompasses many different definitions, concepts, and methods to systemize vulnerability (Birkmann, 2007, Cutter et al., 2003). Vulnerability could be defined as the susceptibility of a system to be affected or susceptible to damage as proposed by Villagran de Leon (2006).
Due to the complexity of vulnerability, its calculation and measurement (and especially for the social component) can be challenging (Brooks et al., 2005). In the field of natural disaster risk assessment one methodology to evaluate the vulnerability is the use of indicator approaches (Adger, 2005, Cardona, 2006, Dilley et al., 2005, Pelling, 2004, Perduzzi, 2006). In general, the data of most interest from the point of view of vulnerability assessment are those relating to mortality and the numbers of people adversely affected by climate-related events. While economic damage is also an important indicator of the severity of the impacts of climate-related disasters, data relating to the cost of disasters are relatively sparse and are also difficult to estimate.
One of the main challenges in the analysis of vulnerability is the identification of appropriate indicators for the operationalization of the concept. Only with valid, reliable and objective indicators can vulnerability be adequately examined. A review of the literature in the field reveals that very different indicators are being employed to capture phenomena of, and trends in, vulnerability. The choice of indicators is influenced by the specific purpose of the studies, the availability of data, but also by the theoretical perspective taken on vulnerability (Prior et al., 2017).
To compose the appropriate for the study vulnerability framework indicators with the following features should be chosen: i) simple to apply, ii) quantitative, iii) sensitive to changes, and iv) representative.