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The concept of quality of life was born in the 1930s as a new, multidimensional and challenging objective for modern societies. In the last decades, the interest in empirical evaluation of quality of life and social indicators has steadily risen, as a result of the inception of novel objectives for the development of societies. Moreover, it represents the answer to the challenge of quantifying the level of well-being of communities and to the increasing information demand based on the implementation of active social policies. This concept becomes even more perplexing in the context of complex urban networks, as the human part constitutes a social network itself.

Social indicators are represented by statistics and other information (Bauer 1966), reflecting the actual conditions of local and global societies. According to De Vaus (De Vaus 2007), they are specific measures of a more abstract concept, which allow social change to be measured (Felson 1993). Therefore, they are measurements of social health that allow investigation of the evolution of social conditions. The international scientific community recognises a huge need to identify a scientifically effective tool for quality of life assessment through the use of indicators.

Indicators have been used since the 1960s to quantify social characteristics that might influence public policy (Newman 1997). However this has never been simple by any means. Working with indicators is very difficult and requires significant attention and valid consideration. More specifically, defining indicators necessitates the implementation of a specific procedure, as outlined by De Vaus (De Vaus 2007), whose first step is implied with the definition of the concept itself which the indicator attempts to describe. Also, according to the methodology that one may want to adopt, it could be important to find a sample of persons for contingent questionnaires to be administered, constructing questionnaires, and managing data.

The indicators also have to be consistent with the dimension of the concept, which one wants to describe, since the number of indicators used depends on it. Moreover reliability and validity are fundamental for the selected set of indicators. National and international authorities have, in fact, underlined a lack of adequate data, concepts and methodologies to quantify the social perception of quality of life (Noll 2002). They have also emphasised the need for collecting homogeneous data, reusable and clearly understandable, in order to both monitor social changes and assess social health and sustainability, so that one can use them within more complex, analytical models, too (Sen 1993, 2008).

Furthermore, the evaluation of quality of life for a specific community can be evaluated in both an objective and a subjective way. In the first case, indicators refer to the efficiency of services and relationships from an exclusively technical perspective, regardless of a person’s perception (Erikson 1993). Otherwise, social indexes are calibrated just setting up the citizens’ judgment, their satisfaction and happiness (Thomas et al. 1928; Ortiz et al. 2009).

Basically, social indicators are empirical measurements of the happiness of people and their level of satisfaction with reference to specific conditions. In particular, within urban contexts, the level of satisfaction of

citizens can be interpreted as a measure of the efficiency and functionality of the city itself. This therefore means that social indicators are able to gauge the social resilience of a community. Moreover, when dealing with extreme events having taken place, resilience depends on several different factors and on the community behaviour itself, and so indicators must refer to all involved mechanisms.

For this reason, the primary objective of this section is related to the definition of a strategy, aimed at identifying a set of adequate indicators that can be also assembled in different categories. Each of these categories can be referred to as a particular social dimension, influencing the level of satisfaction of citizens. This allows for the quantification of the “happiness” of citizens as an integral part of city resilience, and hence, of social sustainability. As an example, one can assemble the category “health and well-being”, which can take into account indicators referring to families with or without smokers, safety perception, police services efficiency, citizens with long term illness, death rate, child health, public medical services efficiency, etc. (McMahon 2002). To take this point further, indicators can also be processed as indexes, aggregations of indicators, and can provide a multidimensional and coherent view of a complex system (Cobb 2000; Mayer 2008), like the city.

In order to more consistently pursue this idea, some fundamental requirements are needed: the number of indicators have to be controlled in order to better manage the collected data (Tanguay et al. 2009), and the indicators possessing the most important linkages with engineering resilience measures have to be chosen to produce a more integrated overview.

Given that the primary intention of the present study is to quantify disaster resilience of urban communities, a further issue to account for is related to the need to perform all the specific measurements in the aftermath of the event. Indicators which describe the post-event phase as best possible have to be selected and evaluated for each step of time within each of the

considered recovery phases. In such a way, the social impact of a recovery strategy within the local environment can be effectively examined, together with its feasibility and effects on urban resilience. Another important feature is related to the capability to identify a threshold for each of the recognised indicators - a scientific measure of the limit value that it can reach. When dealing with resilience in the field of constructions, one can take into account further, more specific indicators: “number of displaced citizens”, “time of displacement”, “percentage of unfeasible buildings”, but also “number of workers”, “quantity of produced ruins”, “building process energy consumption”, and so on. Doing so permits evaluation of the social and environmental impacts, namely the measure of social and environmental sustainability. Also, when dealing with social indicators, it is actually fundamental to consider all sustainability dimensions (Figure 4.4) according to a life-cycle approach (Hodge 1991) within a human perspective. This is because an urban strategy targeting increasing the quality of life is not always the more sustainable one.

As an example, the lighting network of a city can be improved by simply providing a major number of streetlights. In this way, a category of indicators referring to the dimension “society” will increase, of course, as it takes into consideration indicators such as “security”, “community services” and “well-being”. On the other hand, the economic indicator “community expenses” will increase and the environmental indicators “energy consumption” and “environmental impacts” will increase, too. When looking to integrate all approaches and studies investigated, a set of both qualitative and quantitative indicators may be developed:

- economic indicators, accounting for local enterprise presence, accounting for the effects on the local economy of the regional and national economies, employment rate, household income and expenses. Also, a variety of indicators involving national financial capacity can be considered, such as the Gross Domestic Product, gasoline prices, economic welfare, insurance market trends, etc. (Cardona 2013; Sharp 1999);

- social indicators, like urban well-being as perceived by citizens, security, education, health, demographic incidence on national levels, etc.; - environmental indicators within the life cycle of an urban context, ex. ecological footprint, soil use, air quality, noise, waste, etc.

One important issue in dealing with social indicators, in particular, is related to the choice of what are deemed the most significant indicators. Current debate in the social sciences is deeply focused in determining what the indicators should be and which indicators best describe all the variables related to human well-being and quality of life. As a preliminary step, social resilience can be assessed by referring to the most common indicators used by universally recognised institutions, such as the United Nations (UN) and the World Bank (United Nations Conference on Environment and Development 1992; World Bank 1992). Such

institutions make national indicators available to everyone so that they can be used on a mass scale while also serving as reference points for the identification of local indicators, like, for instance, the “human poverty index”, “social disparity”, “unemployment” and so on. These are usually available with national census data, even if not all countries have such data readily available. In the case of the latter, indicators can be acquired through processing locally available data and by designing simple and brief questionnaires and having local administrators fill them out. In addition, well-being can be appraised through interviews with local actors, asking them about their level of satisfaction regarding urban services. Moreover, general information about the constructed environment can be employed. As has already been completed by the Inter-American Development Bank (IDB) for the Caribbean, the “Disaster Deficit Index” (DDI) and the “Local Disaster Index” (LDI) are used to classify mortality risk. However, when dealing with social resilience assessment, there are many problems widely recognised by the scientific community (e.g. gaps in data, knowledge and understanding, conceptual, methodological and application gaps). According to Tapsell et al. (2010), it is important to know the links between risk governance and local activities and processes in order to recognise the way which social vulnerability analysis fit within (and with which) societal aspects.

However, because the procedure to define and quantify indicators is rather complex, as a first step, all indicators that are determined easier to evaluate can be used when performing a hybrid approach, taking into account observed data, expert judgments and scenario analysis. Further, these easier-to-evaluate are employed in such a manner that the less precise and crude results produced can be controlled for by considering the relevant uncertainties during this phase of the study.

4.4 LINKING ENGINEERING METRICS ON NETWORKS AND