Assessment of Urban Neighbourhoods’ Vulnerability through an Integrated Vulnerability Index (IVI): Evidence from Barcelona, Spain

Citation: Piasek, Gonzalo, Iraide Fernández Aragón, Julia Shershneva, and Pilar Garcia-Almirall. 2022. Assessment of Urban Neighbourhoods’ Vulnerability through an Integrated Vulnerability Index (IVI): Evidence from Barcelona, Spain. Social Sciences 11: 476. https://doi.org/10.3390/ socsci11100476 Academic Editors: Haorui Wu, Jeff Karabanow, Jean M. Hughes and Catherine Leviten-Reid Received: 1 August 2022 Accepted: 5 October 2022 Published: 14 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). $ € £¥ social sciences Article Assessment of Urban Neighbourhoods’ Vulnerability through an Integrated Vulnerability Index (IVI): Evidence from Barcelona, Spain Gonzalo Piasek 1,* , Iraide Fernández Aragón2, Julia Shershneva 2and Pilar Garcia-Almirall 1 1Architectural Technology Department, Polytechnic University of Catalonia, 08028 Barcelona, Spain 2Sociology and Social Work Department, University of the Basque Country, 48940 Bilbao, Spain *Correspondence: [email protected] Abstract: Urban inequality, specifically in vulnerable areas, has been a study topic from the earliest days of sociology to the present. This study’s objective is to discuss the scope and limitation of the concept of urban vulnerability, whilst generating an index that detects urban vulnerability in all its dimensions. A factor analysis of the main components was conducted resulting in the formation of four partial indices related to the social class, gentrification, social and employment, and physical & architectural dimensions of urban vulnerability, whilst their sums conform an integrated vulnerability index. This index is applied to the city of Barcelona, allowing its neighbourhoods to be positioned on a vulnerability continuum. Despite being applied in this city, the integrated vulnerability index emerges with the purpose of being replicated to other urban spaces. The mapping of these results using geographic information systems suggests a robust index that allows early identification of problematics, while also providing clues for policy intervention. Keywords: inequality; vulnerability; neighbourhood; factor analysis 1. Introduction Since the advent of cities, their organisation, particularly their inequal and heterogeneous structuring, has been a central study topic in social sciences, giving rise to a vast body of literature, with some studies focusing on ‘the urban’ expression of such inequalities. The key concepts of social studies, such as class struggle, polarisation, or inequality, are crystallised in the urban space, particularly on the neighbourhood scale (Zhang et al. 2018), as its most self-defining and sociological unit. The study of urban inequality has a long and diverse tradition in sociology. It began with the Chicago School in the 1930s based on the idea that physical distances so frequently are—or seem to be—an indicator of social distances (Park 1926). Some of the most important topics of study since then have been the comparison of different urban structures, as well as the identification of causes and effects of urban inequalities and their spatialisation. The analysis of these inequalities was addressed by the neo-Marxist approaches of the new urban sociology, showing how the abandonment of the city to free market has generated speculation and surpluses in urban capital gains. Thus, land in cities has become a specific type of capital, and its value forges urban segregation and differentiates (and ranks) the different neighbourhoods (Urrutia Abaigar 1999) and their inhabitants. In this regard, class struggle, for authors such as Harvey (2013), is particularly manifested in the fight for the appropriation of space in cities. The intensification of the spatial divisions coincides very well with class divisions, resulting in a differential distribution among areas of the city (Harvey 2013). However, the current global and urban scenario raises new challenges. Different authors have already mentioned the increase in spatial and social polarisation at the start of this century (Wacquant 2010;Secchi 2014;Seiz 2020). This increase was partly due Soc. Sci. 2022,11, 476. https://doi.org/10.3390/socsci11100476 https://www.mdpi.com/journal/socsci Soc. Sci. 2022,11, 476 2 of 19 to the globalisation of migrations (Castles and Miller 1998) and to the expansion of the neoliberal city model. Financial globalisation has generated neoliberal city production models, which in turn has intensified social inequalities. These inequalities are likewise heightened in crisis contexts, whether the 2008 economic crisis, in which vulnerable areas and groups were most badly hit (Janoshcka and Hidalgo 2014;Echaves García and Echaves 2017;Fernández Aragón and Shershneva 2017), or the 2020 health crisis, which generated significant inequalities (Guterres 2020;Seiz 2020;Teixeira et al. 2022). The different crisis scenarios reveal the need to pinpoint the most vulnerable groups and areas in order to design public policies to offset the negative impacts of crises. We are currently going through a scenario of fast and abrupt change. Thus, urban vulnerability is revealed as a highly relevant and complex issue, making it necessary to carry out an updated diagnosis tool that considers all of its dimensions. Even though it has been studied by different authors and from different perspectives, a comprehensive approach of urban vulnerability is sometimes missing, making it necessary to take previous discussions into account whilst taking a step forward on both a theoretical and a practical level. After addressing the main conceptual discussions related to urban vulnerability, as well as reconstructing the main efforts towards the construction of different vulnerability indices, a discussion on their limits and opportunities is presented. Thus, these discussions can help us better identify the dimensions of analysis of urban vulnerability, towards the construction of an integrated measure that can be applied in the city of Barcelona. 1.1. Conceptual Framework: Regarding the Concept of Urban Vulnerability As a step prior to setting up an index that allows us to measure urban vulnerability, the concept of vulnerability and its application to the urban sphere should be examined. With regard to its most general perception, vulnerability emerged in the literature of the Englishspeaking world to move beyond the concept of poverty that proved insufficient (Rodríguez 2001). The vulnerability approach, when realising the defencelessness, insecurity, and exposure to risk, shocks, and stress (Chambers 1989;Ochoa-Ramírez and Guzmán-Ramírez 2020) caused by extreme socioeconomic events, provides a more comprehensive view of the living conditions of people in poverty or at risk. Thus, vulnerability would encompass those processes that often occur in parallel, such as the increased risks and threats that could affect an individual or group, the fracture and weakening of the protection instruments, or the difficulty to overcome structural poverty (Rodríguez 2001). The application of this concept to the urban analysis must consider the convergence of personal, social, and physical parameters occurring in the urban space. There are multiple ways of defining urban vulnerability, some giving greater importance to one or another aspect. Thus, from the social perspective, urban vulnerability refers to the potentiality of a given population of a specific urban space of being affected by certain adverse circumstance(s) (Alguacil Gómez et al. 2014). In this regard, it is not only defined by the current critical disadvantage, but more specifically by the threat of exposure to certain exclusion risks (Callejo et al. 2005). The United Nations defines vulnerability as a state of high exposure to risks and uncertainties, in combination with a reduced ability to protect or defend oneself against these and cope with their negative consequences (United Nations 2003). Therefore, this definition stands out on two basic points: on the one hand, the increased risks that may negatively affect individuals or social groups and, on the other hand, the weakening of the tools that enable protection against those risks (Moser 1998). In this regard, the concept of urban vulnerability alludes to a dynamic and complex process, as it combines different residential economic and socially disadvantaged dimensions leading to exclusion (United Nations 2003). Thus, we can speak of at least three basic dimensions of vulnerability: the sociodemographic, the socioeconomic, and the spatial (Pizarro 2001;Rodríguez 2001). The first of them, sociodemographic vulnerability, is more linked with population ageing, migratory movements, and the appearance of household structures presenting great risks of vulnerability. The socioeconomic dimension, in turn, Soc. Sci. 2022,11, 476 3 of 19 refers to job instability and insecurity, exposing individuals to economic exclusion and poverty. Lastly, the residential dimension refers to poor conditions, both of housing and of the physical spaces in which daily social activities take place (Alguacil Gómez et al. 2014). On the other hand, authors (Hernández-Aja 2007;Zhang et al. 2021) have stressed the importance of considering the psychosocial dimension or the subjective perception of the environment in which they live. In relation to the characteristics of urban vulnerability, Castel’s reflections (Castel 1991, 1995) on the dynamic nature of this phenomenon are of great interest. Castel argued that it is not an airtight condition, but rather that the conditions of the individuals change depending on the moment. Thus, the author differentiated three areas of vulnerability resulting from their social and employment conditions. These three areas constitute a continuum that ranges from the integration area, which includes individuals with stable employment and a strong network of social relations, to the marginalisation area at the other end of the scale, including people who lack work and social relations. The vulnerability area is between these two extremes and represents the combination of precarious employment with the fragility of relational support (Castel 1991). In this sense, the characteristics related to a higher risk of vulnerability have to do with the intersection of economic, social, cultural, and environmental factors such as job instability, low level of education, unemployment, chronic diseases, disability, domestic violence, residential segregation, and dependency (Ranci 2002). In the urban space, vulnerability tends to be concentrated in certain areas of the city where there is a combination of unfavourable living, social, and job factors (Edwards 1975; Herbert 1975;Alguacil Gómez et al. 2014). Residential segregation, in turn, contributes to people with similar conditions coinciding in the same (under)privileged urban spaces (Egea Jiménez et al. 2008;Méndez 2013;Temes 2014). In the case of Spain, residential discrimination is a widely studied issue (Fernández Aragón et al. 2021a;Ministerio de Igualdad 2022;Checa Olmos et al. 2010), especially in the context of migration and ethnic minorities. Regarding the causes, one of the factors that influences urban spatial distribution is the price of housing (Martori and Hoberg 2004;Alguacil Gómez et al. 2014), with immigrants and young people on low incomes among the most vulnerable groups (Alguacil Gómez 2006). However, the residential discrimination is another relevant factor explaining residential segregation (Galster and Keeney 1988;Massey 2005). Galster and Keeney (1988) differentiated between public discrimination, which refers to local and state land and housing policies, and private discrimination generated by the real estate and private agents towards certain groups (Munnell et al. 1996). Among the consequences of residential segregation, previous research tended to focus mainly on its negative effects, specifically on socioeconomic integration and people’s wellbeing (Kempen and Ozuekren 1998;Wacquant 2001;Musterd 2005). Gentrification is another process related to urban vulnerability, as it may imply a potential threat to the locals living in a specific neighbourhood, in relation to their permanence or expulsion by the hike in housing and other prices (Hübscher 2018;Lin et al. 2021). Processes such as gentrification and touristification are generated when an urban area is appropriated by a population segment that did not previously live there, consequently expelling local inhabitants (Janoshcka and Hidalgo 2014;Hübscher 2018;Sorando and Ardura 2016). The expulsion is related, most directly, to the high price of rent resulting from the commodification and renewal of the neighbourhood (Callejo et al. 2005), as well as with the appearance of tourist or holiday rentals, which exerts great pressure on the rental market (Piñeira et al. 2021). Thus, gentrification and touristification, which particularly affect urban centres, may result in the modification of some cultural, economic, social and/or urban characteristics of the whole area under pressure. The discussion regarding these phenomena is divided between those who consider them a source of income and urban improvement, and those who view them as a factor for vulnerability and subjugation. Whichever the case, more and more definitions of urban vulnerability consider this dimension of analysis (Lin et al. 2021). Soc. Sci. 2022,11, 476 4 of 19 Therefore, focusing our research on urban vulnerability, trying to understand it in all of its dimensions is particularly important at this moment, as our results may lead to an early identification of urban areas with higher risks of social, economic, and/or residential exclusion. Furthermore, the interest in the Spanish context and the city of Barcelona is very well supported, given their specificities, as well as the possibility to extend our results to other similar contexts, mainly in the Spanish state but also in the Mediterranean. The study of urban differentiation, specifically the use of indices, is well established in urban sociology. These kinds of studies received a significant methodological boost in 1955 with the dissimilarity index (Duncan and Duncan 1955), as the sole indicator to measure residential differential. Subsequently, other complementary measures were added to detect the multiple dimensions of the urban segregation phenomenon (Massey and Denton 1988). The methodological developments of this type and the generation of indices as summarised and simple values, but with great explanatory power, have since then multiplied. In this regard, Table 1shows some relevant efforts to operationalise vulnerability, i.e., to provide an index capable of identifying its distribution in the urban space. Specifically, we differentiate between those studies that, from a more traditional vision, analysed vulnerability on the basis of social and economic indicators and those which, starting from a broader understanding of the concept, analysed it in terms of multiple aspects of the urban environment. All of these studies used available secondary data and operationalised urban vulnerability using a set of indicators, before following different methodologies for the calculation of a synthetic index (Fernández-García et al. 2017). Table 1. Vulnerability and/or inequality indices. Index Author Year Area Method Indicators Socioeconomic The social status index Ley 1986 Canada Correlation model Socioeconomic; residential (gentrification) Small-area index of socioeconomic deprivation Havard 2008 France Factorial analysis Sociodemographic; socioeconomic Deprivation index Townsend 1988 UK Logarithmic transformation Socioeconomic; residential Socioeconomic level index Fernández-Garcia 2017 Spain Factorial analysis Sociodemographic; socioeconomic; residential Vulnerability Urban vulnerability index Egea Jiménez 2008 Spain Factorial analysis Sociodemographic; socioeconomic; residential; subjective Comprehensive urban vulnerability synthetic index Ministry of Development 2010 Spain Multicriteria analysis Sociodemographic; socioeconomic; residential; environmental Indices of deprivation Ministry of Housing 2011 UK Factorial analysis Sociodemographic; socioeconomic; residential; environmental Synthetic index of comprehensive urban vulnerability Fernández Aragón 2021b Spain Factorial analysis Sociodemographic; socioeconomic; sociopolitical; residential Comprehensive vulnerability Hanoon 2022 Iraq Fuzzy logic function Environmental; residential; urban Composite vulnerability index Gerundo 2020 Italy Aggregation method Sociodemographic; urban; residential Source: Prepared by the authors. The socioeconomic dimension has been, traditionally, the most studied dimension in the field of urban sociology. In fact, it is notable how, from the most traditional to the most contemporary theories, the socioeconomic dimension has proven to be the determining factor to explain differences in urban spaces (Checa Olmos 2006). However, social indicators and dimensions, such as the access to services and environmental health (Navarro et al. 2016) and the living space, Soc. Sci. 2022,11, 476 5 of 19 have gained importance in recent years. This has likewise been the case in residential analysis (Garcia-Almirall et al. 2017), which most frequently ends up being linked to the gentrification process (Lin et al. 2021). Along with the socioeconomic, the sociodemographic dimension, with special focus on the presence of a foreign population given its greater vulnerability, is a constant in this type of analysis (Fernández Aragón et al. 2021b). Table 1shows the selection of a set of indices, focusing more on one or another dimension of vulnerability. In the case of the studies that focused more on socioeconomic aspects, a special mention should be made of the classic Townsend deprivation index in the United Kingdom (Townsend 1988) and updated by the government in 2011 (Ministry of Housing of the United Kingdom 2011). This list also includes the Havard small-area index of socioeconomic deprivation for the French case (Havard et al. 2018) and the socioeconomic level index created by Fernández-García and others in Spain (Fernández-García et al. 2017). They all share the priority use of social dimensions, including residential, socioeconomic, and sociodemographic indicators. The case of the social status index (SSI) developed by Ley (1986) for Canada is also very interesting. It clearly links urban poverty with gentrification processes, by taking some residential variables into account (Shevky and Bell 1955;Ley 1988). The table also shows the selection of urban vulnerability indicators that, in addition to the abovementioned, consider dimensions related to the subjective level. This is the case of the urban vulnerability index (IVU) of Egea Jiménez et al. (2008), the urban synthetic vulnerability index included in the urban vulnerability atlas (Temes 2014) produced by the Spanish Ministry of Development, along with the English indices of deprivation (Ministry of Housing of the United Kingdom 2011). The consideration of new dimensions and indicators related to the residents’ perception of their environment—which is known as ‘subjective vulnerability’ and involves the lack of green zones, pollution, antisocial behaviour, and transport—stands out in these proposals (Fernández-García et al. 2017). Although not included in the table, Ruáet al. (2019) proposed participatory techniques for the contrast of their selected indicators to elaborate an index for Castellon (Spain). Furthermore, variables are incorporated related to the residential context, such as the price of the dwellings and other environmental attributes (Fernández Aragón et al. 2021b;Gerundo et al. 2020). A special mention should be made of the synthetic index of comprehensive urban vulnerability (ISVUI) (Fernández Aragón et al. 2021b), which is the direct predecessor of this paper. In addition to the more standard dimensions—sociodemographic, social and employment, and residential, this last index considers participation through formal (electoral) and informal (associative network) spaces. In addition to the dimensions listed in the table, some recent studies linked urban vulnerability to geographical and/or climatological factors, which is particularly relevant due to the consequences of climate change (Romero-Lankao and Qin 2011;Hanoon et al. 2022). This analysis, in the case of European cities, was carried out by considering these variables as future risks (Tapia et al. 2017). Although there are different statistical tools used to integrate the different indicators, as shown in Table 1, the principal component factor analysis, whether exploratory or confirmatory, is the most common one. We can also find other techniques such as multicriteria analysis and correlation models. As we reveal in Section 2, this research is based on a factorial analysis. 1.2. Objectives One of the first hypotheses of this work is that social differences have a spatial correlation. This was one of the starting points that motivated the research in terms of identifying the dimensions of vulnerability and the way in which they combine and result in the distribution of social groups in the territory. Secondly, the revision of literature as well as the study of other vulnerability indices resulted in a more or less conscious decision to include variables related not only to social, economic, and demographic aspects, but also to indicators of the urban and housing spheres, most related to the valorisation and commodification of urban areas (Serin et al. 2020). Thus, some of the questions addressed in this research are related to the weight of urban aspects in the conformation of a vulnerability index. Soc. Sci. 2022,11, 476 6 of 19 This research’s objective is to discuss the scope and limitation of the concept of urban vulnerability, whilst generating an index that detects urban vulnerability in all its dimensions. This index is applied to the city of Barcelona, allowing its neighbourhoods to be positioned on a vulnerability continuum. Despite being applied in Barcelona, the integrated vulnerability index (hereinafter IVI) emerges with the purpose of being replicated in other urban spaces (with its appropriate adaptation to the different contexts). This paper is structured in five parts. A brief review of the main theoretical perspectives on urban vulnerability and of some of the indices constructed in similar studies is provided in Section 1. Subsequently, the methodology used for this study, the secondary sources selected, and the data used for the construction of the IVI applied in the city of Barcelona are presented in Section 2. Then, Section 3includes a description of the variables included in the model. This information was summarised by means of a principal component factor analysis (hereinafter FA) in order to establish the most important factors that influence urban vulnerability, as well as obtain an integrated measure. The main outcomes, together with validation and crosschecking, presented in Section 4, account for a highly consistent and robust index that is noted for measuring the vulnerability in a multidimensional way. Lastly, the main conclusions arising from this study are presented in Section 5. 2. Materials and Methods In order to construct a synthetic index that detects vulnerability and that allows the different neighbourhoods of the city to be positioned on a vulnerability continuum, a quantitative analysis strategy was selected, starting from secondary data fonts in open access. This last point is of great importance, as this index seeks to be replicated; consequently, access to data must be streamlined. Specifically, this exercise was conducted for the city of Barcelona. 2.1. Measurement Unit: The Neighbourhood With regard to the measurement unit for this analysis, the neighbourhood was selected as the reference space, even though it is more a sociological than an administrative category. Urban indices are divided between those using the census section or a micro-scale as unit and those that expand the scale to district or neighbourhood (Temes 2014). We found examples of this type of analysis, using the neighbourhood as unit, in Andalusia (Navarro et al. 2016), Madrid (Uceda Navas 2017), Bilbao (Fernández Aragón et al. 2021b), and the deprivation index applications (Townsend 1988). Access to data is one of the reasons for the decision to work at a neighbourhood level, as the data by census section are often not available, particularly those related to the 2011 Census (de Cos Guerra and Ferrer 2019). In any event, some of that data would be out of date. Consequently, prioritising the applicability of the IVI to other municipalities, the selected measurement unit is the neighbourhood. The choice of this scale is justified by the intention to crosscheck this methodology with other previous work, such as the index constructed by Fernández Aragón et al. (2021b) which is the basis of this research. At the same time, the neighbourhood scale has been the planning scale of different public policy initiatives in the city of Barcelona and in Catalonia, particularly after the 2004 Neighbourhood Act, which is very relevant to the local reality. At the same time, this scale is of particular importance, related to the presence of public service and social welfare at a neighbourhood scale, as well as its recognition as a value of proximity by local residents (Piasek et al. 2021). 2.2. Selection of Variables In order to answer to our objectives, a database at neighbourhood scale was first prepared on the basis of 21 indicators of different types (social, demographic, economic, urban, etc.) as a first step towards the construction of an urban vulnerability index. These indicators were selected following the same model proposed by the synthetic index of comprehensive urban vulnerability (ISVUI) applied to the city of Bilbao (Fernández Aragón Soc. Sci. 2022,11, 476 7 of 19 et al. 2021b). Specifically, its indicators are related to the sociodemographic, social and employment, residential, and participation dimensions. As for our case, the sociodemographic dimension includes the key variables for understanding the composition of the neighbourhood, which are very common in this type of study (Alguacil Gómez et al. 2014). Variables such as age or foreigners would be located in this dimension. Regarding the employment dimension, it is a classic dimension in the elaboration of urban factor analysis, commonly called ‘social rank’ (Shevky and Bell 1955). This includes variables related to employment and income. The residential dimension refers to the characteristics and conditions of housing. International studies show the relationship of housing equipment with the ‘social rank’ category (Shevky and Bell 1955;Hamilton and French 1979) or as an isolated factor, as in the case of Gittus (1965) for Liverpool. Variables such as the price of rent or heating in the dwelling are also included. Lastly, many studies linked poverty and vulnerability with social participation, either through electoral or associative participation (Gómez Fortes and Trujillo Carmona 2011). Thus, variables such as electoral abstention or participation in associations are also included. Some limitations were found regarding the characteristics of the city of Barcelona that were not all represented in previous indices, making it necessary to take previous efforts into account, while proposing an updated tool of analysis. Consequently, urbanistic indicators are added for this research with the intention of shedding some light on a dimension that a priori seemed representative of Barcelona, and which has to do with the urban scale, with the inherent characteristics of the city, its tourist profile, etc. The dimensions used in the Bilbao case were repeated, whilst adding some other points linked to economic trends related to touristification, and the urban dimension (all measurements based on specific indicators such as buildings owned by foreigners, the proportion of youths out of the total population, the percentage of tourist accommodation out of the total building stock, the percentage of land dedicated to urban park, the population density, etc.). Lastly, regarding the selection of variables, in the case of Spain, at the institutional and academic level, it is common to use the synthetic indices of urban vulnerability included in the atlas of urban vulnerability (Ministry of Public Works 2011). These indices are created on the basis of three indicators: percentage of unemployed people, percentage of people with no education, and percentage of dwellings in buildings in poor condition. The authors of this paper consider that, although this basic index allows comparison, this selection of variables is insufficient to explain urban vulnerability as a whole, as it leaves out basic aspects such as the quality of housing, urban dimensions, and social class attributes. Moreover, this approach does not allow vulnerability to be measured as a scale, as it only detects extreme situations. In order to overcome these limitations, the IVI expands the selection of variables as can be seen in Table 2. Nevertheless, this selection does not include variables related to the geographical/climatological dimension. While we consider this to be an interesting dimension, the lack of standardised data at a state level would prevent the index from being replicable in other cities. Likewise, the participation dimension is limited only to electoral participation and presence of local entities or associations, also due to the difficulty of obtaining data. 2.3. Index Creation Process Specifically, the following steps were followed for the elaboration of the index: first, the most relevant variables related to the object of study were selected on the basis of the literature review and the identification of core dimensions. Second, the selected variables were recoded to convert them into numerical variables, an essential condition for carrying out a factor analysis. Third, an exploratory principal component factor analysis was carried out. In this phase, variables with little or no explanatory capacity were eliminated. Specifically, the variables ‘population density’ and ‘urban green areas’ were eliminated. Fourth, a factor analysis was carried out with the explanatory variables resulting in four factors obtained with Varimax rotation. With this second factor analysis, we were able Soc. Sci. 2022,11, 476 8 of 19 to rank and weight the impact of each dimension on the overall urban vulnerability of each neighbourhood. This is what is mapped in the next section. The weight assigned to each dimension by the second-order factor analysis was as follows: social class, with 23.7% explanatory capacity; gentrification, with an explanatory capacity of 23.1%; social and employment, 22.7%; physical and architectural, 11.4%. The calculation of the global index was then carried out considering the relative weight of each of these dimensions. Table 2. Variables selected for the model. Indicator Source House prices per m2(EUR) Sect. of the Urban Agenda for Catalonia—GenCat (2021) People with a university education (%) Municipal Register of Inhabitants (2020) People without basic education (%) Municipal Register of Inhabitants (2020) Available family income (EUR) Open Data Barcelona (2018) Age of the dwellings (years) Cadastral General Directorate (2020) Tourist accommodation (%) Bcn Statistics Department (2020) Entities per neighbourhood (N) Open Data Barcelona (2020) Foreign population (%) Municipal Register of Inhabitants (2020) Youth population (%) Municipal Register of Inhabitants (2020) Premises property of private foreign owners (%) Cadastral General Directorate (2020) People with vocational training (%) Municipal Register of Inhabitants (2020) No. of people unemployed (%) Department of Work—GenCat (2021) Abstention in last elections (%) Ministry of the Interior (2019) Over 65 years old (%) Municipal Register of Inhabitants (2020) Non-contributory pensions (%) Department of Work—GenCat (2021) Sex ratio (%) Municipal Register of Inhabitants (2020) Flats with lift (%) Population and Housing Census (2011) Average surface area of the housing (m2)Cadastral General Directorate (2020) Housing with heating (%) Population and Housing Census (2011) Source: Prepared by the authors. The secondary data used, which are detailed in Table 2, were obtained from statistical information sources available in open format, including the Statistics Department of Barcelona City Council, the Observatory of Districts and Neighbourhoods, the Sociodemographic Survey, the Housing Census, and the city’s Survey Register. Most of the indices used to measure urban poverty or vulnerability use census data, which can only be updated every 10 years, when the census is carried out. Therefore, for the elaboration of the IVI, we opted for other types of sources that allow vulnerability to be monitored in shorter periods of time. The factorial analysis is a data reduction technique, whose ultimate aim consists of searching for the minimum number of dimensions capable of explaining the vast amount of information contained in the data (De la Fuente Fernández 2011). The method seeks to simplify the available information in order to make it easier to handle and interpret. In this regard, it offers huge benefits when working with a large number of variables and cases, which is the reason for selecting it as the most appropriate method to reduce the number of indicators to a series of interlinked dimensions, without losing valuable information. At the same time, some limitations of this methodology can also be indicated, particularly those linked to the use of statistical techniques where the researcher does not handle the information directly. Even though this could be seen as a guarantee of relative objectivity in the method, the ‘capacity of agency’ is an integral part of any research task; both the selection of the indicators and the interpretation of the data are eminently qualitative activities according to earlier studies and readings that set the course, along with the result of certain ‘habitus’ of the researcher (Bourdieu 1978). According to these comments, the FA is, therefore, the most appropriate technique given the research goals of this paper. After having obtained the most appropriate model to explain the dimensions of urban vulnerability, the factors obtained were reduced in an integrated vulnerability index Soc. Sci. 2022,11, 476 9 of 19 consisting of the sum of the relative weights of each of the dimensions arising from the FA. Having checked the contribution of the variables—above 0.5—and the KMO and Bartlett indicators—above 0.6 in the first case and significant in the second—four factors were selected according to the eigenvalue method which altogether explain more than 81% of the variance. 2.4. Presentation of Results Lastly, the results were mapped (the maps in the next section represent the distribution of each of the factors, as well as of the IVI), using geographic information systems (GIS) (Qgis 3.10-A Coruña, Free Software Foundation Inc., Boston, MA, USA), a powerful technology used by different disciplines when the subject matter has a territorial component (geographic, environment, landscape, etc.) (Garcia-Almirall et al. 2017). They can, thus, be used to identify different dimensions in the territory being studied, with huge implications both for the research and for the planning of cities and neighbourhoods. 3. Results This section portrays the main results obtained following the methodological strategy with the intention to answer our research objectives related to the discussion of the concept of vulnerability and the validation of an integrated vulnerability index. After the selection and inclusion of 19 indicators in our model, we conducted a factorial analysis that resulted in the formation of four subindexes and an integrated comprehensive measure of vulnerability, which we named IVI. The implications of these measurements are presented in this section. Secondly, the application of the IVI to the city of Barcelona resulted in the construction of four partial maps—related to the four dimensions of vulnerability previously identified—as well as final map portraying the different neighbourhoods of the city in a vulnerability continuum. These maps are analysed, according to recent data, as well as following recent findings from academic work. Subsequently, an analysis of the implications of our results in terms of research and public policy is presented. 3.1. The IVI Factors After normalising and standardising the values for each of the 21 indicators introduced into the model, two variables were excluded, resulting in aa total of 19 indicators which were introduced into the factorial analysis, obtaining robust results: four subfactors and statistical confidence. To account for the suitability of the FA’s results, two of the most commonly used alternatives are the Kaiser–Meyer–Olkin (KMO) test and the Bartlett test of sphericity. In our case, as can be seen in Table 3, the KMO was greater than 0.7, and we were statistically able to accept the proposed model. At the same time, the significance tended to 0, showing that the model is statistically reliable. Table 3. KMO and Bartlett test. Kaiser–Meyer–Olkin Measure of Sampling Adequacy 0.776 Bartlett’s test of sphericity Approximately chi-square 1716.212 df 171 Sig. <0.001 Source: Prepared by the authors. The factorial analysis resulted in the formation of four factors differentiated from each other, but whose components were related, altogether explaining 81.03% of the total variance of data. As Table 4shows, the first three factors explained between 22.8% and 23.7% of the total variance, while the last one explained 11.4%. Soc. Sci. 2022,11, 476 16 of 19 be crucial in terms of preventing potential physical deterioration, gentrification, or silent expulsion tendencies. Lastly, it should be stressed that the application of the IVI to Barcelona proves that it is important to constantly obtain an updated and ‘early’ identification of complex situations of inequality and vulnerability. Moreover, this should trigger a proactive response by stakeholders involved in the planning and production of the city, from a perspective of human rights and social justice. Author Contributions: Conceptualization, G.P., I.F.A., J.S. and P.G.-A.; methodology, G.P. and I.F.A.; software, G.P.; validation, I.F.A.; formal analysis, G.P.; investigation, G.P. and I.F.A.; resources, P.G.-A.; data curation, G.P.; writing—original draft preparation, G.P., I.F.A. and J.S.; writing—review and editing, G.P., I.F.A. and J.S.; visualization, G.P.; supervision, I.F.A. and P.G.-A.; project administration, G.P. and I.F.A.; funding acquisition, P.G.-A. All authors have read and agreed to the published version of the manuscript. Funding: This research is part of the doctoral thesis of the first author, G.P., with the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. Part of the development of this research was supported by an R&D project: ‘Socio-spatial indicators for the improvement of the housing stock in vulnerable areas. Criteria for action in the cases of the metropolitan areas of Barcelona and Bilbao (Re-Inhabit); RTI 2018-101342-BI00’, supported and funded by the Spanish State Research Agency. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the first author. Furthermore, data used in this paper are available in official open-access data sources, such as the Statistical Department of the city of Barcelona, the national census, and the Observatory of Neighbourhoods and Districts. Acknowledgments: The authors thank M. de la Asunción for her support in the use of GIS software. Conflicts of Interest: The authors declare no conflict of interest. References Alguacil Gómez, Julio. 2006. Barrios desfavorecidos: Diagnóstico de la situación española. In V. Informe FUHEM de políticas sociales: La exclusión social y el estado de bienestar en España. 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