Talk:Synthetic vulnerability index in Barcelona and Madrid

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The COVID-19 pandemic in Spain. First wave: from the first cases to the end of June 2020

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Thematic structure > The COVID-19 pandemic in Spain > Different spatial behaviours > Synthetic vulnerability index in Barcelona and Madrid

Five social and economic variables were selected to develop a synthetic vulnerability index for Barcelona and Madrid: non Spanish population, illiterate or uneducated population, average income per capita, unemployed and social welfare beneficiaries. Data were sourced from the National Statistics Institute (experimental statistics) and the statistics portals from Madrid and Barcelona.

PCA MATRIXa
VARIABLES PRINCIPAL COMPONENTS
1 2
Average income per capita -0.899 0.189
Unemployed 0.880 -0.137
Social welfare beneficiaries 0.830 -0.250
Illiterate or uneducated population 0.272 0.810
Non Spanish population 0.513 0.539
Principal component analysis (PCA) technique. (a) 2 components obtained

The economic variables were chosen to take into account citizens with lower income, unemployed and those in in-work poverty. Immigrants and illiterate or uneducated people are the groups potentially most exposed to poverty, as is confirmed in the VIII Foessa Report: Exclusion and social development in Spain 2019 (Exclusión y desarrollo social en España 2019), and the Oxfam Intermón publication (2020) A fair reconstruction is possible and necessary. This is not the time for austerity; let us choose dignity (Una reconstrucción justa es posible y necesaria. No es momento para la austeridad, elijamos dignidad). A long-lasting lack of income leads to depending on social welfare benefits. These variables have been included and assessed in important studies on vulnerability, such as the Atlas of Urban Vulnerability (Atlas de la Vulnerabilidad Urbana) and the Urban Analysis of Vulnerable Neighbourhoods in Spain (Análisis urbanístico de Barrios Vulnerables en España), published by the Ministry of Development (Ministerio de Fomento, 2012, 2015).

Once the information was compiled and processed, a synthetic vulnerability index was developed using the principal component analysis technique. Variables were displayed in rows and principal components in columns. Ideally, each principal component relates positively to a few variables (with correlation coefficients close to +1 or -1) and poorly to the other variables (with coefficients close to 0). In this case, of the two principal components obtained, the first was considered the most representative one and it was therefore selected. Lastly, the results were transferred to a geographical information system and sorted using the natural breaks classification method.

Map: Synthetic vulnerability index. City of Barcelona. 2017. Barcelona. PDF. More information.

Medical mapping is a key tool for visualising disease spreading, managing epidemiological crises and making decisions. The COVID-19 pandemic compels urban studies to be reformulated. It has breathed new life into old debates on unsolved urban issues, such as inequality in cities; a subject which seemed unthinkable in 21st-century urban discussion until recently. Indeed, the disease is proving to be a significant and telling indicator of urban contrasts. A long-term neglect of the universal human need for shelter, health and safety has made lower-income neighbourhoods much more vulnerable to the COVID-19 pandemic, not only in terms of health but also in relation to a loss of income, increased unemployment rates, weaker social protection, etc. Growing poverty and rising socio-spatial inequality on various scales are taking shape into what is known as spatial injustice. The assumption that poverty and scarcity are causing more infections and victims amongst the most vulnerable is indeed feasible (González and Piñeira, 2020).

Map: Synthetic vulnerability index. City of Madrid. 2017. Madrid. PDF. More information.

Vulnerability in Barcelona and Madrid is particularly evident in the city outskirts, especially in neighbourhoods that were raised during the years of strong economic boost back in the 1960s, which are nowadays typified by high levels of social housing, high population densities and a drift to serve as a place of residence for immigrants. In Barcelona, the social divide lies between a more vulnerable north and south and a more wealthy west. In Madrid, the pattern is rather south-north. For their part, the historical city centres and the 19th-century expansions are areas of scant vulnerability. High rates of COVID-19 infections were limited in historical city centres to very small and specific old areas that have not yet been restored or gentrified, such as El Raval neighbourhood in Barcelona. In short, the most vulnerable areas in Madrid and Barcelona were also the areas to record the highest cumulative incidence of COVID-19 cases.

The index developed depicts Barcelona as a city where high rates of vulnerability are observed in two areas: first, the neighbourhoods in the north (Torre Baró, Ciutat Meridiana, Canyelles, Les Roquetes) and some in the northeast (Bon Pastor), where a high number of immigrants live; second, the area to the south of the city, where the harbour and La Barceloneta neighbourhood lie. These areas registered a cumulative incidence of over 500 cases per 100,000 inhabitants (February-June 2020) and sometimes even over 1,000 (the maximum considered). By contrast, the lowest figures in vulnerability were recorded in census sections to the west of the city (Pedralbes and Sarrià), much of the Eixample and those bordering the northern coastline (Vila Olímpica del Poblenou, Diagonal Mar and Front Marítim del Poblenou). A study on the amount of cases in these less vulnerable areas reveals that they registered the lowest incidence rates of COVID-19 in the whole city of Barcelona, with under 500 cases per 100,000 inhabitants.

This social twofoldness may also be perceived in the city of Madrid. The blue-collar and industrial census sections in the southern outskirts of the city (Vicálvaro, Villa de Vallecas, Villaverde, Puente de Vallecas and Carabanchel) registered the highest figures on vulnerability indicators. These neighbourhoods are located in boroughs where a high cumulative incidence was recorded in absolute terms, yet not necessarily in relative terms. The least vulnerable areas are to be found in the north (southern part of Fuencarral-El Pardo, Chamartín, Hortaleza and Ciudad Lineal), in the 19th-century expansion (Moncloa-Aravaca, Chamberí and Salamanca) and in most of the historical city centre. Generally speaking, these were the boroughs to register a lower incidence.

In short, the two main Spanish cities have major vulnerability problems. The current situation shows that the urban improvements made in recent decades were based on weak structures and on an unequal city model. The recession from 2008-2013 and the so-called post-crisis policies deepened internal inequalities in cities that registered significant social cohesion issues beforehand. And the COVID-19 crisis has further widened the vulnerability gap. New research shall be launched in this regard. There is a risk that the vulnerability detected in the most fragile areas of the city may spread to other neighbourhoods that are nowadays well integrated in the city and dwelled by middle classes.


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Co-authorship of the text in Spanish: Jesús M. González Pérez y María José Piñeira Mantiñán. See the list of members engaged


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Bibliography



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You can download the complete publication The COVID-19 pandemic in Spain. First wave: from the first cases to the end of June 2020 in Libros Digitales del ANE site.

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