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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).
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<div style="text-align:center; float:right"><div style="color:MediumBlue">'''Matriz de componentes'''<span style="color:#636363"><sup>a</sup></span></div>
 
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| rowspan="2" style="background:LightGoldenrodYellow; color:MediumBlue; border: solid; border-width: 1px; text-align:center; width:250px" | '''VARIABLE''' || colspan="2" style="background:LightGoldenrodYellow; color:MediumBlue; border: solid; border-width: 1px; text-align:center" | '''COMPONENTES'''
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| style="background:LightGoldenrodYellow; color:MediumBlue; border: solid; border-width: 1px; text-align:center; width:50px" | '''1''' || style="background:LightGoldenrodYellow; color:MediumBlue; border: solid; border-width: 1px; text-align:center; width:50px" | '''2'''
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| style="color:#000000; background:#ffffff; text-align:left; border: solid; border-width: 1px" | Renta media por persona || style="background:#ffffff; color:#000000; text-align:center; border: solid; border-width: 1px" | -,899 || style="background:#ffffff; color:#000000; text-align:center; border: solid; border-width: 1px" | ,189
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| style="color:#000000; background:Ivory; text-align:left; border: solid; border-width: 1px" | Población en paro || style="background:Ivory; color:#000000; text-align:center; border: solid; border-width: 1px" | ,880 || style="background:Ivory; color:#000000; text-align:center; border: solid; border-width: 1px" | -,137
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| style="color:#000000; background:#ffffff; text-align:left; border: solid; border-width: 1px" | Población con prestaciones || style="background:#ffffff; color:#000000; text-align:center; border: solid; border-width: 1px" | ,830 || style="background:#ffffff; color:#000000; text-align:center; border: solid; border-width: 1px" | -,250
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| style="color:#000000; background:Ivory; text-align:left; border: solid; border-width: 1px" | Población analfabeta/sin estudios || style="background:Ivory; color:#000000; text-align:center; border: solid; border-width: 1px" | ,272 || style="background:Ivory; color:#000000; text-align:center; border: solid; border-width: 1px" | ,810
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| style="color:#000000; background:#ffffff; text-align:left; border: solid; border-width: 1px" | Población extranjera || style="background:#ffffff; color:#000000; text-align:center; border: solid; border-width: 1px" | ,513 || style="background:#ffffff; color:#000000; text-align:center; border: solid; border-width: 1px" | ,539
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<div style="padding-top: 0.4em; padding-right: 0.5em; padding-bottom: 0.3em; padding-left: 0.5em; font-size: 100%"><small style="color:#959595">Método de extracción de componentes principales. (<span style="color:#636363"><sup>a</sup></span>) 2 componentes extraídos</small>
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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.
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