*3.5. Life Satisfaction*

The last variable that we studied was subjective life satisfaction. As mentioned above, the data from the ECVHP reflect the subjective perception of life satisfaction in individuals aged 16 and over, using a scale of degree of satisfaction from 0 to 10, with 0 corresponding to the lowest level of satisfaction and 10 to the highest.

The mean life satisfaction of the Catalan population is fairly high, at around 7.33 (see Figure 6). In this case, the differences with respect to the urban/rural divide are almost irrelevant, as they barely stray from the mean. In contrast, the differences in the classification by population size are more significant, although, even here, there is no clear pattern that makes it possible to relate the two variables. Similarly, although the differences between large geographical settings are relatively more substantial, no logical order can be deduced from them. Once again, the difference that is surely most significant and explanatory is that between the life satisfaction of residents of well-off settings and those of vulnerable settings. In fact, if we leave aside the contrasts between some of the

geographical settings, this difference is the most marked of all, and the one that affects most people.

#### **4. Synthesis and Conclusions**

Our study seeks to contribute to our knowledge of the relationship between spatial variables and a population's living conditions. Using the specific example of Catalonia, we have analysed the relationship between, on the one hand, the population's place of residence and, on the other, a set of social indicators (level of education, socioeconomic position, the risk of poverty, self-perceived health and life satisfaction). To do this, Catalan localities were grouped into four different territorial aggregations that corresponded to geographical settings, population size, intensity of urbanization and residential segregation. The objective was to determine which of these aforementioned spatial factors is most relevant to an explanation of the differences in people's life courses and living conditions, and, therefore, the social cohesion of the country.

The data presented suffer from limitations of spatial and sampling representation that make deeper statistical analysis difficult. In our opinion, however, they show the interest and need to continue exploring the relationship between social inequality and territory, both in Catalonia and in all the countries of the European Union. Other data sources that allow a more in-depth and comparative statistical analysis will be necessary for this.

The results obtained are summarized in Table 2 and in the maps included in the Supplementary Materials. For the purposes of comparison, we have set the values of each of the variables analysed as index numbers (Catalonia = 100) and calculated the mean deviations of the observations. This makes it possible to contrast the deviations obtained in each of the territorial treatments of the data, so that the higher the deviation, the more discriminating the proposed spatial aggregation with respect to social inequalities. As can be seen, the results proved quite illustrative and they enable us to confirm, to a large extent, the initial hypothesis of substantial territorial fractures and differences in Catalonia. However, rather than being related to the variables around which the debate on this issue have traditionally revolved—geographical location, population size and the intensity of urbanization—these differences are linked, above all else, to individuals' and social groups' spatial segregation according to their income.

**Figure 6.** Life satisfaction. Population aged 16 and over. Catalonia. Source: In-house construction based on the ECVHP, 2011.


**Table 2.**

Summary of the indicators analysed and their deviations.

 Catalonia; base: 100.



Source:In-houseconstruction basedontheECVHP,

Thus, in three of the five variables studied—level of studies, socioeconomic position and risk of poverty—the greatest differences are clearly seen in those categories associated with residential segregation. In the other two variables—self-perceived health and life satisfaction—there was greater dispersion related to the geographical settings and other factors such as the mean age of the population. It is significant that the more structural variables seem to be more closely linked to residential segregation, while the differences associated with population size and the traditional urban/rural divide are, generally speaking, less relevant.

If we add the mean deviations associated with each of the spatial aggregations to achieve a synthesis value, we can see how the highest value is the one derived from the territorial grouping based on residential segregation—so much so that the mean of the mean deviations resulting from the analysis of segregation is practically double the means associated with the analysis via geographical settings, population size or degree of urbanization.

The enhanced capacity of residential segregation to explain spatial inequalities is also reflected in the interrelationship between the various variables in each of the territorial aggregations. Thus, the ordering of the variables in the three categories associated with residential segregation always follows the same ordinal precedence, so vulnerable areas are always in the worst position with respect to each of the variables. However, in the other territorial aggregations—geographical areas, population sizes and intensity of urbanization—the behaviour of the variables is more erratic, and their mutual coherence is lower. Spearman's correlation analysis fully confirms this reading, as can be seen in Table S2 included in the Supplementary Materials.

We can conclude, therefore, with the following premises that largely confirm our initial hypothesis:


These results contain relevant policy implications. In particular, they suggest that to reduce spatial inequalities today, improving living conditions in the most vulnerable neighbourhoods and urban areas must be a priority. Obviously, the characteristics of the case under study cannot necessarily be extrapolated to other regions of Spain and Europe. However, the results obtained clearly show the need to consider residential segregation and its effects as key factors in academic and political debates about spatial cleavages.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/urbansci5020045/s1, Table S1: Distribution of the sample according to territorial aggregations, Table S2: Spearman's correlation according to territorial aggregations, Figure S1: Percentage of population (aged 25 or over) without higher education according to Territorial settings, Catalonia, Figure S2: Percentage of population (aged 16 and over) with self-perceived health no good according to Territorial settings, Catalonia, Figure S3: Overall life satisfaction of people (aged 16 and over) according to Territorial settings, Catalonia, Figure S4: Percentage of population risk of poverty (60% median) according to Territorial settings, Catalonia, Figure S5: Percentage of population (aged 16 and over) social structure no qualified (ESEC > 3) according to Territorial settings, Catalonia.

**Author Contributions:** Both authors contributed to the design and conception of the article. J.C. processed the data and created the graphics. O.N. wrote the conceptual section. Both authors contributed to the composition of the rest of the text. Both authors have read and agreed to the published version of the manuscript.

**Funding:** The original research on which this article is based has been possible thanks to the support of the Institut d'Estudis Catalans and its Section of Philosophy and Social Sciences. The preparation and publication of this article has received financial support from the Ministerio de Ciencia e Innovación «Proyectos de i+d+i» Retos de la sociedad, convocatoria 2019. Referencia PID2019-108120RB-C32.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All information about the source of the data, as well as requesting the microdata can be found https://iermb.uab.cat/es/encuestas/cohesion-social-urbana/#2f756a49-ae6 c-6, accessed on 26 May 2021.

**Acknowledgments:** The data from the Survey of Living Conditions and Habits of the Population (ECVHP) have been specifically used for this research, for which the authors would like to express their gratitude to the IERMB, its director and its research team.

**Conflicts of Interest:** The authors declare no conflict of interest.
