Remaking urban divides: Shifting patterns of neighborhood differentiation in Bilbao, Spain

ABSTRACT

Intensifying social and spatial divisions have been on the rise in cities since the 1980s. This trend has evolved in a context of increasing socioeconomic inequality and is exacerbated by the effects of the last economic crisis and austerity policies. Understanding the differential impact of these processes on vulnerable social groups and urban areas is crucial for developing effective policy responses to the challenges of social exclusion and neighborhood decline. This paper examines the spatial dimensions of rising socioeconomic inequality in Bilbao, Spain. Using Census Data and a multivariate approach, it analyzes shifting patterns of socio-spatial differentiation in the city during 2 decades of intense urban restructuring and regeneration dynamics. The results present a characterization of Bilbao’s neighborhoods based on a set of variables that capture various aspects of neighborhood differences, demographics, socioeconomic status and housing attributes, and their evolution, revealing a two-dimensional factorial structure. Neighborhoods are grouped according to these two factorial axes that capture the structure of correlations among the variables. Subsequently, the analysis focuses on a selected number of “extreme” neighborhoods to identify patterns of convergence/divergence and the driving factors behind them, including structural trends and policy initiatives implemented during the 2 decades considered.

KEYWORDS:

• Socio-spatial differentiation
• neighborhood inequality
• Multiple Factorial Analysis (MFA)
• Bilbao

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1. Principal component analysis (PCA) is a technique for dealing with large data matrices it can be viewed as an exploratory technique that seeks to condense information into a small number of new variables that explain the maximum total variability in the data. PCA transforms a set of correlated variables into a new set of uncorrelated variables called principal components. These components are linear combinations derived in decreasing order of importance such that the largest component is the one that explains the greatest possible variability of the total variation contained in the original data. The second principal component is chosen so that it explains the largest possible amount of variation that remains unexplained by the first principal component, subject to the condition that it is uncorrelated with the first principal component. The third component is not correlated with neither the first nor the second and has the third largest variance, and so on (Dallas, 2000).

2. In this section, we present the results of the global analysis obtained using MFA. The partial results for each group of variables can be found in Appendix 1. The complete results are available upon request.

3. For a detailed analysis of socio-spatial stratification in Bilbao’s neighborhoods, see, Altuzarra et al. (2018).

4. Altamira, a neighborhood with one of the lowest values, has not been included in our selection because of its small surface area (0,15 km2) and population (less than 2,000 residents).

Additional information

Notes on contributors

Elena Martínez

Elena Martínez is a senior lecturer in the department of Applied Economics at the University of the Basque Country (UPV/EHU), Spain, and a member of the Institute of Development Studies Hegoa. Her research focuses on labor inequalities regarding gender, the analysis of work-life reconciliation measures in private companies, and urban economy studies.

Arantxa Rodríguez

Arantxa Rodríguez is a retired Professor at the Faculty of Economics and Business of the University of the Basque Country (UPV/EHU), Spain, where she has taught urban and regional economics and planning. Her field of research focuses on socioeconomic restructuring and territorial development policy and planning. In recent years, her research has focused on urban regeneration policies and, particularly, the consequences of neoliberal redevelopment strategies for social and spatial fragmentation in European cities.

Amaia Altuzarra

Amaia Altuzarra is a lecturer at the University of the Basque Country (UPV/EHU), Spain. Her research interest includes the study of competitiveness and the labor market, urban and regional development and technical change and innovation. Some of her recent articles are published in Industrial and Labor Relations Review, International Business Review, Open Economies Review, Social Indicators Review and Sustainability. She is also the author of several books and book chapters.

Irantzu Álvarez

Irantzu Álvarez is a lecturer at the Engineering School of Bilbao, at the University of the Basque Country (UPVEHU), Spain. She develops her research into geospatial technologies, applying them in different fields, like urban and regional planning, transport and mobility studies, among others. She is a member of the Saren Research group of the University of the Basque Country (UPVEHU).