Identification and modeling of drivers of spatial location patterns of immigrant groups in Barcelona

What is it about?

Many European countries with declining birth rates are experiencing increased immigration, but unlike the Gulf countries and South Asia, where labor migration is often temporary, many migrants arriving in Europe seek to settle permanently. Over the past 25 years, this process has been particularly pronounced in Spain, and even more so in Madrid and Barcelona. Given that these settlement patterns ultimately have a significant economic, political, urban, and environmental impact, the residential segregation of immigrant groups has become an important area of research. In this study we sought to determine the key factors underlying the spatial distribution patterns of immigrant populations using a Bayesian analysis methodology. The study also assesses the differences between the main immigrant groups in the city of Barcelona and the surrounding municipalities. Focusing the analysis on census tracts allows us to offer a granular view.

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Photo by Ansar Naib on Unsplash

Photo by Ansar Naib on Unsplash

Why is it important?

First, the degree of residential segregation in the city was measured, revealing high levels of spatial concentration, especially among non-European immigrant groups such as Pakistanis, Moroccans, and Chinese. These groups also reside in distinct neighborhoods, often far from the gentrifying city center, reflecting both socioeconomic barriers and the role of community networks, consistent with the spatial stratification model. Conversely, European immigrants, such as Italians, are concentrated primarily in the old town, highlighting a divergent pattern influenced by economic resources and lifestyle preferences. These results were corroborated by applying a Local Indicators of Spatial Autocorrelation (LISA) analysis, revealing notable differences between the communities analysed. Empirical analysis identified certain variables that influence virtually all the immigrant communities analysed: the prevalence of the rental market, access to public transportation, and housing types. For example, the preference for rental housing among four of the five immigrant communities could highlight the difficulty most immigrants face in accessing homeownership, pushing them toward peripheral areas with more affordable rental options. Conversely, both the Chinese and Italian communities exhibit dynamics different from the other groups. The Chinese community shows a pattern typical of ethnic preference models, settling in both urban and suburban residential areas with a wide range of shops and services specific to their community. Finally, the population from Italy is mainly located in the old town and adjacent central areas, and in some ways represents a new type of immigration from very close European countries, whose motivation for settling in the city differs from that of the other communities analyzed. The results appear robust when distance-based variables are replaced with accessibility metrics calculated using the improved two-step floating catchment area (E2SFCA) method proposed by Luo and Qi (2009).

Perspectives