Evolving demographics: a dynamic clustering approach to analyze residential segregation in Berlin

Víctor H. Masías H., Julia Stier, Pilar Navarro R., Mauricio A. Valle, Sigifredo Laengle, Augusto A. Vargas and Fernando A. Crespo R.

EconStor Open Access Articles and Book Chapters, 2024, vol. 13, issue 1, 1-41

Abstract: This paper examines the phenomenon of residential segregation in Berlin over time using a dynamic clustering analysis approach. Previous research has examined the phenomenon of residential segregation in Berlin at a high spatial and temporal aggregation and statically, i.e. not over time. We propose a methodology to investigate the existence of clusters of residential areas according to migration background, age group, gender, and socio-economic dimension over time. To this end, we have developed a sequential mixed methods approach that includes a multivariate kernel density estimation technique to estimate the density of subpopulations and a dynamic cluster analysis to discover spatial patterns of residential segregation over time (2009-2020). The dynamic analysis shows the emergence of clusters on the dimensions of migration background, age group, gender and socio-economic variables. We also identified a structural change in 2015, resulting in a new cluster in Berlin that reflects the changing distribution of subpopulations with a particular migratory background. Finally, we discuss the findings of this study with previous research and suggest possibilities for policy applications and future research using a dynamic clustering approach for analyzing changes in residential segregation at the city level.

Keywords: Berlin; Data Science; Dynamic Fuzzy C–Means; Residential Segregation; Data Visualization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:espost:312941

DOI: 10.1140/epjds/s13688-024-00455-4

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