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; Fernando A. Crespo R. (EPJ Data Science, 2024)
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: 300; Berlin; Data Science; Dynamic Fuzzy C–Means; Residential Segregation; Data Visualization