https://doi.org/10.1140/epjds/s13688-024-00455-4
Research
Evolving demographics: a dynamic clustering approach to analyze residential segregation in Berlin
1
WZB Berlin Social Science Center, Reichpietschufer 50, 10785, Berlin, Germany
2
E.T.S. of Computer and Telecommunication Engineering, University of Granada, Granada, Spain
3
Facultad de Economía y Negocios, Universidad Finis Terrae, Santiago, Chile
4
Faculty of Economics and Business, University of Chile, Diagonal Paraguay 257, 8320000, Santiago, Chile
5
Departamento de Diseño y Manufactura, Universidad Técnica Federico Santa María, Viña del Mar, Chile
6
Facultad de Economía y Negocios, Departamento de Gestión y Negocios, Universidad Alberto Hurtado, Santiago, Chile
Received:
12
July
2023
Accepted:
19
February
2024
Published online:
12
March
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.
Key words: Berlin / Data Science / Dynamic Fuzzy C–Means / Residential Segregation / Data Visualization
© The Author(s) 2024
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
