Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/312941 
Year of Publication: 
2024
Citation: 
[Journal:] EPJ Data Science [ISSN:] 2193-1127 [Volume:] 13 [Issue:] 1 [Publisher:] SpringerOpen [Place:] Berlin [Year:] 2024 [Pages:] 1-41
Publisher: 
SpringerOpen, Berlin
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.
Subjects: 
Berlin
Data Science
Dynamic Fuzzy C–Means
Residential Segregation
Data Visualization
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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