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.

中文翻译：

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不断变化的人口统计数据：分析柏林居住隔离的动态聚类方法

摘要

本文使用动态聚类分析方法研究了柏林随时间推移的居住隔离现象。先前的研究已经在高空间和时间聚合下静态地（即不随着时间的推移）研究了柏林的居住隔离现象。我们提出了一种根据移民背景、年龄组、性别和社会经济维度随时间变化调查居住区集群存在情况的方法。为此，我们开发了一种顺序混合方法，其中包括用于估计亚种群密度的多元核密度估计技术和用于发现随时间推移（2009-2020）的居住隔离空间模式的动态聚类分析。动态分析显示，在移民背景、年龄组、性别和社会经济变量维度上出现了集群。我们还发现 2015 年发生了结构性变化，导致柏林出现了一个新的集群，反映了具有特定移民背景的亚群分布的变化。最后，我们与之前的研究讨论了本研究的结果，并提出了政策应用和未来研究的可能性，使用动态聚类方法来分析城市层面的居住隔离变化。