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

IF 3 2区计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS EPJ Data Science Pub Date : 2024-03-12 DOI:10.1140/epjds/s13688-024-00455-4

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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.

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不断变化的人口结构：分析柏林住宅隔离的动态聚类方法

摘要本文采用动态聚类分析方法研究了柏林随时间变化的居住隔离现象。以往的研究对柏林的居住隔离现象进行了高度的空间和时间聚合，并且是静态的，即不随时间变化。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研究居住区集群存在情况的方法。为此，我们开发了一种序列混合方法，其中包括一种用于估算亚人群密度的多元核密度估计技术，以及一种用于发现随时间（2009-2020 年）变化的住宅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等方面出现了聚类。我们还发现了 2015 年的结构性变化，在柏林形成了一个新的聚类，反映了具有特定移民背景的亚人群分布的变化。最后，我们将本研究的结果与之前的研究进行了讨论，并提出了使用动态聚类方法分析城市层面居住隔离变化的政策应用和未来研究的可能性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -

CiteScore

6.10

自引率

5.60%

发文量

审稿时长

13 weeks

期刊介绍： EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.