Multivariate analysis of socioeconomic profiles in the Ruhr Area, Germany

Janka Lengyel*, Stéphane Roux, Seraphim Alvanides

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
90 Downloads (Pure)

Abstract

The aim of this article and associate map is to highlight the social and economic diversity of the Ruhr area in Germany through the use of multivariate analysis and visualisation. To this end we combine two different datasets. Demographic parameters stemming from the 2011 German census and socioeconomic indicators obtained from the microdialog of the German post service. Due to the different spatial resolution of the two datasets, we aggregated the data at the neighbourhood (Stadtteil) level. The multivariate analysis was carried out at this scale using Self-Organizing Maps (SOM), an artificial neuron network, which uses an unsupervised learning mechanism for projecting multidimensional data in a low (in our case two) dimensional space. First we used a visualization technique to better comprehend the relationship between our observations via reducing the dimensionality or complexity of our input data. At the same time, we established a global statistical relationships between the indicators. Finally, using these results we built clusters for revealing the distribution of socioeconomic profiles over the whole region. Our results demonstrate that structural inequalities resulting from the processes of industrialization/deindustrialization in the region are still widely persistent and result in characteristic patterns along the three main rivers, the Lippe, Emscher and the Ruhr. In close connection with this, three types of societal segregation patterns become clearly evident in the Ruhr area, namely nationality, age and economic power.
Original languageEnglish
Pages (from-to)576-584
Number of pages9
JournalJournal of Maps
Volume18
Issue number3
Early online date9 Aug 2022
DOIs
Publication statusPublished - 1 Dec 2022

Keywords

  • Multivariate analysis
  • segregation patterns
  • self-organizing maps
  • socioeconomic clusters

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