Social inequality and unequal participation in Münster

One of the main issues facing modern civilization is social inequality. Some people never attain their full potential when they have limited access to healthcare, employment opportunities, or education. As a result, there are disparities that split society and keep the underprivileged from leading fulfilling lives. We analyze and illustrate the social inequality status using data from Münster and as well as political participation as part of social participation in Münster to indicate its correlation. We seek to emphasize its significance from a political and urban planning perspective, as well as offer suggestions in this regard.

Introduction

An uneven distribution of resources and services results from the spatial dispersion of population groupings brought about by socioeconomic disparities. Rich and impoverished neighborhoods in many cities clearly differ from one another in terms of, for example, infrastructure, housing quality and educational opportunities. As we will take a closer look of population groupings in Münster, we define Münster’s districts not just as a physical area but as a social environment.

In this blog post we’ll take a closer look at the different areas of Münster, focusing on social demographics and political participation as part of social participation across different areas. By examining the accessibility of social services and identifying whether these resources are equitably distributed, especially in less well-off neighborhoods, we seek to uncover potential gaps in offers for social participation and service provision. By highlighting areas that might be lacking support, we hope to offer some insights for better city planning and more targeted social programs, ensuring that no part of the city is left behind.

Methodology

We collected data from social services and used mainly official data from Münster's statistical department, OpenData, to show social inequality in the city. We were interested in factors like voter turnout, district-level population density, average unemployment, and the percentage of migrants, distribution of educational and social services and resources (“Bücherbus”, “Kita’s”) among other things. We gathered data from 2014 to 2024 in order to account for temporal changes. For the descriptive analysis, univariate and bivariate analysis techniques were applied like histograms and barplots. We also employed multivariate techniques like multiple regression analysis and applied a Means cluster analysis to group Münster’s areas based on these factors.

Our first methodological step is a social structure analysis to examine various social factors across Münster’s areas like voter turnout, migration rate and other indicators mentioned above. We highlighted our results using heatmaps. We then conducted a correlation analysis to identify potential correlations between these social indicators. After that we analyzed the accessibility and opening hours of “Bücherbus” , “Giveboxen” and accessibility of “Kitaplätze” among Münster’s areas with focus on results of less well-off areas. Using Python, we performed a cluster analysis on Münster’s districts for selected variables from previous analyses to group physical areas as our social environment to find out population groupings. Also, we performed a regression analysis using a multilinear regression model in R to examine the simultaneous effects of the factors we are interested in (e.g., unemployment, the proportion of foreigners, household size, and place of
residence) on participation in the European elections in 2024.

Project Results

Focussing on the period between 2014 and 2024, we find that the factors like unemployment rate, migration rate and voter turnout remain stable over time. Coerde is one example of a less well-off neighborhood due to poor rates, whereas Kreuzviertel is one example as a well-situated neighborhood. Hiltrup is an example of an area in between less well-off situated and well-situated. In total, we elaborated 3 to 4 clusters of areas based on the combination of social factor rate, voter turnout and unemployment rate. By calculating a correlation coefficient of almost 85%, we find potential correlation in migration rate and unemployment rate. Our election rate results in areas with poor social rates are also low, indicating that the percentage of foreigners and high unemployment are related to low voter turnout.

Additionally, based on numbers and opening hours of “Giveboxen” and availability of “Bücherbus” in Münster’s areas, as part of future city planning one might improve accessibility of such social and educational offers in areas like Coerde.

Our results are in line with our expectations and the assumptions from the research literature on voter turnout. Thus, a person’s home district affects their voting behavior as well. Living in Münster’s east (where there is a high social index) is associated to high voter turnout, whereas living in the north is associated with low voter turnout (comparatively low social index).