Visualization of Voting Behavior in the German Bundestag

In the German blog post, I analyze voting patterns of German parliament members using machine learning techniques. By encoding votes as numerical vectors (Yes=1, No=-1, Abstention=0), I apply PCA and t-SNE dimensionality reduction to visualize political clustering, revealing clear faction groupings and identifying potential party outliers in the Bundestag.

This content is only available in German

This article presents a data science analysis of voting behavior in the German Bundestag (federal parliament), including:

Methodology

• Data encoding: Parliamentary votes converted to numerical vectors (1, -1, 0)
• Dimensionality reduction: Both PCA and t-SNE algorithms applied
• Similarity metrics: Quantitative measures for comparing member behavior
• Outlier detection: Identifying members who deviate from their faction

Key Technical Approaches

• Vector embeddings of parliament members based on voting records
• Comparative analysis between different visualization techniques
• Clustering analysis to reveal political faction boundaries
• Distance-based similarity measurements between representatives

Main Findings

The analysis reveals: - Clear faction clustering: Political parties form distinct groups in the visualization - Coalition behavior: Government coalition partners show similar voting patterns
- Opposition clustering: Opposition parties cluster separately from government factions - Individual outliers: Some members deviate significantly from their party line - Algorithmic differences: t-SNE shows more nuanced local relationships than PCA

Research Context

This work was updated with data from March 22, 2018, and represents an exploration of quantitative political analysis using data science methods applied to German parliamentary data.

Future research directions mentioned include: - Entropy analysis to measure faction discipline strength - Speech pattern analysis from parliamentary protocols
- Behavioral analysis during parliamentary sessions - Multi-modal feature combination techniques

For the complete technical analysis, visualizations, and detailed methodology, please read the German version using the language toggle above.