How and Why U.N. Member Nations form Voting Blocks Each Year

Status: Completed, new features being added.

United Nations Voting Record Slider: Yearly Cluster Analysis: A cluster analysis of UN voting records in the General Assembly.

A HDBSCAN cluster analysis of UN member state voting records from 1946 - 2017. Each year's data is passed through a function which generates output of: Which countries reside in each cluster, PCA visualization, identification of which resolutions were most significant in forming clusters.

• git clone https://github.com/JakeRattner/United_Nations_Voting_Records-Cluster_Analysis.git
• Ensure all libraries added/supported
• Open in Jupyter Notebook

• Run code from jupyer notebook until you see the following widget near the bottom of the notebook:
• Use slider at end of notebook to view yearly output:

PCA-based Visualization (Note: Purple pts = Outliers)

HDBSCAN-based Output (Silhouette Coefficient, # of clusters, countries in each cluster)

PCA Variance Analysis (Tells us which resolutions voted on in a given year were most important/responsible for variance)

As I continue to build this project out I am looking for any help / opportunities for collaboration. Please contact me through Github or rattnerjake at gmail.com. I will be adding more detailed notes on opportunities for collab in the future.

• Voeten, Erik; Strezhnev, Anton; Bailey, Michael, 2009, "United Nations General Assembly Voting Data", https://hdl.handle.net/1902.1/12379, Harvard Dataverse, V18, UNF:6:xkt0YWtoBCThQeTJWAuLfg==
• I would also like to credit; Heather Robbins, Mathew Brems, Justin Pounders and Riley Dallas for the suggestions and guidance offered over the course of this project. Thanks guys!