This week, I explored the relationship between three types of political violence events normalized per 1 million people:

• Protests_per_1M
• Riots_per_1M
• Violence_per_1M

Using .corr() in Python, I calculated the pairwise correlation coefficients. All values were positive, indicating that states with high values in one type of event tend to have high values in others as well.

I created three scatter plots to see how the number of protests, riots, and violence events per million people are related across states. All three plots show a positive relationship — meaning when one goes up, the others also tend to go up.

Variable Compared	Pearson r	Strength	Comments
Protests vs Riots	0.78	Strong	Strong upward trend, many points clustered near line
Protests vs Violence	0.89	Very strong	Tight clustering, highest correlation among the three
Riots vs Violence	0.71	Moderate–Strong	Still positive, but more scattered

The first plot shows Protests_per_1M vs. Riots_per_1M, indicating a moderate to strong linear trend.

 

The second plot compares Protests_per_1M vs. Violence_per_1M, which appears to be the strongest and most linear relationship.

 

The third plot shows Riots_per_1M vs. Violence_per_1M, revealing a weaker but still noticeable positive trend.