The completed report was turned in. It featured visualizations such as clustering maps, cumulative plots, and bar charts. Monte Carlo simulations were also used to confirm the results on age differences. In the discussion section, I offered policy-level proposals after coming to the conclusion that there is a notable racial and age bias in the way police shootings take place throughout the United States.

This week, I used Shapiro-Wilk and t-tests for statistical validation and Cohen’s d to assess the effect magnitude of age differences. The statistical significance of the changes I had previously seen was validated by these tests. In order to determine which states had the most instances, I also looked into geographic clustering; Florida, Texas, and California stood out.

I made CDF plots to examine racial disparities in victim age after cleaning the dataset. I noticed significant variances, particularly among Black victims, using Seaborn and Pandas for visualization. I also began looking into whether people were escaping and who was in possession of weapons. These patterns appeared to point to possible bias, which served as the primary driving force behind the course of my investigation.

Some important questions that I wanted to discuss to get a better understanding of the dataset are

1. What is the age distribution of individuals involved in these incidents?
2. Are there any notable patterns in urban vs. rural areas?
3. Are there any patterns in weapon types across different demographic groups?
4. Is there any correlation between race and the type of weapon involved?
5. Is there any relationship between body camera usage and specific police departments? What percentage of incidents had body cameras present?

The dataset poses interesting issues on whether there are temporal patterns in the number of events over different seasons or months, whether certain jurisdictions exhibit greater rates of particular incident kinds, and the relationship between body camera usage and incident outcomes.

 

I started looking through the dataset of police shootings in the United States that was given to us for our first project this week. I took the effort to comprehend the structure and spot any inconsistent or missing values. The inconsistent recording of weapon kinds and fleeing status was one issue I identified early on. In order to facilitate data analysis, I made the decision to standardize these fields. Additionally, I began to formulate preliminary inquiries regarding age-related trends in shootings and racial disparities.