Introduction

The problem domain I am analyzing is the difference in numbers between the amount of white and black people in the county with the highest number of prisoners, Los Angeles. I chose to analyze this to think about why the number is different, and if Los angeles also has something to do with it. I also wanted to analyze the difference between the counties with the largest number of prisoners, which is los angeles, and the lowest number of prisoners, which is Aleutians east borough. I chose this because I wanted to know which counties had these values. Lastly, I wanted to analyze the average number of black people that are prisoners in the dataset.

Summary Information

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Through my analysis I discovered that the county with the highest number of prisoners was Los Angeles County and the county with the lowest number of prisoners was Aleutians East Borough, along with many other counties ass well. Los Angeles is a very big city, so I am not surprised that they had the highest number of prisoners. For the lowest, I had never heard of Aleutians county, so I searched it up and it only has a population of around 3000 as of 2022, which is very low and makes sense why they would have the least amount of prisoners.

I also found that the number of black people from the county with the highest amount of prisoners was 15853 and the number of white people from the county with the highest amount of prisoners was 4326 and I found this interesting because the population in LA has more white people than black people, so proportionally it does not make sense. It seems this is an example of systemic racism.

Lastly, I found that the average amount of black people in prisons was 294.8006571 which seems low, but when taking account they difference between this number and LA’s number of black people, I believe some outliers played a part in this average.

The Dataset

Vera, the institute of justice collected this data, released the Incarceration Trends data tool and a companion publication, which employed data collections the Census of Jails and the Annual Survey of Jails. The data was collected because they wanted more county-level variability, which makes for better studies of the high rates of incarceration across the U.S., compared to using the state as a unit of analysis which does not provide enough detail. There are 153,811 observations, or rows in my data, and 36 features, or columns in my data.

Some ethical questions or questions of power I need to consider when working with this data is how this data was collected, because I was not able to learn too much about how it was collected if it those methods were accurate. Additionally, there are many n/a values in the dataset, which I should consider when working with it because what is the reason that those values in those places were not collected? The possible limitations or problems with this data are that it is slightly messy, with the amount of n/a make it difficult to sort through as I have to filter them. Additionally, with analyzing my problem with racism in the prison scene, I think that using data that excludes outliers is the best way to analyze this problem.

Variable Comparison Chart

I chose this scatterplot to see the relationship between the total prison populations growth over time, and how much of those total prisoners are black prisoners as well as how much they grow or decrease over time. Through this chart I came to the conclusion that as the prison population increases, the amount of black prisoners also increased, except for when it reached a capacity of sorts and suddenly dropped, which makes me wonder what caused this drop in black prisoners amongst the number of total prisoners.

Map

Being able to see the prison population on a geographical scale, and visually, is very interesting to see because it seems like the south side of the United States has a larger population of black prisoners, which can be explained by a lot of things, such as the history of racism and slavery in the south, as well as the structures of the south which somewhat can lead to more racially motivated decisions than other counites.