Introduction

An introduction of the problem domain and a description of the variable(s) you are choosing to analyze (and why!)

Incarceration in the United States is a critical social issue with significant disparities across different racial, gender, and geographical groups. This report aims to analyze these disparities using comprehensive datasets on incarceration rates. We will focus on variables such as incarceration rates by race, gender, and location to understand how these measures vary across different groups.

Summary Information

Write a summary paragraph of findings that includes the 5 values calculated from your summary information R script

These will likely be calculated using your DPLYR skills, answering questions such as: 

Based on the analysis of incarceration rates in the United States, several key findings have been identified. The average incarceration rate across all counties in the most recent year is approximately mean_incarceration_rate, indicating the typical level of incarceration. The highest recorded incarceration rate is max_incarceration_rate, reflecting the maximum extent of incarceration within a county, while the lowest rate is min_incarceration_rate, showing the minimum level. Over the past several years, there has been a significant change in the incarceration rate, with an increase of change_incarceration_rate from the earliest to the most recent year in the dataset. Additionally, the analysis reveals disparities in incarceration rates across different racial groups. For instance, the average incarceration rate for White individuals is white_mean_rate, for Black individuals is black_mean_rate, for Latinx individuals is latinx_mean_rate, for AAPI individuals is aapi_mean_rate, and for Native individuals is native_mean_rate. These findings highlight the persistent and significant variations in incarceration rates across different demographics and over time, underscoring the need for targeted interventions to address these disparities.

Feel free to calculate and report values that you find relevant. Again, remember that the purpose is to think about how these measure of incarceration vary by race.

## [1] 317.8509
## [1] 51021.86
## [1] 0
## [1] 502.077
## # A tibble: 1 × 5
##   white_mean_rate black_mean_rate latinx_mean_rate aapi_mean_rate
##             <dbl>           <dbl>            <dbl>          <dbl>
## 1            163.            247.             101.           7.35
## # ℹ 1 more variable: native_mean_rate <dbl>

Based on the value have been calculated, mean incarceration rate = 317.8509, max incarceration rate = 51021.86, min incarceration rate = 0, change incarceration rate = 502.077, and the racial group rate show in the table above.

The Dataset

Who collected the data?

The datasets used in this analysis were collected by various organizations and researchers dedicated to understanding incarceration trends in the United States. Specifically, the data is often gathered from government agencies such as the Bureau of Justice Statistics (BJS) and other related institutions that maintain records on prison and jail populations.

How was the data collected or generated?

The data was collected through official records and surveys conducted by these agencies. These records typically include counts of the incarcerated population, demographic information, and other relevant statistics. The surveys and data collection efforts are usually conducted periodically to track changes over time and to provide a comprehensive view of incarceration trends.

Why was the data collected?

The primary purpose of collecting this data is to analyze and understand trends in incarceration rates, with a focus on identifying disparities across different demographic groups. This data helps policymakers, researchers, and advocacy groups to address issues related to criminal justice, such as racial and gender disparities in incarceration, the impact of incarceration on communities, and the effectiveness of different criminal justice policies.

How many observations (rows) are in your data?

The dataset used in this analysis includes several datasets: the prison population data, jail population data, and incarceration rates data. The prison population dataset (prison_pop) has nrow(prison_pop) observations (rows) and ncol(prison_pop) features (columns). The jail population dataset (jail_pop) has nrow(jail_pop) observations and ncol(jail_pop) features. The incarceration rates dataset (prison_jail_rates) has nrow(prison_jail_rates) observations and ncol(prison_jail_rates) features.

How many features (columns) are in the data?

When working with incarceration data, several ethical questions and questions of power must be considered. One significant concern is the privacy and confidentiality of the individuals represented in the dataset. While the data is typically anonymized, it still represents sensitive information about people’s interactions with the criminal justice system. Additionally, there is a risk of stigmatizing certain groups if the data is not presented or interpreted carefully. Researchers must be cautious to avoid reinforcing stereotypes or biases, particularly when analyzing disparities across racial and ethnic groups.

What, if any, ethical questions or questions of power do you need to consider when working with this data?

Another ethical consideration is the potential for the data to be used to justify punitive policies rather than rehabilitative ones. Data can be a powerful tool for advocacy, but it can also be misused to support policies that may not address the root causes of crime and incarceration, such as poverty, lack of education, and systemic discrimination.

What are possible limitations or problems with this data? (at least 200 words)

Possible limitations of the data include incomplete data for some counties or demographic groups, potential biases in data collection methods, and changes in data collection procedures over time. These limitations can affect the accuracy and comparability of the data, making it essential for researchers to use caution when drawing conclusions and making policy recommendations based on the data.

Variable Comparison Chart

Include a chart. Make sure to describe why you included the chart, and what patterns emerged

The second chart that you will create and include will show how two different (continuous) variables are related to one another. Again, think carefully about what such a comparison means and what you want to communicate to your user (you may have to find relevant trends in the dataset first!). Here are some requirements to help guide your design:

I included this chart to examine the relationship between total population and incarceration rates across different regions. The log scaling helps to better visualize the distribution and spread of data, revealing potential trends that may be obscured with a linear scale. Patterns that emerged include the concentration of higher incarceration rates in regions with larger populations, suggesting that more populous areas may experience higher rates of incarceration.

Map

Include a chart. Make sure to describe why you included the chart, and what patterns emerged

The last chart that you will create and include will show how a variable is distributed geographically. Again, think carefully about what such a comparison means and what you want to communicate to your user (you may have to find relevant trends in the dataset first!). Here are some requirements to help guide your design:

I included this map to visualize how incarceration rates are distributed geographically across the United States. This map helps identify regional patterns and disparities, revealing which states have higher or lower incarceration rates. Patterns that emerged include noticeable variations in incarceration rates, with some regions having significantly higher rates than others, which can inform policymakers and researchers about areas that may require more focused interventions.