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SMCM_DataScience

The Poverty Trap: Socioeconomic Status and its Impact on Racialized Incarceration, Violent Crime, and Police Killings

Authors: Thomas St. Arnold, William Borwegen, Caroline Goyco, Megan Johnson, JP.

Rates of incarceration, crime, and death at the hands of the police in the United States have been in contention over the years. It is widely known that we as a country incarcerate five to seven times the amount of prisoners that other countries do. Our goal is to prove that there is a correlation between socioeconomic status and its impact on crime rate, incarceration rates, and police killings, and to suggest possible methods to aid in lowering these rates. Through the use of linear regression and k-means clustering, we were able to analyze data found in different datasets, and this will hopefully prove helpful for the making of policy and programs that can aid those below the poverty level. Previous government programs have proved helpful to prevent and rehabilitate ex-offenders, but many cities simply do not have the funds to support these programs, in which case it is our opinion that the federal government should step in to fund these programs.

Used Python version 2.7 for calculating and displaying bar charts, linear regression, kmeans clustering and scatterplots. Used R version 3.3 for calculating and displaying other kmeans clustering and data conversion from SAS files to CSV files/clean-up.

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