A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election
International Journal of Data Science and Analysis
Volume 6, Issue 4, August 2020, Pages: 105-112
Received: Aug. 19, 2020;
Accepted: Sep. 3, 2020;
Published: Sep. 10, 2020
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Bon-A Koo, Northfield Mount Hermon School, Gill, United States
Jana Choe, The Governor’s Academy, Newburyport, United States
Yeseo Kim, Lakefield College School, Ontario, Canada
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The recent death of George Floyd once again reminded the Americans of the chronic racial bias when it comes to police using force during an encounter with an alleged criminal or, in some cases, innocent civilians, and promulgated Black Lives Matter (BLM) movements in the United States. In order to verify such police use of excessive force against a particular racial group, we examined datasets regarding cases of police killings, which were collected from 50 states (and Washington, D. C. separately) across the country. To find out the possible factors that might cause frequent police killings against a particular racial group, we analyzed relevant datasets, observing each state’s demographics, political ideology, education level, and the frequency of police deaths in respect to each state’s frequency of police killings. Although we found numerous factors that might lead such trends in police violence, we discovered a correlation between a state’s political ideology and the frequency of police killings of a particular racial group in the corresponding state. In response to such trends, we evaluated the correlation between each state’s prevalence of police killings and its presidential election outcome in 2016. Using two machine learning methods, random forest and logistic regression, we further predicted each state’s prospective preference toward a particular candidate (Republican or Democrat) and the election outcome of the 2020 presidential election.
BLM, Police, Violence, Data Analysis, Machine Learning
To cite this article
A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election, International Journal of Data Science and Analysis.
Vol. 6, No. 4,
2020, pp. 105-112.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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