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A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election

Received: 19 August 2020     Accepted: 3 September 2020     Published: 10 September 2020
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Abstract

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.

Published in International Journal of Data Science and Analysis (Volume 6, Issue 4)
DOI 10.11648/j.ijdsa.20200604.12
Page(s) 105-112
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

BLM, Police, Violence, Data Analysis, Machine Learning

References
[1] Buchanan, Larry, et al. “Black Lives Matter May Be the Largest Movement in U. S. History.” The New York Times, 8 July 2020, nyti.ms/2D8PhQY.
[2] Anderson, Monica. “Social Media Conversations About Race.” Pew Research Center: Internet, Science & Tech, Pew Research Center, 30 May 2020, www.pewresearch.org/internet/2016/08/15/social-media-conversations-about-race/.
[3] Williams, Joseph P. “Study: Police Violence a Leading Cause of Death for Young Men.” U. S. News & World Report, U. S. News & World Report, 5 Aug. 2019, www.usnews.com/news/healthiest-communities/articles/2019-08-05/police-violence-a-leading-cause-of-death-for-young-men.
[4] Callahan, Molly. “Many People Who Voted in 2016 Were Motivated by the Black Lives Matter Protests. Will the Same Hold True This Year?” News Northeastern, 9 June 2020, news.northeastern.edu/2020/06/09/the-black-lives-matter-protests-motivated-voters-in-2016-will-they-do-the-same-in-2020/.
[5] Siddiqui, Sabrina. “Donald Trump Strikes Muddled Note on 'Divisive' Black Lives Matter.” The Guardian, Guardian News and Media, 13 July 2016, www.theguardian.com/us-news/2016/jul/13/donald-trump-strikes-muddled-note-on-divisive-black-lives-matter.
[6] “Census Bureau.” Five Thirty Eight, 15 July 2019, fivethirtyeight.com/tag/census-bureau/.
[7] The Washington Post, WP Company, www.washingtonpost.com/wp-srv/metro/data/datapost.html.
[8] Patil, Prasad. “What Is Exploratory Data Analysis?” Medium, Towards Data Science, 23 May 2018, towardsdatascience.com/exploratory-data-analysis-8fc1cb20fd15.
[9] Daniel Diorio, Ben Williams. “The Electoral College.” National Conference of State Legislatures, 6 July 2020, www.ncsl.org/research/elections-and-campaigns/the-electoral-college.aspx.
[10] “Presidential Election Results: Donald J. Trump Wins.” The New York Times, The New York Times, 9 Aug. 2017, www.nytimes.com/elections/2016/results/president.
[11] “President - Live Election Results.” The New York Times, The New York Times, 29 Nov. 2012, www.nytimes.com/elections/2012/results/president.html.
[12] “Machine Learning: What It Is and Why It Matters.” SAS, www.sas.com/en_us/insights/analytics/machine-learning.html.
[13] Mangale, Sanchita. “Voting Classifier.” Medium, Medium, 18 May 2019, medium.com/@sanchitamangale12/voting-classifier-1be10db6d7a5.
[14] Yiu, Tony. “Understanding Random Forest.” Medium, Towards Data Science, 14 Aug. 2019, towardsdatascience.com/understanding-random-forest-58381e0602d2.
[15] Shetty, Badreesh. “An in-Depth Guide to Supervised Machine Learning Classification.” Built In, 17 July 2019, builtin.com/data-science/supervised-machine-learning-classification.
Cite This Article
  • APA Style

    Bon-A Koo, Jana Choe, Yeseo Kim. (2020). A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election. International Journal of Data Science and Analysis, 6(4), 105-112. https://doi.org/10.11648/j.ijdsa.20200604.12

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    ACS Style

    Bon-A Koo; Jana Choe; Yeseo Kim. A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election. Int. J. Data Sci. Anal. 2020, 6(4), 105-112. doi: 10.11648/j.ijdsa.20200604.12

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    AMA Style

    Bon-A Koo, Jana Choe, Yeseo Kim. A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election. Int J Data Sci Anal. 2020;6(4):105-112. doi: 10.11648/j.ijdsa.20200604.12

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  • @article{10.11648/j.ijdsa.20200604.12,
      author = {Bon-A Koo and Jana Choe and Yeseo Kim},
      title = {A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election},
      journal = {International Journal of Data Science and Analysis},
      volume = {6},
      number = {4},
      pages = {105-112},
      doi = {10.11648/j.ijdsa.20200604.12},
      url = {https://doi.org/10.11648/j.ijdsa.20200604.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200604.12},
      abstract = {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.},
     year = {2020}
    }
    

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    AB  - 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.
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Author Information
  • Northfield Mount Hermon School, Gill, United States

  • The Governor’s Academy, Newburyport, United States

  • Lakefield College School, Ontario, Canada

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