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

Bon-A Koo; Jana Choe; Yeseo Kim

doi:doi:10.11648/j.ijdsa.20200604.12

| Peer-Reviewed

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

Bon-A Koo, Jana Choe, Yeseo Kim

Published in International Journal of Data Science and Analysis (Volume 6, Issue 4)

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election
AU  - Bon-A Koo
AU  - Jana Choe
AU  - Yeseo Kim
Y1  - 2020/09/10
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200604.12
DO  - 10.11648/j.ijdsa.20200604.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 105
EP  - 112
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200604.12
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.
VL  - 6
IS  - 4
ER  -

Copy | Download

Author Information

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - A Comparative Analysis on Police Related Deaths and Prediction of 2020 Presidential Election
AU  - Bon-A Koo
AU  - Jana Choe
AU  - Yeseo Kim
Y1  - 2020/09/10
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200604.12
DO  - 10.11648/j.ijdsa.20200604.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 105
EP  - 112
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200604.12
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.
VL  - 6
IS  - 4
ER  -

Copy | Download