Data 608 Final: Bias Incidents

Maryluz Cruz

12/5/2020

Bias Incidents

Overview

Over the recent years there have been a lot of bias incidents that have been committed whether it be due to race, religion, sexual orientation. Here I wanted to look into that and see what bias incidents that have been committed the most. Also to see how many bias incidents were committed against a group based on the year, based on the district, based on the act of violence, and based on type of victim like for instance what industry.

In order to come up with the conclusion we are going to use this information was from the Montgomery County of Maryland. You can that a look at the data here. This data is updated frequently. The last date that on record is November 25, 2020.

We are going to look at the and see how many bias incidents occurred toward a specific group going from the years 2016-2020, by District among the many , by Act of Violence, and

About the Data

This information was from the Montgomery County of Maryland, this data includes 539 cases and 16 Columns. There are many columns that will not be used because there is a lot of information that is missing in this data but it is not necessary for this. These incidents have occurred from the years 2016 - 2020.

## [1] 539  16

Summary

##        ID               Incident.Date    District               Bias.Code  
##  Min.   : 16000094   11/13/2017:  4   2D     :119   Anti-Black       :161  
##  1st Qu.: 17015896   2/27/2017 :  4   4D     :106   Anti-Jewish      :159  
##  Median :180034717   6/3/2020  :  4   1D     : 70   Anti-Homosexual  : 52  
##  Mean   :142654198   11/12/2016:  3   5D     : 63   Anti-Multi-Racial: 29  
##  3rd Qu.:190044786   12/5/2019 :  3   6D     : 53   Anti-Hispanic    : 28  
##  Max.   :200047241   3/15/2019 :  3   3D     : 50   Anti-Islamic     : 23  
##                      (Other)   :518   (Other): 78   (Other)          : 87  
##                      Bias.Code_2                                   Bias    
##                            :498   Vandalism                          :217  
##  Anti-Black                : 13   Written Intimidation/Simple Assault: 97  
##  Anti-Jewish               : 10   Verbal Intimidation/Simple Assault : 96  
##  Anti-Homosexual           :  8   Assault (physical)                 : 69  
##  Anti-Multi-Religious Group:  3   Flyer Left Behind                  : 21  
##  Anti-Hispanic             :  2   Other                              : 16  
##  (Other)                   :  5   (Other)                            : 23  
##               Status    X..of.Victims                           Victim.Type 
##  Open            :215   Min.   :0.000   Business/Financial Institution: 18  
##  N/A             :149   1st Qu.:1.000   Government                    : 18  
##  Inactive        : 57   Median :1.000   Individual(s)                 :291  
##  Closed-Arrest   : 41   Mean   :1.218   Other                         : 12  
##  Closed-Admin    : 39   3rd Qu.:1.000   Religious Organization        : 37  
##  Closed-Exception: 19   Max.   :7.000   School/College                :114  
##  (Other)         : 19   NA's   :245     Society                       : 49  
##  X..of.Suspects  X..Suspects...18.of.age X..Suspects.18.35.of.age
##  Min.   :1.000   Min.   :1.000           Min.   :1.00            
##  1st Qu.:1.000   1st Qu.:1.000           1st Qu.:1.00            
##  Median :1.000   Median :1.000           Median :1.00            
##  Mean   :1.352   Mean   :1.586           Mean   :1.13            
##  3rd Qu.:1.000   3rd Qu.:2.000           3rd Qu.:1.00            
##  Max.   :4.000   Max.   :4.000           Max.   :4.00            
##  NA's   :312     NA's   :481             NA's   :493             
##  X..Suspects.36.45.of.age X..Suspects.46.55.of.age X..Suspects...55.of.age
##  Min.   :-1.0000          Min.   :1.000            Min.   :1.000          
##  1st Qu.: 1.0000          1st Qu.:1.000            1st Qu.:1.000          
##  Median : 1.0000          Median :1.000            Median :1.000          
##  Mean   : 0.9048          Mean   :1.056            Mean   :1.037          
##  3rd Qu.: 1.0000          3rd Qu.:1.000            3rd Qu.:1.000          
##  Max.   : 1.0000          Max.   :2.000            Max.   :2.000          
##  NA's   :518              NA's   :521              NA's   :512            
##  Suspect.Known.Unknown
##         :  3          
##  Known  :187          
##  Unknown:349          
##                       
##                       
##                       
##

Bias.Code

x
Anti-Black
Anti-Jewish
Anti-Transgender
Anti-Homosexual
Anti-Other Ethnicity
Anti-Islamic
Anti-Asian
Anti-Multi-Racial
Anti-Catholic
Anti-Hispanic
Anti-White
Anti-Other Religion
Anti-Multi-Religious Group
Anti-Arab
Anti-Gender Non-Conforming
Anti-Other Christian

District

x
1D
3D
2D
4D
6D
5D
GCPD
RCPD
TPPD

Bias: Types of Acts of Violence

x
Assault (simple)
Vandalism
Other
Verbal Intimidation/Simple Assault
Written Intimidation/Simple Assault
Assault (physical)
Flyer Left Behind
Physical Intimidation/Simple Assault
Arson
Display of Noose

Type of Victim

x
Individual(s)
School/College
Society
Religious Organization
Government
Business/Financial Institution
Other

Analysis of the Data

  • By using the dataExplorer package plot_missing allows one to see if there is any data missing, the categories that are in blue will be removed, along with other categories that are not necessary along with Bias.Code_2 even though it says the data is good it has 498 blank rows. The only columns that will be kept Victim.Type, Bias, Bias.Code, District, Incident.Date.

  • With Incident.Date since only the year is needed of the incident date, the year has to be separated out and a new column named Year is created.
  • The columns that will not be used are also removed and we end up with these columns alone.
## [1] "ID"          "District"    "Bias.Code"   "Bias"        "Victim.Type"
## [6] "Year"
##        ID               District               Bias.Code  
##  Min.   : 16000094   2D     :119   Anti-Black       :161  
##  1st Qu.: 17015896   4D     :106   Anti-Jewish      :159  
##  Median :180034717   1D     : 70   Anti-Homosexual  : 52  
##  Mean   :142654198   5D     : 63   Anti-Multi-Racial: 29  
##  3rd Qu.:190044786   6D     : 53   Anti-Hispanic    : 28  
##  Max.   :200047241   3D     : 50   Anti-Islamic     : 23  
##                      (Other): 78   (Other)          : 87  
##                                   Bias                             Victim.Type 
##  Vandalism                          :217   Business/Financial Institution: 18  
##  Written Intimidation/Simple Assault: 97   Government                    : 18  
##  Verbal Intimidation/Simple Assault : 96   Individual(s)                 :291  
##  Assault (physical)                 : 69   Other                         : 12  
##  Flyer Left Behind                  : 21   Religious Organization        : 37  
##  Other                              : 16   School/College                :114  
##  (Other)                            : 23   Society                       : 49  
##       Year     
##  Min.   :2016  
##  1st Qu.:2017  
##  Median :2018  
##  Mean   :2018  
##  3rd Qu.:2019  
##  Max.   :2020  
##
  • For this project dplyr will be used greatly in order to get the information that we want to achieve.

Analysis of the Tables

We will take a closer look at the tables, and we are going to point out the highest number Total Incidents, and we will point out what group/Bias.Code the incident had occurred too.

  • Using tidyverse 4 Tables were created. You can see the exact number of incidents in each table.
    • Year Vs. Bias.Code
    • District Vs. Bias.Code
    • Bias: Type of Violence Vs. Bias.Code
    • Victim.Type Vs. Bias.Code

Year Vs. Bias.Code

Here lets take a look at the Total number of incidents that happended among the years.

Year Bias.Code Total_Incidents
2016 Anti-Asian 1
2016 Anti-Black 23
2016 Anti-Hispanic 9
2016 Anti-Homosexual 6
2016 Anti-Islamic 4
2016 Anti-Jewish 32
2016 Anti-Multi-Racial 8
2016 Anti-Multi-Religious Group 3
2016 Anti-Other Ethnicity 3
2016 Anti-Transgender 4
2016 Anti-White 5
2017 Anti-Asian 3
2017 Anti-Black 39
2017 Anti-Catholic 2
2017 Anti-Hispanic 6
2017 Anti-Homosexual 13
2017 Anti-Islamic 11
2017 Anti-Jewish 36
2017 Anti-Multi-Racial 5
2017 Anti-Multi-Religious Group 1
2017 Anti-Other Ethnicity 2
2017 Anti-Transgender 1
2017 Anti-White 3
2018 Anti-Arab 1
2018 Anti-Asian 3
2018 Anti-Black 24
2018 Anti-Gender Non-Conforming 2
2018 Anti-Hispanic 5
2018 Anti-Homosexual 10
2018 Anti-Islamic 4
2018 Anti-Jewish 26
2018 Anti-Multi-Racial 5
2018 Anti-Other Ethnicity 2
2018 Anti-Other Religion 2
2018 Anti-Transgender 5
2018 Anti-White 4
2019 Anti-Asian 5
2019 Anti-Black 43
2019 Anti-Catholic 2
2019 Anti-Gender Non-Conforming 1
2019 Anti-Hispanic 3
2019 Anti-Homosexual 12
2019 Anti-Islamic 3
2019 Anti-Jewish 34
2019 Anti-Multi-Racial 4
2019 Anti-Other Ethnicity 2
2019 Anti-Other Religion 1
2019 Anti-Transgender 3
2019 Anti-White 1
2020 Anti-Arab 1
2020 Anti-Asian 8
2020 Anti-Black 32
2020 Anti-Catholic 2
2020 Anti-Hispanic 5
2020 Anti-Homosexual 11
2020 Anti-Islamic 1
2020 Anti-Jewish 31
2020 Anti-Multi-Racial 7
2020 Anti-Other Christian 1
2020 Anti-Other Ethnicity 1
2020 Anti-Other Religion 1
2020 Anti-Transgender 2
2020 Anti-White 9
  • It seems like no matter what year there are more Anti-Black, and Anti-Jewish bias incidents, the numbers are pretty high in comparison to the others.
  • In 2017 Anti-Jewish bias incidents was at its highest with 36 incidents.
  • In 2019 Anti-Black bias incidents was at its highest with 43 incidents.

District Vs. Bias Code

District Bias.Code Total_Incidents
1D Anti-Asian 1
1D Anti-Black 21
1D Anti-Catholic 1
1D Anti-Gender Non-Conforming 1
1D Anti-Hispanic 3
1D Anti-Homosexual 2
1D Anti-Islamic 2
1D Anti-Jewish 30
1D Anti-Multi-Racial 4
1D Anti-Multi-Religious Group 1
1D Anti-Other Ethnicity 1
1D Anti-Transgender 2
1D Anti-White 1
2D Anti-Asian 5
2D Anti-Black 38
2D Anti-Hispanic 3
2D Anti-Homosexual 14
2D Anti-Islamic 1
2D Anti-Jewish 48
2D Anti-Multi-Racial 2
2D Anti-Multi-Religious Group 2
2D Anti-Other Ethnicity 2
2D Anti-Transgender 3
2D Anti-White 1
3D Anti-Arab 1
3D Anti-Black 15
3D Anti-Hispanic 5
3D Anti-Homosexual 5
3D Anti-Islamic 4
3D Anti-Jewish 13
3D Anti-Other Religion 1
3D Anti-Transgender 1
3D Anti-White 5
4D Anti-Asian 6
4D Anti-Black 38
4D Anti-Catholic 2
4D Anti-Hispanic 7
4D Anti-Homosexual 12
4D Anti-Islamic 7
4D Anti-Jewish 21
4D Anti-Multi-Racial 5
4D Anti-Other Christian 1
4D Anti-Other Ethnicity 1
4D Anti-Other Religion 1
4D Anti-Transgender 4
4D Anti-White 1
5D Anti-Asian 3
5D Anti-Black 15
5D Anti-Hispanic 5
5D Anti-Homosexual 4
5D Anti-Islamic 5
5D Anti-Jewish 10
5D Anti-Multi-Racial 5
5D Anti-Other Ethnicity 4
5D Anti-Other Religion 2
5D Anti-Transgender 2
5D Anti-White 8
6D Anti-Asian 1
6D Anti-Black 17
6D Anti-Gender Non-Conforming 1
6D Anti-Hispanic 1
6D Anti-Homosexual 10
6D Anti-Islamic 3
6D Anti-Jewish 9
6D Anti-Multi-Racial 6
6D Anti-Multi-Religious Group 1
6D Anti-Other Ethnicity 1
6D Anti-Transgender 1
6D Anti-White 2
GCPD Anti-Arab 1
GCPD Anti-Asian 2
GCPD Anti-Black 3
GCPD Anti-Hispanic 1
GCPD Anti-Jewish 8
GCPD Anti-Multi-Racial 3
GCPD Anti-White 1
RCPD Anti-Asian 1
RCPD Anti-Black 12
RCPD Anti-Catholic 2
RCPD Anti-Gender Non-Conforming 1
RCPD Anti-Hispanic 3
RCPD Anti-Homosexual 2
RCPD Anti-Islamic 1
RCPD Anti-Jewish 17
RCPD Anti-Multi-Racial 4
RCPD Anti-Other Ethnicity 1
RCPD Anti-White 1
TPPD Anti-Asian 1
TPPD Anti-Black 2
TPPD Anti-Catholic 1
TPPD Anti-Homosexual 3
TPPD Anti-Jewish 3
TPPD Anti-Transgender 2
TPPD Anti-White 2
  • In mostly all of the Districts there are to be high numbers of bias incidents that occur of Anti-Black and Anti-Jewish.
  • In a few of the Districts there were Bias Incidents of Anti-Homosexual.
  • In one of the Districts there was a higher number of Anti-White bias incidents.

Bias: Type of Violence Vs. Bias.Code

Bias Bias.Code Total_Incidents
Arson Anti-Catholic 1
Assault (physical) Anti-Asian 3
Assault (physical) Anti-Black 14
Assault (physical) Anti-Gender Non-Conforming 1
Assault (physical) Anti-Hispanic 13
Assault (physical) Anti-Homosexual 15
Assault (physical) Anti-Islamic 5
Assault (physical) Anti-Jewish 1
Assault (physical) Anti-Transgender 9
Assault (physical) Anti-White 8
Assault (simple) Anti-Asian 2
Assault (simple) Anti-Black 7
Assault (simple) Anti-Other Ethnicity 1
Assault (simple) Anti-White 2
Display of Noose Anti-Black 3
Flyer Left Behind Anti-Black 1
Flyer Left Behind Anti-Hispanic 1
Flyer Left Behind Anti-Homosexual 1
Flyer Left Behind Anti-Islamic 1
Flyer Left Behind Anti-Jewish 10
Flyer Left Behind Anti-Multi-Racial 7
Other Anti-Asian 1
Other Anti-Black 8
Other Anti-Homosexual 2
Other Anti-Jewish 2
Other Anti-Multi-Religious Group 1
Other Anti-Other Ethnicity 1
Other Anti-Transgender 1
Physical Intimidation/Simple Assault Anti-Asian 1
Physical Intimidation/Simple Assault Anti-Black 5
Physical Intimidation/Simple Assault Anti-Homosexual 1
Vandalism Anti-Arab 1
Vandalism Anti-Asian 3
Vandalism Anti-Black 54
Vandalism Anti-Catholic 5
Vandalism Anti-Hispanic 3
Vandalism Anti-Homosexual 17
Vandalism Anti-Islamic 4
Vandalism Anti-Jewish 100
Vandalism Anti-Multi-Racial 16
Vandalism Anti-Multi-Religious Group 3
Vandalism Anti-Other Ethnicity 2
Vandalism Anti-Other Religion 3
Vandalism Anti-Transgender 1
Vandalism Anti-White 5
Verbal Intimidation/Simple Assault Anti-Arab 1
Verbal Intimidation/Simple Assault Anti-Asian 8
Verbal Intimidation/Simple Assault Anti-Black 40
Verbal Intimidation/Simple Assault Anti-Gender Non-Conforming 2
Verbal Intimidation/Simple Assault Anti-Hispanic 9
Verbal Intimidation/Simple Assault Anti-Homosexual 4
Verbal Intimidation/Simple Assault Anti-Islamic 4
Verbal Intimidation/Simple Assault Anti-Jewish 14
Verbal Intimidation/Simple Assault Anti-Multi-Racial 1
Verbal Intimidation/Simple Assault Anti-Other Christian 1
Verbal Intimidation/Simple Assault Anti-Other Ethnicity 5
Verbal Intimidation/Simple Assault Anti-Other Religion 1
Verbal Intimidation/Simple Assault Anti-Transgender 3
Verbal Intimidation/Simple Assault Anti-White 3
Written Intimidation/Simple Assault Anti-Asian 2
Written Intimidation/Simple Assault Anti-Black 29
Written Intimidation/Simple Assault Anti-Hispanic 2
Written Intimidation/Simple Assault Anti-Homosexual 12
Written Intimidation/Simple Assault Anti-Islamic 9
Written Intimidation/Simple Assault Anti-Jewish 32
Written Intimidation/Simple Assault Anti-Multi-Racial 5
Written Intimidation/Simple Assault Anti-Other Ethnicity 1
Written Intimidation/Simple Assault Anti-Transgender 1
Written Intimidation/Simple Assault Anti-White 4
  • There was a lot of Anti-Black bias incidents that occurred in the form of Assault (simple).
  • There was an alarming number of Anti-Jewish and Anti-Black bias incidents that occurred in the form of Vandalism
  • There were a few more Anti-Black bias incidents that occurred in the form of Other in comparison to the others.
  • There was a lot of Anti-Black bias incidents that occurred in the form of Verbal Intimidation/Simple Assault.
  • There was a lot of Anti-Black and Anti-Jewish bias incidents that occurred in the form of Written Intimidation/Simple Assault
  • There was a lot of Anti-Black, Anti-Hispanic, and Anti-Homosexual bias incidents that occurred in the form of Assault (physical)
  • There was a lot of Anti-Black bias incidents that occurred in the form of Flyer Left Behind
  • There was a lot of Anti-Black bias incidents that occurred in the form of Physical Intimidation/Simple Assault
  • There was one instance of Anti-Catholic bias incidents that occurred in the form Arson.
  • There were 3 cases of Anti-Black bias incidents that occurred in the form Display of Noose.

Victim.Type Vs. Bias.Code

Victim.Type Bias.Code Total_Incidents
Business/Financial Institution Anti-Asian 2
Business/Financial Institution Anti-Black 7
Business/Financial Institution Anti-Hispanic 1
Business/Financial Institution Anti-Jewish 1
Business/Financial Institution Anti-Multi-Racial 4
Business/Financial Institution Anti-Transgender 1
Business/Financial Institution Anti-White 2
Government Anti-Asian 1
Government Anti-Black 6
Government Anti-Jewish 8
Government Anti-Multi-Racial 1
Government Anti-Multi-Religious Group 2
Individual(s) Anti-Arab 2
Individual(s) Anti-Asian 16
Individual(s) Anti-Black 94
Individual(s) Anti-Catholic 1
Individual(s) Anti-Gender Non-Conforming 3
Individual(s) Anti-Hispanic 22
Individual(s) Anti-Homosexual 42
Individual(s) Anti-Islamic 18
Individual(s) Anti-Jewish 49
Individual(s) Anti-Multi-Racial 4
Individual(s) Anti-Multi-Religious Group 1
Individual(s) Anti-Other Ethnicity 9
Individual(s) Anti-Transgender 14
Individual(s) Anti-White 16
Other Anti-Black 1
Other Anti-Homosexual 1
Other Anti-Jewish 7
Other Anti-Multi-Racial 3
Religious Organization Anti-Black 3
Religious Organization Anti-Catholic 4
Religious Organization Anti-Hispanic 1
Religious Organization Anti-Homosexual 3
Religious Organization Anti-Islamic 5
Religious Organization Anti-Jewish 15
Religious Organization Anti-Multi-Racial 1
Religious Organization Anti-Other Christian 1
Religious Organization Anti-Other Religion 4
School/College Anti-Black 35
School/College Anti-Catholic 1
School/College Anti-Hispanic 1
School/College Anti-Homosexual 3
School/College Anti-Jewish 64
School/College Anti-Multi-Racial 6
School/College Anti-Multi-Religious Group 1
School/College Anti-Other Ethnicity 1
School/College Anti-White 2
Society Anti-Asian 1
Society Anti-Black 15
Society Anti-Hispanic 3
Society Anti-Homosexual 3
Society Anti-Jewish 15
Society Anti-Multi-Racial 10
Society Anti-White 2
  • Individual(s)- Anti-Black, Anti-Hispanic, Anti-Homosexual, Anti-Islamic, Anti-Jewish, Anti-Transgender, Anti-White
  • School/College - Anti-Black and Anti-Jewish had the highest number of bias incidents
  • Society - Anti-Jewish, Anti-Black, Anti-Multiracial
  • Religious Organization- Anti-Jewish
  • Government - Anti-Black, Anti-Jewish
  • Business/Financial Institution - Anti-Black, Anti-Multi-Racial
  • Other - Anti-Jewish

Visualizations

Year Vs. Bias Code

Year Vs. Bias Code Facet Wrap

Year Vs. Bias Code Geom-Point

  • Looking at the visuals it is evident that no matter what year there are more Anti-Black and Anti-Jewish bias incidents.

District Vs. Bias.Code

District Vs. Bias.Code Facet Wrap

District Vs. Bias.Code Geom-Point

  • Looking at the visuals it is evident that no matter what District there are more Anti-Black and Anti-Jewish bias incidents and also Anti-Homosexual

Bias:Type of Violence Vs. Bias.Code

Bias:Type of Violence Vs. Bias.Code Facet Wrap

Bias:Type of Violence Vs. Bias.Code Geom-Point

  • Anti_Black bias incidents have occured in almost all types of violence.

Victim.Type Vs. Bias.Code

Victim.Type Vs. Bias.Code Facet Wrap

Victim.Type Vs. Bias.Code Geom-Point

ShinyApps

For a more interactive approach of the visualizations ShinyApps were created.

If one wants to look at the basic Bar Chart and can go to this ShinyApp: https://luz917.shinyapps.io/data608finalallincidents/

If one wants to look at all of the visualizations and some tables one could look at this ShinyApp: https://luz917.shinyapps.io/data608finalprojectshiny/

Conclusion

It is sometimes surprising that so many bias incidents occur, and considering that this is just data from one particular county in Maryland. One could only imagine how many more bias incidents there would be if once included all of the states. Even if all of the states were included it would still be apparent that there are many more Anti-Black, and Anti-Jewish bias incidents that occur and it does not matter matter what category it would fall under. There were still many Anti-Homosexual bias incidents that occurred. Also when looking at the bias incidents that occurred among Individual(s) there were Anti_Black, Anti-Hispanic, Anti-Homosexual, Anti-Islamic, and Anti-Jewish. One can only wonder if the amount of bias incidents that occur will decrease. Just recently there was an Anti-Jewish bias incident that occurred in Illinois where flyers were put on the only Anne Frank memorial that is in the US. Another good project would be to get all of the data from all of the States and see how much of an increase there would be.