njoaguof

This is a cleaned-up version of the NJ OAG Use of Force database available from https://www.njoag.gov/force/.

Dataset Overview

• incident This is the main data table. Each use of force incident is recorded here. A single incident may involve multiple subjects.
• subject The subjects of the use of force – includes persons and animals.
• incident-specific data tables. Each of these data tables are for multi-value fields associated to incidents. For example – a single incident may have both "Rain" and "Fog" weather conditions, and this will result in two rows in incident_weather.
  • incident_contact_origin
  • incident_lighting
  • incident_location_type
  • incident_officer_injury_type
  • incident_officer_medical_treatment
  • incident_planned_contact
  • incident_type
  • incident_video_type
  • incident_weather
• incident-subject data tables These data tables are for multi-value fields that should be associated to individual incident subjects. Unfortunately, these tables reflect some irreducible messiness of the source data. See the notes below.
  • incident_subject_action
  • incident_subject_force_type
  • incident_subject_injury
  • incident_subject_medical_treatment
  • incident_subject_perceived_condition
  • incident_subject_reason_not_arrested
  • incident_subject_resistance
• officer_name_variants Includes every variation in spelling and capitalization of the officer names found in the source data.
• use_of_force_raw The source data.

Examples

Every incident has a unique form_id, and this field is used to link the subject incident_xxx and incident_subject_xxx tables to specific incidents:

library(njoaguof)
library(dplyr)
# Summarize video_type by agency_county
incident %>%
  select(form_id, agency_county) %>%
  right_join(incident_video_type, by = "form_id") %>%
  count(agency_county, video_type)
#> # A tibble: 187 × 3
#>    agency_county   video_type              n
#>    <fct>           <fct>               <int>
#>  1 Atlantic County Body Worn            2269
#>  2 Atlantic County CED Camera             14
#>  3 Atlantic County Cell Phone             11
#>  4 Atlantic County Commercial Building   100
#>  5 Atlantic County Motor Vehicle         322
#>  6 Atlantic County Residential/Home       13
#>  7 Atlantic County Station House          97
#>  8 Atlantic County Other                  31
#>  9 Bergen County   Body Worn            2855
#> 10 Bergen County   CED Camera             72
#> # ℹ 177 more rows

library(njoaguof)
library(dplyr)
# Summarize subject gender by officer gender
incident %>% 
  select(form_id, officer_gender) %>% 
  right_join(subject, by="form_id") %>%
  count(officer_gender, subject_gender=gender)
#> # A tibble: 16 × 3
#>    officer_gender subject_gender     n
#>    <fct>          <fct>          <int>
#>  1 Male           Male           19940
#>  2 Male           Female          5313
#>  3 Male           Non-Binary/X      36
#>  4 Male           <NA>             874
#>  5 Female         Male            1139
#>  6 Female         Female           694
#>  7 Female         Non-Binary/X       2
#>  8 Female         <NA>              76
#>  9 Other          Male           33845
#> 10 Other          Female          9740
#> 11 Other          Non-Binary/X      42
#> 12 Other          <NA>            1305
#> 13 <NA>           Male              27
#> 14 <NA>           Female             9
#> 15 <NA>           Non-Binary/X       4
#> 16 <NA>           <NA>               7

Notes

The raw data from the NJ OAG is available in table use_of_force_raw, which has one row for each use of force incident. Fields with multiple values are recorded as comma separated lists. For example:

use_of_force_raw %>% count(SubjectGender) %>% head(5)
#> # A tibble: 5 × 2
#>   SubjectGender                              n
#>   <chr>                                  <int>
#> 1 Female                                 15359
#> 2 Female, Female                           114
#> 3 Female, Female, Female                    12
#> 4 Female, Female, Female, Female             4
#> 5 Female, Female, Female, Female, Female     1

These fields are broken out into new data tables in the following categories.

Subject Fields

Several fields contain one value for each subject. We presume that the order is preserved, so that we may create one row for each subject in the subject table.

use_of_force_raw %>% filter(FormID == 16301) %>%
  select(
    FormID,
    SubjectArrested,
    SubjectType,
    SubjectAge,
    SubjectRaceEthnicity,
    SubjectGender
  )
#> # A tibble: 1 × 6
#>   FormID SubjectArrested SubjectType    SubjectAge SubjectRaceEthnicity         
#>    <dbl> <chr>           <chr>          <chr>      <chr>                        
#> 1  16301 False, True     Person, Person 23, 26     Black or African American, H…
#> # ℹ 1 more variable: SubjectGender <chr>

subject %>% filter(form_id == 16301)
#> # A tibble: 2 × 10
#>   form_id index arrested type    age juvenile race  gender injured injured_prior
#>     <dbl> <int> <lgl>    <fct> <int> <lgl>    <fct> <fct>  <lgl>   <lgl>        
#> 1   16301     1 FALSE    Pers…    23 FALSE    Black Female FALSE   FALSE        
#> 2   16301     2 NA       <NA>     26 FALSE    <NA>  <NA>   TRUE    FALSE

Multi-value incident fields

Some fields contain multiple values which apply to the entire incident. For each such column, we create a separate table expressing this many-to-one relationship. For example, this row in the source data has three values for incident_type, and this results in three rows in the incident_type table.

library(tidyverse)
use_of_force_raw %>% filter(FormID == 16301) %>%
  select(IncidentType)
#> # A tibble: 1 × 1
#>   IncidentType                                                                  
#>   <chr>                                                                         
#> 1 Potential Mental Health Incident, Suspicious person, Disturbance (drinking, f…
incident_type %>% filter(form_id == 16301)
#> # A tibble: 3 × 2
#>   form_id type                                        
#>     <dbl> <fct>                                       
#> 1   16301 Potential Mental Health Incident            
#> 2   16301 Suspicious person                           
#> 3   16301 Disturbance (drinking, fighting, disorderly)

Multi-value Incident-Subject Fields

Some fields contain multiple values which apply to individual subjects, but there is no reliable way to assign the values to subjects. For example, in this row of the raw data, there are two subjects and three values in the SubResist field. In this case, we create three rows in the incident_subject_resistance table, indicating the position of each item in the list with the index value.

use_of_force_raw %>% 
  filter(FormID == 19542) %>% 
  select(SubjectType,SubjectResistance)
#> # A tibble: 1 × 2
#>   SubjectType    SubjectResistance                                              
#>   <chr>          <chr>                                                          
#> 1 Person, Person Verbal, Verbal, Aggressive resistance (attempt to attack or ha…
incident_subject_resistance %>% filter(form_id == 19542)
#> # A tibble: 3 × 3
#>   form_id index subject_resistance                               
#>     <dbl> <int> <fct>                                            
#> 1   19542     1 Verbal                                           
#> 2   19542     2 Verbal                                           
#> 3   19542     3 Aggressive resistance (attempt to attack or harm)

Note that it is not clear in the source data if “Aggressive resistance” should be associated to the first subject or to the second subject.

All of the incident_subject_xxx data tables are of this form, with an index column included so the order information is not lost.

Officer name variants

In the raw data, there are two fields which identify the officer: officer_name (an ID field) and Officer_Name2 (a name field). A single officer_name ID can be associated different spellings in the Officer_Name2 field. When building the incident table, we ensure that every officer_name_id is associated with a single spelling of the officer name by choosing the most common form.

But all variants of the officer names appearing in the source data are preserved in the officer_name_variants table.

Installation

You can install the latest version of njoaguof from GitHub with:

# install.packages("devtools")
devtools::install_github("tor-gu/njoaguof")