Executive Summary

This is the first independent analysis of the Salt Lake City Police Department’s internal use of force and street-check data since a Deseret News analysis in \(2000\).

Statistical tests (chi-square) using data provided by the Salt Lake City Police Department (SLCPD) from 2014–2017 reveal statistically significant evidence of bias against Black and Indigenous people of color in use of force and against Black Salt Lakers in street checks (stopping pedestrians on the street).

Asian and Pacific Islander Salt Lakers, which were grouped together in SLCPD’s internal data, are statistically significantly underrepresented in both the use of force and street checks.

This study should be viewed as preliminary in light of multiple limitations and signals the need for more research into this area. Additionally, obtaining the data required the signing of a nondisclosure agreement which prevents the release of raw data; therefore, we are unfortunately unable to make raw data available.

Context

Nationally, racial bias in police use of force exploded into the national conversation in 2014 with the killings of Trayvon Martin, Michael Brown, and Eric Garner, all unarmed black men killed by police.

One year later, the Washington Post found that unarmed Black men were seven times more likely than whites to die by police gunfire.

On August 13, 2017, the Salt Lake City Police Department (SLCPD) shot and killed a Black man, Patrick Harmon, from behind while Harmon was fleeing police. This shooting sparked #BlackLivesMatter and other anti-police brutality protests in the Salt Lake City area.

Context

Our study is the first independent study of SLCPD’s use of racial profiling since \(2000\) (Deseret News, 2000). That study found no evidence of racial bias in ticketing.

SLCPD provides aggregate use of force data by race on their website (SLC Police Use of Force, 2019), but the data may not be cleaned (for example, to remove duplicates) and is limited to the last \(24\) months.

Moreover, this data is not compared to the SLC population to test for statistically significant evidence of bias.

SLCPD shared internal spreadsheets with us in order to determine whether there is evidence of bias in their use of force.

Methodology: The Skeptic’s View

Statistical tests start by assuming there is no bias (the skeptical viewpoint)

SLC’s population is \(3.3\%\) Black, so we would expect the residents against which SLCPD used force to be \(3.3\%\) Black as well (if no bias)

Pearson’s \(\chi^{2}\) test quantifies how unlikely observed use-of-force demographics are given SLC’s demographics (the expected use-of-force demographics under the skeptic’s view).

Quantifying Chance with Pearson’s Chi-Square Test

Cleaning the Data (2014)

We provide information about our analysis for \(2014\). We share provide the results, but not the methodology, of our analysis for \(2015\)-\(2017\).

We sought out and removed exact duplicates in the dataset (e.g., a subject charged with multiple crimes).

We removed entries that said “no force” or “race unknown”.

We then plotted the race distributions in our observed use of force data, which will later be compared to the American Community Survey.

Racial Distribution of Force Subjects, 2014

## race_2014
## Asian or Pacific Islander                     Black                Indigenous 
##                       353                       964                       584 
##                     White 
##                      7019

Racial Distribution of Force Subjects, 2014

Heatmap

We create a geographic heatmap of the number of use-of-force incidents in various neighborhoods of Salt Lake City. A geographic heatmap of the number of use-of-force incidents in various neighborhoods of Salt Lake City

From the heatmap, the largest number of police use-of-force incidents in Salt Lake City occurred in the area of Pioneer Park, bounded roughly by the streets 100 South and 400 South, as well as by 600 West and 300 West. In the timeframe considered for this study, this area included The Road Home shelter, which along with the park was considered a high-crime area (Salt Lake Tribune, \(2014\)).

Comparing to the ACS values

##   AAPI_ACS_2014 Black_ACS_2014 Indigenous_ACS_2014 White_ACS_2014
## 1           7.9            3.3                 1.9           74.6

Hypotheses

The null hypothesis for our dataset is that the proportion of races in our 2014 Use of Force dataset is equal to the proportion of races in the 2014 ACS. In symbols: \[ H_{0}: p_{\text{A}} = 0.079, p_{\text{B}} = 0.033, p_{\text{I}} = 0.019, p_{\text{W}} = 0.74 \]

The alternative hypothesis states that the proportion of races in our Use of Force dataset is not equal to the proportion of races in the 2014 ACS. This could mean that if any of our expected proportions are wrong, then we reject the null in favor of our alternative hypothesis.

Model Assumptions

We used a \(\chi^{2}\) model with \(4-1=3\) degrees of freedom. The three conditions that must be checked are:

  1. Independence: SLCPD’s use of force against one individual is (usually) not correlated with their use of force against another individual. (Or is it..?)
  2. Sample size / distribution: the expected counts (the number of individuals of each of the four races in the ACS) is greater than \(5\) (by far).
  3. Degrees of freedom: we have more than two degrees of freedom.

Chi-square Test Results (Use of Force 2014)

## # A tibble: 1 × 4
##   statistic p.value parameter method                                  
##       <dbl>   <dbl>     <dbl> <chr>                                   
## 1     2237.       0         3 Chi-squared test for given probabilities

The value of our 2014 \(\chi^{2}\) is 2237.4214612 with a \(p\)-value of less than \(0.0001\). We reject the null hypothesis of no difference in racial demographics. There is statistically significant evidence that the distribution of races against whom SLCPD used force is different from the distribution of races in Salt Lake City.

Residuals

The residuals measure the difference between our observed and expected counts. Negative numbers show underrepresentation, while positive numbers represent overrepresentation.

The farther the number is from 0, the more over/underrepresented a group is in our data. Residuals show a relative distance between the observed and expected values.

Black and Indigenous Salt Lakers (the latter coded as American Indians/Alaska Natives in SLCPD’s dataset) were substantially overrepresented compared to their proportion in the population. Asians were fairly underrepresented.

## race_2014
## Asian or Pacific Islander                     Black                Indigenous 
##                -16.033295                 34.054482                 27.916891 
##                     White 
##                 -6.426069

Graphing Residuals (Use of Force 2014)

Racial Distribution of Force Subjects, 2015–2017

## race_2015
## Asian or Pacific Islander                     Black                Indigenous 
##                       475                      1322                       599 
##                     White 
##                      8635

Chi-Square Test Results (Use of Force 2015-2017)

## # A tibble: 1 × 4
##   statistic p.value parameter method                                  
##       <dbl>   <dbl>     <dbl> <chr>                                   
## 1     3196.       0         3 Chi-squared test for given probabilities

The value of our \(\chi^{2}\) is 3196.2268752. The \(p\)-value is less than \(0.001\). We reject the null hypothesis of no discrimination.

Graphing Residuals (Use of Force 2015-2017)

Potential Sources of Error

We may have made a Type I error, rejecting the null hypothesis when the null hypothesis was in fact true. This would mean that there really was no difference in the proportion of races, and that the observed overrepresentation of Black and Indigenous people of color in the use of force dataset was due to chance.

Analysis of Street Checks by Race: Context

“Typically a ‘street check’ or ‘police stop’ is a practice where police stop a person in public, question them, and record their personal information in a police database. A vehicle check stop is not a street check.” (British Columbia Civil Liberties Association, 2018).

A freedom of information request in \(2017\) showed that Edmonton, Alberta, police use street checks disproportionately often against Indigenous and Black people (CBC News, 2017).

Street check data is not made public by SLCPD, but the Department shared their data with us in a collaboration to better understand evidence of potential bias.

As before, we will analyze the street check data from \(2014\) separately from the data from \(2015\)-\(2017\).

Chi-Square Test Results (Street Checks 2014)

The value of our \(\chi^{2}\) is 46.7187277. The \(p\)-value is less than \(0.001\).

The p-value calculated is exceptionally small, meaning that we reject the null in favor of the alternative hypothesis. This means that there is significant evidence that the proportions of races in our data is not equal to the model proportions in the American Community Survey.

Graphing Residuals (Street Checks 2014)

Chi-Square Test Results (Street Checks 2015-2017)

The value of our \(\chi^{2}\) is 857.9825016. The \(p\)-value is less than \(0.001\).

The p-value calculated is exceptionally small, meaning that we reject the null in favor of the alternative hypothesis. This means that there is significant evidence that the proportions of races in our data is not equal to the model proportions in the American Community Survey.

Graphing Residuals (Street Checks 2015-2017)

Limitations

Limitations

Conclusions