Student Spotlight: Racial Bias in the NYPD Stop-and-frisk Policy

Donald Trump recently came out in favor of an old New York Police Department’s (NYPD) “stop-and-frisk” policy that allowed police officers to stop, question and frisk individuals for weapons or illegal items. This policy was under harsh criticism for racial profiling and was declared in violation of the constitution by a federal judge in 2013.

An earlier post by Krit Petrachainan showed a potential racial discrimination against African-Americans within different precincts. Expanding on this topic, we decided to look at data in 2014, one year after the policy had been reformed, but when major official policy changes had not yet taken place.

More specifically, this study examined whether race (Black or White) influenced the chance of being frisked after being stopped in NYC in 2014 after taking physical appearance, population distribution among different race suspect, and suspected crime types into account.

2014 Data From NYPD and Study Results

For this study, we used the 2014 Stop, Question and Frisked dataset retrieved from the city of New York Police Department. After data cleaning, the final dataset has 22054 observations. To address our research question, we built a logistic regression model and ran a drop-in-deviance test to determine the importance of Race variable in our model.

Our results suggest that after the suspect is stopped, race does not significantly influence the chance of being frisked in NYC in 2014. A drop-in-deviance tests after creating a logistic regression model predicting the likelihood of being frisked gave a G-statistic of 8.99, and corresponding p-value of 0.061. This marginally significant p-value shows we do not have enough evidence to conclude that adding terms associated with Race improves the predictive power of the model.

Logistic regression plot predicting probability of being frisked from precinct population Black, compared across race

Figure 1. Logistic regression plot predicting probability of being frisked from precinct population Black, compared across race

To better visualize the relationship in interactions between race and other variables, we created logistic regression plots predicting the probability of being frisked from either Black Pop or Age, and bar charts comparing proportion of suspects frisked across sex and race.

Interestingly, given that the suspects are stopped, as the precinct proportion of Blacks increases, both Black and White suspects are more likely to be frisked. Furthermore, this trend is more profound for Black than White suspects (Figure 1).

Additionally, young Black suspects are much more likely than their White counterparts to be frisked, given that they are stopped. This difference diminishes as suspect age increases (Figure 2).

Logistic regression plot predicting probability of being frisked from age, compared across age

Figure 2. Logistic regression plot predicting probability of being frisked from age, compared across age

Finally, male suspects are much more likely to be frisked than females, given they are stopped (Figure 3). However, the bar charts indicate that the effect of race on the probability of being frisked does not depend on gender.

Proportion frisked by race, compared across sex

Figure 3. Proportion frisked by race, compared across sex

Is stop-and-frisk prone to racial bias?

Our results suggest that given that the suspect is stopped, after taking other external factors into account, race does not significantly influence the chance of being frisked in NYC in 2014. However, after looking at relationships between race and precinct population Black, age, and sex, there is a possibility that the NYPD “stop-and-frisk” practices are prone to racism, posing threat to minority citizens in NYC. It is crucial that the NYPD continue to evaluate its “stop-and-frisk” policy and make appropriate changes to the policy and/or police officer training in order to prevent racial profiling at any level of investigation.

*** This study by Linh Pham, Takahiro Omura, and Han Trinh won 2nd place in the 2016 Undergraduate Class Project Competition (USCLAP).

Check out the 2016 USCLAP Winners here.

Exploring Racial Disparities in New York City’s Stop-and-Frisk Policies


A comparison of the two maps above yields a surprising conclusion: African-Americans are much less likely to be arrested in areas with higher African-American populations!

One of the best examples of the use of statistics in policy research is in the controversy about New York City’s Stop-Question-and-Frisk policies, which give police officers the right to stop, search, or arrest any suspicious person with reasonable grounds for action. These policies were an effort to reduce crime rates, under the philosophy that stopping suspicious persons will prevent smaller crimes from escalating into more violent ones. In recent years, the NYPD had been under fire for alleged racial discrimination in their stops. Research on approximately 175,000 stops from January 1998 through March 1999, for example, showed that Blacks and Hispanics represented 51% and 33% of the stops, while only representing 26% and 24% of the New York City population respectively. The NYPD defended their practices, saying that since crimes mostly happen in black neighborhoods, it is natural that more black people would be found suspicious of crimes.

Using the stop-and-frisk dataset provided by the NYPD and 2010 census data, numbers were compiled into an interactive heat map of arrests directly related to the stop-and-frisk policy in New York City, as an aid to visualizing this disparity in race.

For each precinct, the visualization allows you to compare the racial make-up of the population with the proportion of arrests by race. For instance, this shows that in Precinct 104 while less than 2 % of the population in this precinct is African-Americans, over 15% of the arrests were of African-Americans.



Evidence of racial disparity is clear.  African-Americans are consistently overrepresented in arrests compared to the population in each precinct.

The exception to this trend which was alluded to at the beginning of the post:  in areas with high African-American populations, the disparity disappears, and even reverses in a few precincts! Thus African-Americans are much less likely to be arrested in neighborhoods with high African-American populations.

Use this visualization to explore the trends in arrests due to the stop-and-frisk policies in New York:

Another visualization on stops and arrests in New York City can be accessed here. You can also go here for more information on these data visualizations.  These visualizations were created as part of a Grinnell College Mentored Advanced Project with Ying Long and Zachary Segall under the direction of Shonda Kuiper.