psychic
NYPD
MODULE 1
MODULE 2
MODULE 3
HANDOUTS

NYPD

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

By Shonda Kuiper. Contributors: Yusen He, Allie Jones, Shreyas Agrawal '24, Bowen Mince '22, Wagih Henawi '22, Adam Solar '22, Ying Long '17, Krit Petrachaianan '17, Zachary Segall '18




















Key Idea:key idea icon

Be cautious of researchers who only use graphs and data to support their preconceived ideas.

Part 2A: Introduction

Was there less racial profiling with the removal of the NYPD stop and frisk policy after the 2013 court ruling? Data doesn’t occur in isolation. For any issue as complex as racial profiling, we must consider the multiple variables affecting our data.

It was a cold Halloween night in Cleveland, Ohio, in 1963. Officer McFadden was on duty when he noticed some suspicious behavior. A few men were loitering on a street corner, repeatedly peeking in a store window. McFadden confronted them, suspecting that they were casing the joint for a robbery. When they mumbled noncommittal answers to his inquiries, McFadden grabbed them, proceeded to frisk them, and discovered a pistol.

This controversial case brought up an important question. Did McFadden have the right to follow his gut and search the men? Or did he violate their 4th Amendment rights with “unreasonable searches and seizures”? Terry v. Ohio made its way up to the Supreme Court, where it was ruled legal for officers to stop and frisk people upon suspicion of danger, even without probable cause.

Over the next few decades, many police departments increased this procedure as a preventative measure. In the 1980s, the NYPD made use of this greater freedom and began to stop and frisk more and more people. As the number of stops increased, so did complaints of police discrimination.

On August 12, 2013, a U.S. District Court in New York ruled that the stop-and-frisk practices of its police department were unconstitutional. In 2014, New York Civil Liberties Union stated that New York City Mayor Bill de Blasio had "... made stop-and-frisk reform a central issue in his campaign, and shortly after his election he moved aggressively to honor his campaign promises".8






Part 2B: What Does The Data Say?

Figure 2 shows the number of arrests by race over time. There is a substantial decrease in the number of stop and frisk searches being conducted by NYPD after 2013.

One could look at these graphs and conclude that racial profiling—at least with stop and frisk procedures, has largely been eradicated. However, before making a conclusion, we will first look at the data carefully using the interactive visualization below.



data figures



Part 2C: Explore The Data

Use the app below to make Figure 2 on your own. Then modify the graph to answer the questions.





To make a graph that looks like Figure 1A, select:

  • Y-axis variable:   Stopped
  • X-axis variable:   Race
  • X-axis measurement:   Counts
  • Choose years:   (2009-2016)
  • Facet by:   Year
  • Color by:   Race

    • To better evaulate more recent patterns in racial disparities of the NYPD stops, change "Choose all years" to (2014-2020)





    Instructors Note: Go to faculty resources to access student data





    Part 1E: Data Literacy Breakdown data literacy icon

    Before we make any conclusions about a graph or dataset, it is important to ask critical questions to determine if the data is trustworthy. How would you evaluate the data in the NYPD Bar Chart App


    a) What is the source?

    • Where is this data coming from?
    • What is the purpose of this information? Would this source have any desire to influence how people feel about this issue?
    • How was the data collected? It is reasonable to assume that the data was accurately recorded?
    • Does this data agree with other sources?

    b) What’s the context?

  • What measurements are we most interested in? Is it reasonable to assume that the available data can be used to address our questions?
  • Are the numbers saying something about an entire population or just a restricted subset of a population?
  • How was the data collected? It is reasonable to assume that the data was accurately recorded?
  • Is there any missing data, missing context, or missing information that we need to consider?
  • What do other studies show?

    • c) What assumptions are we making? It can be very easy to produce biased results even with reliable data.

  • How can we be sure that we are not simply using the data to support what we want to be true? Are we incorporating some of our own personal assumptions when drawing conclusions from this data?

    • Part 1F: Get Curiousget curious icon

    1. Which graph should be used to better understand the possible patterns of discrimination in the NYPD, Figure 1A, 1B, or both? Briefly describe how each graph can contribute to addressing Focus Question 1. How does the story change if both graphs are used?

    2. Why is it important to consider the racial distribution of the entire city when looking at these graphs?

    3. When is it important to look at multiple graphs before drawing conclusions from a dataset?

    4. In each report, a suspect is identified by the police as male, female, or unknown. Are there any clear patterns related to the gender of the suspect? Assuming a male was stopped, is he more likely to be arrested than a female? Do these patterns hold true across races? (Hint: try faceting by race.)

    5. Which crime type tends to have the most arrests each year?

    6. Develop your own question that could be answered with the above NYPD Bar Chart app. Write a one paragraph answer to your question.

  • Assume your audience already understands the source and context of the data.
  • Include one or two graphs (cut and pasted from the app above).
  • Clearly state your question, describe the variables in the graph(s), interpret your graph(s), and discuss what conclusions you are able to draw from these graphs.

    • References

    Shannon, Joel, Feb 2019, Adorable animals across the nation are making Super Bowl predictions, USA Today. https://www.usatoday.com/story/news/nation/2019/02/03/animals-predict-super-bowl-outcome/2756507002/


    Continue to Part 3

     

    Data Stories


    NYPD
    Covid-19
    Recidivism
    Brexit

    Stats Games


    Racer
    Greenhouse
    Statistically Grounded
    Psychic

    Questions?


    If you have any questions or comments, please email us at DASIL@grinnell.edu

    Dataspace is supported by the Grinnell College Innovation Fund and was developed by Grinnell College faculty and students. Copyright © 2021. All rights reserved

    This page was last updated on  November 11th  2024.