NYPD Argumentation Activity

Objective

• Combine data analysis and the context behind that data to develop an effective argument.

Background

On August 12th, 2013, US District Court Judge Shira Scheindlin ruled in the case Floyd vs. City of New York that the stop-and-frisk policy used by the New York City Police Department (NYPD) was unconstitutional. The decision was a culmination of years of public outcry against perceived racial profiling by the NYPD.

Stop-and-frisk policies were first implemented in New York as a means of reducing crime rates, with proponents arguing that preventing smaller crimes leads to less escalation and fewer violent crimes (an idea commonly referred to as broken windows theory). Opponents accused the NYPD of racial discrimination in their stops. The NYPD defended their practices, claiming that most crimes occur in predominately Black neighborhoods, so it makes sense for a higher proportion of Black individuals to be stopped.

Prior to the 2013 District Court ruling, a record 685,724 stops occurred in 2011, the majority of which involved Black and Hispanic individuals. After the ruling, a mandate was passed that required officers to thoroughly justify their reasons for making a stop. By the end of 2013, the number of stops had fallen to 22,929, or roughly 3% of the total number of stops a few years earlier.

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Activity

Open the R Shiny app available at the following link:

• https://shiny.grinnell.edu/NYPD_BarCharts/

This app displays summary graphs of data from New York public records on police stops in New York City. The source data is available at the NYC website: https://www.nyc.gov/site/nypd/stats/reports-analysis/stopfrisk.page

A few definitions you should be aware of are:

• Stopped: indicates an individual was stopped by the NYPD
• Frisked: individuals who are stopped may be frisked (patted down)
• Searched: individuals who are stopped may be searched, searches are more invasive than frisks, and may occur if a frisk results in any suspicion of illicit activity
• Arrested: if evidence of illegal activity is found during a frisk or a search an arrest can be made
• Force: if force was used by the officer(s) during a stop

Additional definitions and information about the app can be found at this link

Using the app’s drop down menus, sliders, and tabs, answer the following questions. These questions are structured to give you a sense of different ways to utilize these data as evidence/support.

• Question 1: According to a study using data from the late 1990s (Gelman, Fagan, Kiss, 2012) [1], Blacks and Hispanics represented 51% and 33% of NYPD stops, while, at the time, making up only 26% and 24% of the New York City population (respectively). As of the 2010 Census, the population of New York City was 33.3% non-Hispanic white, 23% non-Hispanic black, 0.6% Native American, 12.7% Asian, 28.6% Hispanic (of any race), and 1.8% other races. Are the trends found by Gelman et al (2012), still present in 2006-2016? Estimate the percentage of stops involving Black and Hispanics individuals using the information from the app.

• Question 2: Is the census data important? That is, would it be accurate to look only at the counts of who was stopped?

• Question 3: Choose year in the Facet By menu. What trends do you see in the number of individuals stopped from 2006-2016? What happened to the numbers of individuals stopped after 2012?

• Question 4: Change the y-variable to Frisked, how does the trend compare to what you noticed in Question 3? (considering 2012 in particular).

• Question 5: Change the y-variable to Arrested, how does the trends you observed in Stopped and Frisk? (again paying special attention to 2012).

• Question 6: In a 2007 study, Ridgeway [2] claims “Non-whites generally experienced slightly more intrusive stops, in terms of having more frequent frisks and searches than similarly situated white suspects”. Using the app to look at both frisks and searches as a percentage of stops, is this claim verified? Why does the Y-axis measurement need to be the percentage of stops, rather than just the raw counts, to evaluate this claim? Note: you should reset the Facet option to “none” for this question.

• Question 7: Keeping the Y-axis set to percentages, facet the graph by year. Have there been any changes over time in the proportion of stops that result in frisks and searches? What might be driving these changes?

• Question 8: Considering everything you’ve seen in Questions 1-7, what stands out to you the most? Limit your answer to 1-2 sentences.

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Assignment

Individually (in consultation with your partner for ideas, but not writing) draft a paragraph that briefly introduces the stop and frisk policy and argues either against the legitimacy or in favor of the legitimacy of the policy using data as your core reasoning and evidence.

• You should take a screenshot of 1-2 graphics created by the R Shiny app from this activity to support your paragraph.
• You should outline the central claim, reasoning/rationale, and evidence/support before you begin your paragraph.
• You should consider utilizing the strategy discussed in today’s reading

On Thursday, we will exchange these paragraphs and critique them with a focus on improving clarity, structure, and conciseness.

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References

• [1] Gelman, Fagan, & Kiss. (2012). An Analysis of the New York City Police Department’s Stop and Frisk Policy in the Context of Claims of Racial Bias, Retrieved from http://www.stat.columbia.edu/~gelman/research/published/frisk9.pdf
• [2] Ridgeway. (2007). Analysis of Racial Disparities in the New York Police Department’s Stop, Question, and Frisk Practices. Retrieved from http://www.rand.org/pubs/technical_reports/TR534.html

Acknowledgements

• The R Shiny app used in this activity was developed by Shonda Kuiper and some questions were inspired by the NYPD dataspace page Other contributors include: 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