Project Abstract

Mass incarceration is a well-recognized public health issue and driver of racial inequities. However, focusing on arrests and subsequent incarceration underestimates the totality of police-community member interactions, and risks obfuscating the full magnitude of disproportionate policing within a city. This project will use publicly available NYPD policing data on multiple endpoints of policing (e.g., arrests, desk appearance tickets, criminal summons) to construct a geographic visualization tool to assess the burden of policing across neighborhoods and time in NYC. Through data linkages with the American Community Survey, this tool will allow for the investigation of predictors of over policing within NYC.

Project Description & Overview

The relationship between communities and police has received a lot of attention following multiple high-profile fatal police shootings of unarmed people of color. The disproportionate burden of police violence and the over-representation of Black and Brown people in correctional facilities has raised large questions regarding the unequal policing of communities. Though some investigators have begun to interrogate the impacts of over-policing, few include the broader set of police interactions not captured by arrests. Furthermore, current data structures severely hinder progress towards understanding variations in exposure to policing across time and place. Understanding how policing varies across communities is a critical first step in reducing the burden of over-policing among those disproportionately impacted. The goals of this project are to a) assess geographic and temporal variation of policing in NYC by constructing a geocoded mapping tool of policing interactions, and b) identify predictors of variation in policing burden by constructing predictive models using data from the American Community Survey.

Participating students will create a single, column-oriented database of publicly available NYPD policing data (from 2013 – 2021) on a range of policing end points (e.g., arrests, court summons, desk appearance tickets). Students will geocode the data and create an online mapping tool that will illustrate variations in policing burden across NYC. Finally, students will be asked to link the database to publicly available data from the American Community Survey and build predictive models to identify geographic and sociodemographic predictors of increased policing burden in New York City.

Datasets

The primary data for this project will be a composite of multiple publicly available NYPD policing datasets. NYPD historic arrests data contains over 5 million individual arrests with date and location data ranging from 2006 to 2020. Additional datasets provide analogous information on cannabis court summons, desk appearance tickets, criminal summons, and stop-question-frisk incidents. Metadata is available for each indicator at the precinct and quarter level for the first three quarters of 2021. Policing data will be linked to census data from the American Communities Survey, including but not limited to racial/ethnic composition, poverty, etc.

Competencies

The ideal student for this project would have strong data science and programming skills, specifically as it relates to geocoding data and building a dashboard and/or web tools (e.g., R shiny apps). Additionally, students should have basic analytic skills and understanding of predictive model building. Lastly, experience with data visualization and spatial analysis will be especially useful for building the map-based web tool.

Learning Outcomes & Deliverables

There are three expected deliverables that will result from this project.

1. Creation of mapping/visualization tools showing the geographic distribution of manifestations of NYPD police interactions (e.g., arrests, cannabis court summons, criminal summons, desk appearance tickets, stop-question-frisks) at varying levels of granularity in NYC (e.g., precinct, census block).
2. Creation of a column-oriented database to store and access complied NYPD policing data linked to data from the American Community Survey.
3. An analytic model identifying geographic and sociodemographic predictors of increased exposure to policing in NYC.

Students

Eaverine Fu, Yiou Wang, Yuxin Zhao