Navigating a New Political Landscape: View real-time updates about the impact of and Northeastern’s response to recent political changes.
Apply

Graduate Research Assistant - Exploring Officer Patrol Behaviors Using Automated Vehicle Locator and Body-Worn Camera Data

We are seeking a motivated Research Assistant to support a mixed-methods research project analyzing police patrol activity and police-citizen interactions in Kansas City, MO. Key responsibilities include: asssisting in the processing and analysis of AVL data to classify officer patrol behaviors (e.g., proactive vs. reactive policing, driving patterns, and response types), conducting systematic social observation (SSO) of BWC footage to assess officer-citizen interaction, organizing and managing large datasets, ensuring accuracy and confidentiality, and collaborating with the research team to synthesize findings and contribute to reports. Ideal candidates will have experience with quantitative and qualitative data analysis, strong attention to detail, and an interest in policing or criminal justice research.

  • Location:

    Boston

  • Semester:

    Summer 2025

Application

Doctoral Program
Current year in program
Doctoral Program Status
Accepted file types: pdf, Max. file size: 250 MB.
Attach a PDF of your resume here
This field is hidden when viewing the form
This field is hidden when viewing the form

  • Project Title

    Exploring Officer Patrol Behaviors Using Automated Vehicle Locator and Body-Worn Camera Data

  • Faculty / Project Lead

    Eric Piza

  • Project Description

    This project is a mixed-methods research project that leverages automated vehicle locator (AVL) data and body-worn camera (BWC) video to analyze police patrol activity and police-citizen interactions in Kansas City, MO over a one-year period. We will first operationalize daily patrol officer travel patterns from AVL data, specifically focusing on committed vs. uncommitted time, proactive vs. reactive deployment, driving speed, single vs. multiple-vehicle responses, and stationary vs. in-motion presence. We will then select a stratified random sample of incidents for further analysis through systematic social observation (SSO) of associated BWC video.

  • Qualifications Necessary

    experience working with large datasets, proficiency in ArcGIS Pro, proficiency in R or Stata, experience conducting systematic social observation research, strong writing skills

  • Hours per Week

    20 Hour Position