A Multi-Method Data Science Pipeline for Analyzing Police Service

Abstract

Despite the fact that most police departments in the U.S. serve jurisdictions with fewer than 10,000 residents, policing practices in small towns are understudied. This is due in part to data limitations and technological barriers that exist in the small-town context. In this article we focus on one small town police department in New England with a history of misconduct, and develop a comprehensive data science pipeline that addresses the stages from design and collection to reporting. We present the reader with specific tools in the open-source Python ecosystem for replicating this pipeline. Once these data are processed, we perform two statistical analyses in an attempt to better understand the provisions of service by the small-town police department of focus. First, we perform ecological inference to estimate the rate at which residents are placing calls for service. Second, we model wait times using a negative binomal regression model to account for overdispersion in the data. We discuss data and model limitations arising through the pipeline creation and analysis process.