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<p>Even though the New York Police Department (NYPD) reform in 2013 led to a substantial reduction in the total number of stops, unnecessary stops and weapon use against innocent citizens remain critical issues. This study analyzes stop-and-frisk records during 2014 – 2019 using tree-based machine learning approaches along with logistic regression and Multi-Layer Perceptron (MLP) models, in order to discover patterns and insights. By developing predictive models for both suspect convictions and the level of force applied by police, this study provides a basis for a discussion whether weapon usage aligns with indicators of guilt or conviction. Findings show that XGBoost outperforms other machine learning techniques in predicting both conviction and the level of force used. Key factors associated with a suspect’s conviction include weapon possession, carrying suspicious objects, and trespassing. However, an excessive number of unnecessary stops appear to be associated with inaccurate assumptions about suspects’ weapon possession, which are also linked to police gunfire against innocent citizens. Refining suspicion criteria for Criminal Possession of Weapon and suspect actions could help reduce unnecessary stops and excessive force.</p>

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Understanding unnecessary stops and police use of force in NYPD Stop, Question, and Frisk with machine learning techniques

  • 期刊论文

  • 发表日期：

    2025年3月9日

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  • Passiri Bodhidatta,
  • Daricha Sutivong

摘要

Even though the New York Police Department (NYPD) reform in 2013 led to a substantial reduction in the total number of stops, unnecessary stops and weapon use against innocent citizens remain critical issues. This study analyzes stop-and-frisk records during 2014 – 2019 using tree-based machine learning approaches along with logistic regression and Multi-Layer Perceptron (MLP) models, in order to discover patterns and insights. By developing predictive models for both suspect convictions and the level of force applied by police, this study provides a basis for a discussion whether weapon usage aligns with indicators of guilt or conviction. Findings show that XGBoost outperforms other machine learning techniques in predicting both conviction and the level of force used. Key factors associated with a suspect’s conviction include weapon possession, carrying suspicious objects, and trespassing. However, an excessive number of unnecessary stops appear to be associated with inaccurate assumptions about suspects’ weapon possession, which are also linked to police gunfire against innocent citizens. Refining suspicion criteria for Criminal Possession of Weapon and suspect actions could help reduce unnecessary stops and excessive force.