Crime distortion within the NYPD: a potential method for estimating crime misclassification within CompStat statistics. (25th May 2021)

• Record Type: Journal Article
• Authors: Thomas, Amanda L.; Wolff, Kevin T.
• Is Part Of: Police practice & research. Volume 22:Number 4(2021)
• Page Start: 1390
• Page End: 1407
• Publication Date: 2021-05-25
• Subjects: CompStat -- NYPD -- crime distortion -- group-based trajectory modeling (GBTM) -- multinomial logistic regression (MNLR); Police -- Periodicals; Police administration -- Periodicals; Law enforcement -- Periodicals; Crime prevention -- Periodicals; 363.2
• DOI: 10.1080/15614263.2020.1798236
• Languages: English
• ISSNs: 1561-4263

Abstract

ABSTRACT: Since the advent of CompStat in 1994, the NYPD contends that felony crimes have decreased throughout NYC, specifically within the seven-major felony index crime categories. Previous work from Eterno and Silverman has suggested that the scope of the decrease was duplicitous and exaggerated due to the department's widespread practice of crime distortion. However, previous research has never attempted to quantify this phenomenon. This study addresses a critical gap within the existing CompStat literature by attempting to capture the magnitude of crime distortion. Using secondary crime data from the NYPD, the current study will examine burglaries and the suspected downward misclassifications, occurring within all NYC precincts, at the aggregate level, during the period between 2000 and 2013. The goals of this research are to identify and summarize any precinct-level patterns of potential crime distortion using semi-parametric group-based trajectory modeling and multinomial logistic regression techniques.