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Critical reflections on evidence-based policing
Evidence-Based Practice (EBP) has over the last decade made an increasing mark in several fields, notably health and medicine, education and social welfare. In recent years it has begun to make its mark in criminal justice. As engagement with EBP has spread, it has begun to evolve from what might be regarded as a somewhat narrow doctrine and orthodoxy to something more complex and various. Often criminological research has been at odds with the assumptions, conventions and methodologies associated with first generation EBP. In that context EBP poses a challenge to the research community and existing evidence base and is, accordingly, hotly controversial. This book is a welcome and timely contribution to current debates on evidence-based practice in policing.
Book
Indicators for estimating trends in alcohol-related assault: evaluation using police data from Queensland, Australia
Monitoring levels of alcohol-related harm in populations requires indicators that are robust to extraneous influence. We investigated the validity of an indicator for police-attributed alcohol-related assault. We summarized offence records from Queensland Police, investigated patterns of missing data, and considered the utility of a surrogate for alcohol-related assault. Of 242 107 assaults from 2004–2014, in 35% of cases the drug used by the offender was recorded as ‘unknown’. Under various assumptions about non-random missingness the proportion of assaults judged to be alcohol-related varied from 30%–65%. We found a sharp increase in missing data from 2007 suggesting the downward trend from that point is artefactual. Conversely, we found a stable and increasing trend using a time-based surrogate. The volume of missing data and other limitations preclude valid estimation of trends using the police indicator, and demonstrate how misleading results can be produced. Our analysis supports the use of an empirically-based surrogate indicator.
Journal Article
A scalable empathic supervision intervention to mitigate recidivism from probation and parole
Incarceration is a pervasive issue in the United States that is enormously costly to families, communities, and society at large. The path from prison back to prison may depend on the relationship a person has with their probation or parole officer (PPO). If the relationship lacks appropriate care and trust, violations and recidivism (return to jail or prison) may be more likely to occur. Here, we test whether an “empathic supervision” intervention with PPOs—that aims to reduce collective blame against and promote empathy for the perspectives of adults on probation or parole (APPs)—can reduce rates of violations and recidivism. The intervention highlights the unreasonable expectation that all APPs will reoffend (collective blame) and the benefits of empathy—valuing APPs’ perspectives. Using both within-subject (monthly official records for 10 mo) and between-subject (treatment versus control) comparisons in a longitudinal study with PPOs in a large US city (NPPOs
= 216; NAPPs
=∼20,478), we find that the empathic supervision intervention reduced collective blame against APPs 10 mo postintervention and reduced between-subject violations and recidivism, a 13% reduction that would translate to less taxpayer costs if scaled. Together, these findings illustrate that very low-cost psychological interventions that target empathy in relationships can be cost effective and combat important societal outcomes in a lasting manner.
Journal Article
A randomized control trial evaluating the effects of police body-worn cameras
Police body-worn cameras (BWCs) have been widely promoted as a technological mechanism to improve policing and the perceived legitimacy of police and legal institutions, yet evidence of their effectiveness is limited. To estimate the effects of BWCs, we conducted a randomized controlled trial involving 2,224 Metropolitan Police Department officers in Washington, DC. Here we show that BWCs have very small and statistically insignificant effects on police use of force and civilian complaints, as well as other policing activities and judicial outcomes. These results suggest we should recalibrate our expectations of BWCs’ ability to induce large-scale behavioral changes in policing, particularly in contexts similar to Washington, DC.
Journal Article
The Impact of Measurement Error in Regression Models Using Police Recorded Crime Rates
Objectives
Assess the extent to which measurement error in police recorded crime rates impact the estimates of regression models exploring the causes and consequences of crime.
Methods
We focus on linear models where crime rates are included either as the response or as an explanatory variable, in their original scale or log-transformed. Two measurement error mechanisms are considered, systematic errors in the form of under-recorded crime, and random errors in the form of recording inconsistencies across areas. The extent to which such measurement error mechanisms impact model parameters is demonstrated algebraically using formal notation, and graphically using simulations.
Results
The impact of measurement error is highly variable across different settings. Depending on the crime type, the spatial resolution, but also where and how police recorded crime rates are introduced in the model, the measurement error induced biases could range from negligible to severe, affecting even estimates from explanatory variables free of measurement error. We also demonstrate how in models where crime rates are introduced as the response variable, the impact of measurement error could be eliminated using log-transformations.
Conclusions
The validity of a large share of the evidence base exploring the effects and consequences of crime is put into question. In interpreting findings from the literature relying on regression models and police recorded crime rates, we urge researchers to consider the biasing effects shown here. Future studies should also anticipate the impact in their findings and employ sensitivity analysis if the expected measurement error induced bias is non-negligible.
Journal Article
Computational text analysis on unstructured police data: a scoping review
Introduction
Police reports made following attendance at various events (e.g., crashes, domestic violence, theft) often contain rich contextual details including indicators of mental health issues or abuse types, and persons/entities involved and their relationships, which are not typically captured in structured administrative data, interviews or official statistics. However, the sheer volume of information along with strict data access protocols render manual analysis impractical. Computational text analysis methods offer a feasible and effective approach to automatically process this underutilized data source.
Aim
This article is an overview of studies using computational text analysis (e.g., text mining, natural language processing (NLP)), on unstructured police data, serving as a guide for researchers interested in employing similar methodologies.
Methods
This scoping review was conducted in accordance with the PRISMA-SCR guidelines, following the two screening processes (title/abstract and full text screening) and the development of a pre-defined protocol. A search was conducted across seven electronic databases (ProQuest, IEEE Xplore, Scopus, PubMed, Web of Science, Criminal Justice Abstracts, Google Scholar) covering the past 20 years.
Results
A total of 5426 records were identified. After removing duplicate entries and screening titles/abstracts and full-text publications, 61 studies met the inclusion criteria. Included studies were published between 2004 and 2024, with most from the United States, Australia and the Netherlands. Most studies used opensource tools: Bidirectional Encoder Representations from Transformers (BERT), natural language tool kit (NLTK), scikit-learn, or General Architecture for Text Engineering (GATE) to analyze unstructured police data. Our review indicates applications of computational text analysis on unstructured police data have moderate to high performance. Common limitations included variable data quality, with reliability depending on the level of detail provided by the police report’s author, and failure to report ethical implications or methodological limitations.
Conclusions
Computational text analysis can extract key information from unstructured police data. However, future research should clearly report ethics approvals and implications, and methodological limitations. Establishing a structured data-sharing framework between law enforcement and researchers is also crucial to facilitate access and support high quality, impactful research in this field.
Journal Article
Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study
The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim's and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes.
The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events.
We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events.
In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%).
A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.
Journal Article
Modeling the Social and Spatial Proximity of Crime
Objectives
Our goal is to understand the social dynamics affecting domestic and sexual violence in urban areas by investigating the role of connections between area nodes, or communities. We use innovative methods adapted from spatial statistics to investigate the importance of social proximity measured based on connectedness pathways between area nodes. In doing so, we seek to extend the standard treatment in the neighborhoods and crime literature of areas like census blocks as independent analytical units or as interdependent primarily due to geographic proximity.
Methods
In this paper, we develop techniques to incorporate two types of proximity, geographic proximity and commuting proximity in spatial generalized linear mixed models (SGLMM) in order to estimate domestic and sexual violence in Detroit, Michigan and Arlington County, Virginia. Analyses are based on three types of CAR models (the Besag, York, and Mollié (BYM), Leroux, and the sparse SGLMM models) and two types of SAR models (the spatial lag and spatial error models) to examine how results vary with different model assumptions. We use data from local and federal sources such as the Police Data Initiative and American Community Survey.
Results
Analyses show that incorporating information on commuting ties, a non-spatially bounded form of social proximity, to spatial models contributes to better deviance information criteria scores (a metric which explicitly accounts for model fit and complexity) in Arlington for sexual and domestic crime as well as overall crime. In Detroit, the fit is improved only for overall crime. The distinctions in model fit are less pronounced when using cross-validated mean absolute error as a comparison criteria.
Conclusion
Overall, the results indicate variations across crime type, urban contexts, and modeling approaches. Nonetheless, in important contexts, commuting ties among neighborhoods are observed to greatly improve our understanding of urban crime. If such ties contribute to the transfer of norms, social support, resources, and behaviors between places, they may then transfer also the effects of crime prevention efforts.
Journal Article
Understanding Intimate Partner Violence Through Police Crime Data: Descriptive and Temporal Insights
Police crime reports are a critical but often underutilized source of information for understanding intimate partner violence (IPV). They provide systematic, population-level data on when, where, and how IPV incidents occur, complementing surveys and clinical studies. This study provides a descriptive analysis of IPV crime reports in Los Angeles (January 2020–December 2023) and models temporal trends using Seasonal Autoregressive Integrated Moving Average (SARIMA). Results showed that a total of 74,776 IPV-related incidents were reported to the LAPD in the four-year period, averaging 51.22 incidents per day (SD = 10.84). Most incidents occurred in residential settings (71.9%), followed by public spaces (18.2%) and transportation settings (6.5%). Females accounted for the majority of incidents (77.35%) compared to males (22.65%), and Physical IPV was the most frequently reported subtype (77.0%). Of these Physical IPV reports, most incidents did not involve a weapon (83.82%), while the use of firearms, bladed weapons, blunt objects, and improvised implements was relatively uncommon. Temporal modeling using SARIMA indicated that month-to-month variation was dominated by stable seasonal and autoregressive dynamics, with no evidence of a distinct pandemic-specific shift in call volume. By integrating descriptive and temporal analyses, the study offers actionable insights for public health, law enforcement, and community organizations working to prevent and respond to IPV.
Journal Article
A National Examination of the Spatial Extent and Similarity of Offenders’ Activity Spaces Using Police Data
It is well established that offenders’ routine activity locations (nodes) shape their crime locations, but research examining the geography of offenders’ routine activity spaces has to date largely been limited to a few core nodes such as homes and prior offense locations, and to small study areas. This paper explores the utility of police data to provide novel insights into the spatial extent of, and overlap between, individual offenders’ activity spaces. It includes a wider set of activity nodes (including relatives’ homes, schools, and non-crime incidents) and broadens the geographical scale to a national level, by comparison to previous studies. Using a police dataset including n = 60,229 burglary, robbery, and extra-familial sex offenders in New Zealand, a wide range of activity nodes were present for most burglary and robbery offenders, but fewer for sex offenders, reflecting sparser histories of police contact. In a novel test of the criminal profiling assumptions of homology and differentiation in a spatial context, we find that those who offend in nearby locations tend to share more activity space than those who offend further apart. However, in finding many offenders’ activity spaces span wide geographic distances, we highlight challenges for crime location choice research and geographic profiling practice.
Journal Article