Re-assessing measurement error in police calls for service: Classifications of events by dispatchers and officers

Police calls for service are an important conduit by which officers and researchers can obtain insight into public requests for police service. Questions remain, however, about the quality of these data, and, particularly, the prevalence of measurement error in the classifications of events. As part of the present research, we assess the accuracy of call-types used by police dispatchers to describe events that are responded to by police officers. Drawing upon a sample of 515,155 calls for police service, we explore the differences among initial call-types, cleared call-types, and crime-types as documented in crime reports. Our analyses reveal that although the majority of calls for service exhibit overlap in their classifications, many still exhibit evidence of misclassification. Our analyses also reveal that such patterns vary as a function of call- and crime-type categories. We discuss our findings in light of the challenges of the classification process and the associated implications.

Are sex offending allegations viewed differently? Exploring the effect of offense type and conviction status on criminal stigmatization

Attitudes toward individuals with sexual convictions play a major role in the formation of legislative action, including sentencing policies and registration and notification procedures. However, there is little research about stigmatization directed at those who are accused of such offenses prior to conviction. In this work we explored this gap by comparing stigmatization towards people accused of sexual offending in comparison to other offense categories, and whether this was further impacted by whether allegations led to a conviction. We recruited 403 community-based participants for a between-subjects experimental survey. We found support for the conclusion that people accused of and convicted for sexual offenses are more heavily stigmatized than allegations related to other crime types, and especially so when allegations involved child victims. Stigmatization took the form of greater levels of support for police-initiated notifications about allegations before conviction, increased desires for social distance, and attributions of negative personality traits. We discuss the theoretical and applied implications of these findings in relation to stigma research and issues related to anonymity for those accused of sexual offenses.

Local Alcohol Policies in New Zealand: An overview of their implementation and effects on crime

We document the implementation of local alcohol policies in New Zealand and then study their impacts on crime. A key contribution of our study is that we construct a detailed data set on local alcohol policies applicable across territorial authorities between July 2014 and January 2019. To our knowledge, we are the first ones to provide such a comprehensive overview. In a subsequent analysis, we find that local alcohol policies as recently implemented in New Zealand do not appear to have reduced crime. This result holds for specific policy dimensions and their stringency (e.g., closing times and geographic restrictions on issuing new licences), and is reasonably robust across crime types, days/times of occurrence, and socioeconomic subgroups. Our failure to identify significant reductions in crime following the imposition of local alcohol policies may partly reflect the policies being non-binding in some cases: for example, licensed premises had sometimes already operated within the restricted trading hours specified by a local alcohol policy.

Racial animus and its association with punitive sentencing and crime types: Do Australian community attitudes reflect the United States’?

The racial animus model argues that public support for punitive sentencing of criminal offenders is shaped by threat perceptions associated with cultural minority groups. This study applies the racial animus model to examine support for the punitive sentencing of criminal offenders in the United States and Australia. It also examines whether racial animus mediates the possible difference in punitive attitudes between each country toward different crime types. Online survey data were obtained in the US and Australia to assess racial animus and punitive attitudes using six different crime scenarios. Results indicate that (a) individuals with higher levels of racial animus demonstrate greater levels of punitiveness; (b) Australians have higher levels of racial animus as compared to their US counterparts; and (c) racial animus mediates the difference of punitive attitudes between the two countries. Overall, punitive attitudes and racial animus vary cross-culturally, with Australians demonstrating more racial animus. Our mediation model provides evidence for the importance of racial animus in the cross-cultural demand for the punishment of criminal offenders.

Prescriptive Analytics in Urban Policing Operations

Problem definition: We consider the case of prescriptive policing, that is, the data-driven assignment of police cars to different areas of a city. We analyze key problems with respect to prediction, optimization, and evaluation as well as trade-offs between different quality measures and crime types. Academic/practical relevance: Data-driven prescriptive analytics is gaining substantial attention in operations management research, and effective policing is at the core of the operations of almost every city in the world. Given the vast amounts of data increasingly collected within smart city initiatives and the growing safety challenges faced by urban centers worldwide, our work provides novel insights on the development and evaluation of prescriptive analytics applications in an urban context. Methodology: We conduct a computational study using crime and auxiliary data on the city of San Francisco. We analyze both strong and weak prediction methods along with two optimization formulations representing the deterrence and response time impact of police vehicle allocations. We analyze trade-offs between these effects and between different crime types. Results: We find that even weaker prediction methods can produce Pareto-efficient outcomes with respect to deterrence and response time. We identify three different archetypes of combinations of prediction methods and optimization objectives that constitute the Pareto frontier among the configurations we analyze. Furthermore, optimizing for multiple crime types biases allocations in a way that generally decreases single-type performance along one outcome metric but can improve it along the other. Managerial implications: Although optimization integrating all relevant crime types is theoretically possible, it is practically challenging because each crime type requires a collectively consistent weight. We present a framework combining prediction and optimization for a subset of key crime types with exploring the impact on the remaining types to support implementation of operations-focused smart city solutions in practice.

Social Research about Online Crime: Global Range of Topics and a Systematic Analysis of Research in Lithuania

Social research about online crime is a multi-disciplinary field addressing a wide array of topics since its inception in the 1980s. Based on a broad review of state-of-the-art literature and gaps identified in review publications (Holt, Bossler 2014; Stratton, Powell, Cameron 2017; Maimon, Louderback 2019, and others), in this paper I outline 41 key topic in social research about online crime, classified into four broad categories: 1) research focusing on specific types of online crime, 2) research about perpetrators, victims, and law enforcement, 3) research about online crime discourses and public perceptions, 4) research putting the local and global specifics of online crime into perspective. Based on the topic map, I undertook a systematic review of literature on research about online crime published in Lithuania from the empirical social scientific perspective. The results show that very few such studies are carried out in Lithuania. From 2004 to 2020, 26 publications have been found in total. 10 of them were theoretical briefs, while 16 were based on empirical data. Out of the 41 key topic, 14 were covered in the publications, while 29 or roughly two thirds remained unaddressed. The dominant contributors were legal scholars writing about the social aspects of online crime across a variety of topics, and mostly focusing on specific crime types. The most developed topic was cyberbullying, with contributions by scholars mostly from the fields of psychology and education. To fill in these glaring gaps, it is vital to develop this field of research with an emphasis on both wider and deeper research agendas, complex, valid and reliable research data and critical theoretical approaches, inviting systematic contributions from criminology, sociology, communication and media studies, and political science.

Examining Deep Learning Architectures for Crime Classification and Prediction

In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction.

Performing unbelonging in court. Observations from a transnational corporate bribery trial—a dramaturgical approach

This article presents a study of the theatrical performance in court in a high-profile transnational corporate bribery case. Data gathered from observations in court were supplemented with interviews with the defense teams and the presiding judge. The paper’s objective is to demonstrate how the defendants performed unbelonging in court via the interactions between the different ‘teams’ in the courtroom; the defense, the prosecutors, the judges, and the company Telia. The analysis draws on Goffman’s theater analogy and his understandings of performance and self-presentation. The authors introduce the concept of ‘performing unbelonging in court’ and show how the defendants performed unbelonging via an indirect expression of status and standing. The authors further analyze the defense teams’ framing of the case, including the framing of the defendants as being scapegoated by the corporation, and of the prosecution as being unfair and illegitimate, which also contributes to the performance of unbelonging in court. The complexity and ambiguity of a transnational corporate bribery case permeated the interactions during the trial; however, at the end of the article, the authors discuss how knowledge from this case is transferable to other crime types or legal cultures.

The Socioeconomic Covariates of Violent and Property Crime Rates in Azerbaijan

The majority of the studies exploring the relationship between socioeconomic factors and crime levels are confined to major industrialized nations. As a post-Soviet, transitional economy with a predominantly Muslim population, Azerbaijan provides a different setting to explore how socioeconomic indicators affect police-recorded violent and property crime levels across cities and districts. This study finds a positive relationship between GRP per capita, the proportion of pupils admitted to university and population size property crime levels. The relationship was linear in all cases. The geographical units with more social benefit (pensions, disability, and family care) recipients had lower acquisitive crime levels, though the significance was marginal. The higher the number of targeted social assistance recipients for poverty alleviation is, the higher the rate of violent crime is, which differs from the findings of similar previous studies. Overall, socioeconomic predictors were significantly better in explaining variations for offences against the property (r=.481) than violent crimes (r=.073). These findings suggest that different crime types are better explained by different economic indicators in the Azerbaijani context. Furthermore, the study shows that most of the covariates function in ways which are observed in the societies covered by the literature.