Crime Science最新文献

Objectives

To explore spatial patterns of crime in a small northern city, and assess the degree of similarity in these patterns across seasons.

Methods

Calls for police service frequently associated with crime (theft, break and enter, domestic dispute, assault, and neighbor disputes) were acquired for a five year time span (2015–2019) for the city of North Bay, Ontario, Canada (population 50,396). Exploratory data analysis was conducted using descriptive statistics and a kernel density mapping technique. Andresen’s spatial point pattern test (SPPT) was then used to assess the degree of similarity between the seasonal patterns (spring, summer, autumn, winter) for each call type at two different spatial scales (dissemination area and census tract).

Results

Exploratory data analysis of crime concentration at street segments showed that calls are generally more dispersed through the city in the warmer seasons of spring and summer. Kernel density mapping also shows increases in the intensity of hotspots at these times, but little overall change in pattern. The SPPT does find some evidence for seasonal differences in crime pattern across the city as a whole, specifically for thefts and break and enters. These differences are focused on the downtown core, as well as the outlying rural areas of the city.

Conclusions

For the various crime types examined, preliminary analysis, kernel density mapping, and the SPPT found differences in crime pattern consistent with the routine activities theory.

Research suggests that stalking inflicts great psychological and financial costs on victims. Yet costs of victimisation are notoriously difficult to estimate and include as intangible costs in cost–benefit analysis. This study reports an innovative cost–benefit analysis that used focus groups with multi-agency teams to collect detailed data on operational resources used to manage stalking cases. This method is illustrated through the presentation of one case study. Best- and worst-case counterfactual scenarios were generated using the risk assessment scores and practitioner expertise. The findings suggest that intervening in high-risk stalking cases was cost-beneficial to the state in all the case studies we analysed (even if it incurs some institutional costs borne by the criminal justice system or health) and was often cost-beneficial to the victims too. We believe that this method might be useful in other fields where a victim- or client-centred approach is fundamental.

Background

The 2021 NIJ recidivism forecasting challenge asks participants to construct predictive models of recidivism while balancing false positive rates across groups of Black and white individuals through a multiplicative fairness score. We investigate the performance of several models for forecasting 1-year recidivism and optimizing the NIJ multiplicative fairness metric.

Methods

We consider standard linear and logistic regression, a penalized regression that optimizes a convex surrogate loss (that we show has an analytical solution), two post-processing techniques, linear regression with re-balanced data, a black-box general purpose optimizer applied directly to the NIJ metric and a gradient boosting machine learning approach.

Results

For the set of models investigated, we find that a simple heuristic of truncating scores at the decision threshold (thus predicting no recidivism across the data) yields as good or better NIJ fairness scores on held out data compared to other, more sophisticated approaches. We also find that when the cutoff is further away from the base rate of recidivism, as is the case in the competition where the base rate is 0.29 and the cutoff is 0.5, then simply optimizing the mean square error gives nearly optimal NIJ fairness metric solutions.

Conclusions

The multiplicative metric in the 2021 NIJ recidivism forecasting competition encourages solutions that simply optimize MSE and/or use truncation, therefore yielding trivial solutions that forecast no one will recidivate.

Poaching is the most direct threat to the persistence of Amur tigers. However, little empirical evidence exists about the modus operandi of the offenders associated with this wildlife crime. Crime science can aid conservation efforts by identifying the patterns and opportunity structures that facilitate poaching. By employing semi-structured interviews and participants observation with those directly involved in the poaching and trafficking of Amur tigers in the Russian Far East (RFE), this article utilizes crime script analysis to break down this criminal event into a process of sequential acts. By using this framework, it is possible account for the decisions made and actions taken by offenders before, during and after a tiger poaching event, with the goal of identifying weak points in the chain of actions to develop targeted intervention strategies. Findings indicate poaching is facilitated by the ability to acquire a firearm, presence of roads that enable access to remote forest regions, availability of specific types of tools/equipment, including heat vision googles or a spotlight and a 4 × 4 car, and a culture that fosters corruption. This crime script analysis elucidates possible intervention points, which are discussed alongside each step in the poaching process.

The Syrian Civil War created an opportunity for increased trafficking of antiquities and has resulted in a renewed awareness on the part of a global audience. The persistence of criminal and organisational networks which facilitate antiquities trafficking networks (ATNs) has been recognised as significant, leading to increased interest in the development of new and improved methods of understanding such networks. While this field of research has traditionally been dominated by relevant areas such as archaeology, law, art and museum studies, there is a noticeable gap in crime prevention research. This paper presents a crime script of Syrian antiquities trafficking networks during the Syrian Civil War which has been generated from open source journalistic data. In creating a broad crime script for such a prevalent issue, this paper aims to demonstrate the need for further crime script analysis and specifically crime prevention research more generally within the study of antiquities trafficking.

Expected crime rates that enable police forces to contrast recorded and anticipated spatial patterns of crime victimisation offer a valuable tool in evaluating the under-reporting of crime and inform/guide crime reduction initiatives. Prior to this study, police forces had no access to expected burglary maps at the neighbourhood level covering all parts of England and Wales. Drawing on analysis of the Crime Survey for England and Wales and employing a population terrain modelling approach, this paper utilises household and area characteristics to predict the mean residential burglary incidences per 1000 population across all neighbourhoods in England and Wales. The analysis identifies distinct differences in recorded and expected neighbourhood burglary incidences at the Output Area level, providing a catalyst for stimulating further reflection by police officers and crime analysts.