Crime Science最新文献

Certain places generate inordinate amounts of crime and disorder. We examine how places differ in their nature of crime and disorder, with three objectives: (1) identifying a typology of profiles of crime and disorder; (2) assessing whether different forms of crime and disorder co-locate at parcels; and (3) determining whether problematic parcels explain crime and disorder across neighborhoods. The study uses 911 and 311 records to quantify physical and social disorder and violent crime at residential parcels in Boston, MA (n = 81,673). K-means cluster analyses identified the typology of problematic parcels and how those types were distributed across census block groups. Cluster analysis identified five types of problematic parcels, four specializing in one form of crime or disorder and one that combined all issues. The second cluster analysis found that the distribution of problematic parcels described the spectrum from low- to high-crime neighborhoods, plus commercial districts with many parcels with public physical disorder. Problematic parcels modestly explained levels of crime across neighborhoods. The results suggest a need for diverse intervention strategies to support different types of problematic parcels; and that neighborhood dynamics pertaining to crime are greater than problematic properties alone.

This article explores the routine precautions taken by sex workers (SW) in Switzerland, a country in which sex work is a legal activity. It is based on approximately 1100 h of non-systematic participant observation spread over 18 months and 14 semi-structured interviews with indoor and outdoor SW. The findings show that SW use a series of routine precautions that overlap with the situational prevention techniques for increasing perpetrators’ efforts or their perception of the risk of offending, reducing the rewards of the crime, and decreasing the provocations and perpetrators’ excuses. Future tests of the efficacy of these routine precautions could help developing specific situational crime prevention techniques for deterring offences against SW.

Objective

To address the gap in the literature and using a novel open-source intelligence web-scraping approach, this paper investigates the longitudinal relationships between availability, value, and disposability, and stealing counts of specific makes and models of gaming consoles.

Methods

Using data from Western Australia (2012–2019) and focusing on specific makes/models of gaming consoles, the relationships between product-specific stealing counts, availability, value, and disposability were examined using time series and cross-sectional analyses.

Results

Support was found for a positive relationship between the changing disposability of specific makes/models of gaming consoles over their lifecycle with corresponding stealing counts, above and beyond changes in availability and value. However, when these attributes were analysed statically, both disposability and value were important.

Conclusions

The results highlight the importance of measuring correlates of ‘hot products’ longitudinally to better understand offenders’ target selection preferences over time—with important implications for theft risk assessment and crime prevention policy and practice. These findings also provide support for the use of similar open-source intelligence web-scraping strategies as a suitable technique for capturing time-specific proxies for product-specific value and disposability.

Objective: Illegal dumping of household and business waste, known as fly-tipping in the UK, is a significant environmental crime. News agencies reported major increases early in the COVID-19 pandemic when waste disposal services were closed or disrupted. This study examines the effect of lockdowns on illegal dumping in the UK.

Method: A freedom of information request was sent to all local authorities in the UK asking for records of reported incidents of fly-tipping for before and after the first national lockdown. ARIMA modelling and year-on-year comparison was used to compare observed and expected levels of fly-tipping. Urban and rural local authorities were compared.

Results: A statistically significant decline in fly-tipping during the first lockdown was followed by a similar increase when lockdown ended. The effects largely cancelled each other out. There was pronounced variation in urban-rural experience: urban areas, with higher rates generally, experienced most of the initial drop in fly-tipping while some rural authorities experienced an increase.

Conclusion: Waste services promote compliance with laws against illegal dumping. When those services were disrupted during lockdown it was expected that fly-tipping would increase but, counter-intuitively, it declined. This enhanced compliance effect was likely due to increased perceived risk in densely populated urban areas. However, as lockdown restrictions were eased, fly-tipping increased to clear the backlog, indicating temporal displacement.

Supplementary information: The online version contains supplementary material available at 10.1186/s40163-022-00170-3.

Background: Cryptocurrency fraud has become a growing global concern, with various governments reporting an increase in the frequency of and losses from cryptocurrency scams. Despite increasing fraudulent activity involving cryptocurrencies, research on the potential of cryptocurrencies for fraud has not been examined in a systematic study. This review examines the current state of knowledge about what kinds of cryptocurrency fraud currently exist, or are expected to exist in the future, and provides comprehensive definitions of the frauds identified.

Methods: The study involved a scoping review of academic research and grey literature on cryptocurrency fraud and a 1.5-day expert consensus exercise. The review followed the PRISMA-ScR protocol, with eligibility criteria based on language, publication type, relevance to cryptocurrency fraud, and evidence provided. Researchers screened 391 academic records, 106 of which went on to the eligibility phase, and 63 of which were ultimately analysed. We screened 394 grey literature sources, 128 of which passed on to the eligibility phase, and 53 of which were included in our review. The expert consensus exercise was attended by high-profile participants from the private sector, government, and academia. It involved problem planning and analysis activities and discussion about the future of cryptocurrency crime.

Results: The academic literature identified 29 different types of cryptocurrency fraud; the grey literature discussed 32 types, 14 of which were not identified in the academic literature (i.e., 47 unique types in total). Ponzi schemes and (synonymous) high yield investment programmes were most discussed across all literature. Participants in the expert consensus exercise ranked pump-and-dump schemes and ransomware as the most profitable and feasible threats, though pump-and-dumps were, notably, perceived as the least harmful type of fraud.

Conclusions: The findings of this scoping review suggest cryptocurrency fraud research is rapidly developing in volume and breadth, though we remain at an early stage of thinking about future problems and scenarios involving cryptocurrencies. The findings of this work emphasise the need for better collaboration across sectors and consensus on definitions surrounding cryptocurrency fraud to address the problems identified.

Oslo, the capital of Norway, is situated in a North European cool climate zone. We investigate the effect of weather on the overall level of crime in the city, as well as the impact of different aspects of weather (temperature, wind speed, precipitation) on the spatial distribution of crime, net of both total level of crime, time of day and seasonality. Geocoded locations of criminal offences were combined with data on temperature, wind speed, and precipitation. Generalized Additive Models (GAMs) allowed us to map level of and the spatial distribution of crime, and how it was impacted by weather, in a more robust manner than in previous studies. There was slightly more crime in pleasurable weather (i.e. low precipitation and wind speed and high temperatures). However, neither temperature, precipitation nor wind speed impacted the spatial distribution of crime in the city.

Supplementary information: The online version contains supplementary material available at 10.1186/s40163-022-00171-2.

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.