Crime Science最新文献

Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to identify high-crime areas, and optimise resource allocation and personnel management for crime prevention. Traditionally, techniques such as the Poisson model and regression analysis have been widely used for crime prediction. However, recent statistical advancements have introduced Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focuses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction. Secondly, it develops a model using no demographic information other than clustering attributes as an exogenous variable. Thirdly, it quantifies prediction uncertainty. Finally, it addresses data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model ranked areas by crime levels, obtaining a 29.3% Mean Absolute Percentage Error (MAPE) and 0.8 (R^2) value for crime predictions. These findings and contributions presents the potential of INLA in advancing evidence-based decision-making for crime prevention.

An interdisciplinary approach has the potential not only to help solve conservation-centric problems but also to enrich and improve evidence-based scientific research. Crime science, an offshoot of criminology, provides a comprehensive, solution-oriented approach that transcends disciplinary boundaries and bridges science and practice for developing effective conservation interventions to real-life problems such as Human Elephant Conflict (HEC). This paper focuses on HEC as a conservation concern, but the resultant behaviors toward elephants, people, and their property are criminology’s concern. Using the Action Research paradigm, a rapid assessment of human–elephant conflict (HEC) in India was undertaken to identify contextual solutions. This study utilized problem-oriented field research methods that enabled the gathering of data on elephant habitat-landscape, villagers’ lifestyle (habitat) in the fringe areas, their current approaches in dealing with the conflict, the challenges forest officials face to mitigate HEC, and the assistance provided by district administrators to protect villagers and their corps and HEC-related deaths. The qualitative inquiry, including observation of village-forest fringe areas, focus group discussions with villagers, and interviews with forest officers and rangers, and district administrators/collectors who are handlers, guardians, and managers of the conflict space, provided rich data in identifying situational practical measures and underscored the role of crime science in providing a conceptual framework to gather evidence in addressing HEC in forest areas. The findings of the research suggest that human–animal convergence space is the source (or location) of conflict and criminology-driven situational crime prevention measures, including increasing effort, risks, reducing rewards and provocations, and removing excuses might mitigate the conflict, requiring coordinated efforts by villagers, forest and district administrators, and local law enforcers.

There is a vast literature evaluating the empirical association between stay-at-home policies and crime during the COVID-19 pandemic. However, these academic efforts have primarily focused on the effects within specific cities or regions rather than adopting a cross-national comparative approach. Moreover, this body of literature not only generally lacks causal estimates but also has overlooked possible heterogeneities across different levels of stringency in mobility restrictions. This paper exploits the spatial and temporal variation of government responses to the pandemic in 45 cities across five continents to identify the causal impact of strict lockdown policies on the number of offenses reported to local police. We find that cities that implemented strict lockdowns experienced larger declines in some crime types (robbery, burglary, vehicle theft) but not others (assault, theft, homicide). This decline in crime rates attributed to more stringent policy responses represents only a small proportion of the effects documented in the literature.

In this article, we investigate situations involving firearm violence in Sweden. The spatiotemporal distribution of records is assessed in different contexts across the country and linked to land use, demographic, and socio-economic characteristics by area and by street segment. The findings emphasize the prevalence of evening shootings, particularly in economically disadvantaged areas where young people congregate in public places often in residential areas, in parks, in playgrounds, and in transit areas. Although two-thirds of shootings took place in larger urban municipalities, our study sheds light on the non-uniform distribution of gun violence along the rural–urban continuum. We conclude by offering suggestions for future research and practical interventions to address this pressing issue that negatively affects people and communities.

Analyses of crime based upon aggregate counts of different crime types have restricted value, because they count all crime types equally irrespective of the harm caused. In response to this problem, a series of weighted measures of crime harm have been proposed. In this short contribution, we contend that the use of some crime harm metrics to inform police deployment practices has the potential to reinforce ethnic disparities in the criminal justice system through the creation of unintended negative feedback loops. We focus our analysis on the Cambridge Crime Harm Index and the Office for National Statistics (ONS) Crime Severity Score, the preeminent crime harm indexes in England and Wales. We conclude that the ONS Crime Severity Score, which is based on mean sentencing outcomes, does give cause for concern in some contexts. There is currently no evidence that the Cambridge Crime Harm Index, based on sentencing guidelines, presents the same problems.