Abstract Objectives Crime prevention strategies often rely on the small set of micro-places where crime is most concentrated, the so-called hotspots, yet it has remained unclear how close existing hotspot detection methods come to the maximum coverage theoretically possible. This study introduces GraphVenn, the first algorithm that identifies the globally optimal placement of N fixed-radius hotspots directly from the empirical crime distribution, without relying on heuristic or approximate approaches. Methods GraphVenn was evaluated on three years of crime data from Malmö, Boston, and New York City (in total 1.75 million crimes) and compared against kernel density estimation (KDE), greedy PAI maximization (PAI-Max), and GraphTrace. Both the globally optimal and the greedy (fast approximation) modes of GraphVenn were evaluated across different spatial resolutions, demonstrating scalability to large urban datasets. Results In optimal mode, GraphVenn identified the absolute maximum coverage of incidents achievable under fixed-radius constraints. The greedy variant reached within 0.1–−1.9% of this optimum while reducing runtimes by up to two orders of magnitude. By contrast, existing methods consistently fell short, e.g., in New York City the optimal GraphVenn captured 51,522 crimes within its top-100 hotspots compared to 35,098 with KDE and 28,241 with GraphTrace, while PAI-Max was excluded due to its runtimes. In practical terms, the baselines therefore missed between 16,000 and 23,000 crime incidents that could have been covered. Conclusions Globally optimal detection of fixed-radius hotspots that maximize the distinct crime count is now computationally feasible at city scale. GraphVenn offers (i) a practical tool for researchers, law enforcement, and crime analysts to identify the most effective fixed-radius hotspot locations with confidence that no better configuration exists, and (ii) a benchmark for evaluating approximate methods against the true maximum crime count. Open-source code is provided to support replication and further research.

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. 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. 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. 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. 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.

Abstract Purpose The proliferation of large language models (LLMs) and generative artificial intelligence (GenAI) applications has provided ample opportunities for crime, including technology-facilitated financial crimes. The present study conducted a quantitative systematic literature review to examine the evolving intersection of GenAI and financial crime. Specifically, the study identified keyword concentrations, latent research topics, and thematic relationships within this emerging domain to explore how current scholarship understands the role of GenAI in financial crimes. Methods Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, this systematic literature review employed three quantitative analytical methods—bibliometric analysis, topic modelling, and knowledge graph analysis—to reveal trends, concentrations, and connections of prominent keywords and topics that emerged from the extant literature. Results With the assistance of the PRISMA 2020 framework, a total of 94 studies were incorporated for the quantitative systematic review. The bibliometric analysis identified five keyword clusters, while the topic modelling and knowledge graph revealed six latent research topics with nuanced patterns, highlighting a growing concentration on automated financial crimes that are distinctive from human-centric social engineering. The results also revealed the dual-use nature of GenAI in both facilitating and preventing financial crimes. On one hand, GenAI has been widely misused in financial cybercrimes such as algorithmic fraud, deepfake attacks, and smart contract exploitation. On the other hand, GenAI has enhanced crime prevention capacities, such as detection capabilities and vulnerability screening. Conclusions While GenAI facilitates various criminal opportunities for financial crime, it also provides insight into effective crime prevention strategies. This study demonstrated that research is increasingly focused on the technical and adversarial dimensions of the dual-use nature of GenAI, outlining a structural distinction between human-centric social engineering and automated financial crimes. The findings shed light on the importance of recognising the evolutionary landscape of financial crimes enabled by GenAI and the significance of embracing forward-looking governance frameworks for regulatory compliance in decentralised financial (DeFi) systems.

This study evaluates the impact of the COVID-19 pandemic and the associated “epidemiological traffic-light” system on homicide rates in Ecuador. We exploit monthly panel data at the canton level for 2020–2021 and estimate an event-study difference-in-differences design using Poisson pseudo-maximum likelihood with two-way fixed effects. The treatment is defined as the first transition from the red (strict) alert level to more flexible yellow or green stages, and we control for COVID-19 incidence and mortality, local economic activity, mobility indices and demographic characteristics. Our preferred event-study specification suggests a temporary increase in homicides in the month of the first relaxation of restrictions, but the estimated effects in subsequent months are imprecise and statistically indistinguishable from zero. The average post-treatment effect across the first five months after the transition is small and not statistically significant. Linear specifications in levels and in log(1 + homicides), as well as wild-cluster bootstrap inference, corroborate the absence of robust effects of the traffic-light policy on homicide rates. These findings indicate that, conditional on epidemiological and economic conditions, the traffic-light mobility regime did not generate systematic or sustained changes in lethal violence at the canton level. We discuss possible mechanisms and implications for the joint design of public-health and public-safety interventions.

Abstract The rise of ‘cryptojacking’ – the covert use of victim resources for unauthorised cryptocurrency mining – has become a significant cyber threat since the introduction of Bitcoin. Such cryptomining malware secretly hijacks a user’s computational power to generate cryptocurrency without their knowledge or consent, leading to reduced and/or degraded performance at the victim’s expense. This paper presents a systematic literature review of 119 articles tracing the evolution of cryptomining malware, past trends in their dissemination and detection, security recommendations, and anticipated future developments. We specifically highlight the dual impact of this threat, which targets not only individual users on devices like IoT, mobile phones, and cloud infrastructures, but also critical national infrastructure, large corporate networks, and high-traffic websites. Our analysis reveals that the threat landscape, which significantly expanded around 2017, continues to grow steadily. Additionally, we systematically identify and discuss detection methods – such as network traffic analysis, CPU utilization monitoring, and machine learning classifiers – as well as security recommendations like browser extensions, patch management, and network-level blocking. Our findings highlight the urgent need for a unified, multi-stakeholder security strategy to mitigate this pervasive and adaptable threat.

Abstract Research on Stop and Search in England and Wales is constrained by substantial barriers to data access, inconsistent geographic coverage, and technical complexity. This paper presents policedatR , an R package that addresses these challenges by providing streamlined access to comprehensive stop and search data from the data.police.uk Application Programming Interface (API). policedatR automates data acquisition across multiple geographic scales, enriches datasets with population estimates and geographic identifiers, and includes functions for analysing the data, including calculating ethnic disproportionality. We describe the architecture and main functionalities of policedatR and demonstrate its capabilities and utility with analyses of temporal trends, geographic variation and ethnic disparities at national (e.g. countrywide, Police Force Area) and local (e.g. sub-local authority) levels. We also provide an example of how data acquired using the package can be harmonised with other datasets (in this case the English Indices of Deprivation) to explore broader questions on stop and search and society.By transforming thousands of individual API calls into a straightforward analytical workflow, policedatR facilitates rigorous empirical research and supports democratic accountability in policing.

This study quantifies the overlap between existing police precinct boundaries and theoretically optimal boundaries derived from Voronoi diagrams based on Euclidean and network distances. Accessibility refers to how easily an individual can reach a police station, with closer points being more accessible. There is a need to understand whether current police precinct boundaries effectively facilitate accessibility to police stations, which has an impact on accurate reporting and police response. A novel network Voronoi algorithm, together with spatial similarity measures, is used to determine regions at risk for access to police stations. For police precincts with low similarity values when compared to the proposed network Voronoi areas, which is validated by analysing the proportional change in the number of crimes reported within the different boundaries. The decrease observed suggests that a significant portion of crimes are being reported to other, nearer, and more accessible police stations. By quantifying these relationships, this research evaluates the effectiveness of current precinct boundaries and their potential influence on the accuracy of crime reporting and true police accessibility.

Abstract Purpose Ivory trafficking remains a persistent transnational crime globally, and especially, in East Africa, driven by organized networks that exploit regulatory loopholes, corruption, and porous borders. Crime science perspectives have largely been ignored in examining ivory trafficking and this study addresses this gap in knowledge. Methods This study employs crime script analysis to systematically deconstruct the modus operandi of ivory traffickers, outlining and detailing the steps taken to prepare, transport and smuggle ivory from source to demand countries. To achieve this goal, this research focuses on the most culpable actors in trafficking and in the period of the highest recorded trafficking activities. This study uses data from twenty (20) prosecuted criminal cases drawn from three countries in East African and relating to at least forty-one (41) tons of illegal ivory. Results Traffickers prepare by obtaining ivory from poachers and storing them is storage facilities passing off as legitimate businesses; falsify export documents, bribe customs officials, disguise ivory as legitimate export or obfuscate it in hidden or modified cargo compartments to secure successful exportation. The findings indicate that traffickers are highly organized with a wide range of actors enabling their criminality and their actions often converges with a variety of other major and serious crimes. Conclusions This study identifies critical intervention points where law enforcement and policy measures can disrupt trafficking networks and proposes various recommendations for policy and practice.