A globally optimal algorithm for hotspot detection and ranking

Enhancing longitudinal cohort studies by linking routine external data to them is increasingly used to evaluate how local environments impact participants' outcomes (e.g. crime on adolescents' perception of security and victimisation). To describe the geographical linkage between the UK Millennium Cohort Study (MCS) and street-level crime incidents reported to the Police in England and Wales, and to estimate crime count and rates around MCS participants' residences. Eight years of monthly street-level police data were linked to the residential postcodes of MCS participants living in England and Wales in surveys 5, 6 and 7 to create individual-level variables of neighbourhood crime counts and rates (28,724 surveys and 11,365 individuals). Radial buffers around participants' residences were created at ages 11, 14 and 17. Crime counts and rates were created prior to the month of interview (at 1, 3, 6, 9, and 12 months prior). A homogenisation of crime categories reported in the police data was conducted to evaluate changes over time and areas. Multivariate models were used to study the association between MCS participants' demographic characteristics and derived measures of neighbourhood crime. While total crime rates and counts around MCS participants remain stable over the period, they hide heterogeneous upward and downward trends in specific sub-categories, with violence and sexual offences showing a larger increase. We observe a negative socioeconomic gradient between household income deciles, recorded at age 11, and subsequent exposure to neighbourhood crime. Linking routine crime data to longitudinal studies, such as the MCS, which follow children and their families through a critical period of development, can provide a new resource to understand how local crime impacts child and adolescent outcomes.

Monetary sanctions in law enforcement, including fines, forfeitures, and related fees, are susceptible to exploitation by agencies for self-serving profit motives. However, a key challenge in addressing this issue is disentangling the agencies’ profit-driven motives from their genuine commitment to upholding law and order. Against this backdrop, this study examines a novel policy design proposal: redirecting revenues from law enforcement to fund local nonprofits. This approach seeks to eliminate conflicts of interest without restricting the use of monetary sanctions as a tool for law enforcement, while simultaneously channeling revenues toward community benefits. Experimental evidence based on a representative sample of US adults (n = 1,030) further highlights this approach’s potential to improve public perceptions of, and attitudes toward, law enforcement agencies. The study concludes by discussing the broader implications of this proposal for the political economy of law enforcement, as well as key considerations and potential challenges for its implementation.

Facial recognition technology holds the promise of transforming criminal identification processes by offering a highly effective tool for  law enforcement. Its capacity to quickly match individuals with mugshots or surveillance footage can significantly speed up investigations and potentially act as a deterrent to criminal activity. This technology can provide crucial support in solving cases and identifying suspects more efficiently. Facial recognition technology has the potential to revolutionize criminal identification by providing a powerful tool for  law  enforcement. Its ability to rapidly match suspects with mugshots or surveillance footage can expedite investigations and potentially deter crime. However, its implementation must be approached with caution to address challenges such  as  accuracy  limitations,   privacy   concerns, and ethical considerations. By carefully considering           the           benefits          and drawbacks, policymakers and law enforcement agencies can harness the power of facial recognition while safeguarding individual rights and privacy.

Place-based interventions may reduce violence, but approaches for capturing nearby incidents using kernel density estimation (KDE) vary. KDE smooths geospatial point data, like crime incidents, using a user-specified bandwidth often selected through data-driven approaches that rely on the underlying point pattern. Because point patterns vary by outcome, time, and context, data-driven methods can produce bandwidth sizes that are misaligned with the spatial extent of a place-based intervention, potentially limiting the ability to detect its effect. To illustrate the inferential challenges associated with data-driven bandwidth selection approaches, this study aimed to (1) quantify variability in bandwidths selected through data-driven methods and (2) examine the impact of bandwidth size on simulated intervention effects. We used violent crime data for Philadelphia (2013-2023). For Aim 1, we calculated bandwidth sizes for each crime-year combination using two default data-driven selection criteria and compared selected sizes across crime types and years. For Aim 2, we used a hypothetical place-based intervention with a known effect (30% reduction in nearby assaults) and ran simulations to examine how the intervention effect, estimated using Poisson regression, changed based on the bandwidth size used to estimate the crime density surface. Bandwidth sizes varied significantly by data-driven selection method, crime type, and year (range: 45.9-48,450 ft). For the simulated intervention, "true effects" (i.e., the reduction of nearby assaults attributed to the intervention) were only detectable at bandwidths between 200 and 2900 ft. Larger bandwidths resulted in estimates that incorrectly suggested the intervention was ineffective or increased crime. Data-driven bandwidth selection can obscure or distort intervention effects. Researchers should be critical and transparent when selecting KDE parameters in place-based violence prevention research.

The global response to the COVID-19 pandemic between January 2020 and late 2021 saw extraordinary measures such as lockdowns and other restrictions being placed on citizens’ movements in many of the world’s major cities. In many of these cities, lockdowns required citizens to stay at home; non-essential business premises were closed, and movement was severely restricted. In this paper, we investigate the effect of these lockdowns and other pandemic response measures on crime counts within the local authorities of England and Wales. Using openly accessible crime records from major police forces in the UK from 2015 to 2023, we discuss the impacts of lockdowns on the incidences of crime. We show that as time passed and citizens’ response to the imposed measures eased, most types of crime gradually returned to pre-pandemic norms whilst others remained below their pre-pandemic levels. Furthermore, our work shows that the effects of pandemic response measures were not uniform across local authorities. We also discuss how the findings of this study contribute to law enforcement initiatives.

The problem of drug addiction in Malaysia is worsening and causes harm to the well-being of the population of Malaysia. A report published in 2018 states that 133,684 or 0.4% of the Malaysian population are drug addicts. Furthermore, 56% of all inmates in the federal prison are locked-up because of drug-related offences. This study aim is to identify the hotspots for drug addicts in Selangor, Malaysia. This study uses three geostatistical techniques, kernel density estimation (KDE), Getis-Ord Gi*, and IDW to map the hotspots for drug addicts. The National Anti-Drug Agency (NADA) provides the data for this study which consists of 2997 cases of drug addict under supervision (DAUS) in 2016. The data are analysed using ArcGIS Pro 2.4 software. The individual DUAS represents a point vector data format with WGS 1984 Web Mercator projection. Hotspot analysis is performed using kernel density estimation (KDE), Getis-Ord Gi* and IDW. The results show eight statistically significant hotspots for drug addicts in the sub-districts (99% confidence level and p-value < 0.001). The locations with significant hotspots for drug addicts are Bandar Serendah, Pandamaran, Bandar Klang, Bandar Kajang, Dengkil, Bandar Ampang, Bandar Damansara, and Semenyih sub-districts. This study provides spatial information that helps law enforcement agencies identify drug hotspot areas and use this information to create and enhance a defensible safe neighbourhood. The outcome of this study facilitates law enforcement through better strategic planning for reducing drug addict hotspot areas. Keywords: drug addicts, drug hotspots and mapping, geographical information system (GIS), Getis-ord Gi*, inverse distance weighted (IDW), kernel density estimation analysis (KDE)

Exploratory spatial data analysis (ESDA) is a useful approach for ­detecting patterns of criminal activity. ESDA includes a number of quantitative techniques and statistical methods that are helpful for identifying significant ­clusters of crime, commonly referred to as hot spots. Perhaps the most popular hot spot detection methods, both in research and practice, are based on tests of spatial autocorrelation and kernel density. Non-hierarchical clustering methods, such as k-means, are less used in many contexts. There is a perception that these approaches are less definitive. This chapter reviews non-hierarchical cluster analysis for crime hot spot detection. We detail alternative non-hierarchical approaches for spatial clustering that can incorporate both event attributes and neighborhood characteristics (i.e., spatial lag) as a modeling parameter. Analysis of violent crime in the city of Lima, Ohio is presented to illustrate this for hot spot detection. We conclude with a discussion of practical considerations in identifying hot spots.

The taser device (CEW), based on electric discharge, is one of the most controversial, legally used law enforcement tools ever, and its specific health risks to the affected subject have received much attention in the scientific and research literature. This paper aims to objectively evaluate the benefits and drawbacks of CEW by reviewing the literature. The SWOT analysis method was used to objectively assess the balance between the negative aspects and benefits of law enforcement, using Fuller’s triangle as a paired assessment method to determine the individual weights of the identified strengths, weaknesses, opportunities, and threats. The resulting strategy for its prospects, based on the SWOT approach, was evaluated as an offensive S-O strategy, focusing on opportunities and on developing and further improving technology to strengthen the tool’s strengths. In general, CEWs can be considered a valuable enforcement tool, but they are not risk-free.

Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (&lt; 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.

Since 2003, anticorruption war has become a major plank of Nigeria’s governance reform. As part of the war, Nigeria has enacted anticorruption laws and established anticorruption agencies. This marks the triumph of ‘law enforcement’ over ‘change management’ approach to fighting corruption. This approach has not produced significant improvement in the integrity of governance and state institutions. Despite massive investment in law enforcement efforts, corruption remains prevalent and Nigeria’s standing in Corruption Perception Index (CPI) has not improved. This casts doubt on the potential of law as effective tool in the fight against corruption. The paper briefly reviews the history of Nigeria’s failed engagement with anticorruption campaigns and identifies the reasons for failure in the law enforcement approach to fighting corruption. Utilizing insights from culture studies and institutional economics, the paper argues against legal formalism as it manifests in excessive reliance on law enforcement techniques and tools to counter corruption in Nigeria. The paper argues further that law enforcement is too limited to constitute the main strategy for fighting corruption because it faces mainly on the symptoms rather than on the underlying causes of corruption as a social pathology. Therefore, an exclusive focus on law in the sense of what prosecutors and judges do, is ill-suited as a cure for pervasive corruption. To effectively control or contain corruption, the paper recommends abandoning legal formalism and contextualizing and socializing law in the light of insights from culture studies and institutional economics to change political culture and improve collective action.

The variety of situations faced by law enforcement officers require that they be prepared to use varying levels of escalating force. Use of non-lethal weapons provides a multitude of options which, if used properly, can greatly aid in the resolution of the situation. Key factors in the use of non-lethal weapons include adequate training and discretion in their application.

Prescription drug monitoring program design and function: A qualitative analysis

This paper suggests ways in which human reliability analysis (HRA) can assist the United State Justice System, and more specifically law enforcement, in enhancing the reliability of the process from evidence gathering through adjudication. HRA is an analytic process identifying, describing, quantifying, and interpreting the state of human performance, and developing and recommending enhancements based on the results of individual HRA. It also draws on lessons learned from compilations of several HRA. Given the high legal standards the Justice System is bound to, human errors that might appear to be trivial in other venues can make the difference between a successful and unsuccessful prosecution. HRA has made a major contribution to the efficiency, favorable cost-benefit ratio, and overall success of many enterprises where humans interface with sophisticated technologies, such as the military, ground transportation, chemical and oil production, nuclear power generation, commercial aviation and space flight. Each of these enterprises presents similar challenges to the humans responsible for executing action and action sequences, especially where problem solving and decision making are concerned. Nowhere are humans confronted, to a greater degree, with problem solving and decision making than are the diverse individuals and teams responsible for arrest and adjudication of criminal proceedings. This paper concludes that because of the parallels between the aforementioned technologies and the adjudication process, especially crime scene evidence gathering, there is reason to believe that the HRA technology, developed and enhanced in other applications, can be transferred to the Justice System with minimal cost and with significant payoff.

Crime Mapping and Law Enforcement Scope and Applications

Software that helps Law Enforcement to keep track of statements in sign language is currently not available in the market. Although other available programs are incredibly efficient, they might be very challenging to determine whether your hand movements are correct.Those programs are less user-friendly because it takes a lot of time to learn how to use it. Apart from that some of the software need hardware support to make the detection accurate. This paper, describe how to employ this model/project effectively while putting together a course on interactive sign language instruction.This project will make it easier to record statements from a deaf or dumb person more accurately and consistently. Additionally, A text-to-speech converter would allow for both listening and seeing the text in sign language. Sign language products such as this appear to be a very helpful tool for law enforcement as well as the deaf persons.Such a translator might be useful for individuals who have been victims of incorrect sign language translation, such as deaf and stupid people.