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Forensic Science is the application of Scientific methods to resolve crime and legal issues. It involves various disciplines, such as Computer Science, Biology, Chemistry and Anthropology. Forensic scientists examine and analyze evidence from crime scenes, such as fingerprints, DNA, blood, or weapons. Digital proof is one of the forms of forensic evidence. It provide real time eye witness of the incident. Video recordings enable investigators to find out what exactly has transpired. Investigators use video evidence as a source for witness statements, and it aids in the search for the missing person or suspect. Video evidence is also used to testify in court and help with investigations and prosecutions. Failure of forensic science results in wrong judgement convicting innocent people and escaping criminals [1]. For most crimes high quality video recordings are often not available. video quality issues such as blurry, speckled, pixelated and low-resolution videos captured at low light are a real challenge in forensic analysis. To address such issues in this research a hybrid model using set of filters including triplemask spatial linear filter, median filter and bilateral filters are used. For denoising images, a novel image filter using sliding window convolution is proposed. For image sharpening a triplemask spatial linear filter is proposed. Triplemask spatial linear filter is created by cascading a series of filters. Identity, shift and fraction-based approach is used in mask processing. For image smoothing and to preserve the edges bilateral filter is used [2]. The performance of convolution operation is compared with distinct convolution, shift rotational convolution and scipy convolution. To handle uncertainty, imprecision, and ambiguity in real-world image data in a precise manner neutrosophic science is used in image analysis. By the generated neutrosophic set of the given input image ambiguous regions in the image are detected. Feature selection is made by calculating the entropy of different image regions. From the generated neutrosophic set entropy the degree of uncertainty, within the input image is quantified. The intensity distributions are measured using entropy values. In feature selection regions with highest and lowest entropy values containing face images are selected, visualized and processed to further aid in forensic analysis in detecting the culprits. Neutrosophic AHP is used for prioritizing criteria based on face detection and indeterminancy. Face detection is performed using single shot detector framework with a resnet base network, trained using caffe deep learning framework. face recognition process is performed using dlibs [9] state-of-the-art face recognition model built with deep learning [10]. Face recognition in this research distance-based similarity measure using neutrosophic sets is performed. These measures are used in conjunction with facenet[59] face recognition algorithm to improve the robustness and accuracy over traditional methods. The model has an accuracy of 99.38% on the labelled faces in the wild benchmark. [ABSTRACT FROM AUTHOR]

Purpose: Crime data analysis has gained significant interest due to its peculiarities. One key characteristic of property crimes is the uncertainty surrounding their exact temporal location, often limited to a time window. Methods: This study introduces a spatio-temporal logistic regression model that addresses the challenges posed by temporal uncertainty in crime data analysis. Inspired by the aoristic method, our Bayesian approach allows for the inclusion of temporal uncertainty in the model. Results: To demonstrate the effectiveness of our proposed model, we apply it to both simulated datasets and a dataset of residential burglaries recorded in Valencia, Spain. We compare our proposal with a complete cases model, which excludes temporally-uncertain events, and also with alternative models that rely on imputation procedures. Our model exhibits superior performance in terms of recovering the true underlying crime risk. Conclusions: The proposed modeling framework effectively handles interval-censored temporal observations while incorporating covariate and space–time effects. This flexible model can be implemented to analyze crime data with uncertainty in temporal locations, providing valuable insights for crime prevention and law enforcement strategies. [ABSTRACT FROM AUTHOR]

This study assessed the influence of socioeconomic and demographic indicators on different types of crime and explored the spatial and temporal dynamics of crime. Between 2014 and 2020, 174,365 criminal events registered in Quito, Ecuador, were collected and aggregated at an administrative area level. Time-series decompositions, spatial autocorrelations, and regression models were applied, considering different types of crime as dependent variables. A marked seasonal component of crime and crime hotspots in the center of the study area was identified. Crime events are likely to increase significantly by 2025. We also found that unemployment, schooling, unsatisfied basic needs, and especially the density of bars and night clubs are socioeconomic indicators influencing crime. Urban crimes present specific spatial and temporal patterns, and crime events can be explained by urban socioeconomic conditions. [ABSTRACT FROM AUTHOR]

Introduction: Illegal, unreported, and unregulated (IUU) fishing poses a significant threat to marine resource management globally. Environmental criminology plays a vital role in understanding and mitigating IUU activities by focusing on the contextual elements surrounding these crimes. This study examines the dynamics of poaching within areas managed through Territorial User Rights for Fisheries (TURFs), with a specific emphasis on the loco (Concholepas concholepas) fishery in Chile. Methods: Employing Crime Script Analysis (CSA) as the primary methodological lens, this research meticulously deconstructs the criminal process involved in TURFs poaching operations. CSA is used to identify key elements, providing a comprehensive understanding of poachers modus operandi. Additionally, by integrating CSA with Situational Precipitators of Crime (SPC), the study identifies critical factors such as environmental conditions, socio-economic disparities, and enforcement weaknesses that shape poaching opportunities within TURFs. Results: The analysis reveals a complex interplay between SPC factors, underscoring their role in shaping poaching dynamics. Key findings highlight the importance of specific environmental conditions, socio-economic disparities, and enforcement weaknesses in facilitating poaching activities. The study identifies various tactics and strategies employed by poachers and the roles of different actors involved in the poaching process. Discussion: Building upon these findings, the study proposes a comprehensive Situational Crime Prevention (SCP) framework aimed at effectively combating TURFs poaching. The framework emphasizes the importance of striking a careful balance between restrictive and inclusive measures to mitigate potential negative consequences. The study contributes valuable insights into understanding and addressing IUU fishing, particularly within TURFs in Chile. [ABSTRACT FROM AUTHOR]

Purpose/Goal: This research investigates the potential link between mobility and violent crimes in Tamil Nadu, India, using an empirical study centred on the COVID-19 pandemic waves (2020–2022). The goal is to understand how these events influenced crime, employing a counterfactual approach. Methods: The study employs the XGBoost algorithm to forecast counterfactual events across different timeframes with varying levels of mobility. The mobility data sources include historical bus and passenger records spanning a decade, along with Google Community Mobility Reports added during the pandemic phases. The foundation for crime analysis is built upon the univariate time series of violent crimes reported as First Information Reports from 2010 to 2022. Findings: Results indicate a significant correlation between mobility and violent crimes when mobility drops below a specific threshold. However, no such correlation is observed when mobility is above this threshold during the non-pandemic periods. The COVID-19 pandemic had a major impact on people's and vehicular mobility, especially during the complete lockdown periods of the first two waves, and also affected crime rates. Conclusions: The decrease in recorded incidents could also be attributed to fewer criminal opportunities. Additionally, this could be due to unfavourable situational factors, such as victims' limited access to appropriate health and law enforcement agencies to report crimes. Furthermore, frontline services were busy with pandemic-related commitments, which could have contributed to a lack of crime registration even when crimes were committed. [ABSTRACT FROM AUTHOR]

Aggravated assault and homicide both increased in 2020 and 2021 in the United States, generating considerable public concern. As such, the current study examines whether (1) the increase in violence during the 2020–2021 period uniquely affected some local street segments more so than others when compared to the previous years 2017–2019 and, in turn, (2) whether specific socio-demographic characteristics are associated with different changes in local violence during the 2020–2021 period. The current study focuses on the City of Little Rock, Arkansas, examining violence across streets nested within block groups. Descriptive analyses are followed by mixed-effect models to examine the potential change (increase, decrease) or stability in violence occurrence, including whether a first incident occurred in the years 2020–2021. The central finding was that the violence surge only impacted a small number of street segments – just over 14% of the total sample in Little Rock – and roughly the same proportion experienced a decrease in violence during this same period of time. The results are further discussed from a crime prevention and policing perspective. [ABSTRACT FROM AUTHOR]

Background: Paratransit is a system that operates along a more or less defined route and then stops to take passengers on demand. They offer on-demand door-to-door service from any origin to any destination in a service area, and are operated by public transit agencies, community groups or not-for-profit organizations, and for-profit private companies or operators. In transit, criminal acts including rape, kidnapping and/or abduction, robbery, carjacking, vehicle theft, and even acts of terrorism are committed. These crimes are occasionally committed by other passengers since not every individual who boards a vehicle is a real traveller. This study examined the incidence of paratransit crimes in Akure, Nigeria using a Secured System Approach. Results: This study employed a household-based form of sampling to select the study locations in Akure. Convenient sampling was used to select two streets in each of the residential areas from which one hundred and twenty (120) houses were sampled purposively. The questionnaire was used to gather the primary data, with descriptive analysis. The study found that most of the transit crimes were perpetuated with taxi and motorcycles. Also, kidnapping was the major transit crimes in Akure. The Oba-Adesida road and Arakale road (situated along the study areas) were the hotspots for transit crimes in Akure. Finally, the respondents did not have a good perception of the proactiveness, integrity, reliability, respectability, and trustworthiness of security agencies regarding paratransit crimes in Akure. Conclusions: Incidence of paratransit crimes in Akure, Nigeria was explored among residents in Akure households. Kidnapping was dominant in Akure along Oba-Adesida and Arakale road, and mostly perpetuated with taxi and motorcycles. Since the participant did not have a good perception concerning the proactiveness, integrity, reliability, respectability, and trustworthiness of security agencies in Akure. Recommendations were suggested. [ABSTRACT FROM AUTHOR]

Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis. [ABSTRACT FROM AUTHOR]

This paper provides a new perspective for robust estimation and inference methods based on Huber regression. An efficient distributed learning with sparsity is proposed, in which the observations are randomly divided among different machines. We modify the proximal alternating direction method of multiplies (ADMM) algorithm to calculate the sparse penalized Huber regression. The convergence property of the algorithm is established. Computationally, our proposed approach requires only a central machine to solve a SCAD or adaptive lasso penalized M-estimation problem, and other node machines to calculate gradients based on local datasets. In terms of communication, the estimation error of our method decreases to that of the centralized method in several rounds of communication. Simulation studies are conducted to evaluate the finite-sample performance of our proposed method. The empirical study shows that our method has good practicability in the practice of a Communities and Crime analysis. [ABSTRACT FROM AUTHOR]

Our research probes the effect of pandemic uncertainty on drug crimes in the top 10 nations facing the highest levels of pandemic uncertainty (UK, USA, South Korea, India, China, France, Germany, Italy, Indonesia, and Russia). Unlike previous research that primarily relied on panel data methods and neglected individual economy nuances, the existing study utilizes the Quantile-on-Quantile tool. This innovative technique allows us to explore the variables' connection within every economy, enhancing the accuracy of this investigation. Consequently, our research bestows an all-inclusive, worldwide standpoint, uncovering nuanced foresight specific to every economy's unique characteristic. The findings of this research showcase a positive association between pandemic uncertainty and drug crimes across different quantiles in the UK, the USA, India, Germany, China, and Indonesia, while a negative relationship in France, Italy, South Korea, and Russia. Additionally, our research underscores varied patterns in these associations across distinct economies. These outcomes highlight the importance for policymakers to undertake thorough assessments and formulate effective strategies when addressing fluctuations in both pandemic uncertainty and drug crimes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: A child is a nation’s supreme asset and future. India homes 444 million children, aged between 0 and 18 years, contributing to 19% of the world’s children. Crime against children is detrimental to their mental and physical health and affects their growth and development. The National Crime Record Bureau recently reported that a crime targeting children happens every 4 minutes. There is a paucity of literature regarding the burden of crime against children. To understand the magnitude and spatial distribution of crime against children, a retrospective surveillance study was conducted in the state of Tamil Nadu, India, from 2017 to 2021. Materials and Methods: This is a cross‑sectional analytical type of study conducted in KIMSRC, Chengalpattu, Tamil Nadu. The data from the yearly crime review bulletin of Tamil Nadu from 2017 to 2021 were cleaned, transformed, and analyzed using Python v3.8 and subjected to geospatial auto‑correlation and hotspot analysis using the Getis‑Ord Gi* in ArcGIS Pro v3.1. The endemicity pattern was studied through cluster analysis with Hierarchical Density Based Scanning in Python and visualization in ArcGIS pro v3.1 in the study area. Results: In Tamil Nadu, only one hotspot district in 2017 [Tiruppattur (95% confidence, P < 0.05)] and one hotspot in 2020 [Villupuram (90% confidence, P < 0.1)] were identified, with others being insignificant. The districts which show very high prevalence of crimes against children are Chennai, Ranipet, Chengalpattu, Viluppuram, Tiruvannamalai, Vellore, Tiruppattur, Krishnagiri, Dharmapuri, Salem, Cuddalore, Thanjavur, Tiruchirappalli, Karur, Tiruppur, Coimbatore, Dindigul, Pudukkottai, Sivaganga, Tenkasi, Thoothukkudi, Tirunelveli, and Kanniyakumari. Conclusion: This study identifies key areas within the state of Tamil Nadu which have a high prevalence of crimes against children and also areas that are hotspots for such crimes. Greater resources and measures can now be targeted toward these areas by stakeholders, which can help in the reduction of crimes against children. [ABSTRACT FROM AUTHOR]

Questions remain on whether the UCR constitutes a legitimate source for analyzing hate crime. While a handful of studies have attempted to uncover the law enforcement characteristics associated with reporting hate crime, scholars have yet to triangulate official data with other sources, namely the NCVS. In this study, I replicate past UCR-NCVS convergence analyses on hate crime incidents, determining whether these series converge and if they do so across different forms of bias motivation. Using 18 years of data from 2003 through 2020 at a national level, results indicate mixed support for convergence, drawing implications for research and policy. [ABSTRACT FROM AUTHOR]

Urban crime is a pressing issue in socially unequal regions like Brazil. We investigate the complex relationships between crime, victims, and urban situations in Rio de Janeiro. We analyze the connections between specific types of crime, victim characteristics, and crime locations through complex network analysis. In a large-scale empirical study, we examine 5000 randomly selected crime incidents in Rio between 2007 and 2018. Our analysis reveals a strong association between sex, race, location, and income inequality in the risk of exposure to crime. The results suggest that specific social groups, notably black and brown women, face a higher-than-average vulnerability to particular types of crime in Rio. Our investigation confirms our hypothesis that crime incidents are not random occurrences. Instead, we have discovered meaningful connections between specific types of crime, victim characteristics, and crime locations. These findings indicate a pattern of underlying factors shaping the distribution of crime and vulnerable social groups. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study is to assess whether CALEA-accredited police departments are more (or less) likely than non-CALEA-accredited police departments to conduct crime analysis after matching on factors associated with engaging in accreditation and conducting crime analysis. Methods: This study draws upon a quasi-experimental approach, cardinality matching, to estimate the relationship between CALEA accreditation status and crime analysis within municipal police departments. Results: CALEA accreditation has a positive and statistically significant association with the use of crime analysis at the 0.05 level. The estimate for the treatment in odds ratios is 4.8, meaning that the odds of using crime analysis was 4.8 times higher among CALEA-accredited municipal police departments compared to non-CALEA-accredited municipal police departments in the sample after matching. Conclusions: CALEA accreditation serves an important signal of support for conducting crime analysis relative to similarly situated non-CALEA-accredited police departments. [ABSTRACT FROM AUTHOR]

This research was designed in a random sampling method for detecting legal drawbacks in Chattogram North Forest Division, Bangladesh. Land use and land cover (LULC) change, normalised difference vegetation indexing (NDVI), hotspot analysis, IDW and Ripley's K function were applied for spatial analysis of forest crimes of five fiscal years. The LULC showed forests degraded from 76% to 12% while the NDVI was 0.37 to 1. In 13 detected hotspots, a 2% probability of occurring offences in neighbouring areas of each hotspot was determined by hotspot analysis and IDW respectively. Ripley's K showed randomly distributed hotspots. [ABSTRACT FROM AUTHOR]

Safety is arguably the single biggest daily concern for commuters. Popular navigation software systems such as Google Maps, TomTom and Waze do not however currently give the same weight of consideration to crime risk when directing road users to the safest and shortest routes as they do existing traffic knowledge. In this study we propose three simple methods to delineate the safest and shortest route between any two locations in South Africa considering crime risk. The first method uses historical crime data to delineate the safest and shortest route; the second method, uses only the location of so-called 'risky facilities' to determine the safest and shortest route; while the third method uses a combination of both historical crime data and facility data to delineate the optimal route. We contrast the resultant routes delineating by these three methods and speculate on the policy implications of integrating this knowledge into future navigation software systems. [ABSTRACT FROM AUTHOR]

Introduction: Drug courts are criminal justice programs to divert people with substance use disorders from incarceration into treatment. Drug courts have become increasingly popular in the US and other countries. However, their effectiveness in reducing important public health outcomes such as recidivism and substance-related health harms remains ambiguous and contested. We used nationwide register data from Sweden to evaluate the effectiveness of contract treatment sanction, the Swedish version of drug court, in reducing substance misuse, adverse somatic and mental health outcomes, and recidivism. Methods: In this prospective cohort study, two quasi-experimental designs were used: difference-in-differences and the within-individual design. In the latter, we compared the risk of outcomes during time on contract treatment to, 1) parole after imprisonment and, 2) probation. Results: The cohort included 11,893 individuals (13% women) who underwent contract treatment. Contract treatment was associated with a reduction of 7 percentage points (95% CI: -.088, -.055) in substance misuse, 5 percentage points (-.064, -.034) in adverse mental health events, 9 percentage points (-.113, -.076) in adverse somatic health events, and 3 fewer charges (-3.16, -2.85) for crime in difference-in-differences analyses. Within-individual associations suggested that the same individual had longer times-to-event for all outcomes during contract treatment than on parole or on probation. Conclusions: Contract treatment is an effective intervention from both public health and criminal justice perspective. Our findings suggest that it is a superior alternative to incarceration in its target group. Further, we find that an implementation approach that is less punitive and more inclusive than what is typical in the US can be successful. [ABSTRACT FROM AUTHOR]

In an extended period, crime and statistical professionals’ analyses have channeled their skills, knowledge, and expertise to anticipate the timing and locations of future criminal incidents, although with varying degrees of success. The surge in criminal activities and the evolving strategies adopted by modern offenders have strained the efficacy of existing predictive methods. This study introduces a novel approach by leveraging the Multi-Layer Perceptron (MLP) architecture, a cutting-edge technology that uses the back-propagation algorithm to develop a predictive model for analyzing crime data. A total of 4,748 records were collected from the Cross River State Police Command. Data training was conducted using MLP, and the dataset was divided into 70% for training and 30% for testing. The outcomes of the MLP model, characterized by a precision of 0.84, an accuracy of 74%, a recall rate of 0.73, and an F1-score of 0.79, underline the suitability and effectiveness of employing MLP as an invaluable tool in crime prediction. [ABSTRACT FROM AUTHOR]

Although there are recent developments for the analysis of first and second-order characteristics of point processes on networks, there are very few attempts in introducing models for network data. Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatiotemporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for which we follow a non-parametric estimation of both the background and the triggering components. Then we consider a semi-parametric version, including a parametric estimation of the background based on covariates, and a non-parametric one of the triggering effects. Our model can be easily adapted to multi-type processes. Our network model outperforms a planar version, improving the fitting of the self-exciting point process model. [ABSTRACT FROM AUTHOR]

The research builds on the previous findings on the number of hate crimes against LGBTQ in Russia before and after the introduction of the so-called "gay propaganda law" in 2013 and significantly enriches them. The previous research studied hate crimes between 2010 and 2016 and was limited to using 2 databases of court decisions. The current research expands both the timeframe and the use of sources: it analyzes such crimes across 11 years (2010–2020) while adding 2 new databases of court rulings to achieve more comprehensive results and generate more accurate statistics of violent acts against LGBTQ in Russia. The previous research identified 263 cases of hate crimes, which was not sufficient to generate accurate statistics and indicated a trend only. The results of the current research significantly expand the existing knowledge by identifying 1056 hate crimes committed against 853 individuals including 365 fatalities. The greater number of cases enabled the current research to present a more thorough descriptive analysis of the crimes committed against LGBTQ in Russia. The paper introduces 3 main categories of hate crimes (Premeditated attacks, Not premeditated attacks, and the so-called "Gay panic defense" cases) and establishes that the number of cases when LGBTQ victim was purposefully selected and targeted grew substantially across the decade. The overall number of victims increased by three times after the introduction of the "gay propaganda law" in 2013 – from 34 in 2010 to 138 in 2015 and remained at relatively the same level for the remaining of the decade. [ABSTRACT FROM AUTHOR]

Criminality is an act of violating the values and norms of society that causes a lot of harm. Much of the criminal data is often just a collection of data that has no information. Analysis of crime data is key in efforts to reduce crime rates that provide an overview of the incidence of crime, patterns, levels of vulnerability, and the level of security of an area. This research proposes data analysis that provides an understanding of crime using data mining techniques, especially the K-means cluster method, both traditional and with principal component analysis (PCA) dimension reduction. Before the PCA process, the values are transformed first with Z score normalization. From the processing through the davies bouldin index (DBI) performance test with 3 clusters, it is concluded that traditional K-means produces a DBI Index value of 0.019 and K-means PCA of 0.299. Meanwhile, to see the optimal cluster, several iterations were performed and resulted in the most optimal DBI index of 4 clusters in K-means of 0.014 and K-means PCA of 0.172. From the performance test value, it means that in the context of clustering the traditional criminal K-means data is declared more optimal than K-means PCA. [ABSTRACT FROM AUTHOR]

The criteria used to identify the crime of stalking have been debated since the 1990s, with most definitions including a so-called "fear standard" as a form of harm experienced by victims. The current study takes the next logical step in this dialogue to examine the varied harms that victims of stalking experience. These analyses operationalize harm through the creation of a harm scale based on several dimensions of fear and emotional distress. By taking this approach and utilizing data from the National Crime Victimization Survey's Supplemental Victimization Survey, the current study identifies the extent of the harm suffered by stalking victims. Further, the determinants of harm following stalking victimization are identified. Finally, the present research examines the relationship between the invasiveness of the stalker's conduct and the harm experienced by victims, finding a positive relationship wherein more invasive encounters correspond with a greater degree of harm to victims. [ABSTRACT FROM AUTHOR]

The true crime genre is becoming increasingly popular with different audiences, especially on-demand platforms. This popularity coincides with the revisiting of stories previously covered by the press but approached differently in true crime narratives. This study aims to identify recurrent frames in media crime narratives by comparing news reports with audiovisual true crime. First, the theoretical framework of framing applied to crime journalism narratives is reviewed. Then, using an inductive method, we observe cases and document available sources to compile reliable frames. Finally, we apply this framework to a relevant media event: the disappearance and murder of Rocío Wanninkhof. Contrasting journalistic accounts with HBO's 'Dolores. The Truth About the Wanninkhof Case' reveals overlapping approaches that are intensified in audiovisual narratives. True crime allows for the redemption of villains, reshaping initial journalistic narratives for better understanding through elaborate context and narrator changes that influence the entire narrative construction. [ABSTRACT FROM AUTHOR]

Hot-spot maps regularly aid many policing resource allocation decisions in today's data-driven age. However, it is unclear what forecasting algorithm(s) should be used to create these maps. To address this gap, we must be able to assess how "good" a generated hot-spot map is. Currently, four main metrics are used for evaluation: the prediction accuracy index (PAI), the recapture rate index (RRI), the prediction efficiency index (PEI), and the prediction efficiency index* (PEI*). This article discusses PAI, RRI, and PEI's strengths and weaknesses, articulates and justifies PEI*, and demonstrates the differences in calculations and interpretations of each metric. We argue that PEI* measures the efficiency of a crime forecasting algorithm while being operationally realistic and should be used in conjunction with other appropriate measures. [ABSTRACT FROM AUTHOR]

The Ironman Triathlon is a globally recognized event; however, there is a lack of research concerning its influence on crime in host cities. This study examines the extent to which the Ironman Triathlon, as a multi-day and mobile event, influences crime across and within the host city. Using city-wide and census tract-level crime data surrounding the May and September 2019 Ironman Competitions held in Chattanooga, Tennessee, USA, autoregressive integrated moving average (ARIMA) analyses explore the potential temporary effects of the event. Results indicate city wide changes in crime, as well as tract-specific effects, but most achieved only marginal statistical significance, and all were limited to the May event. [ABSTRACT FROM AUTHOR]

This paper showcases an organizational life-cycle analysis of corporate offending behavior in small businesses. It analyzes two small food and hospitality firms in China, drawing on deep ethnographic data collected during three years of fieldwork. The paper investigates these two businesses as they go through three phases: pre-existence, existence, and survival. The study shows that organizational life-course analysis is important for understanding the development and root causes of organizational offending. It finds that offending evolves alongside the development of the organization. It shows that an organizational life-cycle analysis should focus not just on changes in the corporation itself, but also on how the regulatory context changes over the course of the organization's development and maturing. Stages in the business cycle coincide with changes in regulatory encounters, and this shapes how corporations view what regulators expect of them and the extent to which they can violate such expectations. This points to a broader form of life-course analysis. It urges the field to moves beyond an analysis of changes in the business to also study the how such changes coincide with changes in the regulatory frameworks that are supposed to monitor and reduce offending. [ABSTRACT FROM AUTHOR]

Using qualitative interview data, this article examines the role of crime analysts in producing knowledge, as well as the challenges they face. Through the collection and organization of data outlining pertinent information about specific districts, analysts aid in the implementation of policing practices. As such, analysts regard themselves as possessing a specialized form of knowledge, which they incorporate and draw on in the outputs they produce. We conclude that analysts do not always employ rigorous, scientific methodologies, while producing their intelligence outputs, suggesting rather that they rely on their familiarity and specialized knowledge of offenders and crimes in their district. Our findings are important to evaluate and understand how 'data-driven' policing is occurring and identifying ways to improve and utilize crime analysis approaches within policing. [ABSTRACT FROM AUTHOR]

Gun and street group violence remains a serious problem in cities across the United States and the focused deterrence strategy has been a widely applied law enforcement intervention to reduce it. Although two meta-analytical studies concluded that the intervention had a significant effect on violence, questions remain about how violence changes across space and time during and after the intervention. This study applies novel geospatial analyses to assess spatiotemporal changes in gun violence before, during, and after the implementation of Philadelphia Focused Deterrence. Emerging hot spot analysis employing Space-Time cubes of ten annual time bins (2009–2018) at the Thiessen polygon level was used to detect and categorize patterns. The analyses revealed a non-significant decreasing trend across the ten-year period. Furthermore, there were ninety-three statistically significant hot spots categorized into four hot spot patterns: fourteen new hot spots; twenty-three consecutive; one persistent; and fifty-three sporadic. There was no evidence showing statistically significant hot spots for the "diminishing" pattern. Knowledge of these patterns that emerge across micro-locations can be used by law enforcement practitioners to complement data-driven problem solving and fine tune these strategies and other place-based programming. Policymakers can use findings to prioritize resources when developing complementary prevention and intervention efforts by tailoring those efforts to the different emergent patterns. [ABSTRACT FROM AUTHOR]

Most terrorist groups are active close to their native area of operation. Why then do some terrorist groups expand to foreign territories? In an exploratory attempt, this article draws parallels between organised crime group transplantation frameworks and the transnational activities of terrorist groups. Utilising transplantation studies, we introduce a conceptual criminological framework that integrates both push and pull factors influencing the transnational movement of terrorist groups. To validate this framework as a pilot test, we apply it to al-Qaeda's operations in the Sahara-Sahel region, relying on open-source data. Our findings, while preliminary, indicate the potential of this unified transplantation approach in offering deeper insights into the transnational behaviours of terrorist groups. [ABSTRACT FROM AUTHOR]

Geographical profiling is a form of crime analysis that can be used to help predict the area within which the home or base of a serial offender resides. Police services globally have incorporated it into their crime analysis capabilities but there remains limited research on how this has been achieved. In this article, we interview 10 geographical profilers working in the field to gather their experiences of the operational use of geographical profiling. Implementation theory is used as the theoretical framework to analyze the interviews. As a result, we identify a field with the potential to add value to criminal investigations, but a lack of knowledge and awareness of frontline officers and staff about the novel capability is affecting its use. In addition, issues relating to technology, tasking acceptance criteria, and methods of data collection create compounding factors serving to potentially reduce its contribution. We provide recommendations to address them including greater levels of training, a loosening of theoretical constraints, academic collaboration, and embracing free-to-access technology and heuristic methods as potential ways to improve the application. [ABSTRACT FROM AUTHOR]

The internet utilization has been developing quickly, mostly in previous eras. Though, by way of the internet develops an important section of the daily life routine, cybercrime is similarly on the grow. The prices of cybercrime will approximately five lakh crores dollars per year through 2022 according to cyber security endeavours details in 2021. The cyber attackers exploit some internet resources as a principal way of transformation through a victim system; therefore intruders generate benefit based on economic, promotional and many more through developing the susceptibilities over devices. The computing cybercrime threats and providing security procedures through physical schemes utilizing previous methodological techniques and moreover examinations have unsuccessful many times to govern cybercrime threats. The previous literature in field of cybercrime threats agonizes from absence of evaluation schemes to guess the cybercrimes, mainly on unstructured information. Hence, an Improved Grey-wolf Optimization based Classification Model (IGO-CM) is developed with the help of chaos system and information entropy utilizing machine learning schemes for cybercrime data analysis to compute the rate of cybercrime by classifying the cybercrime data. The protection examinations by means of the relationship of data analysis methodologies provide services to examine and classify crime data in unstructured form taking from India. The implementation of IGO-CM is performed on MATLAB 2021a tool for unstructured cybercrime data and the outcomes describe the superior performance of IGO-CM depending on accuracy, F-Measure, standard deviation, purity index, intra-cluster distance, root mean square error, and time complexity against a popular classification scheme K-Means, and some optimization schemes like ACO, ALO, PSO and GO. [ABSTRACT FROM AUTHOR]

Both an offender's home area and their daily activity area can impact the spatial distribution of crime. However, existing studies are generally limited to the influence of the offender's home area and its immediate surrounding areas, while ignoring other activity spaces. Recent studies have reported that the routine activities of an offender are similar to those of the residents living in the same vicinity. Based on this finding, our study proposed a flow-based method to measure how offenders are distributed in space according to the spatial mobility of the residents. The study area consists of 2643 communities in ZG City in southeast China; resident flows between every two communities were calculated based on mobile phone data. Offenders' activity locations were inferred from the mobility flows of residents living in the same community. The estimated count of offenders in each community included both the offenders living there and offenders visiting there. Negative binomial regression models were constructed to test the explanatory power of this estimated offender count. Results showed that the flow-based offender count outperformed the home-based offender count. It also outperformed a spatial-lagged count that considers offenders from the immediate neighboring communities. This approach improved the estimation of the spatial distribution of offenders, which is helpful for crime analysis and police practice. [ABSTRACT FROM AUTHOR]

This research work is based on the problems related to the increase in the number of registered complaints of computer crimes, which negatively affects the perception of citizen insecurity and the procedural overload in the justice system, there being a need on the part of the State to address this problem. In response to this, considering that data have been collected that demonstrate that the Immediate Process has obtained effective results in the justice system, the objective of this research work is to expose the viable application of the Immediate Process as an effective tool for the processing of the crimes of Computer Fraud and Proposals to children and adolescents for sexual purposes through technological means regulated by Law No. 30096 -- Computer Crime Law. For this, the crime figures, procedural overload of the justice system, citizen insecurity and the growth of complaints for computer crimes are observed in order to show the problem to be addressed. Then an analysis of the Immediate Process is carried out, in addition, the results obtained in its application in the justice system are observed. Subsequently, an analysis of the crimes of Computer Fraud and Proposals to children and adolescents for sexual purposes through technological means is carried out. Finally, through an analysis of cases related to the aforementioned computer crimes, it is possible to expose the viability of the Immediate Process for the effective prosecution of these computer crimes. [ABSTRACT FROM AUTHOR]

Because bloodstain patterns resulting from close-range shooting are intrinsically different from those of long-range shooting, an accurate interpretation and delineation of these phenomena are essential in forensic science and crime scene analysis. Such a delineation would be helpful, for example, to distinguish whether a suicide or a homicide had happened. If the shooting was from a long-range (most likely a homicide), muzzle gases would not be able to influence blood spatter ejected from a victim. However, in the case of a close-range shooting, muzzle gases would greatly influence blood spatter. Herein, the effect of the muzzle gases on bloodstain patterns is studied. A de Laval nozzle is used to mimic an issue of supersonic "muzzle" gas from a gun barrel. The supersonic gas flow passes through a cylinder containing defibrinated sheep blood, which is blown off and atomized into numerous drops. These drops fly away and settle onto the floor or onto vertical walls at various distances from the cylinder exit. Viscoelasticity of the defibrinated sheep blood is enhanced by adding Xanthan to model different states of blood corresponding to different conditions. An impact of a vertically-released single drop onto an inclined substrate was also studied to elucidate splashing regimes/criteria of the blood drops of different viscoelasticity levels. We found that stronger elastic forces facilitated formation of bloodstains with higher ellipticity at a higher impact angles. [ABSTRACT FROM AUTHOR]

The ever expanding digital space and government initiatives like Digital India have increased connectivity, digitization, remote employment which empowered us with technology and made our lifestyle easy and fast but as digitization is enhancing, cyberattacks are proportionally growing. In the initial stage of this study, it was observed from the data analysis and it was evident that women are particularly a soft target of many cybercriminals and cyber fraudsters. Women feel insecure in cyberspace, hence there is a need to dive deeper into our understanding and statistics of cybercrimes against women in India. It becomes necessary to determine the factors that have led to the clear surge of such crimes in recent years. In recent studies no such analysis is done focusing on geographical factors and top most cybercrime types committed against women. Therefore, in this article, prediction for cybercrime trends against women was performed using statistical tools and techniques to provide a better insight into the current scenario revolving around cybercrimes and women. A regressive statistical analysis of cybercrime data of all states of India to understand the current trend of cybercrimes, identification of the most vulnerable states of India and specific cybercrime with their percentage in whole, also where cybercrime afflicted women stand in those numbers. Further, for preventive measures a robust guidelines is proposed to combat cybercrimes for a better future. [ABSTRACT FROM AUTHOR]

Objective: This article aims to analyze a possible relationship between budget execution and crime levels in the State of Ceará between 2012 and 2021. Methodology: Use of linear regression using dependent variables (budgetary expenditures with the public security function) and independent variables (public revenue, intentional lethal violent crimes, seizure of weapons, violent property crimes, thefts, and sexual crimes). Results: After evaluating the proposed variables, the results showed the influence of 2 (two) of the 6 (six) independent variables analyzed: revenue at 1% and theft at 10%. The other indicators related to intentional lethal violent crimes, gun seizures, violent property crimes, and sex crimes did not show consistent results. Contributions of the Study: Expands the level of knowledge about possible relationships between budget expenditures and crime (its various typologies), in order to better understand whether effectively only spending on public security can influence the level of crime in a state of the federation. [ABSTRACT FROM AUTHOR]

In today's global world, many people can move across borders as travelling has become much easier in many ways. However, the securitization of borders has not been relaxed, implying that multilingual police-civilian interactions are becoming more 'commonplace'. Within the framework of conversation analysis, this article presents a novel study on multilingual police border checks. These are police encounters "on the ground" (not in police custody), and as such, there are no interpreters or language experts present. Focusing on the analysis of a single-case police encounter in which participants have to rely on a second language (English) that none of them are proficient in (i.e., 'novice-novice interaction'), this article examines practices that speakers use to resolve a crime involving the illegal possession of drugs. In addition, some complementary findings from other border checks are presented. Overall, this study shows that participants attempt to achieve intersubjectivity by using interactional (and embodied) practices (e.g., word choice, repair, speech simplification) oriented toward recipient design. By doing so, participants shape the progressivity of the encounter and ultimately achieve their objectives in the interaction. As such, this article shows how a high-stake (police) multilingual situation can also be resolved in the absence of a language expert. [ABSTRACT FROM AUTHOR]

In this world, the rate of crimes is increasing as well as challenging the capabilities of people who are investigating crimes. Proper crime analysis and clustering has to be done in those cases. Crime analysis is the analysis of crime patterns and trends. It also assists in the research and planning necessary for the functioning of tactical forces and administrative services. Crime data grouping and clustering is very important to analyse the crime patterns and trends. By identifying patterns of crime committed in the past and the most common types of crime, crimes can be prevented from recurring in an area. Machine learning plays a key role in efficiently clustering today’s crime data. [ABSTRACT FROM AUTHOR]

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