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Crime data are problematic: Crimes that are never reported undermine its validity and differences in police recording practices affect its reliability. However, the true extent of these problems is not well known, with existing studies suffering from a number of methodological limitations. We examine the quality of police recorded crime data and survey-based crime estimates recorded in England and Wales using a robust latent trait model that effectively represents the competing sources of error. We find that whilst crime rates derived from police data systematically underestimate the true extent of crime, they are substantially more reliable than estimates from survey data. Reliability is lower for violence and criminal damage and is getting worse over time. [ABSTRACT FROM AUTHOR]

Homicide trend research has relied heavily on the same two data systems for decades. The current article highlights the research utility of a newer database—the National Violent Death Reporting System (NVDRS)—for examining the contours of homicide change. First, we describe the history, design, and availability of the NVDRS, and review past homicide and longitudinal studies using these data. Second, we discuss two features of the data system that researchers should consider when conducting homicide trend analyses: (a) data availability and representativeness and (b) data completeness. Third, we demonstrate a strong degree of correspondence between NVDRS homicide victimization trends and trends from other data systems. Fourth, we illustrate the ways in which the NVDRS can enrich our understanding of homicide trends by examining how homicide characteristics changed during the two recent homicide spikes from 2014 to 2016 and 2019 to 2020. [ABSTRACT FROM AUTHOR]

This article considers the operation of the time series processes that underlie U.S. crime rate trends. These processes are important because they carry the influence of the variables that generate the rates. They limit the forms that explanations of crime trends can take, and they open avenues for new theoretical development. Using data from the nation and a panel of large cities, analysis finds that crime trends operate much like random walks or their smoothed cousins; that they rarely deviate from a constant pattern; and that they show little evidence of nonlinearity. The article discusses the substantive implications of these features for understanding crime trends, and it considers directions for expanding the study of their empirical properties. [ABSTRACT FROM AUTHOR]

Based on the Indonesia national police crime database, it is reported that vehicle theft cases have increased during the Covid-19 pandemic. The database reported an increasing trend of vehicle theft, 4,065 cases from January 2019 to January 2020 in the province and regency region. Therefore, to help police officers work and minimize the criminal cases of vehicle theft, an effective strategy is needed to reduce these threats. This study proposes implementing SMS and QRcode technology embedded in the vehicle for validation information. Cloud computing capabilities can offer real-time network access to technology resources that can be physically located anywhere geographically based on business needs. This technology can rapidly search and show detailed information regarding the specific vehicle, including the vehicle owner, the vehicle registration number, and the validation of the driver's license. To implement and examine the effectiveness of the proposed technology, this study was conducted an experimental study in a real-world setting from January 2021 until April 2021 in Makassar city, Indonesia. This study concluded that the proposed technology could successfully be implemented and effectively show detailed information regarding the specific vehicle based on the experimental results. This study concluded the potential use of the proposed technology in the real world as an alternative solution to minimize the criminal cases of vehicle theft. It can be used as an alternative solution to reduce the increase in criminal cases of inter-island private vehicle theft syndicates. [ABSTRACT FROM AUTHOR]

The criminal cases are increasing rapidly in our country due to which number of pending cases are also piling up. The continuous increase in the criminal cases is proving that it would be very difficult to be classified and to be solved. Recognizing the criminal activity patterns of a place is very important in order to prevent it from happening any other case. The crime solving agencies are doing a better work and can do a better work if they have a good knowledge of the pattern of criminal activities that are happening in a particular area or region. The most difficult task will most likely be "proving" to legislators that it works. It's tough to establish the negative when a system is meant to prevent something from happening. A positive feedback loop would certainly benefit companies who are directly involved in providing governments with ML capabilities to monitor areas or predict crime. Improvements in crime prevention technology will almost certainly lead to an increase in overall spending on this technology. The proposed system leverages historical crime data, demographic information, socioeconomic factors, and other relevant features to train and fine-tune machine learning algorithms. Various algorithms such as decision trees, random forests, and support vector machines are employed to analyze and predict crime rates for specific geographical areas. The project aims to provide law enforcement agencies with a valuable tool for proactive crime prevention and resource allocation, allowing them to allocate personnel and resources more efficiently in areas with a higher predicted crime rate. Additionally, the insights gained from this project can aid policymakers in making informed decisions to address the underlying socioeconomic factors contributing to crime in different regions. [ABSTRACT FROM AUTHOR]

Crime has an inherent geographical quality and when a crime occurs, it happens within a particular space making spatiality essential component in crime studies. To prevent and respond to crimes, it is first essential to identify the factors that trigger crimes and then design policy and strategy based on each factor. This project investigates the spatial dimension of violent crime rates in the city of Detroit for 2019. Crime data were obtained from the City of Detroit Data Portal and demographic data relating to social disorganization theory were obtained from the Census Bureau. In the presence of spatial spill over and spatial dependence, the assumptions of classical statistics are violated, and Ordinary Least Squares estimations are inefficient in explaining spatial dimensions of crime. This paper uses explanatory variables relating to the social disorganization theory of crime and spatial autoregressive models to determine the predictors of violent crime in the City for the period. Using GeoDa 1.18 and ArcGIS Desktop 10.7.1 software package, Spatial Lag Models (SLM) and Spatial Error Models were carried out to determine which model has high performance in identifying predictors of violent crime. SLM outperformed SEM in terms of efficiency with (AIC:5268.52; Breusch-Pagan test: 9.8402; R2: 16% & Log Likelihood: −2627.26) > SEM (AIC: 5275.24; Breusch-Pagan test: 9.7601; R2: 15% & Log Likelihood: −2630.6194). Strong support is found for the spatial disorganization theory of crime. High percent ethnic heterogeneity (% black) and high college graduates are the strongest predictors of violent crime in the study area. [ABSTRACT FROM AUTHOR]

Extensive research exists in relation to the Sexual Offences Act 2003, in particular, rape and assault by penetration, the two most serious offences involving non-consensual sexual penetration of the victim. However, the other penetrative offence, causing a person to engage in (penetrative) sexual activity without consent, found in section 4 of the Act, has, to date, been excluded from national statistics and research. This article analyses novel data relating to the section 4 penetrative offence, collected using freedom of information requests from 37 police forces in England and Wales over a 13-year period. The data explore victim and offender demographics and outcomes after detection. The findings challenge understandings around who the victims and perpetrators of penetrative sexual offences are. [ABSTRACT FROM AUTHOR]

Several U.S. states are considering setting or raising a minimum age of juvenile court jurisdiction. However, there is scant evidence to suggest if a state minimum age law would protect children from developmentally inappropriate proceedings beyond existing capacity and competency statutes. To address this central question, this case study focuses on the state of California and considers (a) existing state laws, (b) state juvenile crime data, and (c) opinions of diverse juvenile justice stakeholders. Triangulated analysis found that a low number of California children below the age of 12 years are petitioned in juvenile court and most are referred for misdemeanor or status offenses. Existing legal protections are present yet inconsistently implemented. A minimum age law would address some of these policy gaps. [ABSTRACT FROM AUTHOR]

The Chicago Public School's Safe Passage program is a large-scale intervention designed to improve the safety of students as they travel to and from schools. By placing hundreds of adult monitors on designated streets around schools, the program has the potential to reduce crime. This study evaluates Safe Passage's impact on crime around primary schools during the 2013-2014 expansion of the program. Using longitudinal, geocoded crime data and a difference-in-differences and triple-difference methodology, this study finds suggestive, though not conclusive, evidence that Safe Passage may have contributed to lower crime on designated routes despite some increases in crime around designated "welcoming schools" during the 2013-2014 school year. Implications for the continued expansion of the program and further directions for research of the program are discussed. [ABSTRACT FROM AUTHOR]

China is generally considered a safe place: among the safest for foreigners to visit. For local (long-term) residents and for Chinese criminology scholars, China as a country and its individual cities might be safer compared to many of their foreign peers; however, they might not be as safe as some travel agents claim. To show why this is the case, we crawled and geovisualized the 2015–2016 crime records (n = 24,803) available to the public online. The geovisualization shows that the seven crimes (n = 12,516) that were most likely to be influenced by space in Beijing were aggravated assault, blackmail, cheating and bluffing, dangerous driving, picking quarrels and provoking trouble, robbery and theft. It also shows a Beijing that many might not have known before. [ABSTRACT FROM AUTHOR]

Australia is a country firmly part of the Global North, yet geographically located in the Global South. This North-in-South divide plays out internally within Australia given its status as a British settler-colonial society which continues to perpetrate imperial and colonial practices vis-à-vis the Indigenous peoples and vis-à-vis Australia's neighboring countries in the Asia-Pacific region. This article draws on and discusses five seminal examples forming a case study on Australia to examine big data practices through the lens of Southern Theory from a criminological perspective. We argue that Australia's use of big data cements its status as a North-in-South environment where colonial domination is continued via modern technologies to effect enduring informational imperialism and digital colonialism. We conclude by outlining some promising ways in which data practices can be decolonized through Indigenous Data Sovereignty but acknowledge these are not currently the norm; so Australia's digital colonialism/coloniality endures for the time being. [ABSTRACT FROM AUTHOR]

Although suicide and homicide are two of the leading causes of death, theoretical understanding and rigorous quantitative examination of homicide-suicide—the rare combination of suicide and homicide—are sparse. We ground homicide-suicide in the stream analogy of lethal violence and use three analytical techniques to examine the shared and unique correlates of suicide, homicide-suicide, and homicide. Data come from the National Violent Death Reporting System (NVDRS), a pooled, cross-sectional time-series database of US victims across 46 states from 2003 to 2013. A logistic regression model with robust standard errors clustered across states and year fixed effects on 105,243 suicide and homicide-suicide cases predicts the odds of homicide-suicide relative to suicide across a range of theoretically informed covariates. A multinomial logistic regression model investigates the factors that distinguish 26,243 appended homicide cases (from the NVDRS) from suicide and homicide-suicide cases. And, an item-response-based statistical approach examines the extent to which homicide-suicide perpetrators are primarily homicidal versus primarily suicidal. Results indicated that interpersonal stressors and criminal history increased, while physical and mental health stressors decreased, the odds of homicide-suicide relative to suicide. In addition, the etiology of homicide-suicide more closely resembled homicide than suicide. Finally, almost one-half of the homicide-suicide cases in our sample were classified as "primarily homicidal," while less than one-quarter were classified as "primarily suicidal." Findings suggest that homicide-suicide can be conceptualized as a current in the stream analogy of lethal violence, and that the prevention of homicide-suicide would be better facilitated via screening of violence prevention than suicide prevention programs. [ABSTRACT FROM AUTHOR]

Evidence-based policing has become a commonly used term in academic circles to describe policing practices guided by research evidence demonstrating their effectiveness. Little is known, however, about how police practitioners understand and define the term and the extent to which these definitions align with researcher conceptions of the term. We examine data from a survey on receptivity to research administered to 1355 police officers, supervisors, and leaders from multiple US agencies. Officers familiar with the term 'evidence-based policing' were asked to define it, and we coded each definition for key terms identified in the literature as relevant to evidence-based practice and for overall match to commonly used academic definitions. Our results suggest variability in both familiarity with the term 'evidence-based policing' and use of key terms from academic definitions based on rank. Higher ranking officers were more likely to have definitions that aligned with academic conceptions, although very few officer definitions fully matched those provided by researchers. Multivariate results suggest higher education is associated with more 'correct' definitions. We discuss the implications of our work for efforts to make policing more evidence-based. [ABSTRACT FROM AUTHOR]

Research on the nature of bias homicide has experienced increased interest in the academic literature. To date, few studies have compared the similarities and differences between anti-race/ethnicity bias homicides and average American homicides. Consequently, we know little about how the offender, victim, and situational characteristics compare across these two homicide types. Drawing from doing difference theory of bias crime, the aim of this study is to comparatively analyze the attributes of anti-race/ethnicity homicides to average homicides between 1990 and 2014. Antirace/ethnicity homicide data is extracted from the U.S. Extremist Crime Database and paired with average homicides from the Federal Bureau of Investigation's 2000 Supplementary Homicide Reports. The results of this study suggest that the characteristics of anti-race/ethnicity homicides are both similar and different from average homicides. Implications for bias crime theory, research, and policy are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Criminology is an applied discipline where the findings from the data collected and analyses conducted inform debates about policy and practice. For this to happen, a discipline must have an agreed‐upon set of facts to define the problem and suggest solutions. We can debate the soundness of fundamental data series, but these debates must take place within the confines of scientific inquiry and all data must be subjected to the same scrutiny. Data sources must comprise agreed‐upon standards for collection and be accessible for replication. The increasing use of “big data” has frayed this agreement about quality and accessibility and has made it more difficult for criminology to have its own facts. In this presentation, I define the term “big data” and argue it will be difficult for big data to replace traditional data sources and to live up to their potential for knowledge building. Finally, I suggest a few things that the discipline might do to address these problems of access and quality. [ABSTRACT FROM AUTHOR]

This study replicates and extends Archer and Gartner’s classic work testing whether wars increase postwar homicide rates because they legitimize the use of violence as a means of conflict resolution. Using the Comparative Crime Data File (CCDF), we replicated the original study with data from the two World Wars, as well as 12 smaller wars occurring prior to 1980. Our replication results generally confirmed the hypothesis that wars increase postwar homicide rates, although there were differences in results based on the method of analysis. We then examined the validity of this theory using data on four wars occurring after 1990, but found no support for the legitimation of violence argument. We argue that the null findings encourage theoretical expansion, which is an underappreciated aspect of replication that is just as valuable as empirical validation. [ABSTRACT FROM AUTHOR]

Honor crimes are violent acts that are experienced among different religions and ethnicities. This type of offense can be justified, either before or after the crime, by the offender’s perceived need to protect honor-based values. This study used Sykes and Matza’s neutralization theory to explain the offender’s justification of honor crimes. We used data from the United States Extremist Crime Database, which has incident, victim, and perpetrator level indicators for the population of honor crimes in the United States (N= 16). Using open source search files for the cases, neutralizations and justifications provided by the offenders are presented. [ABSTRACT FROM PUBLISHER]

Administrative databases are used by criminal justice professionals to guide specialist responses to crimes of child sexual abuse. Assumptions might be made that the database will be accurate, contemporaneous, complete, and meaningful; however, this may not be the case. The main aim of the current study was to critically evaluate a database used by practitioners for tracking cases of child sexual abuse, in order to identify evidence that may justify investment in improved data gathering and centralised information management systems. Three data quality dimensions were examined: (1) completeness, measured as data that were not missing and were of adequate breadth and depth, (2) accuracy, namely that the data are correct, and (3) believability, where the data may be regarded as credible or plausible. Results indicated that data quality was of concern for all three dimensions, with missing and inaccurate data found across a range of variables, and issues with believability found on two variables. The implications of these results for development of new data documentation methods are discussed. [ABSTRACT FROM PUBLISHER]

This article examines the intersection of two structural developments: the growth of surveillance and the rise of "big data." Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does--and does not--transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality. [ABSTRACT FROM AUTHOR]

The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas - an administrative system of areas used by the UK police) and the frequency of anti-social behavior crimes. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. The data are from the UK police and contain over 600,000 records before preprocessing. The results, looking at predictive performance as well as processing time, indicate that decision trees (M5P algorithm) can be used to reliably predict crime frequency in general as well as anti-social behavior frequency. [ABSTRACT FROM AUTHOR]

Crime, when someone does any unlawful activity, the intensity level of crime can be from very low to very high. In current society, crime exist everywhere in distinct form, and if we collect all the data related to different crime, that data would be very large in volume which can be managed through data mining techniques. Using various data mining techniques, lot of conclusion can be drawn like rise or fall in particular type of crime, percentage of particular crime, time when crime mostly or less happens, area in which maximum or minimum crime happens etc. In this paper, we use rapid miner data mining tool and naïve Bayes classification algorithm to show the different types of crime. By using crime data on classification algorithm with rapid miner tool we tries to demonstrate that how efficiently naïve Bayes algorithm can manage this data and the accuracy of result. As there is a probability of crime prediction and naïve Bayes algorithm gives result based on probability, thus the help of naïve Bayes classification algorithm the better accuracy of result can be achieved. This research will be very useful in crime control and crime detection but these all depends on the volume and correctness of the data, more data gives the more accurate prediction. [ABSTRACT FROM AUTHOR]

Tools that perform pattern recognition analysis of crimes, comprising at the same time forecasting, clustering, and recommendations on real data such as patrolling routes, are not fully integrated; modules are developed separately, and thus, a single workflow providing all the steps necessary to perform this analysis has not been reported. In this paper, we propose forecasting criminal activity in a particular region by using supervised classification; then, to use this information to automatically cluster and find important hot spots; and finally, to optimize patrolling routes for personnel working in public security. The proposed forecasting model (CR-Ω+) is based on the family of Kora-Ω Logical-Combinatorial algorithms operating on large data volumes from several heterogeneous sources using an inductive learning process. We perform two analyses: punctual prediction and tendency analysis, which show that it is possible to punctually predict one out of four crimes to be perpetrated (crime family, in a specific space and time), and two out of three times the place of crime, despite of the noise of the dataset. The forecasted crimes are then clustered using a density-based clustering algorithm, and finally route patrolling routes were crafted using an ant-colony optimization algorithm. For three different patrolling requirements, we were always able to find optimal routes in shorter time compared to commonly used random walk algorithms. We present a case study based on real crime data from the municipality of Cuautitlán Izcalli, in Mexico. [ABSTRACT FROM AUTHOR]

Purpose To test whether geographical, temporal, and modus operandi ( MO) crime scene behaviours can be used to support behavioural case linkage ( BCL) with crime series that contain several different types of offence. Methods Crime scene data relating to 749 solved commercial burglaries and robberies were extracted from the databases of the Metropolitan Police Service, London, England. From these data, 2,231 linked crime pairs (containing two crimes committed by the same offender) and 273,422 unlinked crime pairs were created (two crimes committed by different offenders). Three measures of similarity were calculated for each crime pair: (1) the kilometre distance between crimes (intercrime distance [ICD]); (2) the number of days between crimes (temporal proximity [TP]); and (3) a statistical measure of similarity in MO behaviour (Jaccard's coefficient). Statistical tests of difference, binary leave-one-out logistic regression, and receiver operating characteristic analysis were used to determine whether the three measures of similarity could be used to distinguish between linked and unlinked crime pairs, some containing only burglaries (burglary pairs), some containing only robberies (robbery pairs), and some containing both burglaries and robberies (cross-crime pairs). Results Linked and unlinked crime pairs could be distinguished with a high level of accuracy ( AUCs > .90), with the highest accuracy when combining ICD, TP, and Jaccard's coefficient. These findings were replicated with the burglary pairs, robbery pairs, and cross-crime pairs. Conclusions Offender behaviour is sufficiently consistent and distinctive to support the use of BCL with versatile crime series, as well as with burglary crime series and robbery crime series. [ABSTRACT FROM AUTHOR]

Objective: To compare the time trends of Google search interest in methamphetamine and criminal offences related to this drug. Methods: Google Trends data for the search term "meth" was compared to methamphetamine-related crime statistics (incl. use, possession, and dealing) in Switzerland, Germany, and Austria for the years 2004–2016. Google data was availably monthly. Crime data was available yearly, and monthly values were imputed. Results: On the country level, internet search trends for "meth" roughly paralleled relevant criminal activity. State-level data, which was available for Austria, showed more heterogeneity. Cross-correlations for yearly data almost always peaked at a lag time of 0 and coefficients were mostly between 0.7 and 1.0 on the country level, and between 0.5 to 1.0 on the state level. Monthly cross-correlations based on imputed values were substantially lower, ranging from 0 to 0.6. Conclusions: These results encourage the further evaluation by law enforcement authorities of Google search activity as a possible predictor of methamphetamine-related crime. However, several limitations, in particular the crude temporal resolution of available crime data, precluded a detailed assessment of the relationship between internet search trends and the development of methamphetamine-related crime in central Europe. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of testing for parameter change in zero-inflated generalized Poisson (ZIGP) autoregressive models. We verify that the ZIGP process is stationary and ergodic and that the conditional maximum likelihood estimator (CMLE) is strongly consistent and asymptotically normal. Based on these results, we construct CMLE- and residual-based cumulative sum tests and show that their limiting null distributions are a function of independent Brownian bridges. The simulation results are provided for illustration. A real data analysis is performed on some crime data of Australia. [ABSTRACT FROM AUTHOR]

In our innovative crime location forecast method, at the outset, the crime features are mined from the crime database and used for performing the adaptive mutation-based artificial bee colony (AMABC) algorithm, in which the database attributes and crime values are bunched together. Subsequently, the frequent closed itemsets lattice (FCIL) is built by the rules support factor values, and from this the frequent rules are extracted. In the course of the FCIL creation, the clustered attributes values are processed like a sliding window. In accordance with the frequent rules, the related crime locations are created. Thus, our proposed sliding with itemsets factor-based FCIL proposed technique is endowed with the superb skill of fruitfully forecasting the locations by means of AMABC and FCIL methods. In our innovative approach, we apply an UCI Machine Learning Repository-Communities and Crime Data Set for the offence investigation. The novel method is analysed and contrasted with the modern mining algorithms such as Apriori, Eclat and conservative FCIL. [ABSTRACT FROM AUTHOR]

Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behaviour of known offenders within their activity spaces. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently select targets in or near places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CrimeTracer outperforms all other methods used for location recommendation we evaluate here. [ABSTRACT FROM AUTHOR]

Using mathematical methods to understand and model crime is a recent idea that has drawn considerable attention from researchers during the last five years. From the plethora of models that have been proposed, perhaps the most successful one has been a diffusion-type differential equations model that describes how the number of criminals evolves in a specific area. We propose a more detailed form of this model that allows for two distinct criminal types associated with major and minor crime. Additionally, we examine a stochastic variant of the model that represents more realistically the ‘generation’ of new criminals. Numerical solutions from both models are presented and compared with actual crime data for the Greater Manchester area. Agreement between simulations and actual data is satisfactory. A preliminary statistical analysis of the data also supports the model's potential to describe crime. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this study is to investigate the cases of “delayed and denied” justice that resulted from a lack of or poor record-keeping in the South African courts and police service with a view to encouraging proper records management. Proper records management plays a significant role in supporting the justice system. Records provide the critical evidence that a particular action or transaction took place and can be used as evidence in a court of law. Without reliable and authentic records, government cannot administer justice and, as a result, offenders can be set free while the victims are denied justice. Design/methodology/approach – Utilising content analysis, this study extracted print media articles (2000-2012) relating to the “records and justice system” from the South African Media database, which is one of the databases hosted by the South African Bibliographic and Information Network. The study selected cases reported in the media to conduct follow-up interviews with a policeman, lawyer and judge to discover the implications of the unavailability of required records in court cases. Furthermore, access was given to three selected cases that were given high profile in the media and these cases were analysed to find out what the final verdict in each case was. Findings – Results of the study suggest that some criminal cases were withdrawn due to missing dockets or cases not properly registered. In some instances, records were reconstructed, resulting in the travesty of justice. The study concludes by arguing that if records are not accounted for, lawyers, prosecutors and magistrates could dispute the authenticity of records. As a result, justice for victims would be delayed and ultimately denied while the perpetrators are freed. Research limitations/implications – The findings and recommendations of this study may go a long way in helping courts in South Africa to manage records properly to support the justice system. Furthermore, the study is a useful compilation of the importance of missing records for social purposes. Originality/value – In an attempt to show the role of records management in the administration of justice in South Africa, this study used a triangulation of data collection tools. This is a new attempt, especially in the South African context. Previous studies in southern Africa only looked at the management of records in supporting justice system. [ABSTRACT FROM AUTHOR]

Purpose – This research paper aims to consider the use of the UK Freedom of Information Act 2000 (FOIA) as a resource providing access to otherwise unavailable data from the UK Police forces. Not seeking to be a critical examination of Police practice, it offers insight to many aspects of records management appertaining to the police service provision of recorded crime. Authors consider whether record management is sufficiently integrated into police practice, given the transparency called for by the FOIA, contemporary societal needs and the growing requirement to provide high value evidence led assessments of activity both within and external to the service. Design/methodology/approach – FOIA was utilised to collect data from all police forces in England and Wales through multiple requests. Carried out over a 15-month period, three requests were collated and responses compared, allowing for examination of compliance with the legislation and reflections on the manner in which records were sought and ultimately disseminated. Findings – Generally, responding to FOIA requests was well managed by English Police Forces. Methods of data management and collection practice were exposed which the authors suggest pose questions on the strength of records management consideration that may be worthy of further work. Configuration management of records is highlighted as an essential function given the disparity of data releases experienced. Research limitations/implications – This research highlights the FOIA as a valuable methodological tool for academic researchers, but it is limited in respect of seeking firm contextual explanation of the Police internal procedures to answer requests. By making common requests over a long time period to the same Police forces, it provides a clear study of FOIA processes and raises potentially significant questions for records management consideration. Practical implications – Findings provide advice on developing use of the FOIA as an academic methodological resource and reflect on the findings impact on internal police use of data and information records. Originality/value – This paper allows for reflection on the importance of high-value records management in the day-to-day business of the police service and questions whether such knowledge areas are suitably considered. Covering an area of little previous academic enquiry, the research informs criminal justice practitioners of areas for potential further discussion and academic researchers on the validity of using the FOIA as a valuable information source. [ABSTRACT FROM AUTHOR]

Geographical masks are a group of location protection methods for the dissemination and publication of confidential and sensitive information, such as health- and crime-related geo-referenced data. The use of such masks ensures that privacy is protected for the individuals involved in the datasets. Nevertheless, the protection process introduces spatial error to the masked dataset. This study quantifies the spatial error of masked datasets using two approaches. First, a perceptual survey was employed where participants ranked the similarity of a diverse sample of masked and original maps. Second, a spatial statistical analysis was performed that provided quantitative results for the same pairs of maps. Spatial statistical similarity is calculated with three divergence indices that employ different spatial clustering methods. All indices are significantly correlated with the perceptual similarity. Finally, the results of the spatial analysis are used as the explanatory variable to estimate the perceptual similarity. Three prediction models are created that indicate upper boundaries for the spatial statistical results upon which the masked data are perceived differently from the original data. The results of the study aim to help potential "maskers" to quantify and evaluate the error of confidential masked visualizations. [ABSTRACT FROM AUTHOR]

Informatics and databases used to track crime are an increasing part of crime prevention strategies and hold promise for identifying individuals likely to be involved in crime so they can be more easily tracked to prevent future crimes. This article uses cost analysis techniques to explore the utility of California's gang prevention database (CalGang) from 1997 to 2010. Costs included the primary operating costs of the database and secondary costs associated with Type I and Type II errors. CalGang tracks more than 235,000 individuals implicated in gang activity statewide. Although the primary costs of operating the database were relatively low, secondary costs resulting from errors were much larger and led to an overall greater cost to society. In addition, it appears that CalGang has not been successful in reducing the rate of gang-related crime in Los Angeles. Resources for CalGang may be better spent on other crime prevention activities that are less costly to society and lead to greater prevention. [ABSTRACT FROM AUTHOR]

Sex offender laws that target recidivism (e.g., community notification and residency restriction regimes) are premised-at least in part-on the idea that sex offender proximity and victimization risk are positively correlated. We examine this relationship by combining past and current address information of registered sex offenders ( RSOs) with crime data from Baltimore County, Maryland, to study how crime rates vary across neighborhoods with different concentrations of resident RSOs. Contrary to the assumptions of policymakers and the public, we find that, all else equal, reported sex offense victimization risk is generally (although not uniformly) lower in neighborhoods where more RSOs live. To further probe the relationship between where RSOs live and where sex crime occurs, we consider whether public knowledge of the identity and proximity of RSOs may make offending in those areas more difficult for (or less attractive to) all potential sex offenders. We exploit the fact that Maryland's registry became searchable via the Internet during our sample period to investigate how laws that publicly identify RSOs may change the relationship between the residential concentration of RSOs and neighborhood victimization risk. Surprisingly, for some categories of sex crime, notification appears to increase the relative risk of victimization in neighborhoods with greater concentrations of RSOs. [ABSTRACT FROM AUTHOR]

The article presents the discussion on continual growth in the availability of address and neighborhood-level crime data allowing for many spatial approaches to crime occurrence given the nature of criminal justice related data.

The article focuses on analysis of crime data from states of the U.S. regarding effectiveness of burglary tool legislations for reducing crime. Topics discussed include introduction of an act in West Virginia in 2013 to enact burglary tool statutes, concern expressed by legislators regarding punishing of innocent people with respect to burglary tool statutes and reference of court cases such as Puglisi v. State, Commonwealth v. Gendrachi and Crossland v. State filed in supreme courts of states.

Using crime data over a period of a decade for Glasgow, this paper explores whether the density of prior offenders in a neighbourhoods has an influence on the propensity of others to (re)commence offending. The study shows that the number of 'newly active' offenders in a neighbourhood in the current quarter is positively associated with the density of prior offenders for both violent and property crime from the previous two years. In the case of newly active property offenders, the relationship with active prior offenders is apparent only when prior offender counts exceed the median. The paper postulates that intraneighbourhood social mechanisms may be at work to create these effects. The results suggest that policies which concentrate offenders in particular neighbourhoods may increase the number of newly active offenders, and point to evidence of a threshold at which these effects take place. [ABSTRACT FROM AUTHOR]

In 2011 the Home Office released the police.uk website, which provided a high-resolution map of recent crime data for the national extents of England, Wales and Northern Ireland. Through this free service, crimes were represented as points plotted on top of a Google map, visible down to a street level of resolution. However, in order to maintain confidentiality and to comply with data disclosure legislation, individual-level crimes were aggregated into points that represented clusters of events that were located over a series of streets. However, with aggregation the representation of crimes as points becomes problematic, engendering spurious precision over where crimes occurred. Given obvious public sensitivity to such information, there are social imperatives for appropriate representation of crime data, and as such, in this paper we present a method of translating the 'point' crime events into a new representational form that is tied to street network geography; presenting these results in an alternate national crime mapping portal . [ABSTRACT FROM AUTHOR]

Routinely collected statistics on crime and the administration of justice are essential for developing laws and policies that are responsive to the crime problem and for holding criminal justice agencies accountable in the execution of those policies. Any statistical system on crime and the administration of justice should include the opportunity for the direct participation of citizens in providing information on the crime problem. This direct participation enhances our ability to check on administrative record data and bolsters the legitimacy of these statistics more generally. Victimization surveys allow citizens to have this direct participation. The specific role of victimization surveys in a statistical system will depend upon the credibility and technical proficiencies of the police, court and correctional agencies. This paper describes the preferred role of victimization surveys and the technological challenges in playing that role under different conditions of legitimacy and technical capabilities. [ABSTRACT FROM AUTHOR]

Kernel smoothing methods can be used to visualise complex point event data that are distributed geographically. The visualisations generated by these methods allow users to observe trends in the data which may not be apparent from numerical summaries or by visualising the point data themselves. Crime data in the UK has recently been made available to the public for the smallest postal delivery areas in the country (postcodes). Visualisations of the number of recorded events at each postcode for a given area are provided online. In this paper we apply some widely used kernel smoothing methods to this data, to assess the use of kernel smoothing methods as applied to open access data. Kernel smoothing methods are applied to crime data from the Greater London metropolitan area using freely available packages for the open source R program. We also investigate the utility of using simple methods to smooth the data over time. The kernel smoothers used are demonstrated to provide effective and useful visualisations of the data, and it is proposed that these methods should be more widely applied in exploratory data visualisation in the future. [ABSTRACT FROM AUTHOR]

This study extends previous research by comparatively examining variations in situational circumstances of bias homicide targeting lesbian, gay, bisexual, and transgender (LGBT) victims. Rather than conceptualize categories of homicide by offender motive, this study disaggregates anti-LGBT homicide based on observable characteristics of victim selection by offenders. Anti-LGBT homicides are conceptualized as predatory and responsive offenses, which capture planned, unprovoked homicides and unplanned crimes in which victims play a role in the escalation of violence. Further situational distinctions are conceptualized within these categories of anti-LGBT homicide, and comparative analyses reveal important differences across these situational circumstances. Considering the large amount of variation found within anti-LGBT homicide, which often goes unrecognized by studies relying on official bias crimes data, these findings suggest that open-source crime data and nuanced examinations of anti-LGBT homicide may be useful tools for fashioning informed policy discussions of victim-based legal protections and law enforcement responses to bias crime. [ABSTRACT FROM PUBLISHER]

This study is a content analysis of inmate and staff free responses to a question about good ways to prevent prison sexual assault. Data from 1,094 inmates and 373 correctional staff were abstracted from a 1998 survey of Midwestern prisons. A total of 2,689 distinctive ideas ( 1,830 from inmates and 859 from staff) were distributed across 12 prevention categories. Inmates' top solutions were classification of inmates and increased sexual outlets. Staff s top choices were increasing security and hiring more and better staff. Similarities and disparities in inmate and staff solutions are discussed in light of Prison Rape Elimination Act-based policies. [ABSTRACT FROM AUTHOR]

A widely cited study by Osgood and Chambers appeared to extend the generalizability of social disorganization theory to youth violence in rural areas. The results of a very similar study we conducted, however, did not show support for social disorganization, and we concluded that the theory may not be as robust an explanation for rural youth violence as believed. In the current article, we take an important first step in addressing the conflicting findings by examining three likely methodological reasons for the inconsistent results: spatial autocorrelation, sample composition, and measurement of the dependent variable. Multiple tests suggest the first two explanations do not influence the results. Our analyses do indicate, however, that the association between social disorganization and violence in rural areas is sensitive to how the dependent variable is measured. We conclude that scholars should not rely solely on official crime data from rural areas when testing sociological and criminological theories. [ABSTRACT FROM PUBLISHER]

The death certificate is mandated by civil law and serves as a medical-scientific document useful for biostatistics and epidemiological research. For a variety of reasons, death certificates can be misclassified. We reviewed data from self-inflicted deaths collected over an 8-year period by the Forensic Institute of the University Sacro Cuore of Rome ( Italy). Four hundred and thirty-five of 2904 were classified as self-inflicted deaths (15%). The comparison with death certificates processed by the local public health authority ( ASL) and by the Italian National Census Bureau ( Istat) and with the judicial investigation results available in the Italian Penal Court archive shows some discrepancies. One-hundred and twenty-four of 435 deaths were not considered to be self-inflicted but due to a crime (29% overrecording suicide) with a higher reduction for women, suggesting that it is easier to confuse a murder for suicide in female cases. Any discrepancies between the mortality and crime data are discussed in details. [ABSTRACT FROM AUTHOR]

Given a collection of Boolean spatiotemporal (ST) event-types, the cascading spatiotemporal pattern (CSTP) discovery process finds partially ordered subsets of these event-types whose instances are located together and occur serially. For example, analysis of crime data sets may reveal frequent occurrence of misdemeanors and drunk driving after and near bar closings on weekends, as well as after and near large gatherings such as football games. Discovering CSTPs from ST data sets is important for application domains such as public safety (e.g., identifying crime attractors and generators) and natural disaster planning, (e.g., preparing for hurricanes). However, CSTP discovery presents multiple challenges; three important ones are 1) the exponential cardinality of candidate patterns with respect to the number of event types, 2) computationally complex ST neighborhood enumeration required to evaluate the interest measure and 3) the difficulty of balancing computational complexity and statistical interpretation. Current approaches for ST data mining focus on mining totally ordered sequences or unordered subsets. In contrast, our recent work explores partially ordered patterns. Recently, we represented CSTPs as directed acyclic graphs (DAGs); proposed a new interest measure, the cascade participation index (CPI); outlined the general structure of a cascading spatiotemporal pattern miner (CSTPM); evaluated filtering strategies to enhance computational savings using a real-world crime data set and proposed a nested loop-based CSTPM to address the challenge posed by exponential cardinality of candidate patterns. This paper adds to our recent work by offering a new computational insight, namely, that the computational bottleneck for CSTP discovery lies in the interest measure evaluation. With this insight, we propose a new CSTPM based on spatiotemporal partitioning that significantly lowers the cost of interest measure evaluation. Analytical evaluation shows that our new CSTPM is correct and complete. Results from significant amount of new experimental evaluation with both synthetic and real data show that our new ST partitioning-based CSTPM outperforms the CSTPM from our previous work. We also present a case study that verifies the applicability of CSTP discovery process. [ABSTRACT FROM AUTHOR]

Predictive policing seeks to allocate scarce resources where and when they are most needed. Yet analysis is often based on recorded crime data that typically understate the concentration of crime at the same places or against the same people. This study outlines a means of developing more accurate estimates, termed the Recorded Repeats Adjustment Calculator (RRAC), and applies it to burglary data for Metro Vancouver. Whereas repeat burglaries constituted 20% of recorded burglaries, after adjustment they were half of burglaries. Moreover, households with five or more burglaries accounted for less than 1% of recorded but, after adjustment, one in five actual burglaries (21%). These results are closer to those found by crime victim surveys but still likely to be conservative. The study aspires to produce a tool for analysts that produces more accurate information on crime's concentration and, thereby, more informed crime control efforts. [ABSTRACT FROM AUTHOR]

In Yeomni-dong Sogeum-gil, Korea, the first generation CPTED (Crime Prevention Through Environmental Design) project was implemented in 2012, focusing on improving the physical environment. Later, spreading nationwide, it was developed into the second generation CPTED, emphasizing the role of resident participation and improving upon the weak points of the first project. This study makes a comparative analysis of crime reduction and diffusion before and after the second generation CPTED conducted in S-dong, Dongjak-gu, Seoul, Korea, using crime location data to verify the crime prevention effect. Most previous studies on Korean CPTED projects sought verification through surveys that involved subjective opinions of the researchers or participants, creating the need for verification through quantitative and objective analysis based on crime data. This follow-up research examines the effects of the first generation CPTED Project by making an objective analysis of the differences in crime prevention effects between the first and the second project. Findings revealed that the second CPTED had a positive effect in reducing the rate of burglary and violent crime. The second generation CPTED project also led to the crime control benefits of crime diffusion, in contrast to the earlier project, where crime displacement occurred. [ABSTRACT FROM AUTHOR]

The article presents a study on developments in forensic intelligence with reference to international researches related to it. Topics discussed include changes that need to be addressed in forensic intelligence such as demonstrating the advantages of digital model to policing agencies, use of digital forensic data for ameliorating efficacy of the practices, usefulness of databases such as improved efficiency of crime solving processes and required changes such as focusing on data operators.