The Australian methylamphetamine market: the national picture

The Australian Methylamphetamine Market provide a concise understanding of the nature of organised criminal involvement in the Australian methylamphetamine market. The report consolidates open source information with operational and strategic intelligence to inform the Australian public on what the Australian Crime Commission sees as a significant threat to the Australian community. It is intended for the report to inform the national response to the methylamphetamine problem. Aim This report aims to provide a concise understanding of the nature of organised crime involvement in the Australian methylamphetamine market. The ACC monitors all illicit drug markets through its High Risk and Emerging Drugs Special Operation. Through this work, the ACC has assessed that methylamphetamine poses the greatest threat to the Australian public of all illicit drug types. The ACC’s annual Illicit Drug Data Report provides a detailed and comprehensive statistical picture of the illicit drug threat to Australia and provides an in-depth statistical analysis of the illicit drug market. The Australian Methylamphetamine Market: The National Picture is a complementary intelligence report. This report provides a brief summation of the national picture of the methylamphetamine threat. It explores the international and national dimensions of the methylamphetamine market, outlines the role of organised crime in driving the Australian market, the nature of the market, and the harms associated with methylamphetamine use. It also examines the diversion of precursor chemicals required to produce methylamphetamine in clandestine laboratories. It does this by consolidating open source information with operational and strategic intelligence collected by the ACC and Commonwealth, state and territory law enforcement agencies. The release of this report is designed to: ƒƒ inform the widest possible audience, including those who are not privy to classified law enforcement intelligence ƒƒ generate discussion and dialogue about what can be done to tackle the methylamphetamine problem ƒƒ enable individuals, friends and families to understand the nature of the harms caused by methylamphetamine and influence those around them to minimise harm ƒƒ inform the national response to the methylamphetamine market

Cyber-crime Science = Crime Science + Information Security

Cyber-crime Science is an emerging area of study aiming to prevent cyber-crime by combining security protection techniques from Information Security with empirical research methods used in Crime Science. Information security research has developed techniques for protecting the confidentiality, integrity, and availability of information assets but is less strong on the empirical study of the effectiveness of these techniques. Crime Science studies the effect of crime prevention techniques empirically in the real world, and proposes improvements to these techniques based on this. Combining both approaches, Cyber-crime Science transfers and further develops Information Security techniques to prevent cyber-crime, and empirically studies the effectiveness of these techniques in the real world. In this paper we review the main contributions of Crime Science as of today, illustrate its application to a typical Information Security problem, namely phishing, explore the interdisciplinary structure of Cyber-crime Science, and present an agenda for research in Cyber-crime Science in the form of a set of suggested research questions

Crime prediction through urban metrics and statistical learning

Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multicollinearity in urban indicators, it is common to find controversial conclusions about the influence of some urban indicators on crime. Machine learning ensemble-based algorithms can handle well such problems. Here, we use a random forest regressor to predict crime and quantify the influence of urban indicators on homicides. Our approach can have up to 97% of accuracy on crime prediction, and the importance of urban indicators is ranked and clustered in groups of equal influence, which are robust under slightly changes in the data sample analyzed. Our results determine the rank of importance of urban indicators to predict crime, unveiling that unemployment and illiteracy are the most important variables for describing homicides in Brazilian cities. We further believe that our approach helps in producing more robust conclusions regarding the effects of urban indicators on crime, having potential applications for guiding public policies for crime control.Comment: Accepted for publication in Physica

SimCrime: A Spatial Microsimulation Model for the Analysing of Crime in Leeds.

This Working Paper is a part of PhD thesis 'Modelling Crime: A Spatial Microsimulation Approach' which aims to investigate the potential of spatial microsimulation for modelling crime. This Working Paper presents SimCrime, a static spatial microsimulation model for crime in Leeds. It is designed to estimate the likelihood of being a victim of crime and crime rates at the small area level in Leeds and to answer what-if questions about the effects of changes in the demographic and socio-economic characteristics of the future population. The model is based on individual microdata. Specifically, SimCrime combines individual microdata from the British Crime Survey (BCS) for which location data is only at the scale of large areas, with census statistics for smaller areas to create synthetic microdata estimates for output areas ?(OAs) in Leeds using a simulated annealing method. The new microdata dataset includes all the attributes from the original datasets. This allows variables such as crime victimisation from the BCS to be directly estimated for OAs

Do sports stadiums generate crime on days without matches? A natural experiment on the delayed exploitation of criminal opportunities

Crime pattern theory claims that busy places generate crime through immediate and delayed exploitation. In delayed exploitation, offenders notice criminal opportunities during opening hours but return to exploit them later. This study investigates delayed exploitation by testing whether soccer stadiums locally increase police recorded property crime on non-game days. A soccer stadium closure created a natural experiment. We estimate linear regression difference-in-difference models to compare crime rates on non-game days around the stadium, before and after the closure. The closure reduced non-game day property crime beyond the citywide property crime drop. We conclude that criminogenic effects of busy places extend beyond their opening hours, confirming the delayed exploitation mechanism, and that crime prevention strategies should also target these places outside opening hours

Indicators of School Crime and Safety: 2014

A joint effort by the Bureau of Justice Statistics and National Center for Education Statistics, this annual report examines crime occurring in schools and colleges. This report presents data on crime at school from the perspectives of students, teachers, principals, and the general population from an array of sources--the National Crime Victimization Survey, the School Crime Supplement to the National Crime Victimization Survey, the Youth Risk Behavior Survey, the School Survey on Crime and Safety, the Schools and Staffing Survey, EDFacts, and the Campus Safety and Security Survey. The report covers topics such as victimization, bullying, school conditions, fights, weapons, the presence of security staff at school, availability and student use of drugs and alcohol, student perceptions of personal safety at school, and criminal incidents at postsecondary institutions