Dealings on the Dark Web: An Examination of the Trust, Consumer Satisfaction, and the Efficacy of Interventions Against a Dark Web Cryptomarket

Abstract Objective. The overarching goal of this thesis is to better understand not only the network dynamics which undergird the function and operation of cryptomarkets but the nature of consumer satisfaction and trust on these platforms. More specifically, I endeavour to push the cryptomarket literature beyond its current theoretical and methodological limits by documenting the network structure of a cryptomarket, the factors which predicts for vendor trust, the efficacy of targeted strategies on the transactional network of a cryptomarket, and the dynamics which facilitate consumer satisfaction despite information asymmetry. Moreover, we also aim to test the generalizability of findings made in prior cryptomarket studies (Duxbury and Haynie, 2017; 2020; Norbutas, 2018). Methods. I realize the aims of this research by using a buyer-seller dataset from the Abraxas cryptomarket (Branwen et al., 2015). Given the differences between the topics and the research questions featured, this thesis employs a variety of methodological techniques. Chapter two uses a combination of descriptive network analysis, community detection analysis, statistical modelling, and trajectory modelling. Chapter three utilizes three text analytic strategies: descriptive text analysis, sentiment analysis, and textual feature extraction. Finally, chapter four employs sequential node deletion pursuant to six law enforcement strategies: lead k (degree centrality), eccentricity, unique items bought/sold, cumulative reputation score, total purchase price, and random targeting. Results. Social network analysis of the Abraxas cryptomarket revealed a large and diffuse network where the majority of buyers purchased from a small cohort of vendors. This theme of preferential selection of vendors on the part of buyers is repeated in other findings within this study. More generally, the Abraxas transactional network can then be viewed as set of transactional islands as opposed to a large, densely connected conglomeration of vendors and buyers. With regard buyer feedback, buyers are generally pleased with their transactions on Abraxas as long as the product arrives on time and is as advertised. In general, vendors have a relatively low bar to achieve when it comes to satisfying their customers. Based on the results of the sequential node deletion, random targeting was found to be ineffective across the five outcome measures, producing minimal and a slow disruptive effect. Finally, these strategies are based on a power law where a small percentage of deleted nodes is responsible for an outsized proportion of the disruptive impact. Conclusion. As with all applied research examining emergent phenomena, this thesis lends itself to a more refined understanding of dark web cryptomarkets. While the results and conclusions drawn from these results are not perfectly generalizable to all cryptomarkets, they should serve to inform law enforcement on the dynamics which undergird these markets. To this extent, a sombre consideration of trust, consumer satisfaction, and tactical effectiveness of interventions is a necessary step towards the development of more effective countermeasures against these illicit online marketplaces. For law enforcement to be more effective against cryptomarkets, it is advised that an evidence-based approach be taken

Victims, offenders and victim-offender overlaps of knife crime: A social network analysis approach using police records.

Knife crime is a source of concern for the police in England and Wales, however little published research exists on this crime type. Who are the offenders who use knives to commit crime, when and why? Who are their victims, and is there a victim-offender overlap? What is the social network formation for people who are exposed to knife crime? Using a multidimensional approach, our aim is to answer these questions about one of England and Wales' largest jurisdictions: Thames Valley. We first provide a state-of-the-art narrative review of the knife crime literature, followed by an analysis of population-level data on central tendency and dispersion of knife crimes reported to the police (2015-2019), on offences, offenders, victims, victim-offender overlaps and gang-related assaults. Social network analysis was used to explore the formations of offender-victim networks. Our findings show that knife crime represents a small proportion of crime (1.86%) and is associated largely with violence offenses. 16-34 year-old white males are at greatest risk of being the victims, offenders or victim-offenders of knife crime, with similar relative risks between these three categories. Both knife offenders and victims are likely to have a criminal record. Knife crimes are usually not gang-related (less than 20%), and experienced mostly between strangers, with the altercation often a non-retaliatory 'one-off event'. Even gang-related knife crimes do not follow 'tit-for-tat' relationships-except when the individuals involved have extensive offending histories and then are likely to retaliate instantaneously. We conclude that while rare, an incident of knife crime remains predicable, as a substantial ratio of offenders and victims of future knife crime can be found in police records. Prevention strategies should not be focused on gang-related criminals, but on either prolific violent offenders or repeat victims who are known to the police-and therefore more susceptible to knife crime exposure

Targeting Fatal Traffic Collision Risk from Prior Non-Fatal Collisions in Toronto

Funder: University of CambridgeAbstract Research question How accurately can all locations of 44 fatal collisions in 1 year be forecasted across 1403 micro-areas in Toronto, based upon locations of all 1482 non-fatal collisions in the preceding 4 years? Data All 1482 non-fatal traffic collisions from 2008 through 2011 and all 44 fatal traffic collisions in 2012 in the City of Toronto, Ontario, were geocoded from public records to 1403 micro-areas called ‘hexagonal tessellations’. Methods The total number of non-fatal traffic collisions in Period 1 (2008 through 2011) was summed within each micro-area. The areas were then classified into seven categories of frequency of non-fatal collisions: 0, 1, 2, 3, 4, 5, and 6 or more. We then divided the number of micro-areas in each category in Period 1 into the total number of fatal traffic collisions in each category in Period 2 (2012). The sensitivity and specificity of forecasting fatal collision risk based on prior non-fatal collisions were then calculated for five different targeting strategies. Findings The micro-locations of 70.5% of fatal collisions in Period 2 had experienced at least 1 non-fatal collision in Period 1. In micro-areas that had zero non-fatal collisions during Period 1, only 1.7% had a fatal collision in Period 2. Across all areas, the probability of a fatal collision in the area during Period 2 increased with the number of non-fatal collisions in Period 1, with 6 or more non-fatal collisions in Period 1 yielding a risk of fatal collision in Period 2 that was 8.7 times higher than in areas with no non-fatal collisions. This pattern is evidence that targeting 25% of micro-areas effectively could cut total traffic fatalities in a given year by up to 50%. Conclusion Highly elevated risks of traffic fatalities can be forecasted based on prior non-fatal collisions, targeting a smaller portion of the city for more concentrated investment in saving lives. </jats:sec

Targeting Knife-Enabled Homicides for Preventive Policing: A Stratified Resource Allocation Model

Funder: University of CambridgeAbstract Research Question How can police translate differing risk levels for knife homicide into a resource allocation model that follows the evidence? Data The data for this publication are taken from the open access tables published in this journal by Massey et al. Cambridge Journal of Evidence-Based Policing, 3:1-20, (2019). Those data show the linear relationship between the number of non-fatal knife assaults in a lower super output area (LSOA) in 1 year and the risk of a knife-enable homicide in the subsequent year, as well as how many of the 4835 LSOAs fell into each of five levels of increasing homicide risk. Methods The data from Massey’s research are re-calculated to show how a hypothetical number of 15-min police patrols could be allocated across all areas on the basis of a combination of knife-enabled (KE) homicide risk level and the volume of LSOAs at each of the five levels of knife homicide risk. We display these results using both tables and multi-layered “wedding cake” images to show the size of different dimensions of each level, including proportion of total homicides and directed patrol frequency per LSOA at each of the five risk levels. Findings Based on the hypothetical allocation of 10,000 patrol visits of 15 min in length, the highest risk group, with a forecasted 6% of all KE homicides, would receive 600 police patrols, divided by the 41 LSOAs at that risk level = 15 patrols across every 10 days. At the lowest level of risk, the 2787 LSOAs would share the 3000 patrols that a group of LSOAs would recieve for having 30% of homicides, which equals 1.1 patrols every 10 days. The hypothetical premise is that every LSOA gets some patrol, but the highest risk areas get 15 times more patrol to follow the evidence of risk. The formula is to (1) allocate resources by proportion of homicide at each risk level; (2) divide the allocated resources by the number of areas in each risk level group; and (3) allocate the resulting resources per day to each area in each of the 5 levels. Conclusion Police face difficult tradeoffs between targeting more policing to fewer areas of higher risk (with more efficiency) or to more areas of lower risk (with more effectiveness). The use of a formula combining risk and volume can help guide such decisions, illustrated by a layered “wedding cake” visualization for gaining clarity and legitimacy in communications. </jats:sec

Tracking Police Arrests of Intimate Partner Domestic Abuse Suspects in London: a Situational Factors Analysis

Funder: University of CambridgeAbstract Research Question What were the odds of named suspects being arrested for a reported crime of intimate partner abuse over one 12-month period in one area of London (UK), and how did those odds vary across twelve predictive situational characteristics, including the presence or absence of the suspect? Data This study analyses 1000 intimate partner domestic abuse (DA) crimes recorded in the South West Basic Command Unit of the London Metropolitan Police Service in the 12 months between 1 February 2018 and 31 January 2019. Methods Twelve factors present at the time of police recording an intimate partner abuse crime were analysed in an odds ratio analysis predicting whether a named suspect would be arrested for the crime. Separate analyses were conducted for cases in which the suspect was present at the time police arrived to interview the victim and in cases in which the suspect was absent. Analyses were also conducted by crime type and crime severity scores, with a descriptive analysis of reasons police gave for not making arrests. Findings Police arrested the suspect in 90% of the 287 cases in which suspects were present when police arrived, and in 47% of the 713 cases in which the suspect was absent on arrival. There was no difference in crime severity scores between cases in which arrests were made vs. not. After suspect absence, victim unwillingness to press charges was the second strongest predictor of no arrest being made, with 21% reported unwilling to cooperate when suspects were present and 29% unwilling when suspects were absent. Arrests were more likely when suspects were male than female and less likely with male victims than female victims. Conclusions These data show that in an area housing about 12% of London’s population, police make arrests in 90% of intimate partner crimes when the suspect is still present at the scene, and almost half of those in which suspects leave before police arrive. The primary predictor of non-arrest, controlling for offender absence, is victim unwillingness to support prosecution. Various policy options can be considered in light of these findings. </jats:sec

Effects of One-a-Day Foot Patrols on Hot Spots of Serious Violence and Crime Harm: a Randomised Crossover Trial

Abstract Research Question Does one foot patrol per day (15–20 min) conducted in serious violence harm spots reduce street-visible crime harm and frequency relative to no foot patrol in the same hot spots, and if so by how much? Data We identified 20 hot spots of 150m2 each on the basis of community violence defined as serious assaults, robbery, and drug dealing in the Southend-on-Sea area of Essex Police, with boundaries geo-fenced to collect GPS measures of foot patrol presence generated by hand-held electronic trackers issued to officers directed to perform patrols. All street-visible crimes were counted for each of the 90 days of the experiment in each hot spot. Methods Daily random assignment of each hot spot to either control or treatment conditions (N = 90 X 20 = 1800 place-days) prescribed 720 place-days to receive extra patrols by Operational Support Group officers, which were compared to 1080 place-days with no extra patrols, using an intent-to-treat design, with 98% compliance with assigned treatments. Independent measures of other police presence in the area were tracked by the force-wide GPS telematics measures. All crimes were coded with the Cambridge Crime Harm Index for their CHI value. Findings The 20 harm spots comprised just 2.6% of the geographical area of the Southend-on-Sea area, with 41% of all its Cambridge CHI crime harm in the year preceding the experiment. Background patrol presence was about 2 min per day on control days and 1 min per treatment day. Crime harm scores for serious community violence were 88.5% lower on experimental days with extra patrols (mean = 1.07 CHI per treatment place-day) than without it (mean = 9.30 CHI per control place-day). Crime harm scores for all street-visible offences were 35.6% lower on treatment days (mean = 7.94 CHI per treatment place-day) than control days (mean = 12.33 CHI per control place-day), while the mean count of all street-visible offences was 31% lower on treatment days (mean count = 0.09 crimes per treatment place-day) than on control days (mean count = 0.13 crimes per control place-day). The estimated effect of the 720 days with 15-min patrols was to prevent crimes with recommended imprisonment of 3161 days, or 8.66 years. Conclusion The use of two-officer foot patrol can be highly effective at preventing serious violence in street-visible settings in small areas in which such violence is heavily concentrated. </jats:sec

Comparing panic alarm systems for high-risk domestic abuse victims: a randomised controlled trial on prevention and criminal justice system outcomes.

BACKGROUND: The use of panic alarm systems for victims of domestic abuse is becoming increasingly popular. However, tests of these devices are rare. Consequently, it is presently unknown whether domestic abuse offenders are deterred by warning stickers informing them that a panic alarm system is installed on the premises, or whether alarm systems reduce domestic abuse recidivism. There is also a lack of data regarding whether adding an audio-recording feature to the panic alarm results in more prosecutions of domestic abuse offenders compared to standard panic alarm systems. Measuring the efficacy of warning stickers and audio recordings will enhance understanding of the overall effectiveness of panic alarm systems for domestic abuse. METHODS: This study used a pre-test-post-test, control group design, in which 300 eligible high-risk domestic abuse victims in London, UK, were randomly allocated to either a standard panic alarm system or a panic alarm system with audio-recording capabilities and a red warning sticker on a durable, A6-size sign displayed at eye level at the entrance to the premises. Each sticker was well lit to ensure maximum visibility. The gain scores of multiple measures at 6 months prior and 6 months post-randomisation were used to assess the treatment effects (including the number of calls for service, recorded crimes, and harm score), and a negative binomial generalised linear model was utilised to estimate the likelihood of criminal charges for domestic abuse offenders in the two systems. OUTCOMES: Pre-post comparisons of recidivism suggested an overall reduction in both treatment arms, but there were no statistically significant differences between the two types of alarm systems across these crime measures. Nevertheless, the estimation model indicated a significant 57% increase in charges using the audio-recording alarm relative to the standard panic alarm system. CONCLUSIONS: Using deterrent stickers to warn domestic abuse offenders of panic alarm systems does not lead to a reduction in subsequent harm to victims. Compared to ordinary panic alarms for high-risk domestic abuse victims, audio-recording systems provide valuable evidence that increases subsequent charges, and thus, these systems should be explored further. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11292-022-09505-1

Overcoming unreported violence using place‐based ambulance data: The case for mapping hotspots based on health data for crime prevention initiatives

AbstractA key concern in crime analysis is the “hidden crime” problem. Crime events unaccounted for in police records limit the external validity of official statistics and, more importantly, hinder the ability of the police to manage crime and utilize their resources effectively. The problem is exacerbated in proactive initiatives aimed at curbing violence through hotspot policing, where inaccuracies and imprecision, or, worse, no data at all, diminish prevention efforts. Previous studies have sought to overcome the data problem by juxtaposing police records with ambulance data on assault callouts and have found profound disparities. Specifically, researchers matched “crime hotspots” with “ambulance hotspots” (rather than individual events) because patient confidentiality considerations have prevented health professionals from sharing subject‐level data with the police. However, health services can safely share spatial data on wider areas that do not disclose personal information. We build on this line of inquiry by analyzing data from the Thames Valley, United Kingdom, and observing spatial hotspots of different sizes. The results demonstrate that while the police and ambulance services attend to the same communities and similar types of facilities, the police are “blinded” to the location of nearly 8 out of 10 assaults. The incongruency is shown even with severe assaults, but to a lesser extent. We then simulate the reduction in injuries if the police had access to health data at different spatial levels and show that even under the most conservative set of assumptions, such an approach can prevent between 113 and 116 violent injuries each year that might otherwise require hospitalization.</jats:p

Comparing panic alarm systems for high-risk domestic abuse victims: a randomised controlled trial on prevention and criminal justice system outcomes

Background: The use of panic alarm systems for victims of domestic abuse is becoming increasingly popular. However, tests of these devices are rare. Consequently, it is presently unknown whether domestic abuse offenders are deterred by warning stickers informing them that a panic alarm system is installed on the premises, or whether alarm systems reduce domestic abuse recidivism. There is also a lack of data regarding whether adding an audio-recording feature to the panic alarm results in more prosecutions of domestic abuse offenders compared to standard panic alarm systems. Measuring the efficacy of warning stickers and audio recordings will enhance understanding of the overall effectiveness of panic alarm systems for domestic abuse. Methods: This study used a pre-test-post-test, control group design, in which 300 eligible high-risk domestic abuse victims in London, UK, were randomly allocated to either a standard panic alarm system or a panic alarm system with audio-recording capabilities and a red warning sticker on a durable, A6-size sign displayed at eye level at the entrance to the premises. Each sticker was well lit to ensure maximum visibility. The gain scores of multiple measures at 6 months prior and 6 months post-randomisation were used to assess the treatment effects (including the number of calls for service, recorded crimes, and harm score), and a negative binomial generalised linear model was utilised to estimate the likelihood of criminal charges for domestic abuse offenders in the two systems. Outcomes: Pre-post comparisons of recidivism suggested an overall reduction in both treatment arms, but there were no statistically significant differences between the two types of alarm systems across these crime measures. Nevertheless, the estimation model indicated a significant 57% increase in charges using the audio-recording alarm relative to the standard panic alarm system. Conclusions: Using deterrent stickers to warn domestic abuse offenders of panic alarm systems does not lead to a reduction in subsequent harm to victims. Compared to ordinary panic alarms for high-risk domestic abuse victims, audio-recording systems provide valuable evidence that increases subsequent charges, and thus, these systems should be explored further

Spatial distribution and developmental trajectories of crime versus crime severity: do not abandon the count-based model just yet

Abstract Purpose/background A new body of research that focuses on crime harm scores rather than counts of crime incidents has emerged. Specifically in the context of spatial analysis of crime, focusing on crime harm suggests that harm is more concentrated than counts, at the level of crime hot spots. It remains presently unclear what drives the concentration distributions, and whether the count-based model should be abandoned. Methods Cross-sectional and longitudinal analysis of 6 year of spatiotemporal crime data in Toronto, Canada, to compare patterns and concentration of crime harm (measured in terms of the Crime Severity Index (CSI) against crime counts. Conditional probabilities, trajectory analyses, power few concentrations, and spatial Global Moran’s I are used to infer generalised trends from the data. Findings Overall CSI and crime counts tend to exhibit similar concentrations at the spatial micro levels, except against-the-body crimes such as violence which seems to drive nearly all the variations between the two measurement types. Violence harm spots tend to be more dispersed citywide and often do not remain constant year-to-year, whereas overall crime hotspots are more stable over time. Nevertheless, variations in disproportionally high crime hot spots are associated with total variations in crime, with as little as 1% increase in crime levels in these hot spots translating into substantial overall gains in recorded crime citywide. Conclusions Abandoning count-based models in spatial analysis of crime can lead to an incomplete picture of crime concentrations. Both models are needed not just for understanding spatial crime distributions but also for cost-effective allocation of policing resources. </jats:sec