16 research outputs found

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

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    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

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

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    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

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

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    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
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