Search results

Abstract
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.

Abstract
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.

AbstractResearch QuestionHow can police translate differing risk levels for knife homicide into a resource allocation model that follows the evidence?DataThe 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.MethodsThe 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 acombinationof 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.FindingsBased 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.ConclusionPolice face difficult tradeoffs between targeting more policing tofewerareas of higher risk (with more efficiency) or tomoreareas 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.

AbstractWhile criminology and policing studies focus primarily on offenders and their behaviours, there has been an increasing focus on victims and victimology. In this paper, we argue that practitioners and scholars alike can benefit from shifting their focus on police records towards victims. Observing data on victims can lead to greater police efficiencies, particularly in the area of prevention. We review some of the arguments for such a change, then explore evidence on 380,169 victims in Kent, UK, during a 6-year period, to illustrate how to achieve new and feasible targets by focusing on a victim rather than an offender as the unit of analysis. Finally, we explore policy implications, in terms of harm reduction, prevention of repeat victimization, and triaging opportunities.

AbstractThis study examines the police activities most associated with the recovery of stolen vehicles in Manchester, UK. The dataset consists of 6,104 reports of theft of motor vehicles whereby a random sample of 250 cases of stolen vehicles later recovered and 250 cases of stolen vehicles in which no recovery occurred were taken from 1 January 2018 to 31 December 2018. Twenty independent variables were selected using the findings from existing solvability literature, and additional variables were discovered from legacy data provided by the Greater Manchester Police recording systems. A case–control study was developed based on exploratory identification of police activities commonly used in cases of recovered vehicles. The findings indicate that motor vehicles were typically found by police during an investigation or through non-related proactive activities. Furthermore, vehicles were recovered by the victims themselves or by a member of the public reporting the vehicle to the police.

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.

Abstract
Research Question
Do police supervisors reach different conclusions about the legality of a decision to stop and search in survey vignettes using similar facts with the ethnicity of subjects described as either black or white?

Data
We utilize a vignette survey design, presenting 15 real-world stop and search examples from within the South Basic Command Unit policing area to 118 frontline uniformed supervisors (Sergeants and Inspectors) in the Metropolitan Police Service of London.

Methods
We introduce a randomised characteristic assignment of the ethnicity of the subject featured in the vignette to compare officer decision-making when the suspect is black or white. Using both Likert scale and free-text responses, a combination of descriptive statistics, inferential methods, and text mining is applied to the survey data.

Findings
We found no substantive difference in the justification of stop and search powers by officers between white suspects and black suspects. We did find substantial variability in supervisor assessments of whether vignettes provided sufficient legal grounds for conducting a stop and search.

Conclusions
Within the limits of the methodology, we conclude that there is no racial disparity in perceptions of legal thresholds for conducting searches across a range of circumstances. We must also conclude that there is a substantial range of opinion regarding different specific circumstances that are understood to provide a threshold of sufficient evidence to search.

Chapter 1: Introduction -- Chapter 2 Literature Review -- Chapter 3 Study 1 - Effect of BWVs on Assaults and Injuries -- Chapter 4: Study 2 - BWV Activation Trends -- Chapter 5: Study 3 - Perceptions of Frontline Staff Members (Survey) -- Chapters 6: Study 4 - Focus group discussions -- Chapter 7: Study 5 – The Distal Effects of BWVs -- Chapter 8: Discussion and Policy Recommendations -- .

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.