The global crime drop and changes in the distribution of victimisation

Abstract

Over three decades crime counts in England and Wales, as throughout the Western world, have fallen. Less attention has been paid to the distribution of crime across households, though this is crucial in determining optimal distribution of limited policing resources in pursuing the aim of distributive justice. The writers have previously demonstrated that in England and Wales the distribution of crime victimisation has remained pretty much unchanged over the period of the crime drop. The present paper seeks to extend the study of changes in the distribution of victimisation over time using data from 25 countries contributing data to the International Crime Victimisation Survey (ICVS) sweeps (1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000). While fragmentary, the data mirror the trends discerned in England and Wales. The trends are not an artefact of the inclusion of particular countries in particular sweeps. The demographic, economical, geographical and social household characteristics associated with victimisation are consistent across time. The suggested policy implication is the need for greater emphasis on preventing multiple victimisation.

Can we detect contract cheating using existing assessment data? Applying crime prevention theory to an academic integrity issue

“…Crime is also non-randomly distributed with respect to victimisation (e.g., Farrell, Phillips, & Pease, 1995) and targets (e.g., Townsley, Homel, & Chaseling, 2003) with a very small number of targets accounting for a large proportion of victimisation. For example, Pease and Ignatans (2016) recently demonstrated that 1% of UK households experienced just under one-quarter of vehicle crimes, almost half of property crimes, and more than one-third of personal crimes. As with the non-random patterns of contract cheating offending, Lancaster and Clarke (2012) examined 627 postings on a commercial site offering 'writing solutions' for students and identified nonrandomness with respect to subject area (24.5% of all postings related to 'Business and Admin studies' assignments), level of study (71.0% of postings related to undergraduate assessments), and country of origin (where 42.3% of postings that could be attribute to a country had originated from the UK).…”

Whatever happened to repeat victimisation?

“…The proportion of property related victimisation experienced by the one per cent of most victimised increased from 33 to 48 per cent with vehicle related victimisation also increasing from 18 to 22 per cent in the same time period. Similar personal and socio-demographic characteristics were 0 10 20 30 40 50 60 1994 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Proportion Personal Property Vehicle found to differ between the 'super-target' victims and the general population (Pease and Ignatans, 2016).…”

Measuring the Distribution of Crime and Its Concentration

“…For example, a landmark analysis of victimisation data from the British Crime Survey showed that 2% of people who suffer the highest number of personal crimes, in fact, suffered 66% of the total reported for that type of crime (Pease 1998). This has often been attributed to the attractiveness of a place or a person (Brantingham and Brantingham 2010), the interaction in space and time of motivated offenders with suitable targets and the absence of any deterrence system, such as a police, a security guardian or perhaps even ordinary citizens (Hindelang et al 1978;Cohen and Felson 1979) or based on some other theory (Stark 1987).…”

“…A similar type of metric is often used when the number of crimes suffered by the most victimised people is reported (Pease and Ignatans 2016;Pease 1998), or the most criminal individuals (Wolfgang et al 1987) or families (Farrington et al 2001). This metric, however, has some severe issues, such as the lack of agreement on the percentage that gets reported (Fox and Tracy 1988); the metric might not be comparable between different cities (Hipp and Kim 2016); it might be the result of a certain degree of randomness (Levin et al 2016) and it does not work as an adequate metric when the data is extremely sparse.…”