O et al. Crime Sci (2017) 6:9
DOI 10.1186/s40163-017-0071-3
Open Access
SYSTEMATIC REVIEW
How concentrated is crime
among victims? A systematic review from 1977
to 2014
SooHyun O1, Natalie N. Martinez1, YongJei Lee2 and John E. Eck1*
Abstract
Background: Considerable research shows that crime is concentrated among a few victims. However, no one has
systematically compared these studies to determine the level of concentration and its variation across studies. To
address this void in our knowledge of repeat victimization, we conducted a systematic review and meta-analysis of
the evidence that crime is concentrated among victims.
Methods: We distinguished between studies of victimization prevalence, which examine both victims and nonvictims, and studies of victimization frequency, which only examine subjects that were victimized once or more. We
identified 20 prevalence studies and 20 frequency studies that provided quantitative information sufficient for analysis. We organized data using visual binning and fitted logarithmic curves to the median values of the bins.
Results: We found that crime is concentrated within a small proportion of the subjects in both the prevalence studies and frequency studies, but also that it is more concentrated in the former. When we compared studies of business
victimization to studies of household victimization, we found that victimization is more concentrated among households than among businesses in prevalence studies, but that the reverse is true for frequency studies. A comparison
between personal and property victimizations shows that the patterns of re-victimizations are similar. Crime is more
concentrated in the United States compared to the United Kingdom in prevalence studies, but the opposite is true
when frequency studies are examined. Finally, the concentration of victimization changes over time for both the US
and the UK, but the nature of that change depends on whether one is examining prevalence or frequency studies.
Conclusions: Not surprisingly, our systemic review supports the notion that a large proportion of victimizations are
of a relatively small portion of the population and of a small portion of all those victimized at least once. There is no
question that crime is concentrated among a few victims. However, there is also variation in concentration that we
also explored.
Keywords: Concentration of crime, Victim, Systematic review, Meta-analysis, Visual binning
The importance of repeat victimization
Crime victimization is a relatively rare event in the general population. Among those who experience it, most
do so only once. For example, Tseloni et al. (2004) found
that 92% of British households reported experiencing no
victimizations over a 1-year period. Of those households
that were victimized, about 80% experienced it only once.
*Correspondence: john.eck@uc.edu
1
School of Criminal Justice, University of Cincinnati, Cincinnati, OH, USA
Full list of author information is available at the end of the article
Conversely, the few households that were repeatedly victimized in a year accounted for 40% of the crimes in that
period (Tseloni et al. 2004).
The proportion of the population that is ever victimized
and the proportion that is victimized repeatedly varies
over studies. Several studies suggest that over 8% of the
population experiences victimization and that more than
half of all victims experienced crime more than twice
(Lauritsen and Quinet 1995; Osborn et al. 1996; Sparks
1981). For example, Lauritsen and Quinet (1995) found
that about half of the National Youth Survey participants
© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made.
O et al. Crime Sci (2017) 6:9
experienced at least one larceny victimization. Among
these victims almost 60% experienced larceny victimization more than once. However, most repeat victimizations happen to a small fraction of those ever victimized
(Ellingworth et al. 1995; Farrell 1995). Ellingworth et al.
(1995) found that the 10% of people who experienced the
most personal crime victimizations accounted for half
of all personal crime victimizations in 1984. A study of
small businesses found that 1% of businesses accounted
for 45% of all robberies committed, and three percent of
businesses accounted for 81% of all violent attacks committed (Wood et al. 1997). These findings imply that
opportunities for crime are highly concentrated among a
small proportion of the most afflicted repeat victims.
Scholars introduced the concept of “repeat victimization” in the late 1970s (Sparks et al. 1977). Hindelang
et al. (1978) argued that looking at “multiple and recurrent victimization” might benefit public crime prevention
policy by helping identify the causes of victimization.
Over a decade later, the Kirkholt Project in Great Britain, initiated a successful crime control strategy focusing
on repeat victims (Forrester et al. 1988, 1990). Later, the
approach was extended to domestic violence (Lloyd et al.
1994), racial attacks (Sampson and Philips 1992, 1995),
burglary (Webb 1997), and vehicle crimes (Chenery et al.
1997). These efforts also produced substantial crime
reductions. For example, Pease (1998) evaluated a prevention effort targeting repeat victims in Stockport, England and found that this project reduced overall crime
by reducing repeat victimization by 44%. In sum, there is
substantial evidence that repeat victimization accounts
for most crimes, and that preventing repeat victimization
can reduce crime.
State dependence, population heterogeneity, and repeat
victimization
Research suggests two general explanations for repeat
victimization—state dependence and population heterogeneity (Lauritsen and Quinet 1995; Osborn and Tseloni
1998; Wittebrood and Nieuwbeerta 2000). These are
distinct concepts in theory, but they can be intertwined
in practice (Tseloni and Pease 2003). State dependence is the idea that prior victimization predicts future
risk because it alters something about the victim. This
implies that initial victimization “boosts” the probability of experiencing a subsequent victimization (Pease
1998). For example, if an initial victimization makes a
person more fearful to confront offenders, this changed
behavioral pattern increases that person’s vulnerability
and attractiveness (Schwartz et al. 1993). However, some
scholars (e.g., Nelson 1980; Sparks 1981) suggest that
state dependence does not explain all repeat victimization scenarios. For example, Sparks (1981) argued that it
Page 2 of 16
does not explain repeat victimization involving different
crime types (e.g., experiencing a robbery, followed by a
burglary, followed by a car theft).
Alternatively, it may be that prior victimization
changes something about offenders, rather than victims.
Strong evidence suggests that repeated crimes are disproportionately the work of prolific offenders (Ashton et al.
1998, see Martinez et al. [2017, this issue] for a review
of crime concentration among offenders). For example,
about half of all residential burglary offenders return
to the same houses (Winkel 1991) and about half of all
bank robbers strike the same banks (Gill and Matthews
1993). Offenders may also provide each other with useful
information about places they have previously burgled,
robbed, or otherwise victimized. Thus, repeat victimization may involve different offenders (Bennett 1995;
Sparks 1981). In the case of repeat violent victimization,
Felson and Clarke (1998) explain that offenders’ previous
experiences may help them to identify victims who are
least likely to resist. This boost account of offenders holds
for across other types of crimes such as armed robberies
(Gill and Pease 1998).
The other general explanation of repeat victimization is population heterogeneity. This explanation is also
known as a “flag account” (Pease 1998) and claims that
possessing certain characteristics make some people or
households are more at risk for victimization. These characteristics can include biological factors (e.g., individual
size or physical vulnerability), psychological propensity
(e.g., submissive or aggressive personality), lifestyle (e.g.,
staying out late drinking), or occupation (e.g., delivering
pizzas). Most people have characteristics that make them
unlikely to be victimized, but some people have characteristics that make them susceptible to many victimizations. For example, Hindelang et al. (1978) explained
that differences in lifestyle patterns lead a concentration
of victimization among certain people and households.
That is, the risk of revictimization appears to be stable for
people who do not change their lifestyles in response to a
previous victimization (Nelson 1980).
Two measures of victimization
Two measures of crime concentration are commonly
used in the victimization literature. The first measure is
“prevalence,” or the number of people with at least one
victimization divided by the total number of people in
a population. The second measure is “frequency,” or
the total number of victimizations divided by the total
number of victims (Hope 1995; Osborn and Tseloni
1998; Tseloni and Pease 2015; Trickett et al. 1992, 1995).
These two measures suggest different crime prevention
approaches. The higher the prevalence of victimization,
the greater the proportion of the population at risk for
O et al. Crime Sci (2017) 6:9
being victimized. If a high prevalence of victimization
is driving crime rates, crime can be reduced by focusing efforts on preventing a non-victim from becoming a
victim. However, if crime rates are mainly due to a high
frequency of victimization, crime prevention strategies
should concentrate on keeping victims from being revictimized (Hope 1995; Trickett et al. 1992, 1995).
The current study
Numerous studies have demonstrated that a large proportion of victimizations happen to a relatively small
portion of the population. Furthermore, a small proportion of those affected are victimized at more than once.
There seems to be no question that crime is concentrated among a few victims. However, it is important for
the advancement of science that we test ideas that have
gained general acceptance to make sure the community
of scholars have not made a collective error. No one has
systematically reviewed the repeat victimization literature and meta-analyzed the findings. Consequently, there
is an a priori chance that the common understanding of
repeat victimization could be wrong.
Further, looking at individual studies does not tell us
how concentrated victimization is generally. Therefore,
this paper synthesizes the findings from multiple studies
of repeat victimization to estimate overall proportion of
crime that is attributed to a few repeat victims. Equally
as important is the variation in concentration within
populations and among victims, which may vary among
studies for several reasons. First, some studies look at the
prevalence of victimization and its frequency, while others only examine frequency. As a shorthand, we refer to
the first set of studies as prevalence studies and the second as frequency studies.
A second reason studies may show variation in victimization concentration has to do with the type of victim.
Two broad types of victimization surveys are common in
the literature: surveys of households and surveys of businesses (Weisel 2005). These two types of victimization
are also related to two different types of places. Thus, variation in concentration between household and business
victimization might reveal how criminal opportunities
vary depending on the features of places. Other kinds of
victimization this study examined are property and personal victimizations. A comparison of these two types of
victimization are important because different targets of
crime may have different patterns of concentration.
A third reason is that the concentration of crime may
vary across countries. For instance, a cross national comparative victimization study by Tseloni et al. (2004) found
that the UK has higher burglary victimization concentration than the US. The comparison was based on two
different nationally representative victimization surveys:
Page 3 of 16
The U.S. National Crime Victimization Survey (NCVS)
and the British Crime Survey (BCS). Thus, variation
in concentration may be attributable to local nature of
crime (Weisel 2005) or to differences in data collection
processes between different surveys (Lee 2000). In this
study, each country includes various types of surveys and
thus, we expect greater variation between countries.
Finally, concentration of crime may vary across decades. For example, the US experienced a sharp nationwide decline in crime during the 1990s (Farrell et al.
2014). Importantly, this decline was consistent across
two different measures of crime, the FBI’s Uniform
Crime Reports (UCR) and the National Crime Victimization Survey (NCVS). Further, the NCVS shows an even
greater reduction in crime between 1991 and 2001 than
the UCR does. Possible explanations for the sharp drop in
crime include the use of innovative policing strategies, an
increase in the number of police, increased incarceration
rates, changes in crack and other drug markets, tougher
gun control laws, and a stronger economy; however, the
effectiveness of each of these strategies is debatable (Farrell et al. 2014; Zimring 2006; Blumstein and Wallman
2006). Accordingly, the drop in crime may have been
associate with changes in crime concentration across
decades.
With many studies available, we can begin to explain
the concentration of victimization phenomenon by systematically reviewing and analyzing their research findings. The next section describes the methods used,
including the literature search and inclusion strategy,
how data was extracted, and how concentration was
measured. The third section describes the analysis of this
literature and our findings. In the final section, we draw
conclusions and state their implications for research and
policy.
Data and methods
Criteria for inclusion and exclusion
Our goal is to determine the concentration of victimization based on previous research. We need quantitative
information that can describe the distribution of crime
across a sample of victims. To achieve this, we required
studies to have specific information describing crime
among victims, which are reflected in our three criteria
for inclusion in our analysis. First, the study must be written in English. Second, the study had to include empirical
data from which it drew its findings—we had to be able
to retrieve relevant statistics from the text of the study,
or access its original data set to calculate them. Third, the
study must provide statistics on the percentage of victims
(X%) in its sample and percentage of crimes (Y%) associated with those victims. We use the combinations of
these X and Y percentages as ordered pairs to plot points
O et al. Crime Sci (2017) 6:9
Page 4 of 16
Table 1 Characteristics of the studies and ordered pairs identified and analyzed
Characteristics
Prevalence
Number of studies
Frequency
Number of coordinates
Number of studies
Number of coordinates
Type of victim
Household
10
156
10
143
Business
3
40
4
43
Type of crime
Property
3
71
6
116
Personal
7
109
7
93
Two nations
US
8
188
7
144
UK
12
193
12
180
US across decades
1970s
5
140
5
122
1990s
3
48
3
42
UK across decades
1970s
2
27
1
24
1980s
3
48
3
35
1990s
6
55
6
64
2000s
3
55
3
51
20 (66)
397 (548)
20 (25)
359 (371)
Total
Studies analyzed (studies identified)
on a logarithmic crime concentration curve. For example,
Tseloni and her coauthors (2004) provided a cumulative
distribution of 1412 burglary victimizations over 12,845
households in England and Wales from the 1994 British
Crime Survey. In Table 1 of their study, each of the 11
rows in the first column provides the percentage of burglaries explained by the percentage of households, thus it
is possible to retrieve and record these 11 X–Y ordered
pairs into our database.
Since, for any single study, there may be an insufficient
number of X–Y ordered pairs to reliably represent the
distribution of crime across the victims/households—
a single X–Y ordered pair does not reliably represent
the victim-crime distribution of the study—we applied
another criterion to filter out the studies with too few
X–Y ordered pairs. Specifically, in addition to the points
where the percent of victims is 100% or the percent of
crimes is 100%, relevant studies must supply at least two
X–Y ordered pairs to represent the victim–crime distribution of the data (for example, 15% of the respondents
[X] had 45% of the victimizations [Y] and 50% of the
respondents [X] had 100% of the victimizations [Y]).
Data sources and search strategy
We searched for empirical studies addressing the concentration of victimization in journal articles, academic
institutions, and government reports. First, we used
keywords to conduct an electronic search for studies. To
determine our keywords, we first consulted the earliest
studies on victimization. We chose the baseline keywords
‘victimization’ from Sparks et al. (1977) and ‘repeat victimization’ from Hindelang et al. (1978). In our searches,
we spelled “victimization” with a z, as used in North
America, and with an s, as used in Great Britain. We
examined the titles, abstracts, and methods sections of
each article in our search results to determine if it fits our
inclusion criteria. Once we found further studies using
these keywords, we chose new keywords from the studies
we found and then conducted another round of online
searches. In summary, we used the following keywords in
our searches: victimization, re-victimization, repeated
victimization, repeat victimization, concentration of victim, multiple victimization, distribution of victimization,
heterogeneity of victimization, state-dependence of victimization, and frequency of victimization.1 The databases
we searched were: Criminal Justice Abstracts, EBSCO,
ProQuest, Google, and Google Scholar.
1
Our study is not dependent on any particular search term, but on the set
of terms used. Further, even a term that unveils a single study might be very
valuable, if that single study is largely unknown, it contains findings at variance to other studies, or it suggests other search terms that lead to many
other studies. In short, we treated the search terms not as independent
items, but as part of a large web of terms.
O et al. Crime Sci (2017) 6:9
Second, we manually examined bibliographies of
retrieved studies for additional studies to include. If we
found a relevant study from the bibliography of a
retrieved study, we then looked at the bibliography of the
new study and repeated the process. During this iterative
approach, if we found new possible keywords, we
repeated the computerized searching process again
across the databases.2 The bibliographies of several publications were particularly useful because they specifically
focused on the phenomenon of victimization concentration (i.e., Ellingworth et al. 1995; Farrell 1995; Farrell and
Pease 1993; Pease 1998; Tseloni 2000, 2006).
In addition, we presented a preliminary version of this
study at the 2015 Environmental Criminology and Crime
Analysis international symposium in Christchurch, New
Zealand and at the 71st Annual Conference of the American Society of Criminology at Washington, DC and asked
attendees if they knew any gap in our literature.
These search methods resulted in a total of 70 studies with 560 X–Y ordered pairs. However, many of
these studies did not satisfy our stringent third criterion
requiring at least two X–Y ordered pairs. As shown in
Table 1, when including only those studies that did, we
had 20 prevalence studies with 397 ordered pairs and 20
frequency studies with 359 ordered pairs. These studies
and ordered pairs are the data we examine in this paper.
Coding protocol
Our comparative analysis of crime concentration among
population or victims has no precedent in the literature.
Conventional meta-analysis calculates a variety of statistics including t-statistics, estimated coefficient, standard
errors, and confidence intervals and then weights the
data points to compensate for uncertainly in the data
(Higgins and Green 2011; Mulrow and Oxman 1997).
However, because we used actual values of X–Y ordered
pairs to estimate the general distribution of victimizations over possible victims, rather than estimated coefficients (as is standard in meta-analysis), it is unclear if
weights improve the validity of our analysis. As our test
of this indicated that weights were not helpful, we did not
use them.3
2
Because recording the number of studies from multiple databases without
duplicates is cumbersome, particularly when using an iterative process, and
it does not shed additional light on the validity of the findings of our study,
we did not record the number of studies found per search engine or database.
3
We tested whether weighting our data would change our results. We
weighted X–Y pairs of each study by the study’s sample size. We used the
study’s sample size (w) to weight Y value of each coordinate point within
i) to represent the
each bin (i), then calculated the weighted median (wy
weighted central tendency of each bin. We did not find any substantiate difference in the findings with weighted ordered pairs compared to the findings with un-weighted points (see Appendix 1).
Page 5 of 16
For our meta-analysis, we recorded the X–Y ordered
pairs for each study in two ways.4 To analyze the prevalence of victimization, we coded the X and Y pairs based
on the number of the potential victims (e.g., people or
households who could have been victimized). Twenty
studies had sufficient information for this purpose, yielding 397 X–Y pairs. To analyze the frequency of victimization, we coded the values of X based on the number of
victimization for those who experienced at least one
crime (i.e., people or household who did not experience
crime were dropped). Twenty studies provided frequency
distributions with 359 X–Y ordered pairs (19 of these
studies were also used to analyze victimization prevalence). We also coded the data with regards to the type of
victim, type of crime, country of origin, and years of data
collected for each study. Table 1 shows the characteristics
of the studies reviewed in this paper.
Synthesis of the evidence
To answer the question of how concentrated crime is
among victims, we estimated the cumulative distribution of crime using visual binning tool in SPSS 21. Each
bin on the horizontal axis represents a 1% interval over
the range from 0 to 100% of the victims. These bins are
arrayed from victims who experienced the most crimes
to non-victims with zero crimes (i.e., the first bin contains the most crime afflicted 1% of the victims and the
last bin contains 1% of the victims, all of which have no
crimes). We then tabulated the median values of Y for
each bin. We used this technique for two specific reasons.
First, we assumed that Y values within each one-percent
range bin on the horizontal (X) axis vary, so we needed a
measure of the central tendency of each one percent bin.
Second, we chose the median as a representative statistic
for each bin to remedy possibly skewed distributions of Y
values in each bin. A visual representation of the process
we used can be found in Fig. 1 of Lee et al. (2017, this
issue).
After calculating median values of each bin, we estimated the cumulative curve by interpolating the median
values. We used the logarithmic and the power law functions as possible candidates to fit our lines. These two
functions are mathematically connected: power-law
behavior in either nature or social systems can be often
transformed into a logarithmic scale for easier understanding on the phenomenon (Newman 2005).
To determine which function would produce a better
fit, we compared their R-square statistics. Though this
statistic is high for both functions, the R-square for the
4
The leading author retrieved and coded X–Y ordered pairs from the studies, and then the other co-authors reviewed the database, and calculated the
raw distribution of X–Y ordered pairs to cumulative distribution of victimizations if needed.
O et al. Crime Sci (2017) 6:9
Page 6 of 16
100
90
Cumulative percentage of crime
80
70
60
50
Prevalence y = 17.783ln(x) + 32.886
R² = 0.6566
40
Frequency y = 25.345ln(x) - 23.531
R² = 0.8938
30
20
10
0
0
10
20
30
40
50
60
70
80
90
100
Cumula ve percentage of vic ms
Fig. 1 Concentration of crime among victims: prevalence vs.
frequency
logarithmic function is greater (see panel D in Fig. 1 of
Lee et al. 2017, this issue). Therefore, we used it to estimate the distribution curve between the cumulative percentage of (binned) victims and crime. We selected only
a single functional form to use throughout the analysis
because we wanted to have a common standard metric
for our comparisons that was simple to interpret. Further,
as we anticipated comparing victim concentration to
place and offender concentrations (see Eck et al. 2017, in
this issue) we did not want to introduce variation in functional form.
Results
Using the 20 studies with 397 corresponding X–Y pairs
for prevalence and the 20 studies with 359 corresponding X–Y pairs for frequency, we first provide an overall
comparison of the extent of crime concentration. Then,
we examine how victimization concentration varies
depending on victim type, crime type, between nations,
and across decades in the US and the UK.
Prevalence and frequency
Figure 1 shows the concentration curves for the overall
prevalence and frequency of victimization. Visually and
analytically, it is obvious that crime is more concentrated when examining the population of possible targets than when only examining targets with at least one
victimization. The dots on the prevalence and frequency
curves (representing the bin medians) do not overlap
much, and the fitted curves are clearly distinct. Estimated coefficients also provide evidence that the prevalence and frequency curves are substantially different in
the victimization concentration. Using 5% of the targets
as a benchmark, the difference is quite dramatic: the
prevalence curve shows that 5% of the population experiences 61.5% of all victimization, whereas the frequency
curve shows that top 5% of all victims’ experience 17.3%
of the victimizations (see Appendix 2). When the prevalence curve hits 100% of victimizations, about half of the
population has experienced some victimization. On the
frequency curve, half of the victims has experienced only
75.6% of victimization.
This illustrates two sources of concentration previously
identified in the literature. First, there is concentration
due to the fact that most possible targets are not victimized. Population heterogeneity may be the source of this.
Second, even when this is accounted for in the frequency
curve, we still see concentration. Some of this may be due
to state dependence.
Although both curves fit the data reasonably well
(using the R-square statistics), we still can see variation
around the fitted curves. This implies that the amount of
concentration varies across studies. Note that this variation is understated in Fig. 1 because the dots represent
median values for bins and there is variation around
these median values. We turn to possible explanations for
this variation next.
Household victimization vs. business victimization
One source of variation is the type of victim. Two common data sources in the literature are household and
business victimization surveys (Weisel 2005). As shown
in Table 1, 10 studies of households provided 156 X and Y
ordered pairs and three studies of businesses provided 40
X and Y pairs for the prevalence curve. For the frequency
curve, the 10 studies of households provided 143 X and
Y pairs and the four studies of businesses provided 43 X
and Y pairs. The types of crimes included in the studies
of households included domestic violence (Lloyd et al.
1994; Mayhew et al. 1993), household burglary (Johnson 2008; Mayhew et al. 1993; Nelson 1980; Sidebottom
2012; Tseloni et al. 2004; and other types of victimization
occurring to households (Ellingworth et al. 1995; Hindelang et al. 1978; Percy 1980; Tseloni 2006). The studies of businesses included commercial burglary (Laycock
2001), pub violence and work-based violence (Mayhew
et al. 1993), business burglary and robbery (Nelson 1980)
and manufacturing commercial victimization and retail
commercial victimization (Pease 1998).
Figure 2 shows the distributions for prevalence and
frequency for each type of victim. In the left panel of
prevalence curves, we see that the two distributions are
quite similar when considering the targets most involved
with crime (at 5% of the targets have about 60% of the
victimizations). When we consider victims above the 10%
Page 7 of 16
100
100
90
90
80
80
Percentage of cumulative crime
Percentage of cumulative crime
O et al. Crime Sci (2017) 6:9
70
60
50
Household Crime y = 23.977ln(x) + 26.208
R² = 0.6957
40
Business Crime y = 18.006ln(x) + 28.949
R² = 0.8158
30
70
60
50
40
Household Crime
30
20
20
10
10
y = 21.058ln(x) - 15.135
R² = 0.8575
Business Crime y = 22.121ln(x) - 4.8568
R² = 0.663
0
0
0
10
20
30
40
50
60
70
80
90
Percentage of cumulave vicms (Prevalence)
100
0
10
20
30
40
50
60
70
80
90
Percentage of cumulave vicms (Frequency)
100
Fig. 2 Concentration of crime: household crime vs. business crime
value on the horizontal axis, the two curves diverge substantially. Importantly, about 50% of the businesses have
no crime involvement whereas about 80% of households
have no crime. This finding is interesting for crime prevention: it suggests that when one selects a small fraction
of the most crime involved, there is no useful distinction
between households and businesses. Stated differently,
addressing the most crime involved 5% of households or
businesses would theoretically yield equivalent results.
The frequency curves show different results. When we
only consider businesses and households with at least
one victimization, business victimization is more concentrated than household victimization. The most victimized
5% of businesses accounts for about 30.7% of the business
victimization, whereas the most victimized 5% of the
households only account for about 18.5% of the household victimizations (see Appendix 2). This suggests that
repeat victimization interventions might be more useful
for businesses than households. However, there is more
variation around the business victimization frequency
curve than the corresponding curve for households so
we have less confidence in the conclusions draw from the
business studies.
Property victimization vs. personal victimization
We also compared property victimization and personal
victimization. For the prevalence curve, three studies of
property victimization provided 71 X–Y ordered pairs
(Tseloni et al. 2004; Tseloni 2006; Ellingworth et al. 1995)
and seven studies of personal victimization provided 109
X–Y ordered pairs (Ellingworth et al. 1995; Hindelang
et al. 1978; Nelson 1980, 1984; Tseloni 2000; Tseloni and
Pease 2005, 2015). For the frequency curve, six studies of
property victimization provided 116 X–Y ordered pairs
(Ellingworth et al. 1995; Johnson 2008; Mayhew et al.
1993; Nelson 1980; Tseloni et al. 2004; Tseloni 2006) and
seven studies of personal victimization provided 93 X–Y
pairs (Ellingworth et al. 1995; Nelson 1980, 1984; Tseloni
2000; Tseloni and Pease 2005; Tseloni and Pease 2015).
Figure 3 shows the prevalence and frequency distributions. In the prevalence curve panel, we see that the two
distributions are quite different up to 10% of the victims
(on the horizontal axis), and that personal victimizations
are more concentrated than property victimizations.
Because both curves hit the 100% value on the vertical
axis when their horizontal values are about 25%, approximately 75% of targets for both types of victims experience
no crime. This finding requires caution in its interpretation because the R square for personal victimization
curve is only 0.36 and the estimated beta is not significant (beta = 12.206, t-statistic = 2.12).
The frequency curves provide a somewhat different
story. When we consider up to 20% of targets in both
property and personal victimization, we do not find any
substantial difference in the patterns of victim concentration. The most victimized 20% of properties and persons
account for 46.7 and 51.5% of victimizations, respectively.
This small difference in victimization suggests that the
patterns of property and personal re-victimizations are
similar once a target has been victimized once. Though
we see more variation around the personal victimization
curve than the property victimization curve, relatively
large R-squares suggest that both frequency curves fit
well through the median values of each bin.
Page 8 of 16
100
100
90
90
80
80
Cumulative percentage of crime
Cumulative percentage of crime
O et al. Crime Sci (2017) 6:9
70
60
50
Personal
y = 12.206ln(x) + 60.982
R² = 0.3597
Property
y = 23.087ln(x) + 26.099
R² = 0.7605
40
30
70
60
50
y = 22.045ln(x) - 14.562
R² = 0.7805
Property
y = 20.476ln(x) - 14.652
R² = 0.8394
40
30
20
20
10
10
0
Personal
0
0
10
20
30
40
50
60
70
80
90
100
Cumulave percentage of vicms (Prevalence)
0
10
20
30
40
50
60
70
80
90
100
Cumulave percentage of vicms (Frequency)
Fig. 3 Concentration of crime: personal crime vs. property crime
US vs. UK
Another source of variation in victimization concentration relates to the data’s country of origin. The US and the
UK each have their own nationally representative victimization surveys (the National Crime Victimization Survey
and the British Crime Survey, respectively). In addition
to studies based on these surveys, we identified other
studies using other surveys either from US or from UK
and we include them in this analysis.
As shown in Table 1, the eight studies using the data
from the US provided 188 X and Y ordered pairs for
prevalence curve, and seven studies provided 144 X and
Y ordered pairs for frequency curve. Twelve studies using
the data from the UK provides 193 X and Y pairs for prevalence curve and 180 X and Y pairs for frequency curve.
The eight US studies use data from the National Crime
Survey (Hindelang et al. 1978; Nelson 1980; Nelson 1984),
the NCVS (Tseloni 2000; Tseloni and Pease 2003; Tseloni
et al. 2004), National Youth Survey (Lauritsen and Quinet
1995), the National Crime Survey of Business Victimizations (Nelson 1980) and other sources, including a general citizen survey (Percy 1980) in the US The twelve UK
studies use the BCS (Ellingworth et al. 1995; Farrell 1995;
Farrell and Pease 1993, Mayhew et al. 1993; Tseloni et al.
2004; Tseloni 2006; Tseloni and Pease 2015), local surveys
(Farrell 1995; Sparks et al. 1977), a business crime survey
(Laycock 2001), a commercial victimization survey (Pease
1998), calls to the police data (Lloyd et al. 1994) or policerecorded crime data (Johnson 2008) in the U. K.
Figure 4 shows the prevalence and frequency distributions
for each country. Looking at the prevalence curves, we see
that the two distributions are quite different. Victimization
seems to be more concentratedin the US than the UK. The
most victimized 5% of the targets in the US account for
65.8% of all victimizations, whereas the most victimized 5%
of the targets in the UK account for 55.4% of victimizations
(see Appendix 2). At the other extreme, about 76% of the US
respondents experience no crime whereas only half of the
UK respondents experience no crime. These differences in
number of non-victims account for differences in victimization concentration in these prevalence curves.
The frequency curves appear to show that victimization is more concentrated in the UK than in the US when
we only consider people with at least one victimization.
However, the difference is not large as in prevalence curve
comparison. The most victimized 5% of victims accounts
for 21.7% of the victimization in the UK, whereas the
most victimized 5% of the victims accounts for 15.1% of
the victimizations in the US (see Appendix 2). Overall,
the comparisons in the prevalence and frequency curves
show that there is variation in the concentration between
the two nations. However, given the variation in the data
for each country, we should be cautious about drawing a
firm conclusion.
Across decades
Because previous studies contended that there is variation in victimization across decades (e.g., Blumstein and
Wallman 2006; Zimring 2006), we look at the variation in
concentration in the US and the UK over decades.
US across decades
First, we looked at the victimization concentration
among the population of possible targets and the targets
Page 9 of 16
100
100
90
90
80
80
Percentage of cumulative crime
Percentage of cumulative crime
O et al. Crime Sci (2017) 6:9
70
60
US
50
y = 17.847ln(x) + 37.119
R² = 0.4602
40
UK
y = 19.148ln(x) + 24.993
R² = 0.7706
70
60
50
40
30
y = 23.273ln(x) - 22.307
R² = 0.8478
20
20
UK y = 23.526ln(x) - 14.941
R² = 0.8882
10
10
30
0
US
0
0
10
20
30
40
50
60
70
80
90
100
Percentage of cumulave vicms (Prevalence)
0
10
20
30
40
50
60
70
80
90
100
Percentage of cumulave vicms (Frequency)
Fig. 4 Concentration of crime: US vs. UK
with at least one victimization in the US. We looked at
the victimization concentration for only two decades (the
1970s and 1990s) due to the lack of studies in other decades. As shown in Table 1, we found eight studies using
the data from the US with 188 X–Y ordered pairs (Hindelang et al. 1978; Lauritsen and Quinet 1995; Nelson
1980, 1984; Percy 1980; Tseloni 2000; Tseloni et al. 2004;
Tseloni and Pease 2003). Five studies used the data collected from 1970s (Hindelang et al. 1978; Lauritsen and
Quinet 1995; Nelson 1980, 1984; Percy 1980) and three
studies used data from 1990s (Tseloni 2000; Tseloni et al.
2004; Tseloni and Pease 2003).
Figure 5 shows the distributions for prevalence and frequency for each. In the prevalence curves, we see that the
two distributions are quite different. In fact, the most victimized 5% of possible targets account for 60.2% of the
victimizations during 1970 whereas the top 5% account
for 81.7% of the victimizations in the 1990s (see Appendix 2). About 75% of the population experienced no victimization during 1990s whereas 70% of the population
experienced no victimizations during 1970s. This finding
is consistent with victimization trend across decades with
the sharp decline in all categories of crime and all parts of
the nation during 1990s (Rennison 2001).
The difference between the two curves is less when we
examine frequency of victimization (right panel) than
between prevalence curves (left panel). This is logical
because we are only looking the subset of the population
who had at least one victimization. Victimization appears
slightly more concentrated in the 1970s than in the 1990s.
However, the right end of these curves is less reliable and
of less consequence than the left end. When we look at
the top 5% of the victims we see that these victims experienced 17% of the crime in the 1990s and 15.6% of the
crime in the 1970s: not a large or meaningful difference
(see Appendix 2). These two frequency curves diverge
more rapidly beyond the 5% value on the horizontal. The
interpretation of these frequency curves is that once victimized, the likelihood of re-victimization did not change
substantially in 1990s compared to 1970s.
UK across decades
We also looked at changes in the victimization concentrations among the population of possible targets and
the targets with at least one victimization in the UK. In
contrast to the US studies, we were able to examine each
decade from the 1970s to the 2000s. We found twelve
studies for the frequency curve. Among those, Sparks
et al.’s (1977) study used data from the 1973 local survey
in England for all offenses and three other studies used
data from the 1982, 1984 and 1988 British Crime Surveys
(Ellingworth et al. 1995; Farrell 1995; Farrell and Pease
1993). Six studies used data collected from 1990s (Ellingworth et al. 1995; Laycock 2001; Lloyd et al. 1994; Mayhew et al. 1993; Pease 1998; Tseloni et al. 2004) and three
studies used data collected from 2000s (Tseloni 2006;
Johnson 2008; Tseloni and Pease 2015). When a study
used the data collected for several years across different
decades (e.g., 1999–2003), the study was assigned to the
decade for median year. In addition, some include data
from more than two decades (Ellingworth et al. 1995)
and two nations (Tseloni et al. 2004). In these cases, we
Page 10 of 16
100
100
90
90
80
80
Percentage of cumulative crime
Percentage of cumulative crime
O et al. Crime Sci (2017) 6:9
70
60
50
US's 1970s y = 21.337ln(x) + 25.842
R² = 0.6075
40
US's 1990s y = 11.558ln(x) + 63.147
R² = 0.2385
30
70
60
50
40
y = 23.722ln(x) - 22.547
R² = 0.8656
30
20
20
10
10
0
US's 1970s
US's 1990s y = 19.159ln(x) - 13.793
R² = 0.8338
0
0
10
20
30
40
50
60
70
80
90
Percentage of cumulave vicms (Prevalence)
100
0
10
20
30
40
50
60
70
80
90
Percentage of cumulave vicms (Frequency)
100
Fig. 5 Concentrations of crime among victims across different decades in the US
use the relevant data for each country or time period
(e.g., if a study displayed results for both the UK and the
US, the UK data was included in the UK analysis and the
US data was included in the US analysis).
Figure 6 shows the distributions for prevalence and
frequency for four decades. In the left panel, we see that
three decades (1980s, 1990s and the 2000s) have quite
similar quite similar distributions, but seem to be different than the decade of the 1970s (see Appendix 2). In
fact, the most victimized 5% in the UK during the 1980s,
1990s and 2000s have approximately 59–65% of crime,
whereas most victimized 5% during the 1970s have only
about 38.6% of crime (see Appendix 2). About 40% of the
population has zero crime during 1970s whereas 50–60%
of the population has zero crime during other decades.
According to the frequency curves, the 2000s show the
least concentration compared to the other three decades
when we only consider targets with at least one victimization. In fact, the most victimized 5% of victims in the
1980s and 1990s experienced 27.4 and 31% of victimization respectively, whereas most victimized 5% of repeat
victims during the 2000s experienced only about 20.3%
of crime (see Appendix 2). Overall, the UK frequency
curves show that victimization concentration increased
gradually from 1970s to 1990s, then dropped substantially into the 2000s (see Appendix 2).
Limitations
The heterogeneity of the literature on victimization and
the sheer scarcity of studies found for particular categories of victimization create limitations to our findings.
We alluded to most of these limitations in the previous
sections, but they warrant reiteration here.
First, the 70 studies we found included various types
of victimizations, including sexual victimization (Fisher
et al. 1998; Gagné et al. 2005; Gidycz et al. 1993; Tillyer
et al. 2016) and peer victimization (Bond et al. 2001;
Espelage et al. 2013; Fekkes et al. 2004; Fisher et al. 2015;
Pabian and Vandebosch 2016; Li et al. 2003). However,
many of those studies did not satisfy our third criteria
requiring at least two empirical ordered pairs, and so we
excluded them. Thus, we ended up with few types of victimization for our meta-analysis.
Second, visual binning can reduce the true variation
in the X and Y points. Losing variations in the raw data
reduces the degrees of freedom, and can lead to a less
accurate estimation of the curve. For example, we found
that some of the estimated betas (in Figs. 3 and 5) were
not statistically significant (see italicized estimates in
Appendix 2). Theoretically, this finding does not make
sense because it suggests that there is no significant evidence of victim concentration. Despite this limitation, we
used bin medians rather than means because there is no
Page 11 of 16
100
100
90
90
80
80
Percentage of cumulative crime
Percentage of cumulative crime
O et al. Crime Sci (2017) 6:9
70
60
UK's 1970s y = 25.01ln(x) - 1.6901
R² = 0.962
50
40
UK's 1980s y = 14.837ln(x) + 41.143
R² = 0.5603
30
UK's 1990s y = 17.7ln(x) + 30.984
20
UK's 2000s y = 19.657ln(x) + 27.444
R² = 0.692
R² = 0.7858
70
60
50
10
0
y = 24.083ln(x) - 17.833
R² = 0.9734
UK's 1980s
y = 21.524ln(x) - 7.2586
R² = 0.9012
UK's 1990s
y = 21.144ln(x) - 3.0212
R² = 0.7877
UK's 2000s
y = 20.34ln(x) - 12.426
R² = 0.9108
30
20
10
UK's 1970s
40
0
0
10
20
30
40
50
60
70
80
90
Percentage of cumulave vicms (Prevalence)
100
0
10
20
30
40
50
60
70
80
90
Percentage of cumulave vicms (Frequency)
100
Fig. 6 Concentrations of crime among victims across different decades in the UK
other metric to represent the typicality of each bin, given
the skewness of the distributions within bins.
Third, we did not weight our data nor X–Y pairs.
Because data from a large sample can be more reliable
and have more statistical power for a meta-analysis than
data from a small sample, weighting by sample size would
be reasonable. However, because we did not find any substantial difference in the findings by using the weighting
method, we used the unweighted data points for metaanalysis. Based on our analysis, we believe weighting
makes no difference, but there is always the possibility
that we could be wrong.
Fourth, we used the logarithmic function throughout
the meta-analyses. Because the logarithmic transformation is not possible for zero, all curves in the figures
are marginally away from the origin either vertically or
horizontally. Using functional forms tailored to suit different categories of victimization concentration may be
better than a using a standard logarithmic function. The
extreme upper right in our estimated curves are likely
to be biased in most of the figures. The most important
part of the curves is toward the lower left were the most
victimized subjects appear. The logarithmic functions fit
the data well here, as judged by the dispersion of cases
around the fitted lines. In future research, other functional forms should be tested.
Finally, our findings are limited by the populations
researchers have examined with sufficient frequency
that we could make comparisons. We could only compare concentration levels of the UK to the US, for example, because sufficient number of X–Y pairs are given for
reliable comparison. Other national comparisons would
be interesting, but there are insufficient studies to make
such comparisons.
Discussion and conclusions
This is the first study to systematically review studies on
the concentration of victimization and to synthesize their
findings using a form of meta-analysis. One of the reasons researchers use systematic reviews and meta-analysis is to avoid potentially biased conclusions that can arise
from standard narrative reviews (Wilson 2001). Prior to
conducting a meta-analysis, it is quite possible that the
community of scholars examining a topic is wrong in
their conclusions. Now that we have conducted a metaanalysis of repeat victimization studies, our findings suggest that the scholars who promote the importance of
repeat victimization are correct. Though this may seem
obvious, the obviousness of our findings is not the point:
in principle, the findings could have been otherwise.
The studies collectively show that a relatively few
households and businesses have a disproportionate
O et al. Crime Sci (2017) 6:9
number of victimizations. When all possible victims are
included (regardless of whether they have experienced
victimization during a study’s reference period), 5% of the
subjects have 60% of the victimizations. When only those
who have experience at least one victimization are examined, the 5% most victimized subjects have 12% of the
victimizations. On average, therefore, the biggest source
of victimization concentration is due to the non-involvement in crime of most subjects. It seems plausible that
much of the cause of the first source of concentration is
due to population heterogeneity—some people, households, or businesses are at less risk of crime than others
due to some characteristics they do not share with others
who are at greater risk. In fact, our findings suggest that
about 50% of population of businesses and 20% of population of households have experienced victimization and
this can be attributed to the difference in the features of
places in terms of crime opportunities. However, additional concentration is due to repeated victimization of
a minority of victims after the first victimization. And
state dependence is more likely to be an explanation for
repeated victimization following the first victimization.
Furthermore, we found that high involvement in
crime is associated with high repetition once involved.
Though this is a rough summary of our findings, it is
tantalizing and deserving of further enquiry. The reasoning for this is twofold, implying (1) that involvement and repetition are not separate processes that
require different explanations and (2) that mixed processes of flag and boost account (i.e., population heterogeneity and state dependence, respectively) operate
at the aggregate level. Fortunately, we are not the only
researchers to point out this association. Trickett et al.
(1992, 1995) found that high crime rates can be attributed to both measures of victimization, high crime
prevalence and high crime repetition. These findings
suggest that crime prevention should focus on preventing initial victimization and on preventing subsequent
(repeat) victimizations as well. Economic efficiency,
however, suggests focusing more on the previously
victimized, as this is a much smaller portion of the
population so it is easier to concentrate prevention
programs. Accordingly, having two different measures
of victimization concentration is necessary. Though
this conclusion reiterates what others have said about
victimization, no study has systematically analyzed and
confirmed this conclusion. When we began, it was possible that our review could contradict what researchers
thought they would know, or it could confirm it. The
Page 12 of 16
fact that in this case the community was probably right,
is a useful finding. From this standpoint, reasserting
the need for two different measures for victimization
concentration is useful.
Based on the victimization comparisons between frequency and prevalence, households and businesses,
property and personal, the UK and the USA, and across
the decades (i.e. over time) in each of these countries, we
conclude our paper as follows.
First, the variation between households and businesses among the targets with at least one victimization,
suggests that on average, households do better at avoiding subsequent crimes than managers of businesses: a
smaller proportion of households are at the upper end
of repeat involvement than is the case with businesses.
For businesses that experience repeat victimization,
changing management practices through the adoption
of more protective measures may be costly and inconvenient. Especially when they do not make much profit
in more crime ridden places, they might prefer to put
up with repeat victimization. In contrast, households
may put forth more effort to reduce criminal opportunities because repeat victimizations are more expensive
and inconvenient. This finding emphasizes the role of
place management in reducing repeat victimization at
places (Madensen and Eck 2013). Thus, increasing the
responsibility of place managers or owners through the
application of publicity, user fees, or even civil actions
might reduce a substantial amount of business victimization (Weisel 2005). However, it is still possible that
the difference between households and businesses can
be attributed to the different data collection process
from different surveys.
Second, the comparison between personal and property
victimizations suggests that the patterns of revictimization are similar once a target has been victimized. If we
assume that personal crime is a crime against person and
that property crime is a crime at a place, this is consistent
with the findings in Eck et al. (2017, in this issue). In other
words, victim concentration is not substantially different
from place concentration in the frequency curves.
Third, in the variation between decades in the US, the
findings suggest that during 1990s, the percentage of the
population that was victimized decreased and the targets
with at least one victimization generally experienced less
repeat victimization. Thus, findings are consistent with
victimization trend across decades with the sharp decline
in all categories of crime and all parts of the nation during 1990s (Zimring 2006).
O et al. Crime Sci (2017) 6:9
Authors’ contributions
This paper was conducted by a team. SO was the lead writer for this paper,
contributed to the development of the methods used, and provided expertise
on victims. NNM provided expertise on offenders, assisted in the development
of the research methods, and provided editorial reviews. YJL was the lead
analyst for the team and provided editorial assistance. JEE headed the team
and provided overall guidance and editorial assistance. All authors read and
approved the final manuscript.
Author details
1
School of Criminal Justice, University of Cincinnati, Cincinnati, OH, USA.
2
School of Public Affairs, University of Colorado, Colorado Springs, USA.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
Articles used in the systematic review are noted in the references. For other
information regarding data, please contact the lead author.
Ethics approval and consent to participate
Does not apply. As a review of summary data from previously conducted
research, no humans (or their tissues) participated as subjects in this research.
Funding
No funds were solicited or provided for this research.
100
90
Cumulative percentage of crime
80
70
60
50
Prevalence y = 17.783ln(x) + 32.886
R² = 0.6566
40
Weighted y = 17.986ln(x) + 30.388
R² = 0.6358
Prevalence
30
20
10
0
0
10
20
30
40
50
60
70
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix 1
Estimated distributions of crime at victim for prevalence and frequency schema: A comparison of fitted lines
between un-weighted and weighted X–Y ordered pairs
(Figs. 7, 8)
90
100
Fig. 7 Concentration including non-involved
100
90
80
70
60
50
Frequency y = 25.345ln(x) - 23.531
R² = 0.8938
40
Weighted
Frequency
30
y = 25.065ln(x) - 23.498
R² = 0.8262
20
10
0
0
10
20
30
40
50
60
70
Cumulave percentage of vicms
Publisher’s Note
80
Cumulave percentage of vicms
Cumulative percentage of crime
This first meta-analysis of repeat victimization raises
a number of questions about the variation in crime concentration among people, households, and businesses.
It does, however, show that, when data is available to
draw a conclusion, concentration of crime among people, households and businesses is standard. We found no
study that contradicted this finding. Nor did we find any
study that suggests that the concentration is due only to
prevalence (the proportion of subjects who were victimized one or more times) or only due to frequency (the
repetition of victimization given an initial victimization).
Though it should not need repeating, given crime policy
makers proclivity to fads, we do repeat that the concentration of crime among a relatively small proportion of
possible crime targets must be part of any sensible prevention policy.
Page 13 of 16
Fig. 8 Concentration excluding non-involved
80
90
100
O et al. Crime Sci (2017) 6:9
Page 14 of 16
Appendix 2
See Table 2
Table 2 Estimated coefficients and summary statistics of the models specifications in Figs. 1, 2, 3, 4 and 5
Figure number
Key
Constant
Beta
Std. error
Confidence
interval
t‑statistic
Percentage of crime explained
by:
5%
Figure 1
Figure 2
Figure 5
Figure 6
50%
100.0
Prevalence
32.89
17.78
2.11
13.56
22.01
8.41
61.5
73.8
86.2
−23.53
25.35
1.35
22.65
28.04
18.81
17.3
34.8
52.4
75.6
23.69
24.61
3.68
17.25
31.97
6.69
63.3
80.4
97.4
100.0
Household (P)
Business (P)
Figure 4
20%
Frequency
Household (F)
Figure 3
10%
28.95
−15.28
18.01
2.58
12.85
23.16
6.98
57.9
70.4
82.9
99.4
21.017
1.66
17.70
24.33
12.68
18.5
33.1
47.7
66.9
Business (F)
−4.86
22.121
3.72
14.69
29.56
5.95
30.7
46.1
61.4
81.7
Property (P)
26.10
23.087
4.10
14.89
31.28
5.64
63.3
79.3
95.3
100.0
Personal (P)
60.98
12.0206
5.76
0.50
23.54
2.12
80.3
88.7
97.0
100.0
Property (F)
−14.65
20.476
1.91
16.66
24.29
10.73
18.3
32.5
46.7
65.5
Personal (F)
−14.56
22.045
2.69
16.67
27.42
8.22
20.9
36.2
51.5
71.7
US (P)
37.12
17.847
4.32
9.20
26.49
4.13
65.8
78.2
90.6
100.0
Non-US (P)
23.97
19.503
1.77
15.96
23.05
11.00
55.4
68.9
82.4
100.0
US (F)
−22.31
23.273
1.93
19.41
27.14
12.03
15.1
31.3
47.4
68.7
Non-US (F)
−16.82
23.962
1.34
21.29
26.64
17.92
21.7
38.4
55.0
76.9
US 1970’ (P)
25.84
21.337
3.84
13.67
29.01
5.56
60.2
75.0
89.8
100.0
US 1990’ (P)
63.15
11.558
11.93
−12.29
35.41
0.97
81.7
89.8
97.8
100.0
US 1970’ (F)
−22.55
23.722
1.91
19.91
27.54
12.43
15.6
32.1
48.5
70.3
US 1990’ (F)
−13.79
19.159
2.71
13.75
24.57
7.08
17.0
30.3
43.6
61.2
UK 1970’ (P)
−1.69
25.01
1.21
22.60
27.42
20.74
38.6
55.9
73.2
96.1
UK 1980’ (P)
41.14
14.837
3.19
8.46
21.21
4.65
65.0
75.3
85.6
99.2
UK 1990’ (P)
30.98
17.7
2.24
13.22
22.18
7.90
59.5
71.7
84.0
100.0
UK 2000’ (P)
27.44
19.657
4.64
10.38
28.93
4.24
59.1
72.7
86.3
100.0
UK 1970’ (F)
−17.83
24.083
1.06
21.96
26.21
22.63
20.9
37.6
54.3
76.4
UK 1980’ (F)
−7.26
21.524
1.59
18.34
24.71
13.51
27.4
42.3
57.2
76.9
UK 1990’ (F)
−3.02
21.144
2.04
17.07
25.22
10.37
31.0
45.7
60.3
79.7
UK 2000’ (F)
−12.43
20.34
1.92
16.50
24.18
10.60
20.3
34.4
48.5
67.1
Received: 17 February 2017 Accepted: 6 July 2017
References
†
Denotes a study we identified through keyword search; *Denotes a
study included in both the systematic review and meta‑analysis
†
Averdijk, M. (2011). Reciprocal effects of victimization and routine activities.
Journal of Quantitative Criminology, 27(2), 125–149.
†
Akers, R. L., Sellers, C., & Cochran, J. (1987). Fear of crime and victimization
among the elderly in different types of communities. Criminology, 25(3),
487–505.
Ashton, J., Brown, I., Senior, B., & Pease, K. (1998). Repeat victimisation: offender
accounts. International Journal of Risk Security and Crime Prevention, 3,
269–280.
Bennett, T. (1995). Identifying, explaining, and targeting burglary ‘hot spots’.
European Journal on Criminal Policy and Research, 3(3), 113–123.
†
Bennett, D. C., Guran, E. L., Ramos, M. C., & Margolin, G. (2011). College students’ electronic victimization in friendships and dating relationships:
Anticipated distress and associations with risky behaviors. Violence and
Victims, 26(4), 410–429.
Blumstein, A., & Wallman, J. (2006). The crime drop in America. Cambridge:
Cambridge University Press.
†
Bond, L., Carlin, J. B., Thomas, L., Rubin, K., & Patton, G. (2001). Does bullying
cause emotional problems? A prospective study of young teenagers.
British Medical Journal, 323(7311), 480.
†
Brzozowski, J. A., Taylor-Butts, A., & Johnson, S. (2006). Victimization and
offending among the aboriginal population in Canada. Juristat: Canadian Centre for Justice Statistics, 26(3), 1–31.
Chenery, S., Holt, J., & Pease, K. (1997). Biting back II: Reducing repeat victimisation
in Huddersfield (Vol. 2). London: Home Office Police Research Group.
Eck, J. E., Lee, Y., SooHyun, O., & Martinez, N. N. (2017). Compared to what? Estimating the relative concentration of crime at places using systematic
and other reviews. Crime Science, 6, 6.
†
* Ellingworth, D., Farrell, G., & Pease, K. (1995). Victim is a victim is a victim?
Chronic victimization in four sweeps of the British Crime Survey. British
Journal of Criminology, 35(3), 360–365.
†
Espelage, D. L., Rao, M. A., & De La Rue, L. (2013). Current research on schoolbased bullying: A social-ecological perspective. Journal of Social Distress
and the Homeless, 22(1), 21–27.
O et al. Crime Sci (2017) 6:9
†
Fagan, A. A., & Mazerolle, P. (2011). Repeat offending and repeat victimization:
Assessing similarities and differences in psychosocial risk factors. Crime
& Delinquency, 57(5), 732–755.
†
Farrell, G. (1992). Multiple victimisation: its extent and significance. International Review of Victimology, 2(2), 85–102.
*†Farrell, G. (1995). Preventing repeat victimization. Crime and Justice, 19,
469–534.
*†Farrell, G., & Pease, K. (1993). Once bitten, twice bitten: Repeat victimisation and
its implications for crime prevention. London: Home Office.
Farrell, G., Tilley, N., & Tseloni, A. (2014). Why the crime drop? In M. H. Tonry (Ed.),
Crime and justice: A review of research (Vol. 43, pp. 421–490). Chicago:
University of Chicago Press.
†
Fekkes, M., Pijpers, F. I., & Verloove-Vanhorick, S. P. (2004). Bullying behavior
and associations with psychosomatic complaints and depression in
victims. The Journal of Pediatrics, 144(1), 17–22.
Felson, M., & Clarke, R. (1998). Opportunity makes the thief (Police Research
Series Paper 98, Policing andreducing crime unit, research, development and statistics directorate). London: Home Office.
†
Fisher, B. S., Daigle, L. E., & Cullen, F. T. (2010). What distinguishes single from
recurrent sexual victims? The role of lifestyle-routine activities and firstincident characteristics. Justice Quarterly, 27(1), 102–129.
†
Fisher, B. S., Sloan, J. J., Cullen, F. T., & Lu, C. (1998). Crime in the ivory tower: The
level and sources of student victimization. Criminology, 36(3), 671–710.
†
Fisher, S., Middleton, K., Ricks, E., Malone, C., Briggs, C., & Barnes, J. (2015).
Not just black and white: peer victimization and the intersectionality
of school diversity and race. Journal of Youth and Adolescence, 44(6),
1241–1250.
†
Forero, R., McLellan, L., Rissel, C., & Bauman, A. (1999). Bullying behaviour and
psychosocial health among school students in New South Wales, Australia: cross sectional survey. British Medical Journal, 319(7206), 344.
Forrester, D., Chatterton, M., Pease, K., & Brown, R. (1988). The Kirkholt burglary
prevention project, Rochdale. London: Home Office.
Forrester, D., Frenz, S., O’Connell, M., & Pease, K. (1990). The Kirkholt burglary
prevention project: Phase II. London: Home Office.
†
Gagné, M. H., Lavoie, F., & Hébert, M. (2005). Victimization during childhood
and revictimization in dating relationships in adolescent girls. Child
Abuse & Neglect, 29(10), 1155–1172.
Gill, M., & Matthews, R. (1993). Raids on banks. Leicester: University of Leicester:
Centre for the Study of Public Order.
Gill, M., & Pease, K. (1998). Repeat robbers: Are they different? In Crime at
work (pp. 143–153). London: Palgrave Macmillan UK.
†
Gidycz, C. A., Coble, C. N., Latham, L., & Layman, M. J. (1993). Sexual assault
experience in adulthood and prior victimization experiences. Psychology of Women Quarterly, 17(2), 151–168.
†
Gottfredson, M. R., & Grande-Bretagne, (1984). Home office victims of crime: The
dimensions of risk. London, UK: HM Stationery Office.
Higgins, J. P., & Green, S. (2011). Cochrane handbook for systematic reviews of
interventions: Version 5.1.0 (updated March 2011). The Cochrane collaboration. www.cochrane-handbook.org.
*†Hindelang, M. J., Gottfredson, M. R., & Garofalo, J. (1978). Victims of personal
crime: An empirical foundation for a theory of personal victimization.
Cambridge: Ballinger.
†
Holt, M. K., Finkelhor, D., & Kantor, G. K. (2007). Multiple victimization experiences of urban elementary school students: Associations with psychosocial functioning and academic performance. Child Abuse & Neglect,
31(5), 503–515.
Hope, T. (1995). The flux of victimization. British Journal of Criminology, 35(3),
327–342.
†
Hope, T., Bryan, J., Trickett, A., & Osborn, D. R. (2001). The phenomena of
multiple victimization. The relationship between personal and property
crime risk. British Journal of Criminology, 41(4), 595–617.
†
Hough, M. (1986). Victims of violent crime: Findings from the British Crime
Survey. In I. Anttila (Ed.), From crime policy to victim policy (pp. 117–132).
London: Palgrave Macmillan UK.
†
* Johnson, S. D. (2008). Repeat burglary victimisation: a tale of two theories.
Journal of Experimental Criminology, 4(3), 215–240.
†
Johnson, S. D., Bowers, K., & Hirschfield, A. (1997). New insights into the spatial
and temporal distribution of repeat victimization. British Journal of
Criminology, 37(2), 224–241.
†
Jones, T., MacLean, B., & Young, J. (1986). The Islington crime survey. Aldershot:
Gower.
Page 15 of 16
†
Kelly, L., Burton, S., & Regan, L. (1996). Beyond victim or survivor: Sexual
violence, identity and feminist theory and practice. In L. Adkins & V.
Merchant (Eds.), Sexualizing the social (pp. 77–101). London: Palgrave
Macmillan UK.
†
Kilpatrick, D. G., Saunders, B. E., Veronen, L. J., Best, C. L., & Von, J. M. (1987).
Criminal victimization: Lifetime prevalence, reporting to police, and
psychological impact. Crime & Delinquency, 33(4), 479–489.
†
Kimerling, R., Alvarez, J., Pavao, J., Kaminski, A., & Baumrind, N. (2007). Epidemiology and consequences of women’s revictimization. Women’s Health
Issues, 17(2), 101–106.
†
Koss, M. P., Gidycz, C. A., & Wisniewski, N. (1987). The scope of rape: incidence
and prevalence of sexual aggression and victimization in a national
sample of higher education students. Journal of Consulting and Clinical
Psychology, 55(2), 162–170.
*†Lauritsen, J. L., & Quinet, K. F. D. (1995). Repeat victimization among adolescents and young adults. Journal of Quantitative Criminology, 11(2),
143–166.
*†Laycock, G. (2001). Hypothesis-based research: The repeat victimization
story. Criminology and Criminal Justice, 1(1), 59–82.
Lee, M. R. (2000). Community cohesion and violent predatory victimization:
A theoretical extension and cross-national test of opportunity theory.
Social Forces, 79(2), 683–706.
Lee, Y., Eck, J. E., SooHyun, O., & Martinez, N. N. (2017). How concentrated is
crime at places? A systematic review from 1970 to 2015. Crime Science.
doi:10.1186/s40163-017-0069-x.
†
Li, T., Trivedi, P. K., & Guo, J. (2003). Modeling response bias in count: a structural approach with an application to the national crime victimization
survey data. Sociological Methods & Research, 31(4), 514–544.
†
* Lloyd, S., Farrell, G., & Pease, K. (1994). Preventing repeated domestic violence: A
demonstration project on Merseyside. London: Home Office.
Madensen, T. D. & Eck, J. E. (2013). Crime places and place management. In F. T.
Cullen and P. Wilcox (Eds.), Oxford handbook of criminological theory, (pp.
554–578). New York.
Martinez, N.N., Lee, Y., Eck, J.E., & SooHyun, O. (2017). Ravenous wolves revisited: A systematic review of offending concentration. Crime Science
(forthcoming).
†
* Mayhew, P., Maung, N. A., & Mirrlees-Black, C. (1993). The 1992 British crime
survey. London: HM Stationery Office.
†
Menard, S. (2000). The “normality” of repeat victimization from adolescence
through early adulthood. Justice Quarterly, 17(3), 543–574.
Mulrow, C. D., & Oxman, A. (1997). How to conduct a Cochrane systematic review:
Version 3.0.2. San Antonio: The Cochrane Collaboration.
†
Nansel, T. R., Overpeck, M., Pilla, R. S., Ruan, W. J., Simons-Morton, B., & Scheidt,
P. (2001). Bullying behaviors among US youth: Prevalence and association with psychosocial adjustment. JAMA, 285(16), 2094–2100.
*†Nelson, J. F. (1980). Multiple victimization in American cities: a statistical
analysis of rare events. American Journal of Sociology, 85(4), 870–891.
*†Nelson, J. F. (1984). Modeling individual and aggregate victimization rates.
Social Science Research, 13(4), 352–372.
Newman, M. E. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46(5), 323–351.
Osborn, D. R., & Tseloni, A. (1998). The distribution of household property
crimes. Journal of Quantitative Criminology, 14(3), 307–330.
†
Osborn, D. R., Ellingworth, D., Hope, T., & Trickett, A. (1996). Are repeatedly
victimized households different? Journal of Quantitative Criminology,
12(2), 223–245.
†
Outlaw, M., Ruback, B., & Britt, C. (2002). Repeat and multiple victimizations:
the role of individual and contextual factors. Violence and Victims, 17(2),
187–204.
†
Pabian, S., & Vandebosch, H. (2016). An investigation of short-term longitudinal associations between social anxiety and victimization and
perpetration of traditional bullying and cyberbullying. Journal of Youth
and Adolescence, 45(2), 328–339.
*†Pease, K. (1998). Repeat victimization: Taking stock. London: Home Office.
†
Pease, K., & Laycock, G. (1999). Revictimization, reducing the heat on hot victims
(pp. 1–6). Canberra: Australian Institute of Criminology.
*†Percy, S. L. (1980). Response time and citizen evaluation of police. Journal of
Police Science and Administration, 8(1), 75–86.
†
Pereda, N., & Gallardo-Pujol, D. (2014). One hit makes the difference: the role
of polyvictimization in childhood in lifetime revictimization on a southern European sample. Violence and Victims, 29(2), 217–231.
O et al. Crime Sci (2017) 6:9
†
Planty, M., & Strom, K. J. (2007). Understanding the role of repeat victims in
the production of annual U.S. victimization rates. Journal of Quantitative
Criminology, 23(3), 179–200.
†
Rand, M. R., & Rennison, C. M. (2005). Bigger is not necessarily better: An
analysis of violence against women estimates from the National Crime
Victimization Survey and the National Violence Against Women Survey.
Journal of Quantitative Criminology, 21(3), 267–291.
Rennison, C. M. (2001). Violent victimization and race, 1993–98. US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.
†
Rigby, K. E. N. (2000). Effects of peer victimization in schools and perceived
social support on adolescent well-being. Journal of Adolescence, 23(1),
57–68.
†
Russell, D. E. (1984). Sexual exploitation. Beverly Hills: Sage.
†
Sampson, A. (1991). Lessons from a victim support crime prevention project.
London: Home Office.
Sampson, A., & Phillips, C. (1992). Multiple victimisation: racial attacks on an East
London estate. London: Home Office.
Sampson, A., & Phillips, C. (1995). Reducing repeat racial victimisation on an East
London estate. London: Home Office.
†
Sandberg, D. A., Matorin, A. I., & Lynn, S. J. (1999). Dissociation, posttraumatic
symptomatology, and sexual revictimization: a prospective examination of mediator and moderator effects. Journal of Traumatic Stress,
12(1), 127–138.
Schwartz, D., Dodge, K. A., & Coie, J. D. (1993). The emergence of chronic peer
victimization in boys’ play groups. Child Development, 64(6), 1755–1772.
*†Sidebottom, A. (2012). Repeat burglary victimization in Malawi and the influence of housing type and area-level affluence. Security Journal, 25(3),
265–281.
†
Siegal, J. M., Sorenson, S. B., Golding, J. M., Burnham, M. A., & Stein, J. A. (1987).
The prevalence of child sexual abuse: the Los Angeles epidemiological catchment area project. American Journal of Epidemiology, 126,
1141–1153.
†
Sobsey, D. (1994). Sexual abuse of individuals with intellectual disability. In
Craft, A. (Ed.). Practice issues in sexuality and learning disabilities (pp.
93–115). Routledge.
Sparks, R. F. (1981). Multiple victimization: Evidence, theory, and future
research. The Journal of Criminal Law and Criminology (1973-), 72(2),
762–778.
*†Sparks, R. F., Genn, H. G., & Dodd, D. J. (1977). Surveying victims: A study of the
measurement of criminal victimization, perceptions of crime, and attitudes
to criminal justice. London: Wiley.
†
Spelman, W. (1995). Once bitten, then what? cross-sectional and time-course
explanations of repeat victimization. The British Journal of Criminology,
366–383.
†
Tilley, N. (1993). The prevention of crime against small businesses: The safer cities
experience. London: Home Office.
†
Tillyer, M. S., Gialopsos, B. M., & Wilcox, P. (2016). The short-term repeat sexual
victimization of adolescents in school. Crime & Delinquency, 62(1),
81–106.
Page 16 of 16
†
Toplu-Demirtas, E., Hatipoğlu-Sümer, Z., & White, J. W. (2013). The relation
between dating violence victimization and commitment among Turkish college women: does the investment model matter? International
Journal of Conflict and Violence, 7(2), 203–215.
Trickett, A., Osborn, D. R., Seymour, J., & Pease, K. (1992). What is different about
high crime areas? British Journal of Criminology, 32(1), 81–89.
†
Trickett, A., Ellingworth, D., Hope, T., & Pease, K. (1995). Crime victimization in
the eighties: Changes in area and regional inequality. British Journal of
Criminology, 35(3), 343–359.
*†Tseloni, A. (2000). Personal criminal victimization in the United States: fixed
and random effects of individual and household characteristics. Journal
of Quantitative Criminology, 16(4), 415–442.
†
* Tseloni, A. (2006). Multilevel modelling of the number of property crimes:
household and area effects. Journal of the Royal Statistical Society: Series
A, 169(2), 205–233.
*†Tseloni, A., & Pease, K. (2003). Repeat personal victimization. ‘boosts’ or ‘flags’?
British Journal of Criminology, 43(1), 196–212.
†
Tseloni, A., & Pease, K. (2005). Population inequality: the case of repeat crime
victimization. International Review of Victimology, 12(1), 75–90.
*†Tseloni, A., & Pease, K. (2015). Area and individual differences in personal
crime victimization incidence: the role of individual, lifestyle/routine
activities and contextual predictors. International review of victimology,
21(1), 3–29.
*†Tseloni, A., Wittebrood, K., Farrell, G., & Pease, K. (2004). Burglary victimization
in England and Wales, the United States and the Netherlands. British
Journal of Criminology, 44(1), 66–91.
Webb, J. (1997). Direct line homesafe: an evaluation of the first year. Unpublished paper.
Weisel, D. L. (2005). Analyzing repeat victimization. Washington, DC: US Department of Justice, Office of Community Oriented Policing Services.
Wilson, D. B. (2001). Meta-analytic tools for criminology. Annals of the American
Academy of Political and Social Science, 578, 71–89.
Winkel, F. W. (1991). Police, victims, and crime prevention: some researchbased recommendations on victim-orientated interventions. British
Journal of Criminology, 31(3), 250–265.
†
Wittebrood, K., & Nieuwbeerta, P. (2000). Criminal victimization during one’s
life course: The effects of previous victimization and patterns of routine
activities. Journal of Research in Crime and Delinquency, 37(1), 91–122.
†
Wolke, D., Woods, S., Stanford, K., & Schulz, H. (2001). Bullying and victimization of primary school children in England and Germany: prevalence
and school factors. British Journal of Psychology, 92(4), 673–696.
Wood, J., Wheelwright, G., & Burrows, J. (1997). Crime against small business: facing the challenge: findings of a crime survey conducted in the Belgrave and
West End areas of Leicester. Los Angeles: Crime Concern.
Zimring, F. E. (2006). The great American crime decline. USA: Oxford University
Press.