Chainey and da Silva Crime Sci (2016) 5:1
Examining the extent ofrepeat andnear
repeat victimisation ofdomestic burglaries
inBelo Horizonte, Brazil
Spencer Paul Chainey1* and Braulio Figueiredo Alves da Silva2
Substantial research suggests that a burglary event is a useful predictor of burglaries to the same or nearby properties
in the near future. To date, the research that has suggested this predictive quality has been based on studies that have
focused on crime patterns in western industrialised countries, such as the UK, USA and Australia. These studies have
in turn informed the design of eﬀective burglary reduction programmes that have a speciﬁc focus towards counter-
ing the risk of repeats and near repeats. This current study adds to the existing research knowledge by examining
whether patterns of burglary repeats and near repeats are evident in Belo Horizonte, a large Brazilian city. Domestic
dwellings in Brazilian cities, as typiﬁed by those in Belo Horizonte, are quite diﬀerent to dwellings in western coun-
tries—many city-dwelling Brazilians live in apartments in high rise buildings, most houses and apartment blocks
are surrounded by high perimeter fencing, and a reasonable proportion of dwellings are irregular self-constructed
houses. As a consequence, a diﬀerent infrastructure of domestic living may result in diﬀerences in patterns of domes-
tic burglary when compared to patterns in western countries. The research identiﬁes that the extent of repeat and
near repeat patterns in the city of Belo Horizonte are lower than those in comparable western urban contexts. We
discuss the implications of these ﬁndings and how they impact on the translating of practice on crime prevention
and crime prediction to the urban Latin American context.
Keywords: Repeat victimisation, Near repeat victimisation, Crime prediction, Crime prevention, Policing, Burglary,
Boost account, Flag account, Foraging theory
© 2016 Chainey and da Silva. 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.
Repeat victimisation is the empirically observed pattern
of a person or other target (e.g., a building) being sub-
ject to victimisation a number of times (Farrell and Pease
1993; Polvi etal. 1991). Near repeat victimisation is the
observed ﬁnding that targets near to a recent incident
are also at a heightened risk of being victimized (Bowers
etal. 2004). ese patterns of repeat and near repeat vic-
timisation have been observed for a range of crime types,
including domestic burglary (Johnson and Bowers 2004a;
Pease 1998; Johnson etal. 2007), vehicle crime (Johnson
etal. 2009), and shootings (Haberman and Ratcliﬀe 2012;
Ratcliﬀe and Rengert 2008).
e empirical ﬁndings from research into the patterns
of repeats and near repeats has led some commenta-
tors to suggest that recent incidents provide a powerful
indicator for predicting where and when crime is likely
to take place (Bowers et al. 2004; Johnson and Bowers
2004b; Pease 1998; Skogan 1996). In turn, these observed
patterns of repeats and near repeats have resulted in
many police agencies designing crime prevention pro-
grammes, in particular for burglary, to counter the pre-
dicted heightened risk of further incidents following an
initial oﬀence with reported successes including reduc-
tions in burglary of 27% in Traﬀord (UK) (Fielding and
Jones 2012) and 66% in Edmonton (Canada) (UCL 2014).
Additionally, several software companies have drawn
from the research ﬁndings into repeats and near repeats
1 Department of Security and Crime Science, University College London,
35 Tavistock Square, London, England, UK
Full list of author information is available at the end of the article
Page 2 of 10
Chainey and da Silva Crime Sci (2016) 5:1
to design applications for predicting crime, for example
PredPol (2013) and HunchLab (Azavea 2015).
To date, the analysis of repeat and near repeat patterns
of crime has been applied in many western countries,
including Europe (Bernasco 2008; Bowers and John-
son 2005), the USA (Haberman and Ratcliﬀe 2012; Rat-
cliﬀe and Rengert 2008; Wells etal. 2011), and Australia
(Townsley et al. 2003) but has received very little ana-
lytical attention in non-western contexts, such as Latin
American countries. In this paper we describe research
that examined whether patterns of repeat victimisation
and near repeat victimisation of domestic burglary are
evident in Belo Horizonte, Brazil—a typical Brazilian
city. We hypothesise that patterns of burglary repeats and
near repeats are evident in Belo Horizonte, but the extent
of these patterns are likely to be diﬀerent to those found
in western countries. We consider whether diﬀerences
in patterns of burglary repeats and near repeats are due
to contextual urban infrastructure diﬀerences between
Latin American countries and western countries. If pat-
terns of burglary repeat and near repeat victimisation
are less evident in Latin American countries, this would
indicate that crime prediction software tools and police
strategies for countering repeats and near repeats that
have been developed to suit western contexts may be less
suitable in Latin American countries for supporting bur-
Section“Repeats, near repeats, theories that underpin
their patterns, and crime prevention initiatives designed
to counter these patterns” of the paper describes patterns
of repeat and near repeat victimisation from a range of
studies in western countries to oﬀer a benchmark against
which the research ﬁndings can be compared. In
“Repeats, near repeats, theories that underpin their pat-
terns, and crime prevention initiatives designed to coun-
ter these patterns” section we also describe the theory
that underpin the patterns of repeat and near repeat vic-
timisation and describe several burglary prevention pro-
grammes that have been designed to counter the risk of
repeats and near repeats. In “Policing, burglary, and
crime reporting in Brazil” section we describe burglary
patterns in Brazil and consider whether crime reporting
and recoding practices for our study area may have an
impact on our research ﬁndings. Section“Study area and
the domestic living infrastructure in Brazilian cities”
describes our study area of Belo Horizonte, including
details about the city’s urban infrastructure and typical
domestic security arrangements.1 Section“Method: data,
and spatial-temporal analysis of domestic burglary”
1 e review of Brazilian urban infrastructure, domestic living and common
burglary prevention eﬀorts allows us to draw conclusions from the analysis
ﬁndings and consider the practical replication of burglary prevention eﬀorts
that have been applied in western contexts.
describes the method used for examining the extent of
burglary repeat and near repeat victimisation in Belo
Horizonte. In “Results: analysis of repeat victimisation
and near repeat victimisation”section the results are
described, and in “Discussion and implications” section
the results are discussed, along with the implications of
these ﬁndings on crime prediction and burglary preven-
tion in Brazil and elsewhere in Latin America. Conclu-
sions from the research are provided in “Conclusions”
Repeats, nearrepeats, theories thatunderpin their
patterns, andcrime prevention initiatives designed
tocounter these patterns
Patterns ofrepeat andnear repeat victimisation
Repeat victimisation is the concept of a person or build-
ing being subject to victimisation a number of times.
Research into repeat victimisation has shown that, over-
all, risk doubles following a victimisation, and that
repeats occur swiftly after the initial incident (Farrell and
Pease 1993; Polvi etal. 1991). Table1 shows the extent of
burglary repeat victimisation has been recorded to be
between 7 and 33% of all burglaries in several western
and developed countries.2 Interviews with oﬀenders have
also supported the empirical observations of patterns of
repeat victimisation, with Ericsson (1995), for example,
ﬁnding that 76% of oﬀenders interviewed returned to a
number of houses to burgle them 2–5 times.
Near repeat victimisation is the observed ﬁnding that
targets near to a recent incident are at a heightened risk
of being victimised. e level of risk to neighbouring tar-
gets is lower than the risk of victimisation to the recent
victimised target, and decays with distance from this
original target. Similar to repeat victimisation, this
heightened risk to neighbouring targets decays over time
(Bowers etal. 2004). Listed in Table2 are examples of the
extent of burglary near repeat victimisation sourced from
police agencies in the UK and New Zealand (from analy-
sis conducted within police agencies)3 and show, when
deﬁned as burglaries committed within 200m and 7days
of an originator oﬀence, near repeats can account for up
to a quarter of all burglaries. Table2 also shows variation
exists between locations, but that in general, near repeats
2 While the time-window for measuring repeat victimisation is known to
have an eﬀect on results (Farrell etal. 2002), the studies selected for illustra-
tion in Table1 all used a 1year time-window, therefore, enabling compari-
son. Results from studies at national and local levels are provided in order
to illustrate a range of burglary repeat victimisation levels, and to illustrate
how levels at the local level may vary when compared to national levels of
burglary repeat victimisation.
3 To date, while many studies have reported statistical evidence of near
repeat patterns (for example, Bowers and Johnson 2005; Haberman and
Ratcliﬀe 2012; and Townsley etal. 2003), very few have recorded the extent
to which near repeats account for all crime.
Page 3 of 10
Chainey and da Silva Crime Sci (2016) 5:1
within 200 m and 7 days of an originator oﬀence
accounted for at least 12% of all burglaries.
Theories thatexplain the patterns ofrepeats andnear
e reasons why repeats and near repeats occur can prin-
cipally be explained by the boost account and optimal
foraging theory, and the ﬂag account. e boost account
refers to an oﬀender deciding to return to the same loca-
tion, boosted by the success of previous crime commis-
sion (Bowers and Johnson 2004; Pease 1998). is boost
principle also applies to near repeats, based on the idea
that the oﬀender is boosted to return because of their
familiarity with the area (following the initial oﬀence),
the means of breaking in and the layout of the neighbour-
ing properties are likely to be similar, and the neighbours
are likely to have possessions worth stealing, similar to
those stolen in the initial burglary (Chainey 2012).
Optimal foraging theory provides a means of explain-
ing why the boost account occurs (Bowers and Johnson
2004; Johnson etal. 2009). is approach likens oﬀend-
ers to foraging animals. As a forager, an oﬀender makes a
trade-oﬀ between the rewards of crime commission that
are most obvious and immediately available (i.e., return-
ing to commit a repeat), and the eﬀort and risks that will
be expended in seeking better opportunities. Once an
area has been exhausted of the best theft opportunities,
the forager moves on (i.e., seeking to commit burglary
e ﬂag account suggests there is some enduring qual-
ity about the target that highlights (ﬂags) its high level of
vulnerability to would-be oﬀenders (Pease 1998).4 Diﬀer-
ent to the boost account, the ﬂag account suggests a
repeat oﬀence is likely to be committed by a diﬀerent
oﬀender than who committed the initial oﬀence. In addi-
tion, the ﬂag account suggests the time between an initial
oﬀence and a repeat is more likely to be random, rather
than following swiftly after an initial incident.
In practice, a combination of boost, optimal forag-
ing and ﬂag theories are likely at play in explaining why
repeats and near repeats occur. For instance, the ﬂag
characteristics of a property may initially attract an
oﬀender because it is seen as an easier target, with the
risk of future burglary being boosted following an initial
incident. Foraging behaviour then helps explain why an
oﬀender may return to the same or nearby locations for a
short period after a previous oﬀence to carry out a spate
of further oﬀences.
4 In terms of burglary, these qualities could include the property being sit-
uated at the end of a terrace, which has an alley running along the back,
and the property appearing to have poor door and window security—all
of which are signals for easy property access opportunities to a would-be
Table 1 The extent of burglary repeat victimisation
asreported froma range ofstudies inwestern anddevel‑
For each study the time measurement window for analysing repeat victimisation
Location ofstudy andsource Proportion ofburglary
thatwas repeat victimi-
Birmingham, England: 2011
England and Wales: 2012/13
(Farrell and Bouloukos 2001)22
Newcastle, England: 2010
(Safe Newcastle Partnership 2011)15
(Farrell and Bouloukos 2001)21
Northumberland County, England: 2009
(Northumberland Community Safety Partner-
South Auckland, New Zealand: 2014
(Chainey and Silva 2015)10
(Farrell and Bouloukos 2001)13
(Farrell and Bouloukos 2001)33
Table 2 The extent of burglary near repeat victimisation
as reported from a range of analysis studies conducted
bypolice agencies inthe UK andNew Zealand
For each study, data for 1year was used to measure the extent of near repeats
Measure ofnear inspace andnear
intime tooriginator incident,
andlocation ofstudy andsource
Proportion ofburglary that
was nearrepeat victimisa-
Within 200 m and 7 days 14
Within 200 m and 14 days 23
Herefordshire, UK (West Mercia Police
Within 300 m and 10 days 16
Kettering, UK (Northamptonshire Police
Within 200 m and 7 days 23
Newcastle, UK (Chainey 2014)
Within 100 m and 1 day 1
Within 100 m and 7 days 5
Within 200 m and 7 days 12
Auckland, New Zealand (New Zealand
Within 100 m and 1 day 2
Within 100 m and 7 days 5
Within 200 m and 7 days 12
Wellington, New Zealand (New Zealand
Page 4 of 10
Chainey and da Silva Crime Sci (2016) 5:1
Crime prevention initiatives designed tocounter the risk
ofrepeat andnear repeat victimisation
e key ﬁnding from studies that have examined repeat
victimisation is that the risk of a second burglary occur-
ring is substantially higher than the risk of the ﬁrst bur-
glary, but that this risk of a second burglary decays over
time. is ﬁnding presents opportunities for crime pre-
vention by attempting to counter the risk of the second
and other subsequent burglaries to the same property.
One of the ﬁrst crime prevention initiatives that took
advantage to prevent domestic burglary by countering
the risk of repeats was the Kirkholt Burglary Prevention
Project in Rochdale, UK (Forrester etal. 1988). After an
analysis that identiﬁed the high level of repeat victimisa-
tion on the Kirkholt housing estate, crime prevention
eﬀorts, including improvements in dwelling security,
were targeted to those properties that had previously
experienced burglary repeats. e result was an 80 %
reduction in burglary repeat victimisation, which con-
tributed to an overall reduction of 53% in burglary across
the Kirkholt estate (Forrester etal. 1988). Several bur-
glary prevention initiatives that have since followed the
Kirkholt experience5 have similarly focused on prevent-
ing the heightened risk of further burglaries occurring
(following an initial incident).6
e key ﬁnding from research into near repeat victimi-
sation is that the risk of burglary to properties near to a
recently burgled property is signiﬁcantly higher than the
risk to those properties further away, but that the risk of
burglaries to these neighbouring properties decays over
time. is ﬁnding presents opportunities for burglary
prevention by attempting to counter the risk of burglary
to nearby properties. One of the ﬁrst initiatives designed
to speciﬁcally counter both repeats and near repeats was
introduced by Greater Manchester Police in Traﬀord, UK
in 2011. In addition to the targeting of a crime prevention
oﬃcer to recently burgled properties, oﬃcers from the
local policing team would conduct door-to-door visits to
neighbouring houses on the day after the initial burglary,
informing residents about the recent burglary, reassuring
them over their likely risk of burglary, and providing
practical crime prevention advice to prevent these neigh-
bouring residents being burgled (Fielding and Jones 2012;
5 In a systematic review of repeat victimisation prevention, Grove et al.
(2012) further identiﬁed the promising opportunities for burglary preven-
tion by indicating that initiatives that were designed to prevent repeats
experienced an average reduction in burglary of 22%.
6 A commonly used prevention tactic involves the deployment of crime
prevention oﬃcers to burgled homes within 24 h of the burglary occur-
ring (Chainey, 2012). e tactic of deploying a crime prevention oﬃcer to
a recently burgled property aims to respond in a manner that is timely to
when the risk of a repeat incident is at its highest, and that any immediate
improvements in security and requests for residents to be extra vigilant will
resonate for several days after the visit of the crime prevention oﬃcer.
Chainey 2012).7 e impact of the crime prevention ini-
tiative to counter burglary repeats and near repeats in
Traﬀord was a reduction in burglaries of 42% in the areas
that were targeted.8 Similar burglary prevention tactics to
those introduced in Traﬀord have been implemented by
other police forces in the UK, USA, and Canada, and
have contributed to similar reductions (UCL 2014).9
Policing, burglary, andcrime reporting inBrazil
For the twenty-six states in Brazil, each has a Military Police
agency (Policia Militar) that acts as both the front line of law
enforcement and the agency that responds to incidents of
crime. While a militaristic approach to policing, with a focus
towards repression and enforcement has tended to be the
tradition in Brazilian police practice, in more recent years an
appreciation and adoption of prevention-focused, problem-
oriented and community policing principles have begun to
be adopted by Brazil’s Policia Militar (Beato Filho 2008).
Although violent crime is of a particular concern in Brazil
with rates exceeding those of many western countries,10
Brazil experiences relatively low levels of domestic bur-
glary. In 2012, the recorded domestic burglary rate in Brazil
was 11 burglaries per 100,000 population, compared to the
United States (494 per 100,000 population), Australia (659
per 100,000 population), New Zealand (886 per 100,000
population), and England and Wales (402 per 100,000 pop-
ulation) (UNODC 2014). e low domestic burglary rate in
Brazil is though comparable to most rates experienced in
other Latin American countries such as Colombia (47 bur-
glaries per 100,000 population), Mexico (95 per 100,000
population), Panama (9 per 100,000 population), and Para-
guay (28.5 per 100,000 population) (UNODC 2014).
Like in western countries, the under-reporting of crime
is a problem in Brazil. Results from the 2010 Brazil Vic-
timization Survey11 state that only 33% of burglary vic-
7 e timing of the visits to nearby properties was chosen to coincide
with when the previous burglary took place, acting as a possible deterrent
(through the presence of a police oﬃcer in high visibility uniform) to any
8 e overall burglary reduction across Traﬀord was 27% (Chainey 2012).
9 West Yorkshire Police in Leeds modelled a burglary prevention initiative
on the practice from Traﬀord and experienced a 48% reduction in burglary
(Professional Security 2012).
10 Crime and violence are among the main concerns of Brazilians (CNT/
SENSUS 2010). e issue of violence is also reﬂected in changes in the hom-
icide rate in Brazil, increasing from 12 homicides per 100,000 inhabitants in
1980 to 30 homicides per 100,000 inhabitants in 2012 (SIM/DataSUS 2014).
In the most recent Brazilian Victimisation Survey, 60% of respondents
stated that safety and crime problems were becoming worse in the country,
aﬀecting their sense of security and increasing their fear of crime (Silva and
Beato Filho 2013).
11 e Brazil Victimisation Survey is completed by visiting residents in their
home. e sample unit of the survey is the individual, conducted on persons
aged 16 or over and resident in a town of over 15,000 inhabitants. e sur-
vey involved conducting 78,008 interviews and was considered representa-
tive of Brazil to a conﬁdence level of 95 and 0.4% margin of error.
Page 5 of 10
Chainey and da Silva Crime Sci (2016) 5:1
tims reported the crime to the police (Silva and Beato
Filho 2013). is is, however, comparable to a reporting
rate for domestic burglary of 36% in England and Wales
(ONS 2015),12 and, therefore, suggests comparisons
between patterns observed in police recorded burglary
data between Brazil and western countries (in particular
the UK) are feasible. While additionally there are some
concerns over the quality and completeness of crime data
recorded by the state police agencies in Brazil (as noted
by Murray etal. 2013), the crime data we use in the pre-
sent study is for the state of Minas Gerais which is rated
as having high quality police recorded crime data (Fórum
Brasileiro de Segurança Pública 2011).
Study area andthe domestic living infrastructure
e city of Belo Horizonte in Brazil was the chosen area
of study. is is because recorded crime data required
for the analysis were available, the authors’ knowledge
of the city, and because Belo Horizonte is a city that is
representative of urban living in Brazil. Belo Horizonte is
in the southeastern region of Brazil, is capital of the state
of Minas Gerais, and is Brazil’s third largest metropolitan
area with a population of 5.5 million (Brookings 2012).
An important contextual diﬀerence between Brazil-
ian cities and many cities in western countries relates to
urban living. In Brazil, eight of the country’s cities feature
in the top ﬁfty cities in the world with the most high rise
buildings, many of which are for residential purposes. In
contrast, only New York City and Chicago are the two US
cities included and only London is included as the single
UK city in the same top 50. Belo Horizonte is ranked 15th
in the world for cities with the most high rise buildings
A third of the population of Belo Horizonte live in
apartments (in high rise buildings), 9 % of the city’s
inhabitants live in favelas (irregular settlements), with the
remainder mainly living in semi-detached or detached
houses (IBGE 2010). In addition to the diﬀerences in
housing context are the security features that are com-
mon to homes in Brazilian cities, typiﬁed in Belo Hori-
zonte. Results from the 2010 Brazil Victimisation Survey
showed that over 55% of respondents use security mech-
anisms in their homes, such as reinforced bars, guard
dogs and high fences in order to increase protection
against domestic burglary (CRISP/Datafolha/Ministério
da Justiça 2011). e fortiﬁcation of domestic proper-
ties includes fencing around apartment blocks as well as
around semi-detached and detached properties (Caldeira
12 Based on an estimated 560,000 incidents of domestic burglary in a dwell-
ing determined by the Crime Survey of England and Wales and 204,136
domestic burglaries recorded by police forces in England and Wales, for the
year ending September 2014 (ONS 2015).
2000; Paixão 1991). Indeed, results from a recent victimi-
sation survey conducted in Belo Horizonte revealed that
more than a half of respondents stated that their homes
or apartments had high walls or fences over two metres
high, and that 14% had installed electric fences. 42% of
respondents from the survey also stated they had metal
bars installed across the windows of their residence, 37%
reported having extra locks on doors and locks on their
windows, and 13% had installed a burglar alarm in their
homes (CRISP 2006).
In addition to the physical security features that are
built into many Brazilian homes to help protect resi-
dents from burglary, many apartment blocks in Brazil
have a security guard stationed at the entrance to the
building, for all hours of the day. is is a feature that is
not exclusive to just apartment blocks that house the
wealthy, but is common to all types of apartment
blocks. 31% of respondents to the Belo Horizonte vic-
timisation survey also stated they had seen the pres-
ence of private security in their neighbourhood (CRISP
2006). In addition to the presence of security oﬃcers is
the presence of domestic staﬀ in Brazilian homes. Most
residents with at least an average level of income in
Brazil, particularly families, employ at least one domes-
tic helper (Brazil Business 2014). is means that when
the home owners are at work during the day, these
trusted domestic staﬀ occupy their home, and in-
doing-so provide informal security through their
Method: data, andspatial‑temporal analysis
Recorded crime data
Recorded crime data on domestic burglary for 2012 to
2014 were provided by the Policia Militar de Minas Ger-
ais (PMMG) for the city of Belo Horizonte. e crime
data included the address of the home that had been bur-
gled, and the date and time of the oﬀence. Between 2012
and 2014 there were 19453 recorded domestic burglaries
in Belo Horizonte.
PMMG employ a robust geocoding process to pinpoint
within a computer-based mapping environment the exact
location of the oﬀence.14 An assessment of the geocoding
accuracy of the domestic burglary data determined the
13 13% of women working in the metropolitan region of Belo Horizonte are
employed as domestic servants (DIEESE 2013).
14 e geocoding process utilises the address as recorded in the crime
record to determine the spatial coordinates for the oﬀence. In addition,
PMMG use Global Positioning System technology and satellite imagery to
help locate the position of a burglary oﬀence occurred when committed to
homes in favelas.
Page 6 of 10
Chainey and da Silva Crime Sci (2016) 5:1
data to be at least 95% accurate15 and suﬃcient in quality
for the purposes of the research.
Measuring repeat victimisation
Repeat incidents of burglary were identiﬁed using a
two stage approach. e ﬁrst stage identiﬁed repeats by
selecting those records where the geographic coordinates
for the burglary were the same as that for another oﬀence.
e address details for each of these oﬀences were then
checked to determine that each oﬀence corresponded to
the same dwelling, and to remove any that did not (e.g.,
where the same geographic coordinates referred to two
diﬀerent apartments within a high rise building). e sec-
ond stage identiﬁed repeats based on the same text string
recorded in the address ﬁeld of the crime record. is
allowed for addresses to be corroborated between the
two selection approaches. e combination of the two
approaches resulted in a list of all recorded burglaries at
addresses where more than one burglary had occurred.
e list of repeat incidents of burglaries was then ana-
lysed to determine the number of addresses that had
experienced more than one burglary, the number of
repeats (not including the ﬁrst incident), and the propor-
tion of repeats against the total number of burglaries. e
analysis of burglary repeats was conducted for the whole
3 year dataset and for each 1 year period over the
3years.16 Further analysis was conducted to determine if
the pattern of repeats was statistically signiﬁcant, using
the Near Repeat Calculator (Ratcliﬀe 2009).
Measuring nearrepeat victimisation
Near repeats were measured using the Near Repeat Cal-
culator (Ratcliﬀe 2009). is near repeat analysis involved
examining the distance and time between burglaries to
determine if the pattern of near repeats was statistically
signiﬁcant. e spatial bandwidth in the Near Repeat Cal-
culator was set to 100m and ﬁve bands were applied. e
temporal bandwidth in the Near Repeat Calculator was
set to 7days and four bands were applied. Analysis was
also conducted to determine the number of oﬀences com-
mitted within 100m and 7days, within 200m and 7days,
and within 300 m and 7 days of an originator oﬀence.
e spatial and temporal bandwidths and the number of
bands were chosen as they provided a means for compar-
ing the extent of near repeats in Belo Horizonte to several
15 Following the geocoding accuracy measurement process described by
Chainey and Ratcliﬀe 2005.
16 is analysis approach allowed for the eﬀects of the time-window for
measuring repeats to be assessed for its impact on the extent of repeat vic-
timisation in Belo Horizonte, and to allow comparison to the levels of repeat
victimisation reported in Table1 where each study was based on measuring
repeats using one year of crime data.
of the results from near repeat victimisation studies for
other areas in the world (as reported in Table2).
Results: analysis ofrepeat victimisation andnear
Table3 shows that during the 2012 to 2014 period, 1226
homes were known to have experienced more than one
burglary, and accounted for 2894 burglaries in total,
equivalent to 14.9% of all recorded domestic burglaries
in Belo Horizonte. ere were 1668 repeat domestic bur-
glaries in Belo Horizonte between 2012 and 2014, equat-
ing to 8.6% of all recorded domestic burglaries during
In any 1year, 201 to 341 repeat burglaries occurred
in Belo Horizonte, accounting for 5.4% of all recorded
domestic burglaries in 2012, 4.8% in 2013 and 3.2% in
2014 (see Table3c). is level of burglary repeats experi-
enced in Belo Horizonte was lower than levels of burglary
repeats in western countries reported in Table1 (where
the range of repeat victimisation from these previous
studies was 7–33%).
An analysis of the statistical signiﬁcance of repeat bur-
glaries (see Table4) revealed that for each year, the pat-
tern of repeat victimisation was signiﬁcant (p = 0.05)
within 0–7 days of an originator burglary oﬀence. e
Table 3 The extent ofdomestic burglary repeat victimisa‑
tion inBelo Horizonte, Brazil
2012–2014 2012 2013 2014
(a) Number of locations that experienced a repeat burglary
n locations 1226 295 293 189
(b) Proportion of burglary that took place at locations that experienced
more than one burglary
n 2894 636 620 390
% 14.9 10.0 9.0 6.2
All burglary 19453 6349 6854 6250
(c) Proportion of burglaries that were repeats
n 1668 341 327 201
% 8.6 5.4 4.8 3.2
Table 4 The statistical signicance ofburglary repeat vic‑
timisation inBelo Horizonte, Brazil
p=0.05. Temporal bandwidth 7days, four bands
2012 2013 2014
0–7 days p ≤ 0.05 p ≤ 0.05 p ≤ 0.05
8–14 days p ≤ 0.05 p ≤ 0.05 n.s.
15–21 days n.s. n.s. p ≤ 0.05
22–28 days p ≤ 0.05 n.s. n.s.
Page 7 of 10
Chainey and da Silva Crime Sci (2016) 5:1
pattern of repeat victimisation was not statistically sig-
niﬁcant (p=0.05) for each year between 8–14, 15–21
and 22–28days of an originator burglary. ese results
indicate that in Belo Horizonte, a repeat burglary is more
likely to occur swiftly after (and within 7days) of a previ-
ous burglary oﬀence, rather than at any other time.
Near repeat victimisation
Table5 shows the pattern of near repeats in Belo Hori-
zonte was statistically signiﬁcant (p≤0.05) for fourteen
of the twenty diﬀerent spatial and temporal bands. All of
the six bands within 0 to 21days and within 1 to 200m of
originator incidents were statistically signiﬁcant and dis-
played the highest levels of risk in comparison to other
Table 6 shows the number and proportion of near
repeats (of all domestic burglary) for diﬀerent spatial and
temporal bands. Although the pattern of near repeats
was statistically signiﬁcant for most of the spatial and
temporal bands that were closest (in space and time) to
originator incidents, less than 6% of all recorded domes-
tic burglaries were near repeats within 200 m and 7days
of an originator oﬀence. ese levels of near repeat bur-
glaries experienced in Belo Horizonte were much lower
than levels of near repeat burglaries found from analy-
sis in the UK and New Zealand (as reported in Table2),
where the comparable levels (for burglaries within 200m
and 7days of an originator oﬀence) were between 12 and
23% of all domestic burglaries.
e analysis of burglary for the city of Belo Horizonte
has revealed that patterns of repeat and near repeat vic-
timisation are statistically signiﬁcant, with these patterns
following the common typology of the greater level of
risk being soon after an initial burglary oﬀence, and addi-
tionally for near repeats, the highest level of risk being
to those properties that are closest to where the previ-
ous burglary oﬀence took place. However, the analysis
has also revealed that levels of burglary repeats and near
repeats were much lower than those found from studies
in western countries. For example, the level of burglary
repeats in Belo Horizonte in 2014 was half of that meas-
ured in the rural UK county of Northumberland and a
ninth of the levels of repeats for the city of Birmingham
(UK). Similarly, the extent of burglary near repeats in
Belo Horizonte was no more than half the levels of those
found from studies in the UK and New Zealand.
An initial indication of diﬀerences between experi-
ences of domestic burglary in Brazil and experiences of
domestic burglary in the UK, USA and other western
countries was illustrated by the diﬀerences in domes-
tic burglary rates between these countries. e under-
reporting of crime is an issue in Brazil, like it is in many
other western countries, however, the similarity in crime
reporting levels between Brazil and England and Wales
suggests the under-reporting of crime in Brazil is unlikely
to fully explain the diﬀerences in domestic burglary rates.
In addition, the assessment of the police crime record-
ing standards of the data used in the research suggests
that conﬁdence can be placed in the ﬁndings, and that a
real diﬀerence does exist in the levels of repeats and near
repeats experienced in Belo Horizonte in comparison to
similar studies in western countries.
e domestic housing infrastructure in Brazilian cities
is very diﬀerent to that in many western countries. Many
more domestic properties in Brazil have situational pre-
vention measures, such as perimeter fences and security
guards, implemented as standard to improve domestic
safety. While the current research study has not statisti-
cally examined the relationship between diﬀerences in
housing infrastructure in Belo Horizonte and western
countries, we oﬀer a theoretical reason for explaining
the diﬀerences in repeats and near repeats in relation
to these international contextual diﬀerences in housing
e primary theories that explain repeat and near
repeat victimisation are the boost account and foraging
theory, and the ﬂag account. For a property to be singled
Table 5 Spatial and temporal bands for which near
repeats ofburglaries were statistically signicant inBelo
0–7days 8–14days 15–21days 22–28days
1–100 m 1.42
p ≤ 0.05 1.18
p ≤ 0.05 1.21
p ≤ 0.05 n.s.
101–200 m 1.24
p ≤ 0.05 1.18
p ≤ 0.05 1.11
p ≤ 0.05 n.s.
201–300 m 1.15
p ≤ 0.05 n.s. n.s. 1.09
p ≤ 0.05
301–400 m 1.11
p ≤ 0.05 1.11
p ≤ 0.05 1.07
p ≤ 0.05 n.s.
401–500 m 1.18
p ≤ 0.05 1.09
p ≤ 0.05 1.07
p ≤ 0.05 n.s.
Table 6 The proportion ofnear repeats forthree dierent
denitions ofnear inspace andnear intime
Near repeat denition Number ofnear repeats
andproportion ofall burglary
Within 100 m and 7 days 242 (2.2 %)
Within 200 m and 7 days 657 (5.8 %)
Within 300 m and 7 days 1430 (12.7 %)
Page 8 of 10
Chainey and da Silva Crime Sci (2016) 5:1
out by an oﬀender as a suitable target for an initial bur-
glary requires that property to typically display some
enduring characteristic that makes it more vulnerable
than others. In Belo Horizonte, the opportunities to com-
mit burglary are considered to be lower due to the higher
levels of in-built situational crime prevention that for-
tify homes from would-be oﬀenders. In addition, within
favelas, the close proximity of dwellings and an often
high level of social capital can ward oﬀ burglars (Villa-
real and Silva 2006). Furthermore, the common style of
living in high rise apartment blocks in Brazilian cities
such as Belo Horizonte, may also act in naturally limiting
the opportunities for burglary. For instance, it is antici-
pated to be more diﬃcult for an oﬀender to determine if
a home is unoccupied (and, therefore, a potential target
for burglary) if it is on a level above the ground ﬂoor. It
is, therefore, likely that the combination of a greater level
of situational crime prevention and more high rise apart-
ments in a Brazilian urban setting results in a lower prev-
alence of ﬂag account opportunities that explain burglary
oﬀending, and as a result lower levels of burglary repeat
victimisation that are associated with the ﬂag account
In Brazil, when a home is burgled it has typically
required the oﬀender to overcome the perimeter fencing
that is present, the metal bars across doors and windows,
the security guard at the entrance to the apartment block
or domestic staﬀ who are present in the dwelling, both
on entry and exit. is comes with risks and extra eﬀort
to overcome, and although an oﬀender may have escaped
undetected after committing an initial oﬀence, the expe-
rience of this recent oﬀence is likely to increase the vigi-
lance of the home owners and the other people who are
present (i.e., security and domestic staﬀ) in preventing
a repeat oﬀence from occurring soon. is suggests that
the boost account for explaining burglary repeats is likely
to be less prevalent in the Brazilian context. Foraging
behaviour, involving seeking additional nearby oppor-
tunities, may also be limited due to the risks and eﬀorts
required to overcome the in-built situational crime pre-
vention measures that are present at the nearby proper-
ties. Combined, if the limited opportunities in utilizing
boosts and further foraging following the commission
of a previous burglary oﬀence inﬂuences oﬀender deci-
sion-making, then it is likely that fewer repeats and near
repeats will occur swiftly after an initial oﬀence.
Impressive results in crime reduction quickly gain
interest across the international police community. How-
ever, the sharing of good practice on what has worked
to reduce crime requires not only an understanding of
the tactics and initiatives that were applied, but also an
appreciation of whether the context in which replication
is to take place is likely to produce similar results. is
means that tactics and strategies that have been used
elsewhere to predict and prevent domestic burglary by
countering patterns of repeats and near repeats will only
have a high level of impact if the extent of repeats and
near repeats account for a large proportion of all domes-
Eﬀective burglary prevention programmes such as
those in Manchester and Edmonton displayed high levels
of burglary repeats and near repeats prior to the imple-
mentation of the initiatives to counter the risk of repeats
and near repeats. In areas where the levels of repeats and
near repeats are not as high, the impact of the same tac-
tics and strategies are likely to have less impact (i.e., there
are fewer burglaries that can be countered using tactics
to prevent repeats and near repeats). is means that
burglary prevention programmes that focus on reduc-
ing repeat and near repeat victimisation are likely to have
less of an impact in reducing burglary in Belo Horizonte
and in other Latin American cities where levels of repeats
and near repeats are low. As an estimate for Belo Hori-
zonte, taking the example of 2014 where burglary repeat
victimisation and near repeat victimisation accounted
for 3.2 and 5.8% respectively of all burglaries, a crime
prevention programme designed speciﬁcally to counter
repeats and near repeats may only yield an overall bur-
glary reduction of 9%. In addition, the results from Belo
Horizonte also suggest that predictive policing software
that includes algorithms for predicting burglary based
on the patterns of repeats and near repeats may not be as
eﬀective in Latin American countries where domestic liv-
ing and housing infrastructure diﬀer greatly to the west-
ern cities on which the software has been designed.
At present, victimisation surveys in Brazil do not probe
on experiences of repeat victimisation, so little else is
available other than recorded crime data that allows for
analysis of the extent of these experiences. Also, to date,
research in Brazil has not been conducted that has
involved interviewing oﬀenders about their decision-
making in selecting properties to burgle and whether the
concepts of the boost and ﬂag accounts feature in this
decision-making. is type of questioning of oﬀenders is
an obvious area for future research that will allow the
examination of whether these contextual diﬀerences
between Brazil (and other Latin American countries) and
western countries inﬂuence oﬀender decision-making.17
Additional areas for future research could involve
17 In particular, little is known about the diﬀerences in the levels of
attempted burglary between Latin American and western countries, and
the impact a failed attempt at committing a burglary has on boosting the
oﬀender to return to the same property or seek other burglary opportunities
nearby. In the Latin American context where the successful commission of
burglary may be lower, examining the impact of failed attempts to commit
burglary on how it then inﬂuences future oﬀending behavior would beneﬁt
from further research.
Page 9 of 10
Chainey and da Silva Crime Sci (2016) 5:1
analysis of burglary near repeat patterns in relation to
diﬀerences in burglary rates, housing density and hous-
ing type, and research that aims to distinguish whether it
is the preponderance of high rise buildings, or whether
the presence of situational domestic security (such as
perimeter fencing, security guards, or the presence of
domestic staﬀ) are the reasons for lower rates of burglary
and lower levels of repeat and near repeat victimisation
in Brazilian cities.
Policing agencies often draw from the successful practice
of others, and apply this practice to the crime problems
they experience. However, rather than just applying what
has worked for someone else in reducing crime, police
decision-makers must also determine how the practice
works and if it is applicable to their context. Patterns of
repeat victimisation and near repeat victimisation have
been observed in many studies conducted in western
countries, with these patterns considered to provide a
practical means for police agencies to predict and pre-
vent further burglaries from occurring.
is research has shown that the extent of burglary
repeat and near repeat victimisation in Belo Horizonte
was much lower than observed in similar studies in cit-
ies in western countries. e lower rates of burglary in
Brazil, and the lower levels of burglary repeats and near
repeats in Belo Horizonte suggest there is an impor-
tant contextual diﬀerence between the commission of
and opportunties for burglary in Brazil when compared
to western countries. We argue that this contextual dif-
ference is likely to be due to diﬀerences in domestic liv-
ing and housing infrastructure, with dwellings in Brazil
tending to be designed or purposefully adapted to pro-
vide higher levels of situational domestic security when
compared to dwellings in cities in western countries.
While further research into understanding how diﬀer-
ences in the domestic living infrastructure in Brazil inﬂu-
ences oﬀender decision-making would be useful, the
study illustrates the importance of examining whether
the crime patterns on which successful crime preven-
tion practice is based are similarly present where this
practice is being considered for replication. In terms of
applying the prevention practice for reducing burglary
by predicting where burglaries are likely to occur (based
on patterns of repeat and near repeat victimisation), the
current study shows that in a Brazilian urban context
the same practice will likely yield lower levels of burglary
Spencer Chainey is the lead author of this article. Spencer conceived the idea
of the research, planned its design, conducted the majority of the analysis
and drafted the manuscript. Braulio Silva sourced the recorded crime data,
provided comment on the research results, and drafted sections relating to
the contextual characteristics of Brazil and Belo Horizonte. Spencer has visited
Belo Horizonte on several occasions, met with the Policia Militar of Minas Ger-
ais (with Braulio) to support the research and discuss the results. Braulio has
approved the ﬁnal version of the article. We are accountable for all aspects of
the work and ensure that questions related to the accuracy or integrity of any
part of the work will be appropriately investigated and resolved. Both authors
read and approved the ﬁnal manuscript.
1 Department of Security and Crime Science, University College London, 35
Tavistock Square, London, England, UK. 2 Department of Sociology, Federal
University of Minas Gerais, Belo Horizonte, Brazil.
The research (involving a visit to Belo Horizonte to conceive the research,
examine police recorded crime data, conduct preliminary analysis and
conduct ﬁeld visits to areas of diﬀerent housing types) was supported by an
award from the Santander Universities Research Catalyst Awards for 2014. The
completion of the research and manuscript preparation was supported by
each of the author’s universities.
The authors declare that they have no competing interests.
Received: 2 July 2015 Accepted: 27 January 2016
Azavea. (2015). Next generation predictive policing. https://www.hunchlab.com.
Accessed May 27, 2015.
BBC. (2012). Could ‘predictive policing’ help prevent burglary? http://www.bbc.
co.uk/news/uk-19623631 Accessed May 27, 2015.
Beato Filho, C. C. (2008). Compreendendo e Avaliando Projetos de Segurança
Publica. Belo Horizonte: Editora UFMG.
Bernasco, W. (2008). Them again? Same-oﬀender involvement in repeat and
near repeat burglaries. European Journal of Criminology, 5, 411–431.
Bowers, K. J., & Johnson, S. D. (2004). Who commits near repeats? A test of the
boost explanation. Western Criminology Review, 5, 3.
Bowers, K. J., & Johnson, S. D. (2005). Domestic burglary repeats and space-
time clusters: The dimensions of risk. European Journal of Criminology, 2(1),
Bowers, K. J., Johnson, S., & Pease, K. (2004). Prospective hotspotting: The future
of crime mapping? British Journal of Criminology, 44(5), 641–658.
Brazil Business. (2014). Rules for domestic workers in Brazil. http://thebrazilbusi-
ness.com/article/rules-for-domestic-workers-in-brazil. Accessed May 27,
Brookings. (2012). Belo Horizonte metropolitan area proﬁle. http://www.
Horizonte.pdf. Accessed May 27, 2015.
Caldeira, T. P. (2000). A cidade de muros: Crime, segregação e cidadania em São
Paulo. São Paulo: EDUSP.
Chainey, S. P. (2012). JDI Brief: Predictive mapping (predictive policing). London:
UCL. http://discovery.ucl.ac.uk/1344080/. Accessed May 27, 2015.
Chainey, S. P. (2014). Examining the extent to which hotspot analysis can support
spatial predictions of crime. Ph.D. thesis, Department of Security and Crime
Science, University College London.
Chainey, S. P., & Ratcliﬀe, J. H. (2005). GIS and crime mapping. London: Wiley.
Chainey, S. P., & Silva, B. (2015). The predictability of domestic burglaries in
Belo Horizonte: translating repeats, near repeats and hotspot analysis to
a Brazilian context. Presentation at the international crime and intel-
ligence analysis conference, Manchester, 2015. http://www.ucl.ac.uk/
May 27, 2015.
CNT/SENSUS. (2010). Pesquisa de Opinião Pública Nacional—Rodada 100.
Relatório de cruzamentos. 25 a 29 de janeiro de 2010. Available at http://
Page 10 of 10
Chainey and da Silva Crime Sci (2016) 5:1
Sensus/2010/100%20Cruzamento.pdf. Accessed March 30, 2011.
CRISP. (2006). Pesquisa de Vitimização na Região Metropolitana de Belo Horizonte.
Belo Horizonte: UFMG.
CRISP/Datafolha/Ministério da Justiça. (2011). Pesquisa Nacional de Viti-
Relat%C3%B3rio-PNV-Senasp_ﬁnal.pdf. Accessed March 30, 2015.
DIEESE—Departamento Intersindical de Estatísticas e Estudos Socioeconômi-
cos. (2013). Relatório: Emprego Doméstico no Brasil. http://www.dieese.
February 25, 2015.
Emporis. (2015). Cities with the most high-rise buildings. https://en.wikipedia.
May 27, 2015.
Ericsson, U. (1995). Straight from the Horse’s Mouth. Forensic Update, 43, 23–25.
Farrell, G., & Bouloukos, A. (2001). International overview: A cross-national
comparison of rates of repeat victimization. In G. Farrell & K. Pease (Eds.),
Repeat victimization. Crime prevention studies (Vol. 12). New York: Criminal
Farrell, G., & Pease, K. (1993). Once bitten, twice bitten: Repeat victimisation and its
implications for crime prevention, Crime Prevention Unit Paper 46. London:
Farrell, G., Sousa, W. H., & Lamm Weisel, D. (2002). The time-window eﬀect in
the measurement of repeat victimization: A methodology for its meas-
urement and an empirical study. Crime Prevention Studies, 13, 15–27.
Fielding, M., & Jones, V. (2012). Disrupting the optimal forager: Predictive risk
mapping and domestic burglary reduction in Traﬀord, Greater Manches-
ter. International Journal of Police Science and Management, 14(1), 30–41.
Forrester, D., Chatterton, M., & Pease, K. (1988). The Kirkholt Burglar y Pre vention
Project, Rochdale, Crime Prevention Unit Paper 13. London: Home Oﬃce.
Fórum Brasileiro de Segurança Pública. (2011). Anuário brasileiro de segurança
pública [Annual Brazilian report on public security]. São Paulo, Brasil: Fórum
Brasileiro de Segurança Pública.
Grove, L. E., Farrell, G., Farrington, D. P., & Johnson, S. D. (2012). Preventing repeat
victimisation: A systematic review. Stockholm, Sweden: The Swedish Coun-
cil for Crime Prevention.
Haberman, C. P., & Ratcliﬀe, J. H. (2012). The predictive policing challenges
of near repeat armed street robberies. Policing: A Journal of Policy and
Practice, 6(2), 151–166.
IBGE—Instituto Brasileiro de Geograﬁa e Estatística. (2010). Censo Demográﬁco
2010—Belo Horizonte. http://www.cidades.ibge.gov.br/. Accessed May
Johnson, S. D., Bernasco, W., Bowers, K. J., Elﬀers, H., Ratcliﬀe, J. H., Rengert, G.
F., & Townsley, M. (2007). Space–time patterns of risk: A cross national
assessment of residential burglary victimization. Journal of Quantitative
Criminology, 23(3), 201–219.
Johnson, S. D., & Bowers, K. J. (2004a). The stability of space-time clusters of
burglary. British Journal of Criminology, 44(1), 55–65.
Johnson, S. D., & Bowers, K. J. (2004b). The burglary as clue to the future: The
beginnings of prospective hotspotting. European Journal of Criminology,
Johnson, S., Bowers, K. J., Birks, D., & Pease, K. (2009). Predictive mapping: Accu-
racy, units of analysis and the environmental backcloth. In D. Weisburd, W.
Bernasco, & G. Bruinsma (Eds.), Putting crime in it’s place: Units of analysis in
geographical criminology. New York, NY: Springer.
Murray, J., de Castro Cerqueira, D. R., & Kahn, T. (2013). Crime and violence in
Brazil: Systematic review of time trends, prevalence rates and risk factors.
Aggression and Violent Behavior, 18(5), 471–483.
New Zealand Police. (2014a). Analysis of domestic burglary repeats and near
repeats in Auckland Central and Counties Manukau Central, Wellington:
New Zealand Police [Restricted].
New Zealand Police. (2014b). Analysis of domestic burglary repeats and near
repeats in Wellington and Kapiti-Mana. Wellington: New Zealand Police
Northamptonshire Police. (2013). Problem proﬁle of domestic burglary in Ketter-
ing. Northampton: Northamptonshire Police [Restricted]
Northumberland Community Safety Partnership. (2009). Strategic assessment of
crime and community safety. Morpeth: Northumberland County Council.
ONS—Oﬃce for National Statistics. (2013). Crime Statistics, Focus on
Property Crime, 2012/13. http://www.ons.gov.uk/ons/rel/crime-stats/
May 27, 2015.
ONS—Oﬃce for National Statistics. (2015). Crime Statistics for England and
Wales, year ending September 2014. http://www.ons.gov.uk/ons/rel/crime-
April 9, 2015.
Paixão, L. A. R. (1991). Segurança privada, direitos humanos e democracia:
Notas preliminares sobre novos dilemas politicos. Novos Estudos CEBRAP,
Pease, K. (1998). Repeat victimization: Taking stock, Home Oﬃce Police Research
Group, crime detection and prevention series, paper 90. London: Home
Polvi, N., Looman, T., Humphries, Ch., & Pease, K. (1991). The time-course of
repeat burglary victimization. British Journal of Criminology, 31, 411–414.
PredPol. (2013). Predict crime in real time. http://www.predpol.com/. Accessed
January 22, 2014.
Professional Security. (2012). Tackling burglary. http://www.professionalsecu-
rity.co.uk/news/case-studies/tackling-burglary/. Accessed January 22,
Ratcliﬀe, J. H. (2009). Near Repeat Calculator (version 1.3). Temple University,
Philadelphia, PA and the National Institute of Justice, Washington, DC.
http://www.jratcliﬀe.net/software/. Accessed May 27, 2015.
Ratcliﬀe, J. H., & Rengert, G. F. (2008). Near-repeat patterns in Philadelphia
shootings. Security Journal, 21, 58–76.
Safe Newcastle Partnership. (2011). Strategic assessment of crime and commu-
nity safety. Newcastle-upon-Tyne: Newcastle City Council. [Restricted]
da Silva, B. F. A., & Beato Filho, C. C. (2013). Ecologia social do medo: avaliando
a associação entre contexto de bairro e medo de crime. Revista Brasileira
de Estudos de População, 30, 155–170.
SIM/DataSUS. (2014). Ministério da Saúde. Sistema de Informação sobre Mortali-
dade. Brasília-DF. www.datasus.gov.br Accessed January 22, 2014.
Skogan, W. G. (1996). The Decade’s most important criminological insight.
National Institute of Justice: Research in Action. Washington: Department
Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries. A test of
the near repeat hypothesis. British Journal of Criminology, 43(3), 615–633.
University College London. (2014). Forecasting the time and place of crime hot-
crime-hotspots. Accessed May 27, 2015.
UNODC—United Nations Oﬃce on Drugs and Crime. (2014). UNODC interna-
tional burglary, car theft and housebreaking statistics. https://www.unodc.
org/unodc/en/data-and-analysis/statistics/data.html. Accessed May 27,
Villarreal, A., & Silva, B. F. A. (2006). Social cohesion, criminal victimization and
perceived risk of crime in Brazilian neighborhoods. Social Forces, 84(3),
Wells, W., Wu, L., & Ye, X. (2011). Patterns of near-repeat gun assaults in Hou-
ston. Journal of Research in Crime and Delinquency, 49, 186–212.
West Mercia Police. (2012). Near repeat analysis of domestic burglary in Hereford-
shire. Worcester: West Mercia Police [Restricted].
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at