Return of the Burglar.
Transcript of Return of the Burglar.
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Return Of The Burglar
Steve Kong
London, SE16 5SQ
Dissertation submitted in part-fulfillment of the
Masters Course in Crime Science, UCL, September 2005
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I, Steve Kong hereby declare that this dissertation is my own original work and that all
source material used has been clearly identified and acknowledged. No part of this
dissertation contains material previously submitted to the examiners of this or any other
University, or any material previously submitted for any other examination.
Signed __________________
Steve Kong
15th September 2005
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Abstract
The study reports on the repeat victimisation and near-repeat hypotheses, that suggests
burglars return to the same venue or nearby locations to carry out further
burglaries. The approach for this research provides an analysis of recorded
crime data for 574 residential burglaries in a central London borough in the
United Kingdom. The burglaries were committed by 60 offenders processed
by the police as being those responsible for the residential burglaries. Each
offender was categorised into one of three groups pertaining to the extent of
offending frequency, group one boasting the most prolific individuals, to
group three the least confirmed in their criminal careers.
Each residential burglary was geographically referenced to the British National Grid and
the linear distance between offences was calculated, as were the temporal
differences between subsequent offences. The statistical software package
SPSS was used to analyse whether the variation of time to and length
between subsequent residential burglaries for the 60 burglars were
significantly different to what would be expected on the basis of chance. A
further analysis was undertaken to distinguish whether the 60 offenders
carried out their subsequent burglaries in a similar fashion.
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The analysis proved that regardless of how frequently burglars offend, they do indeed
return to the same venue to carry out further repeat residential burglaries
within a short period of time from their previous offence, and those more
prolific in their criminality commit more repeats at the same location. Results
also showed that offenders were likely to return to nearby locations of an
initial crime to commit further burglaries, with those offenders more prolific
in their career likely to travel further. Thus an initial burglary serves to
increase the risk of another to the same venue and nearby properties more
than can be ascribed on the basis of chance, with prolific offenders more
likely to travel longer distances to do so. The way in which subsequent
burglaries are undertaken is seen to be similar to the previous, suggesting a
signature mark for individual offenders not only in space and time, but by the
way in which they carry out their crimes. The results and likely predictions
are examined, with the benefits for practitioners discussed.
Introduction
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The aim of the dissertation is to ascertain whether offenders committing residential
burglary are more likely to return to the same location or nearby location to
carry out further burglaries from the original offence committed at a specific
location. By way of examining residential burglary recorded crime data from
a central London Borough in the United Kingdom, analysis of offending
patterns in space and time are explored. This presents something new because
it tests assumptions in the literature research on burglary repeat victimisation,
that being not only the same venue is more likely to be targeted again by the
same offender, but also those addresses nearby. Thus, the main hypothesis
that will be tested here is that burglars return to the same location or a nearby
location to commit subsequent burglaries, more than one would expect on the
basis of chance. A further hypothesis of whether offenders carry out their
burglaries in a similar way to the next is also undertaken. This analysis
therefore tests whether individual offenders have a signature mark not only in
space and time, but by the way in which they carry out their crimes.
Repeat victimisation is a repeated act by someone who exploits or victimises someone or
something else. Within the context of crime this can be against individuals such as a
burglary at some persons home, or an organisation such as a bank robbery. The Home
Office definition for repeat victimisation is when the same person or place suffers from
more than one incident over a specified period of time(Bridgeman and Hobbs, 1997: 1).
The British Crime Survey is a periodic survey of reported and unreported offences to the
police. An often-quoted statistic from the 1992 British Crime Survey (Farrell and Pease,
1993) is that 4% of victims suffered 44% of crime demonstrating that repeat victimisation
is a feature of crime generally (Laycock, 2001). There has been a substantial amount of
research regarding repeated victimisation (see Pease, 1998). Knowledge gained thus far,
reveals incidents of reoccurring crime patterns clustering within space and time, targeted
towards the same location or victim. The observed time-course relationship for example
usually consists of a subsequent offence soon after the initial (Polvi et al., 1991; Farrell &
Pease 1993). In their study on commercial burglary Laycock & Farrell (2002) found that
as the volume of repeatedly burgled premises falls, the probability for a further attack of
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those continuing to be victimised increases. In other words without intervention, the more
a premises is victimised, the increased likelihood it will be again soon after.
More recent research suggests that the existence of an initial burglary may serve not only
to increase the chance of the same crime happening again at the same location, but also
spread the heightened risk to neighbours nearby. An analogy used to describe the effect
of the near-repeat hypothesis is that seen with disease in a population, a spreading of the
infection to vulnerable nearby targets (Townsley, 2005).
The growing literature on repeat victimisation suggests that it is the same offender who
returns to commit further burglaries at the same location (Polvi et al., 1991; Pease, 1998;
Ashton at al., 1998) and nearby (Townsley, 2005 and Bowers and Johnson, 2004) rather
than something significant to the properties marking them out as being vulnerable, thus
an opportunity to be repeatedly victimised by different criminals. Many sources of
information from different countries have been used to demonstrate the patterns in repeat
victimisation providing confidence of external validity, that is how far we can generalise
about the results for dissimilar conditions, such as whether the same patterns would be
seen in different neighbourhoods (Sherman et al., 1998).
The study of repeat victimisation however, has mostly utilised data concerning victims
(Andrommachi, & Pease, 2003; Ellingworth, 1995; Forrester et al., 1988; Shaw & Pease,
2002), whilst examination of offender information has relied mostly on interviews
(Ashton at al.,1996; Ericsson, 1995). The predicament faced when analysing only victim
data is the need to make assumptions that it is the same offender returning without
actually knowing. The concern when interviewing offenders rather than having data on
convictions is the reliance that they answer questions honestly.
By way of collecting and analysing recorded burglary crime data where an accused
offender has been processed by the police, this paper acknowledges the space-time
cluster phenomenon and seeks to further establish the near-repeat hypothesis that not only
do burglars return to the same venue to commit further burglaries, but also carry out
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subsequent burglaries nearby to a greater degree than would be expected on the basis of
chance. The paper asserts that burglars do indeed return to the same area to continue their
offending, and they undertake their crimes in a similar way to their previous burglaries,
thereby providing a signature mark not only in space and time, but by the method in
which they carry out subsequent offending of the same crime. Considering that a small
percentage of offenders are responsible for a very large proportion of crime (Tarling,
1993), the benefits for the police and practitioners alike are clear. Knowing when, where
and how offenders are likely to strike allows the police and other practitioners to
effectively target those repeat offenders responsible for the majority of the crime. In
doing so, this provides a pinch-point for large reductions in burglary.
Literature Review
Repeat Victimisation: Time-course, Boosts and Flags
Studies have generally shown that when repeat victimisation happens, it does so soon
after the prior event (Pease, 1998; Polvi et al., 1991; Bowers & Johnson, 2004). The
pattern of repeat burglary victimisation is that a subsequent offence at the same location
has shown to take place with elevated risk of up to twelve times that expected within the
first month, particularly within the first week after the initial crime, showing a downward
exponential trend reducing over time ceasing after six months (Polvi et al., 1991).
This pattern is generally referred to as the time-course phenomenon (Polvi et al., 1991).
Early speculation by Polvi et al. (1991) of the repeat victimisation time-course
phenomenon suggested three reasons why it was reoccurring:
1) The first was that the same offender was returning, perhaps upon recognition of
neglected opportunities
2) The second was that offenders tell others of the house and others then burgle the
premises
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3) Thirdly, features of the house are such as to mark it out as an attractive target to
all those attempting to burgle it, leading to repeat victimisations linked only by
the seductiveness of the target.
Polvi et al., (1991) conjectured that repeat victimisation was as almost exclusively of the
first type. That is in some way the initial burglary has boosted (Pease, 1998) the chance
of a further occurrence from the knowledge gained from the first event. This boost is
event-dependent, meaning subsequent victimisation happens because of something that
takes place from the initial offence. Offenders themselves have explained reasons for the
boost account, which suggests a rational choice perspective motivating offenders
(Felson & Clarke, 1998) selecting burglary victims on the basis of the likely costs and
benefits of their actions. Having the rational choice perspective means that the risks will
be lowered through increased knowledge of the target. For example, Ashton at al., (1996)
interviewed a sample of officially processed offenders that had been responsible for at
least one burglary. They found that repeating against the same target was common,
particularly for those more frequent offenders. Reasons given included; the first time was
easy and profitable, once the lie of the land was known it became easy, returning to take
whatever was not taken the first time and that new goods would be available after
replacement. Similar findings are replicated in other studies (Hearnden & Magill, 2004;
Ericsson, 1995)Recent publications by Johnson and Bowers (2004: 242), suggest a
burglars predatory instincts are similar to an animals optimal foraging strategy, which is
to increase the rate of reward whilst minimizing both search time for food and risk of
being attacked or being eaten by other animalsThe analogy is obvious, with stolen
property the likely reward and the risk being identified or caught by the police.
Polvi et al., (1991) rejected the second reason explaining the time-course phenomenon,
that offenders tell others of the house and others then burgle the premises, simply because
it was regarded as a less frequent occurrence in their study, although no evidence was
provided. It is feasible however that the time-course curve would be less likely to
conform to this reasoning. Although possible that some burglars will share information
about the vulnerability of a particular location, the transfer of information from one
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offender to the next would need to be instantaneous to correspond to the time-course
phenomenon, i.e. the knowledge would need to be shared immediately and acted upon
very soon after. However in doing so would also mean a shared benefit of the goods
obtained, and therefore the choice would be in some way less rational than not telling
peers and keeping the rewards for ones self. Research literature suggests that even if this
surrogate offending takes place, the overall contribution to repeat victimisation of sharing
information about potential targets is marginal (Everson, 1995).
The third reason for the time-course pattern speculated by Polvi et al. (1991), was that
features of the house are such as to mark it out as an attractive target to all those
attempting to burgle it, leading to repeated victimisations linked only by the
seductiveness of the target1. This is often referred to as risk heterogeneity. If this theory
is correct then the onset of repeatedly burgled premises is independent of the initial event,
and therefore something about the vulnerability of the property is what Pease (1998)
terms flagged to the offender. Polvi et al. (1991) proposed that if this were the case then
the volume of re-victimisations would be proportional to the level of which dwellings
vary in their seductiveness. However the data analysed for their work was taken from an
area where the variation of properties in the city was limited, suggesting that if properties
were similar in many ways, then something else was responsible for the time-course
phenomenon.
We now know that the structure of the dwelling is not the main reason offenders select
their targets. Influencing the decision to burgle premises is generally associated with low
risk (Ericsson, 1995), high rewards and the absence of certain things such as alarms or
people (Ashton at al., 1996). Hearnden and Magill (2004) found it was the overriding
belief that suitable goods were in the premises, thus inferences about the occupants were
more important than objective features of the building. Bowers & Johnson (2004) reveal
how patterns of burglary repeat victimisations provide further evidence for the boost
theory rather than flag; they explain how with the flag account an equal number of crimes
would be anticipated over a variety of intervals. Therefore given the startling downward
1 This is excluding Artifice burglary which is expounded later in the document
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exponential pattern of repeated victimisation, whereby a subsequent crime is seen soon
after the first, it would again indicate something different to the flag account.
Ashton at al., (1998) found that repeat intimation of the same location would cease only
when an offender notices change or the presumption of such. For example, a complex
alarm being fitted, occupancy in the home or the belief of being tapped by the police. The
result of any of these would mean an offender selecting another suitable target.
In summary, this means that some burglars have a tendency to undertake repeat
victimisations at the same location, which is a distinct patterned strategy, which
maximises rewards and minimises risk. Importantly, the evidence suggests that the same
burglar will re-visit the same property multiple times. When conducting repeated
burglaries at the same location offenders will do so within a short period of time after the
event, meaning that effective change to the dwelling in order to reduce or eradicate
further incidents needs to happen immediately after the event, and this alteration needs to
be noticeable to the offender.
Near-Repeat Hypothesis
A relatively new idea pertaining to repeat victimisation is the near-repeat hypothesis,
where risk in burglary victimisation is communicable not just affecting one home but
nearby properties (Townsley, 2005; Johnson & Bowers, 2005). Near-repeats for burglary
state that proximity to a burgled premise increases the risk to those areas close by, and
that this risk follows the same temporal patterns seen for the heightened risk for the same
burgled property.
These infectious burglaries have been likened to the contagion model in epidemiology;
where near repeats can be seen as the result of being passed from victim to victim similar
to that of disease, consequently nearby properties being infected from the original
burgled premises (Townsley, 2003). The presupposition of near repeats is based on the
principle that when an offender has greater exposure to certain streets, he or she has an
enhanced body of knowledge about a particular area, leading to scrutiny of potential
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nearby targets lacking in surveillance with signs of weakness. Likely observations made
include potential escape routes, similar features of internal and external architecture for
ease of maneuverability, and the lack of people or CCTV. At some point, there will also
be a presumption that police will be alerted to the premises, meaning that intervention
may have taken place or the perception that it has. Hearnden and Magill (2004)
interviews of burglars found that rather than the fear of being captured or the knowledge
gained from previous burglaries, the belief that valuable goods were present was the main
reason for committing further burglaries. This is also seen in other studies (Ashton at al.,
1996; Ericsson, 1995). Therefore, is it likely that burglars will travel longer distances to
find goods of greater value, rather than commit burglaries nearby?
Optimal foraging theory strategies suggest that the increased rate of reward will not only
come from the value of the goods but the effort in which to obtain them, therefore the
calculated benefit will not be without consideration of the costs. If the risk of committing
burglaries to nearby properties is reduced from virtue of the fact an offender has
previously been successful at committing crime at a similar property, goods obtained and
the unlikely event that he or she was to be apprehended by the police, then surely
committing further burglaries in nearby areas is a more rational choice than to travel long
distances to unknown territory where the previously found anonymity is less likely?
Influencing the decision to burgle a particular property will undoubtedly be an
individuals knowledge of the cost of committing the act and the knowledge gained from
previous burglaries about the type of goods available, the ease in which to obtain them,
and the chance of being observed serves a logic that is hard to diverge from. There may
however come a time when an offender will perhaps leave an area for fear that the
anonymity of their presence has been jeopardised over time, thus removing them from a
particular hunting ground. Bowers and Johnson (2004), have speculated that the time-
course drop in repeat victimisations after six months is because an offender is slippery
in nature, therefore he or she will leave an area for a period of time before returning at a
later date. Hearnden and Magills (2004) findings suggest that offenders would not burgle
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if there were fewer escape routes or if there was more chance of being observed by
residents, but did return to a property to take further goods estimated within one month.
Proximity and Isomorphic Repetition
There are some circumstances where the subsequent action of an offender who targets a
victim or location is very similar to the previous one. In other words there are noticeable
similarities between the characteristics of two or more offences committed by the same
offender. This has been referred to as Isomorphic repetition (Ashton at al., 1996).
Johnson and Bowers (2004) found that properties on the same side and houses with
probably identical layouts such as those two doors away were slightly more at risk than
those with the mirror image layout. Anderson et al., 1995 found that an address two doors
away was more likely to be burgled than those next door. They conjectured that semi-
detached houses two doors away were more similar and therefore the layout of the house
known and easier to maneuver around.
Eversons (2002: 190) study on repeat victimisation and prolific offending found that the
majority of repeat or multiple offences committed against the same victim or location,
where the perpetrator was known, were the responsibility of the same offence. He also
discovered that houses along the same street and the same side were more at risk of
becoming victim to burglary by the same offender. He identified that so long as no two
offences occurred on the same day, up to half the burglaries were preventable if houses
within ten numbers were protected. Although the distance between house numbers were
unknown, it was assumed that they would be close, leading to the suggestion that the
street [or area] might be a more appropriate unit of analysis rather than the individual
address. The work of Everson (2002) is unique in that he analyses recorded crime data
concerning offenders rather than victims alone, providing an account consistent with
assumptions found in the near repeat and repeat victimisation literature research, notably
that the same offenders will return to the same street to commit further burglaries.
A drawback in his work is that although the same street was analysed, and the assumption
that most door numbers would be close to each other in distance, it is not possible to
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disentangle whether near-repeat burglaries in other streets were closer than some of the
burglaries along the same street, particularly if the street having the initial burglary was
very long. For example, consider that the burgled dwelling is on the junction of two or
more streets. Using Eversons (2002) analysis you would not be able to show whether the
distance between two subsequent burglaries on the original road was actually closer than
that between the initial burglary and one on the other streets on the junction, even when
assuming that address numbers on the same street were close in proximity to each other.
What has been needed for practical purposes and beyond the scope of Eversons (2002)
work, is an analysis of the linear distance between burglaries from a previously
victimised house that is the exact distances between dwellings; this paper provides the
missing gap. Having this removes the necessary assumption that house numbers are close
together, and by analysing the surroundings of burgled property presents a more accurate
picture of the distances traveled to subsequent burglaries.
Linking Near Repeats to Modus Operandi
It is said that in forensic psychology there will be a high degree of similarity in the way in
which offenders carry out their crimes. Bennel and Jones (2005) for instance found that
shorter distances between crimes would signal an increased likelihood burglaries were
linked. In further testing the theory on near repeats, Bowers and Johnson (2004) found
evidence that burglaries committed closer together in space and time were more likely to
have the same modus operandi, i.e. carried out in the same way. This suggests that if
offenders use a particular signature for committing their crimes, near repeats to
neighbours are more likely to be undertaken by the same person. This assumes that
similarity in modus operandi of offences is a possible indicator of the same offender
being responsible. A weakness in the work of Bowers and Johnson (2004) is this; it
would be useful to know if the same offenders offences are likely to be similar in their
modus operandi. Without testing a sample of offenders patterns, we cannot be certain
that it is indeed the same offender operating in a similar way.
Bennell and Jones (2005) found that short distances between subsequent burglaries were
more likely to have association. Less useful was the method in which offenders carried
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out their crimes. However, Bennell and Jones (2005: 38) later explain that the degree of
variance associated with the linking features in question may explain their relative
capacity to discriminate between linked and unlinked crimes. This suggests that the
results may only reflect a limited dataset, although a description of the data was
unavailable for scrutiny. For example, if only the method of entry variables were from the
front and rear then this does not provide a detailed datasets regarding the method in
which burglars carried out their crimes. Although it would be more likely that a match of
just rear and front would be seen, it is difficult to discriminate which are linked. A better
dataset for comparison would be the front, rear and additional variables such as entry
through the upper or lower window, through the upper or lower door, through the
skylight, and a comparison of amalgamated fields such as upper front window, lower
back door. Indeed Bennell and Jones (2005: 37) suggest it is possible that the low levels
of predictive accuracy associated with traditional MO indicators in the present study are
due to the limited information available in UK police records on the details of burglary-
related actions. If this is the case a more refined approach to recording what does or
does not happen in these crimes may yield more promising results. Perhaps a merger of
what was available may have proven useful. As Bowers and Johnson (2004) found when
amalgamating many variables relating to modus operandi, there is a greater chance that
the methods are similar thus more confidently linked.
Johnson and Bowers (2004) have also shown that not only the same venue but also those
nearby are more vulnerable to attack clusters within two months and 300 - 400 meters
from a prior burglary. The research suggests that after an initial burglary nearby locations
are elevated in risk of further residential burglaries in the near future and close proximity.
Therefore if offenders can be seen to use similar modus operandi and these are clustered
in space and time, then this provides a framework for linking burglaries to particular
individuals. How long offenders concentrate similar offending patterns in particular areas
is likely to vary. The time-course phenomenon suggests soon after an event the same
venue and those nearby are at heightened risk for up to six months. Johnson and Bowers
(2004) have speculated that burglars are slippery over time, meaning that they commit
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offences in an area for a while then move on to other locations not returning for six
months, possibly because of the fear of being caught.
This section of the paper has shown that premises in close proximity to previously
burgled dwellings, particularly those of a similar nature such in layout design are at
heightened risk to being victimised than those who are not within a limited time frame.
Previous research has assumed that it is the same offender or group of offenders
committing burglaries in close proximity to one another due to the patterns in space-time
clustering, and possible similarities in modus operandi, which according to forensic
psychology provides a likely signature to the same individual. Research using recorded
crime data on offenders provides analysis that the same individual commits offences
nearby, but how near is unknown because the only measure thus far has been the same
street using house numbers as the distances (Everson, 2002). Using data on victims only,
Johnson & Bowers, 2004 have suggested this is up to 400 meters away. However, to
confirm the near repeat theory it is still necessary to establish whether the time course
pattern is seen using offender data, not only along the same street but calculated linear
distances between burglaries which could mean closer burglaries in streets just around the
corner.
By way of analysing the space-time patterns and modus operandi from offenders who
have been responsible for burglaries, this paper presents something new, in that it tests
assumptions in the literature research on burglary repeat victimisation, that not only the
same venue is more likely to be targeted again by the same offender, but those addresses
nearby more than would be expected on the basis of chance. Therefore, the hypotheses
that will be tested here is that burglars return to the same location or nearby location to
commit subsequent burglaries, more than one would expect on the basis of chance.
Drugs and burglary
An area that needs to be remarked upon is the effect of drug-dependent burglars,
particularly considering the borough of Camden in which this study reports, is
documented as being an area of London subject to both open and closed drugs markets
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(Camden Crime Audit, 2004). For those drug dependent offenders, the rational choice for
committing burglary may vary on the immediate need for drugs, where quick proceeds
are sought. It could be argued that choices are dependent on the drug habit, where the
offenders behaviour is probably more chaotic in lifestyle choices. One would expect that
those more chaotic offenders would commit crime closer and more often together in
space and time, looking for the easiest opportunity with a less rational perspective
because the driving force is more impulsive than an offender who is not drug dependent.
Those less addicted to or fewer drugs dependent will be more rational and more
organised with their overarching objective to steal desirable consumables that can easily
be sold on for profit or used by the individual (Clarke, 1991). It is possible that those that
are not dependent on drugs would be more careful in their crimes and therefore more
willing to travel further distances to commit their offences.
Hearnden & Magill, 2004 found that reasons to start burgling were friends were doing it,
boredom or to fund drugs. Initially they would start to burgle with others and when more
confident they would commit offences on their own. Grabonsky (1995) advocates that a
drug dependent burglar will need quick proceeds, and consequently they will be more
persistent, while the more professional burglar less likely to be drug-dependent will be
more systematic and analytical in their selection of target and modus operandi. Everson
(2002) found greater specialisation among those who did not repeatedly target the same
victim. Thus, the evidence suggests those who are more specialised are more organised,
willing to travel further to seek bigger rewards rather than less prolific whos rational
consideration is more towards the benefit of gain rather than the consequential cost of his
or her actions.
Bowers and Johnson (2004) found that repeat victimisation tended to occur in deprived
areas, whereas the space-time clustering was more evident in affluent areas. Repeat
victimisation of the same location tended to occur in areas where crime is high (Pease,
1998), and high crime levels are generally seen in areas of lower social status than
affluent (Mukherjee & Carachch, 1998). Drug markets are usually situated in lower
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socioeconomic areas with high crime rates (Lupton et al., 2002), perhaps suggesting that
drug dependent offenders commit burglaries in more deprived areas where they are more
likely to reside. The borough of Camden is a high volume crime area, with a mixture of
wealth and deprivation, mixed with both an open and closed drugs markets.
In summary, if an offender is drug dependent there is likely to be more space-time
clusters than those who are less or not drug dependent. This is because the benefit of
obtaining goods to sell for drug dependent burglars far outweighs that of the more
organised burglar who may travel further distances and consider the costs of their actions
a lot more. However, the space-time clustering of burglary should be no different to that
of other studies, other than the fact that for those more dependent on drugs, a closer
spatial and temporal pattern would be expected. Therefore, regardless of whether the
offender is drug-dependent or not, patterns of offending for burglars are still based on a
cost and benefit with the gain for those drug-dependent far exceeding the cost of being
seen or apprehended. These findings could potentially be observed within this study of
Camden with drug problems for this area well documented (Crime & Disorder Audit,
2004). The author felt that further investigation into the links between drugs and burglary
worthwhile, however this was outside the remit of this paper.
The Context
The area of analysis is the central London borough of Camden. Camden is a large inner
city borough that has a greater than average crime rate against the London average (Safer
Camden Strategy). The borough has a dense population of about 200,000 increasing
during the day with many commuters working in the borough, and evening with a lively
nightlife, representing the night time economy of many Town Centres.
The borough is diverse in many of its 2,180 hectares of space, split into eighteen different
council ward boundaries with approximately 94,000 households. In the South is
Bloomsbury, which contains a large university, shopping areas and night time
entertainment. In the center is Camden Town, a hotbed of activity during the day and
night time hosting a variety of food, markets, shopping and night time entertainment
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clearly visible from the street, because of foliage or high walls (Grabonsky1995).
Camden with its high turnover of people and high volume of converted flats has many of
these features.
The data
Background to dataset
The Metropolitan Police record incidents where an offender has been arrested and
subsequently proceeded against by charging them with the crime known as person
accused. These do not include situations where people are arrested but no further action
is taken, for example if the victim did not wish to pursue the allegation made. Clear-ups
are situations where the police have gathered sufficient evidence to charge someone but
charges are not brought, for example if the accused person has died. Therefore there will
inevitably be more clear-up offences than there will be accused. The accused detection
rate for crime in Camden over a five-year financial period April 2000 to March 2005
ranged from 4.2% to 8.6% of the total burglaries per year, with an overall average of
6.5%. Clear-ups ranged from 8.1% to 12.6% with an overall detection rate of 10.6% for
the five-year period. Put another way, at least 4.2% and probably more like 10.6% of
accused offenders were recorded as being known to have committed burglaries in
Camden during a five-year period April 2000 and March 20053.
Recorded residential burglary data where an offender with proceedings, e.g. someone
charged, summonsed or cautioned or where courts take offences into consideration
following conviction for other crimes, was gathered for the period 1st January 2000 to 31st
January 2005. All recorded burglary incidents where the offender is known by the police
to be responsible for the crime over a five-year period were analysed. Incidents where the
offender was a suspect or eliminated for whatever reason were not used, thus the five-
year recorded sample is likely to be an underestimate. The author felt it necessary to do
this to ensure that the data was robust. The data has been extracted from the Metropolitan
Police Crime Recording Information System (CRIS) relevant to Camden Borough,
3 Statistics obtained from the official online internet publication website of the Metropolitan Police
www.met.police.uk/crimestatistics/index.htm
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London, England. The borough of Camden was chosen because the author of this paper is
an employee of the Metropolitan Police Authority, working as a Higher Analyst in the
borough of Camden, therefore having direct access to the data. The author is familiar
with handling sensitive data and every necessary step has been taken to assure no
personalised information is retrievable within this paper, thus adhering to the 1998 Data
Protection Act.
All recorded residential burglaries during the five-year period where an offender who had
been processed was selected. These 1547 incidents were taken from the CRIS database
along with other relevant recorded fields for analysis. This included:
Unique Crime Number
Unique Identifier of offender
Surname & Forename of offender
Date of Birth of offender
Address of residential burglary
Fields relating to Method of Entry
Type of property
Dates of burglary
Home Address of Offender
The unique crime number listed above is the crime reference number relating to an
individual crime that took place. Burglaries where more than one burglar was recorded
were removed from the analysis. The purpose of this was for ease of comparison and the
fact that the behaviour of a team of burglars is likely to be different to an individual
because the risk involved is shared. This analysis focused on sole offenders. Different
types of burglary such as breaking and entering, trespasser types, commercial burglary,
and artifice may have different probabilities of repeat victimisation and time courses
(Farrell, 1995). Therefore, any crimes that were flagged as being artifice types were
removed. These types of burglary are more specialised and unique in nature to other
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burglaries. They usually target the elderly posing as an official of some type, for example
a police or electrician, conning their way into the home and distracting the victim while
stealing their valuables. Removal of the burglary teams and artifice burglars reduced the
original total by 21%, leaving 1214 incidents of burglary where only one person was
known to be the offender.
The information was then used for the analysis of offenders patterns of burglary.
Accurate information including specific address locations was gathered because the
distance between each subsequent burglary incidents need to be calculated from the
geographical referencing points of two locations for all 1214 burglaries. In order to do
this, each burglary address was geocoded to within one square meter using MapInfo a
Geographical Information System (GIS) that plots the x and y coordinate points of the
crime to a location on the British National Grid. A calculation of distance between the
two geocoded references was then undertaken using the formula4:
SQRT(((xa-xb)*(xa-xb))+(ya-yb)*(ya-yb))
It is accepted that separate dwellings at the same address (for example shared flats with
communal entrance) were under-represented because of this. These types of addressees
are common in the police borough of Camden.
Data Limitations
Inadequacies with recorded police data have been previously discussed in the literature
(see Anderson et al., 1994; Pease, 1998). These include:
a) Data formatting and cleansing means some repeat incidents are missed, false
names and other details can also be given
b) The Time Window, meaning although 5 years worth of data was used for
analysis, 10 would probably show more repeats in the burglaries simply because
there is more chance a further burglary at the same location may take place
4 The formula represents the calculation of distance between the coordinates from the British National Grid,
with the a representing the initial burglary location and b the subsequent burglary location.
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c) Actual figures are likely to be underrepresented because data only exists when
burglaries have been detected or owned up to by offender
d) Recorded burglaries do not include the dark figure, i.e. unreported, thus
unrecorded crimes
e) Multiple Area Unit problems; offenders could commit more burglaries just across
the borders of the area under investigation, thus outside the edge of Camden
borough.
Camden was no exception to data quality shortfalls. Firstly, the collection of crime data
for the CRIS system relies upon human intervention to input details about the incident,
such as information about the location of the crime and offender responsible if
apprehended. These are not always entered the same way, for example the names of
individuals, streets, buildings and the like are fraught with opportunities for misspellings.
Therefore cleansing of the recorded burglary data was needed.
This process intended to correct the spelling mistakes of offenders or the address
locations. Regarding the address, the important piece of information required for the
distance calculation was the x, y coordinates of the burglary location. These are not
automatically generated in the CRIS database and another piece of software called
Omnidata was utilised. This software has an address database gazetteer of Camden
holding for every address location in the borough denoted by a string of text and an x, y
coordinate reference is provided for each. The string of text of the address is split into
different categories such as house number, building name, street name, and postcode.
Therefore matching address data from CRIS in the same format as those in the gazetteer
would result in the x, y coordinate. If the data was not available for every field or the
address field was spelt incorrectly, e.g. a street name of Euston was spelt Eewston the
program would utilise the other fields in the address such as postcode and street number
and use these as reference points instead. If many of the fields were spelt incorrectly then
the program would try different combinations of each category in an attempt to find the
best fit or utilise a fuzzy search on the names that were very similar to a particular street
for example and correct this. Each x, y coordinate was given a code relating to how the
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reference was calculated. The majority were calculated from the house number and street
name or postcode. Those that were not were double checked for accuracy and corrected if
necessary.
A further methodological issue when analysing repeat victimisation for recorded crime
data includes the time-window. This concerns the period that is currently under
investigation, for this paper a five-year sample January 2000 to January 2005. The level
of repeat victimisation will be that concerning this period of time, thus burglaries outside
this time-window, prior to and after will not be included and therefore the true figure of
repeats are likely to be an under representation (Pease, 1998)
Crime itself is under-reported to the police, who in turn do not always record those that
are reported. Thus, some crime that is reported does not get recorded. Consider the
following from Farrell and Pease (1993) when analysing repeat burglary victimisations of
the same location. A burglary has roughly a 70% or 0.7 chance of being recorded in
police statistics. If the same household suffers a second burglary then this too has a 0.7
chance of being recorded in police statistics. This means that the chance of both being
recorded is 0.49, the chance of one burglary being recorded (0.7) multiplied by the
chance of a second burglary being recorded (0.7). Although improbable that continuing
these calculations would bear out in all reality, simply because a victim might not report
the first burglary but may the second or third, it demonstrates the difficulty in estimating
the dark figure of unreported, thus unrecorded crime. Hence, under-reporting of crime
is a particular issue with repeat victimisation.
The isolation of individual offenders from the data was achieved with some criminals
having their unique reference from the Police National Computer identification (PNC)
recorded on the police crimes database CRIS. The PNC is a separate national database of
criminals in the United Kingdom that hold information on the criminal record of
individual offenders. Unfortunately not every PNC reference was recorded on CRIS. For
those who did not have a unique reference, the surname, forename, date of birth and
address were compared and subjective searches made on whether two individuals were
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the same. For example if there was a James L Smith, date of birth 08/01/1978, 10
London Road in one row and in another it was seen as Jimmy Leroy Smith, date of birth
08/01/1978, 10 London Rd it was taken to be the same individual. This was not a perfect
process, although in cases where there was a borderline decision between two rows, such
as different date of births for example instead of 08/01/1978 it was seen as 08/09/1978,
then they would not be linked to make sure the data being used was as robust as possible.
Methodology
Offender Selection
After the data was cleaned, the number of offenders who were held responsible for the
1214 incidents of recorded burglary was calculated. 601 different offenders were
responsible meaning that these were 613 repeat offences. Due to time constraints it was
not possible to analyse the offending patterns of all 601 burglars, therefore a sample of 60
were selected.
To analyse the differences between those offenders more and less prolific in their careers,
three groups of 20 were selected rather than taking a random sample of 60. The selection
of these three groups of 20 was achieved in the following way. The 601 offenders were
put in descending order of the number of offences they had committed, thus the
individual with most burglaries attributed to him was placed on top with 54 burglaries,
while at the bottom were individuals who had only been processed for committing one
burglary. The top 20 were taken, and the bottom 20 where an offender had committed at
least three, a rule set by the author for comparative purposes. You cannot measure the
distance between subsequent burglaries if an individual has only one burglary. Then the
middle 20 between these samples were also taken. Group 1, the twenty most prolific
committed between 8 and 54 burglaries each, the middle Group 2, between 5 and 7
burglaries each while offenders in the less prolific Group 3 mostly 3 burglaries with a
couple of 4s. The top 20 or most prolific offenders had committed 395 burglaries
between them. The middle 20 had been recorded as committing 118 burglaries, and the
least prolific 20 committing 65 burglaries between them.
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Calculating the differences between subsequent burglaries
A dilemma when analysing police recorded crime data is the Modifiable Unit Area
problem. The MAU problem occurs when a smaller geographical area with arbitrary
boundaries are imposed because of administrative purposes. So in the context of this
paper, the borough of Camden has geographical police boundaries aligning to local
authority borders that aid the allocation of resources to particular areas. This means that
those offenders selected for their crimes committed within the boundaries of Camden,
may have committed other burglaries outside these boundaries that may be recorded and
known only if detected on police databases, but generalised about for a larger area.
Therefore, the difficulty of an offender who commits his or her crimes on the border of
the boundary is that they may in fact have other crimes just across the periphery of
Camden. A difficulty here is establishing whether the distance and time traveled between
burglaries would be significantly different if an offender is found to have committed
crimes outside the arbitrary borders of Camden. There is always likely to be some
burglaries within police databases not recorded and many without a known offender
simply because they may not be reported or the burglaries have not been detected.
To alleviate this problem Bowers and Johnson (2004) adopted techniques first developed
to examine communicable diseases (Mantel, 1967). This process works by calculating the
average expected distances and times between subsequent burglaries on the basis of
chance in the given boundaries in this case Camden. It does this by comparing the
average distances from those offences committed by the same offender and those of a
random sample to see if those average distances and times for subsequent offences of the
burglar are outside what would be expected on the basis of chance.
To calculate the differences between subsequent burglaries, the following process was
adopted. The date the offence took place was put into chronological sequential order of
events. One of the decisions that needed to be made with recorded burglary information
is which incident date to use. The is because the date of the burglary may not be known if
say, the offence took place during a period of vacation for the victim only to discover the
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burglary on return days or weeks later. For this reason a committed on and committed to
date is recorded on the CRIS system. For the ease of comparison and consistency
throughout the data the committed from and committed to time of the burglary was
extracted and a mid range between the two used for analyses. The temporal differences
between the on and to dates were mostly recorded as being 0 or 1 day (91%), i.e. the
discovery was on the same day or day after likely to be within twenty four hours. 98% of
the differences were recorded within six days apart of the committed on and to dates.
Each of the 60 burglars had their burglary incidents sorted into chronological order of
events from the first recorded to the last. The distance between each subsequent burglary
was then calculated, and a similar process adopted for the days between each. This
involved applying a formula5 to the calculation of distance between x, y coordinates and a
simple subtraction for the days of subsequent burglaries.
Distance and Time
In order to test whether the distances and days between each subsequent burglary were
significantly patterned for the 60 offenders, the average distances and days would need to
be tested against what would be expected on the basis chance (see Bowers and Johnson,
2004). In total there were 518 comparisons made between subsequent burglaries out of
574. In the next stage of the method, the average distances and days of the 518 crimes by
the 60 offenders were compared to another 518 random burglaries identified by selecting
a random 518 from the total number of 1214 burglaries. The process that produced
random comparisons of subsequent burglaries followed the same procedure for each of
the 60 offenders. In essence, distances and times between events were calculated between
each of the subsequent events in the random sample. Using the statistics software
package SPSS,calculations between the actual 518 residential burglaries of the 60
offenders and a relative random comparison were undertaken to identify significant
differences between distance, time and modus operandi of subsequent burglaries. Thus, if
statistical evidence found, one could be reasonably confident that patterns seen fall
outside what would be expected on the basis of chance.
5SQRT(((x1-x2)*(x1-x2))+(y1-y2)*(y1-y2))
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Modus Operandi
Very similar to the method employed to calculate the differences in distance and time
from a random sample, the hypothesis to be tested here is that we would expect a greater
degree of similarity in the way pairs of subsequent modus operandi incidents committed
would be the same than would be expected on the basis of chance.
The crimes database CRIS, collates modus operandi information in various ways such as
unstructured free text, by way of a long string in a particular field, for example the
window was smashed to the rear using a sharp tool to enter, untidy search and suspects
made good their escape via the rear door. These unstructured texts cause difficulties
because there are no guidelines to those entering crime reports in how to complete the
fields, thus extracting a comparative method extremely difficult and time consuming
(Adderley & Musgrove, 2003). Fortunately, the CRIS system uses a front-end computer
package called Business Objects, a program that enables a much better search facility.
Breaking down particular fields within the crime report allows for improved extracting of
information. Part of this database allows for the extraction of structured text relating to
the method in which a crime was conducted. Table 1 illustrates six structured fields that
were used from the CRIS database and possible combinations for each. Four of the fields
were extracted from CRIS, whilst the 1st Approach and 2nd Approach is a split of the
Joined Approach category done separately after the extraction. The author felt that doing
this provides a more detailed breakdown of the modus operandi, particularly considering
some of the fields in the joined approach were unknown. If for example the burglary MO
for an initial offence was Rear/Above Grd approach, comparison to a subsequent
burglary that was Rear/Unknown would not show a link in the methodology used. But
when split it will make the link that the burglary approach was from the Rear in both
cases.
The process for calculating the matches works something like the following. The method
of committing each burglary is broken down into six different fields shown in Table 1.
When an offenders crimes are placed in date chronological order, the method is
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compared to the subsequent burglary method. If they are the same a 1 was recorded as a
hit, if they were not a 0 was recorded as a miss. These were then collated in a table
summarising this information for all of the 60 offender burglaries in the sample. A
comparative random selection was also completed in much the same way, however the
selection was based on a random selection of burglaries from the whole 1214 sample,
therefore we are left with two samples of hit rates for similarities in subsequent
burglaries, one for all 60 offenders and their individual burglaries and the other a random
sample of subsequent burglaries.
Table 1. Modus Operandi and Target selection type and combinations within
The results
This section of the report will review the following:
28
Entry Method Joined Approach 1st Approach 2nd Approach Entry Point Location Type
Break-In Adj Prems/Above Grd Adj Prems Above Grd Door Bedsit
Empty Front/Above Grd Front Below Grd Fire Exit Council Owned
Not Applicable Front/Below Grd Not App Front Letter Box Detached
Not Known Front/Grd Level Rear Grd Level Louvre Flat/Maisonette
Walk-In Front/Not App Side Not App Not App. Garden
Not App/Above Grd Unknown NotApplicable Not Known Hostel/Res Home
Not App/Not App Unknown Other Hotel/Guesthouse
Rear/Above Grd Patio Door House/Bungalow
Rear/Below Grd Roof Privately Owned
Rear/Grd Level Skylight Semi-Detached
Rear/Not App Unknown StreetSide/Above Grd Window Terraced
Side/Below Grd
Side/Grd Level
Unknown
Unknown/Above Grd
Unknown/Grd Level
Unknown/NotApplicable
Unknown/Unknown
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1. Whether there are many repeat incidents of burglary at the same location in the
borough of Camden, and whether as the research suggests, the more prolific an
offender the more repeats there will be
2. Testing the hypothesis that burglars will return to the same venue or nearby
location to commit further burglaries within a short period after the initial
3. Testing the hypothesis that an offender will carry out his or her subsequent
burglary in a similar way to the initial, by way of comparing the method used and
the property type selection of target
4. Throughout the results there will be comparison between those who are more
prolific against those less prolific
The following sections review the first three issues in turn.
1. Repeat Victimisation at the same location
Table 2, page 29, illustrates the proportional differences of burglary repeat victimisation
of the same target in Camden between three offender groups. Each group encompassing
20 individual offenders each, whose total offences during the analysed period differ due
to the amount of crimes committed over the five-year period. Put another way each group
has offenders who are more or less prolific in their criminal careers than the next group.
Group 1 is the most prolific with 20 offenders responsible for 395 burglaries individually
ranging from 8 to 54 burglaries each, group 2 the medium group totaling 118 ranging
between 5 and 7, while the least prolific totaled 65 burglaries committing 3 or 4
burglaries each.
Table 2. Proportion of repeat residential burglaries at the same location
29
Repeats Group 1 p Group 2 p Group 3 p
0 353 .89 111 .94 60 .95
1 16 .04 4 .03 1 .02
2 6 .03 - 1 .03
3 1 .01 1 .03 - -
4 1 .01 - - -
5 - - - -
6 - - - -
7 1 .02 - - -
n= 395 118 65
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P= proportion
The proportion of repeat burglaries was calculated for each group of offenders. Only the
repeats and not the initial burglary were calculated, thereby removing those initial
burglaries for a particular location by each offender. These removed burglaries can be
seen in the row of repeats denoted by 0. This is why the totals for each are greater than
the proportions in the table. For example, taking group 1, we can see there were 7
repeated burglaries at one venue, however in total there were 8 burglaries at this location,
the initial being included in the 0 repeat row, as this was not a repeat for the offences of
that single individual. To calculate the proportion of total repeat burglaries for each
group the numbers of repeats were, multiplied by the number of individuals in each group
that were responsible for the number of repeats. For example, in group 1 the situation
where there has been two repeat offences at the same location has occurred six times.
This is therefore 12 repeated burglaries in total. This is a proportion of .03 against the
total 395 incidents of burglary by that group. All of the repeat incidents are matched to
individual offenders and not the same location within each group, thus only those repeats
by the same person were used controlling for the fact that two or more offenders could
have targeted the same premises.
The results from Table 2 concur with previous literature findings demonstrating that the
more frequent the offender in committing their crimes the greater number of repeat
burglaries at the same location (Everson, 2002; Ashton et al., 1996). This can be simply
observed when looking at the proportion pattern of those incidents which are not repeats
from each group, .89, for group 1, .94 group 2, and .95 group 3. The proportions increase
the less prolific an offender, meaning larger proportions attributed to the more prolific
offenders.
2. Space-Time clustering
The main hypothesis to be tested in this paper is that the spatial and temporal distance
between subsequent burglaries committed by the same offender is closer than would be
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expected on the basis of chance. Thus, if sufficiently strong statistical evidence can be
shown for such relationship, it will lead to the conclusion that those offenders do return to
the same location or are nearby to the previous burglary to commit a further crime, as
opposed to other site alternatives. By the same method, the analysis will also review the
time taken to commit further burglaries, thus determining whether space-time clustering
is apparent for Camden Borough. If this is shown to have equally strong statistical
evidence, it will demonstrate that Camden burglaries by individual offenders return to the
same location or to nearby households to commit further burglaries sooner than expected
after an initial burglary.
The following descriptive statistics in Table 3 present, for all 60 burglars, calculations of
the average distance (in meters and miles) and days between the subsequent burglaries.
As described in the methodology section, a random sample comparison of the same is
allocated indicated by Rand at the beginning of the titles days, distance and miles. Each
of the 60 burglars committed a range of offenders recorded on the crimes database, from
the least prolific of 3 burglaries to the more prolific, in one case 54 burglaries.
Table 3. Frequency statistics for the differences in subsequent residential burglaries and
random comparisons
31
Days RandDays Distance RandDistance Miles RandMiles
N 518.0 518.0 518.0 518.0 518.0 518.0
Mean 53.0 146.2 1083.9 2648.3 0.7 1.6
Median 5.3 73.0 670.3 2388.6 0.4 1.5Mode 1.0 12.0 0.0 504.7 0.0 0.3
Std. Deviation 168.4 195.0 1161.3 1456.0 0.7 0.9
Minimum 0.0 0.0 0.0 181.5 0.0 0.1
Maximum 1398.0 1392.5 6184.2 7896.1 3.8 4.9
Percentiles 25.0 1.0 26.4 249.8 1587.7 0.2 1.0
75.0 23.3 192.9 1614.0 3794.8 1.0 2.4
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Table 3 shows the average distance to the next burglary for all of the 60 burglars
regardless of how prolific is 0.7 miles (or 1083.9 meters). A random comparison shows
on average 1.6 miles (0r 2648.3 meters). The days between subsequent burglaries also
shows a difference, 53 on average for the 60 offenders, contrasted with a random
comparison of 146. A histogram of the distance data shows that the data is not normal,
and skewed to the right with outliers seen. These outliers are distances between
subsequent burglaries that are a lot more extreme than the majority of the dataset.
Therefore to provide further representation of the data, the median and percentiles were
included, which indicates an alternative average calculated by lining each distance in
sequential order and taking the mid and quarterly points from the data. The median of 0.4
miles showed a smaller distance than the mean 0.7 miles for the 60 offenders. These
trends were also reflected, in the random comparison. The most significant change seen
with the median was in the days, with the distance for the 60 offenders reducing ten fold
to 5.3 days, and by half for the random comparison to 73 days. The days demonstrate the
skewing of the mean, with the 75th percentile being 23.3, less than the actual average of
53 days. This shows that in this case, the median of 5.3 is perhaps a better indication of
the average days between subsequent burglaries.
A Wilcoxon non-parametric test was completed to test whether the 518 burglary incidents
of the 60 offenders were significantly different than an equal 518 random sample.
Comparison of the burglary distances for the 60 offenders and a random sample of the
same showed that there was a strong evidence of a difference, i.e. a reduced distance
between subsequent burglaries than one would expect by chance. A Wilcoxon Signed
Ranks test confirmed that the change was statistically significant (Z=-15.363, p
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chance6. If the same calculation using the median rather than the mean as an average this
increases to over three and a half times more than would be expected on the basis of
chance. The days to subsequent offending were also shown to be significant with a
Wilcoxon Signed Ranks test (Z=-14.880, p
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Group differences of space-time clustering
A statistical summary for each offending group (Table 4) shows that the days between
subsequent burglaries reduces the more prolific the offender, whilst the average distances
between each burglary increases the more prolific a group is. However using the medianrather than the mean, the difference between the mid and least prolific groups shows a
reverse of this, although the difference is minimal. These findings concur with the
literature that more prolific offenders travel further to commit crimes than those less
prolific (Snook, 2004)
Table 4. Space-Time cluster differences of groups different in their criminal career length
34
0
10
20
30
40
50
60
70
80
90
0 10 20 30 41 51 70 86 127 230 408 922
Days between subsequent burglaries committed by the same offender
ObservedIncidents
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This suggests that although more prolific the offender the more likely they will commit
burglaries at the same location soon after, they will also travel on average longer
distances to commit further burglaries. This perhaps suggests that burglars use the initial
burglary as an anchor point (Snook, 2004) from where they spread the misery further and
faster than less prolific. To examine the differences between prolific and less prolific
offenders, non-paired independent sampled tests were completed. A Kruskal-Wallis Test
measuring how much the group ranks differ from the average rank of all groups was
completed for distance, although no statistical significance was seen (Z= 1.825, 2 df, p
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Table 5. Results from each group comparing every burglary incident with every other.
The results suggest a similar pattern to what was seen when comparing only subsequent
crimes, where the more prolific an offender the closer in time to other burglaries, and on
average he or she would travel longer distances to commit crime. A Wilcoxon test for
significance showed that there was strong statistical evidence to reject the null hypothesis
that the distance and time do not vary from what you would expect by chance 99% of the
time. Rather there is a strong significance for distance (Z=-44.080, p=.001), and a strong
significance for time (Z=-56.448, p=.001). This means that offenders tend to commit
their crimes in a small clustered geographical area, an example of which is illustrated in
Map 1 on page 36, with those more prolific traveling further to do so.
Map 1 An example of three offenders burglaries in Camden, one from each group7
7 Crown Copyright Metropolitan Police Service, PA01055C, August 2005
36
N Mean Median Mode sd Min Max Mean Median Mode sd Min Max
Group 1 5016.0 1324.4 1084.1 0.0 1148.7 36.7 7896.1 167.9 51.0 1.0 290.8 0.0 1575.0
Group 2 296.0 959.1 668.9 0.0 993.5 0.0 5707.8 126.7 43.8 0.0 213.4 0.0 1151.0
Group 3 75.0 798.0 619.5 0.0 690.7 0.0 2643.9 272.8 76.5 0.0 355.0 0.0 1383.0
Distance Days
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Offender Home Address
The distance offenders travel from their home address to commit crime is usually short.
Wiles and Costello (2000) study of offenders in Sheffield, England found that offenders
do not travel far and that burglaries generally occurred near to an offenders home
address. They found that transportation was often made by car, but this was not related to
the distance traveled to offend. It was more related to a speedier escape, the weight of
goods and the fact that offenders were less likely to draw attention to themselves in a car
with valuable items in their possession than walking around in public on foot. Though
some reached premises on foot found that they had a greater ability to stash goods,
addresses were more approachable say via an alleyway, and they had a better chance of
establishing attractive premises in a non-suspicious manner.
Snook (2004) also found that serial burglars travel further between burglaries for greater
rewards, similar ranges were found for the distances between home addresses for
offenders with prolific offending histories and those less prolific. Wiles and Costello
(2000) found that one individual had traveled by car up to 30 miles to commit their
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burglary, highlighting that those younger offenders will have less opportunity to travel
these distances unless over the legal driving age of 17 years.
It has been speculated that the longer the criminal career, the further an offender will
travel. Reasons for this suggest a broadened cognitive map of an area with the potential
for greater rewards. If this is true, then results from testing the data for Camden
offenders distance to crime should reflect a greater gap to subsequent burglaries.
Furthermore, if this is seen, by virtue of the results, it will explain the patterns seen
earlier of larger distances to subsequent burglaries for more prolific offenders. That is
prolific offenders generally travel longer distances to commit their crimes.
One offender from each group was selected from each group. This was done by random
with one criterion that the offenders home address had not changed throughout their
period of committing burglaries in Camden during the five-year period analysed. The
below Table 6 shows that the more prolific the offender the more he or she will travel
from their home address, thus by definition the offender will travel further between
burglaries. A Kruskwallis test showed significance between offending groups (Z=
17.160, 2 df, p
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than younger individuals. This is perhaps linked to the criminal career of the offender,
obtaining a growing knowledge and skill of committing burglaries will provide further
confidence to expand their hunting ground, and the increased opportunity for using cars.
A test to see whether there are significant differences between the ages for the different
prolific groups showed that there was only one significant difference between age groups
1 and 2 (t= 5.678, df 511, p
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subsequent offending. When cross tabulating the distance (in meters) and time (in days)
to a subsequent burglary for all offenders a clear pattern can be seen (Table 8). The
largest proportion, 14.1%, or 73 subsequent of the total number of burglaries is
committed between 0 to 2 days, within a distance of 0 360 meters. The expected
number for this category is 57.4 burglaries. For the row of showing subsequent burglaries
committed within 0 to 2 days, it is clear that the proportion of this category reduces as the
distance increases, suggesting the shorter the distance between subsequent offending the
shorter the period of time.
The Chi-square test showed that there was a significant association between the time and
days between subsequent offence and the distance traveled to subsequent residential
burglaries (X2(4)=10.603, p
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The most prolific group 1, followed a similar trend to the overall pattern for the
amalgamated groups, with the short period of days and time showing the largest cluster,
amounting to 13.1% of the total burglaries analysed for the group. The Chi-square test
showed that there was not a significant association between the time of days between
subsequent offences between the distance traveled to subsequent residential burglaries
(X2(4)=4.727, p
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42
A (0 - 360) B (372 - 1232) C (1235+) Total
A (0-2) Count 49.0 41.0 35.0 125.0
Expected Count 41.7 41.3 42.0 125.0
% of Total 13.1 10.9 9.3 33.3
B(2-8) Count 35.0 45.0 45.0 125.0
Expected Count 41.7 41.3 42.0 125.0% of Total 9.3 12.0 12.0 33.3
C(8+) Count 41.0 38.0 46.0 125.0
Expected Count 41.7 41.3 42.0 125.0
% of Total 10.9 10.1 12.3 33.3
Total Count 125.0 124.0 126.0 375.0
Expected Count 125.0 124.0 126.0 375.0
% of Total 33.3 33.1 33.6 100.0
A (0 - 361) B (368 - 1095) C (1108 +) Total
A(0-6) Count 18.0 8.0 6.0 32.0
Expected Count 10.8 10.4 10.8 32.0% of Total 18.4 8.2 6.1 32.7
B(7-38) Count 8.0 12.0 14.0 34.0
Expected Count 11.4 11.1 11.4 34.0
% of Total 8.2 12.2 14.3 34.7
C(41+) Count 7.0 12.0 13.0 32.0
Expected Count 10.8 10.4 10.8 32.0
% of Total 7.1 12.2 13.3 32.7
Total Count 33.0 32.0 33.0 98.0
Expected Count 33.0 32.0 33.0 98.0
% of Total 33.7 32.7 33.7 100.0
A (0 - 345) B (350 - 743) C (813 +) Total
A (0-10) Count 6.0 4.0 4.0 14.0
Expected Count 4.4 4.7 5.0 14.0
% of Total 13.3 8.9 8.9 31.1
B(14 - 100) Count 6.0 5.0 4.0 15.0
Expected Count 4.7 5.0 5.3 15.0
% of Total 13.3 11.1 8.9 33.3
C(117 +) Count 2.0 6.0 8.0 16.0
Expected Count 5.0 5.3 5.7 16.0
% of Total 4.4 13.3 17.8 35.6
Total Count 14.0 15.0 16.0 45.0
Expected Count 14.0 15.0 16.0 45.0% of Total 31.1 33.3 35.6 100.0
Group 3
time (Days)
Group 2
time (Days)
Distance (meters)
Distance (meters)
Distance (meters)
Group 1
time (Days)
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3. Modus Operandi
Bowers and Johnson (2004: 20) found that burglaries committed close together in space
and time were more likely to be done so in the same way. One of the central
assumptions made in relation to this finding is that the same offenders, or their
associates, are responsible for crimes that form part of a space-time cluster series (near
repeats). If this conclusion is valid, then we would expect to see certain patterns in the
way that crimes are committed. Crimes within one month and 400m of each other twice
as likely to share same MO as those in same time but further away. Thus the analyses
suggest that crimes committed near to each other both in space and time are more likely
to be conducted in the same way than other. Therefore, if by testing whether the same
offenders from those who have committed burglaries in Camden, have done so in a
similar way for his or her subsequent burglaries, this will provide evidence of the theory.
It may also explain that in Camden many burgled premises are similar in design so the
offender will stay in the area where he or she is familiar.
A Wilcoxon test found that the subsequent burglary committed by the same offender was
more similar in nature to what would be expected on the basis of chance for each of the
six fields, see Table 10.
Table 10. Modus Operandi and Property type selection statistical significance from acomparative random sample
The author felt that it was necessary to recreate the analysis removing those fields that
were unknown, not known, empty or not applicable. This was because the methodology
is likely to show some false positives, these are positive matches when in fact it should be
negative. This could occur because the results may show a hit or 1 simply because when
contrasted the subsequent burglary a match will be given if say an unknown in one
method is contrasted with the subsequent and in the same category another unknown is
43
Entry Method Join Approach 1st Approach 2nd Approach Entry Point Venue Location
Z -3.78 -3.25 -4.229 -4.16 -3.281 -2.729
P Value .000 .001 .000 .000 .000 .006
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shown. This of course would be inaccurate because although they maybe the same we are
unsure. The results overall when removing the unknowns, empty or not known, were only
slightly less significant, although each p-value fell outside .01 (Table 11). This shows that
offenders carry out their burglaries overall in a similar way more than would be expected
on the basis of chance.
Table 11. Modus Operandi and Property type selection statistical significance from a
comparative random sample removing those fields unknown, empty or not known
Conclusions
The predictability of crime is the most fundamentally important facet in controlling or
reducing it. Knowing when, where, how and potentially who will commit the next
burglary has obvious implications for crime control. This paper has confirmed that
burglars do return to the same location or nearby location to carry out further burglaries
within a short period of time, usually within seven days after committing an initial one.
Prolific offenders were more likely to repeatedly target the same venue, and also travel
further than those less confirmed in their criminal careers to commit subsequent
burglaries. The days between subsequent offences reduced the more prolific an offender.
The method in which burglars carry out their subsequent crimes, and selection of a
particular type of premises demonstrate similarities, suggesting a signature may be
attributed to a particular offender. Therefore, when considering how to control burglary,
the police need not look much further away from the previous burglary to implement
immediate intervention to gain maximum impact for the resource available.
Many police officers, crime analysts and practitioners would argue that this is exactly
what is currently being done, with police daily briefings reflecting where crimes the
previous days took place, therefore the knowledge gained is of little benefit. However,
44
Entry Method Join Approach 1st Approach 2nd Approach Entry Point Venue Location
Z -2.634 -3.182 -3.373 -3.293 -2.416 -2.729
P Value .008 .001 .001 .001 .016 .006
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knowing where a previous burglary has occurred is very different to predicting when the
next one will. The importance of the findings is an understanding of the rational choice of
why offenders return to the same or nearby locations to commit further burglaries. The
time-course phenomenon informs of when offenders are likely to return to a previously
victimised property or a likely nearby target, and the way in which the burglary is
conducted provides a signature mark that identifies individuals by their selection and
methods of committing the crime.
From their review of intelligence analysis within the Metropolitan police, Innes et al.,
(2005) suggest that what tends to happen is a regurgitation of what is being analysed,
simplifying and clarifying particular dimensions of what is being analysed. Part of this is
perhaps the knowledge of what to analyse, where to start and what to do. This paper
offers a practical guide to what needs doing in respect of residential burglars, at least in
Camden. The Metropolitan Police crimes database CRIS, is arranged in such a way to
easily extract data in a structured manner to observe links in methodology, and the use of
GIS allows the calculation of distances. The first steps to offer greater rewards for least
effort should be concentrated on those crimes closer together in space and time, and then
compare the modus operandi of each.
Bowers and Johnson (2004) have used Graphical Information Systems combined with the
knowledge of repeat victimisation to predict with up to sixty to eighty percent more
accurately where burglaries are likely to happen. Combined with the inclusion of modus
operandi of burglaries, this scientific knowledge can be used to determine the most likely
predictive patterns of offenders who commit residential burglary.
Discussion
Research suggests that offenders do not travel far from their home addresses to carry out
burglaries. The age curve demonstrates that offenders who remain for long periods in
their criminal careers, tend to do so for longer periods, thus the best predictor for future
crimes are high volumes of past offending (Blumstein, 1996). Thus if it can be
45
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established that offenders continue to carry out their offences in a similar way over time,
over a period far greater than the five years analysed in this study, then the police have
the ingredients to track those offenders who continue to return to an area over time to
commit further burglaries. Individual patterns in space, time and the way in which
particular individuals carry out burglaries can be compared to historical links in offending
patterns of previously identified associated crimes. Possessing knowledge that offenders
do not vary their offending patterns over time, could provide an increased chance that
residential burglars could be identified if historical patterns re-emerge in old hunting
grounds.
References
Adderley, R. and P. Musgrove. 2003. MO modelling of group offending: A data mining
case studyThe International Journal of Police Science and Management 5(4), 267-276.
Anderson, D., S. Chenery, and K. Pease (1995a) Biting Back: Tackling Repeat Burglary
and Car Crime. Crime Detection and Prevention Series Paper 58. London: Home Office
Ashton. J, et al. (1998) Repeat Victimisation: Offender Accounts, International Journal
of Risk, Security & Crime Prevention
Barr, R. and K. Pease (1990). 'Crime placement, displacement and deflection.'In: M.
Tony and N. Morris (Eds.). Crime and Justice: A Review of Research, vol. 12. Chicago:
University of Chicago Press.
Bennell, C. and N.J. Jones (2005) Between a ROC and a Hard Place: A Method for
Linking Serial Burglaries by Modus Operandi, Journal of Investigative Psychology and
Offender Profiling, 2: 23-41
Blumstien et al. (1996) Criminal Careers and Career Criminals(2Vols). Washington
DC: National Academy Press.
46
-
7/29/2019 Return of the Burglar.
47/52
Bridgeman, C. and L. Hobbs (1997) Preventing Repeat Victimisation: the police officers
guide, Home Office Police Research Group.
Bowers, K. and S. Johnson (2004) Who Commits Near Repeats? A test of the Boost
Explanation, Western Criminology Review 5 (3), 12-24 (2004)
Bowers, K. and S. Johnson (2005) Domestic Burglary Repeats and Space-Time
Clusters: The Dimensions of Risk, Jill Dando Institute of Crime Science, University
College London. European Journal of Criminology, Volume 2(1).
Bowers, K., S. Johnson, and K. Pease. (2004) Prospective Hot-Spotting: The Future of
Crime Mapping?British Journal of Criminology, 44, 641-658
Bowers, K., S. Johnson, and A. Hirschfield (2004). The measurement of crime
prevention intensity and its impact on levels of crime. The British Journal of
Criminology, 44(3), 1-22.
Bowers, K. and S. Johnson (2005) Domestic Burglary Repeats and Space-Time
Clusters, The Dimensions and Risk, European Journal of Criminology, Volume 2 (1):
67-92.
Canter, D. (2004) Offender Profiling and Investigative Psychology, Journal of
Investigative Psychology and Offender Profiling, 1: 1-15 (2004)
Crime Audit (2004) Camden Borough Crime Audit