return of the burglar

7/29/2019 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

20


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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

22


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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.

24


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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))

26


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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

37


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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


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


% of Total 13.3 11.1 8.9 33.3

C(117 +) Count 2.0 6.0 8.0 16.0


% of Total 4.4 13.3 17.8 35.6

Total Count 14.0 15.0 16.0 45.0


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

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return of the burglar

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