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Linking Personal Robbery Offences Using Offender Behaviour
AMY BURRELL
1,
*, RAY BULL
1
and JOHN BOND
2
1
School of Psychology, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester
LE1 9HN, UK
2
Department of Chemistry, University of Leicester, Leicester LE1 7RH, UK
Abstract
Case linkage uses crime scene behaviours to identify series of crimes committed by the same
offender. This paper tests the underlying assumptions of case linkage (behavioural consistency
and behavioural distinctiveness) by comparing the behavioural similarity of linked pairs of
offences (i.e. two offences committed by the same offender) with the behavioural similarity of
unlinked pairs of offences (i.e. two offences committed by different offenders). It is hypothesised
that linked pairs will be more behaviourally similar than unlinked pairs thereby providing
evidence for the two assumptions. The current research uses logistic regression and receiver
operating characteristic analyses to explore which behaviours can be used to reliably link
personal robbery offences using a sample of 166 solved offences committed by 83 offenders.
The method of generating unlinked pairs is then refinedtoreflect how the police work at a local
level, and the success of predictive factors re-tested. Both phases of the research provide
evidence of behavioural consistency and behavioural distinctiveness with linked pairs display-
ing more similarity than unlinked pairs across a range of behavioural domains. Inter-crime
distance and target selection emerge as the most useful linkage factors with promising results
also found for temporal proximity and control. No evidence was found to indicate that the
property stolen is useful for linkage. Copyright © 2012 John Wiley & Sons, Ltd.
Key words: case linkage; behavioural analysis; robbery; mugging; street crime
INTRODUCTION
Case linkage
Identifying serial crime and linking these offences to a single offender is a crucial part of police
work (Bennell, Jones, & Melnyk, 2009; Santtila, Fritzon, & Tamelander, 2004; Sorochinski &
Salfati, 2010; Yokota & Watanabe, 2002). Establishing that a number of offences are attribut-
able to the same person supports the implementation of efficient and productive investigative
*Correspondence to: Amy Burrell, School of Psychology, University of Leicester, Henry Wellcome Building,
Lancaster Road, Leicester, LE1 9HN, UK.
E-mail: amb58@le.ac.uk
Journal of Investigative Psychology and Offender Profiling
J. Investig. Psych. Offender Profil. (2012)
Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/jip.1365
Copyright © 2012 John Wiley & Sons, Ltd.
strategies (Labuschagne, 2012; Santtila, Junkkila, & Sandnabba, 2005), for example, pooling
information from all the crime scenes (Bennell et al., 2009), which can lead to faster identifi-
cation and apprehension of the offender and/or strengthening the evidential case for court
(Woodhams, Bull, & Hollin, 2007a). Linking offences can be relatively simple if forensic
and/or physical evidence is found at the scene (Grubin, Kelly, & Brunsdon, 2001). However,
forensic evidence is often lacking (Ewart, Oatley, & Burn, 2005; Hazelwood & Warren, 2003).
When this is the case, behavioural analysis may be used to identify a linked series of offences
(Bennell & Jones, 2005; Grubin et al., 2001; Hazelwood & Warren, 2003; Woodhams et al.,
2007a; Woodhams & Toye, 2007). This is known as case linkage.
The central assumptions of case linkage are behavioural consistency and behavioural
distinctiveness. The offender consistency hypothesis (Canter, 1995) postulates that offenders
will behave consistently across their crimes, and behavioural distinctiveness assumes that the
way an offender behaves is heterogeneous from the way in which other offenders commit
crime (Goodwill & Alison, 2006; Salfati & Bateman, 2005). Taken together, these assump-
tions allow individual offenders’offences to be (1) linked together and (2) distinguished from
offences committed by other offenders. Evidence of behavioural consistency has been found
for a range of offence types including sexual assault (Grubin et al., 2001), homicide (Salfati &
Bateman, 2005), arson (Santtila et al., 2004), burglary (Bennell & Canter, 2002; Bennell &
Jones, 2005; Markson, Woodhams, & Bond, 2010), vehicle crime (Tonkin, Grant, & Bond,
2008), and commercial robbery (Woodhams & Toye, 2007). Evidence has also been found
in support of behavioural distinctiveness (Bennell & Canter, 2002; Bennell & Jones, 2005;
Bennell, Gauthier, Gauthier, Melnyk, & Musolino, 2010; Grubin et al., 2001; Santtila,
et al., 2004; Santtila, et al., 2005; Woodhams & Toye, 2007).
Robbery
Robbery is the theft of property using force or the threat of force. There are two categories of
robbery in the UK: commercial and personal. Commercial robberies are committed against
businesses (e.g. a bank robbery with the violence directed at bank employees), whereas
personal offences are committed against individuals (e.g. ‘mugging’)(HomeOffice, 2011).
Although there may be some parallels between commercial and personal robberies—for
example, both typically involve groups of offenders (Gill, 2000; Smith, 2003) and motivations
for committing offences overlap (e.g. money, addiction, and excitement)—there are also
differences. For example, commercial robberies are more probably planned; the financial
rewards are usually larger; and in the UK, the weapon ofchoice is a firearm (Gill, 2000) rather
than a knife. It is not surprising therefore that the two categories are separated for research
purposes. In fact, Matthews (2002) goes so far as to say that research that does not differentiate
between the two categories should be interpreted with caution.
Personal robbery is commonly referred to as ‘mugging’or ‘street crime’(e.g. Tilley, Smith,
Finer, Erol, Charles, & Dobby, 2004), and small, portable, high-value items (such as mobile
telephones) are commonly targeted (Monk, Heinonen, & Eck, 2010; Smith, 2003). Weapons
are used/displayed in approximately one-third of personal robberies in the UK (Flatley,
Kershaw, Smith, Chaplin, & Moon, 2010), and knives are commonly associated with this type
of offence (Barker, Geraghty, Webb, & Key, 1993; Flatley et al., 2010). With this level of
weapon use, it is not surprising that 40% of robbery victims receive some kind of injury
(Smith, 2003).
It is important now to explore the potential to conduct case linkage on personal robbery for a
number of reasons. First, personal robbery typically accounts for around 2% of recorded crime
A. Burrell et al.
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
in England and Wales per annum. Second, personal robbery has a significant impact on
victims (Barker et al., 1993; Dolan, Loomes, Peasgood, & Tsuchiya, 2005; Monk et al.,
2010), including loss of goods, injury, fear (Monk et al., 2010), and in some cases long-term
psychological trauma (Barker et al., 1993). There is also evidence that robbers commonly
commit series of robbery offences (Wright & Decker, 1997), so if accurate methods of case
linkage could be developed, it is possible that robbery offences could fall substantially with
the apprehension of relatively few offenders. Third, in the professional experience of the third
author, forensic evidence is often absent in personal robberies because of a lack of physical
contact between the offender(s) and the victim, thus limiting the scope to link robberies using
such evidence.
Personal robbery in the UK is typically committed by small groups of young men (aged
younger than 20 years) against other men (Smith, 2003) although some offenders do
operate alone. The impact of group dynamics on behavioural consistency is largely
unknown. In the US, research has found that although co-offending does not seem to have
a significant impact on victim selection, it often does increase planning (Alarid, Burton, &
Hochstetler, 2009). This means that the robberies an offender commits with a group might
differ from those they commit alone, potentially making their crimes more difficult to link.
However, research in the UK on behavioural coherence (in rape) has demonstrated the
existence of thematic similarities between offenders committing multiple crimes with the
same co-offenders (Porter & Alison, 2004). Porter and Alison (2006) went on to examine
behavioural coherence in robbery; the results of which suggested that offenders within the
same group might indeed behave in a homogenous fashion. Furthermore, Alarid et al.
(2009) reported that if offenders commit robberies in a short span of time, they probably
select co-offenders from the same group of associates. This suggests that co-offending
might not negatively impact on behavioural consistency (and therefore the ability to link
offences) so long as the offences are committed in relatively quick succession by the same
group of offenders.
Numerous UK studies have linked personal robbery to street culture (e.g. Deakin,
Smithson, Spencer, & Medina-Ariza, 2007; Wright, Brookman, & Bennett, 2006). Motiv-
ation ranges from material gain (Alarid et al., 2009; Monk et al., 2010) to alleviating boredom
(Tilley et al., 2004) or for the ‘buzz’(Deakin et al., 2007). Personal robberies are often
described as spontaneous and unplanned (Alarid et al., 2009; Woodhams & Toye, 2007)
and consequently situation dependent. If offences are very situation dependent,any factor that
affects the similarity/dissimilarity of the situation potentially impacts on the ability to identify
linked offences. However, rational choice theory (Cornish & Clarke, 1986)—which presents
offenders as decision makers—would suggest that, even where a spontaneous offence occurs
in response to a presented opportunity, the offender(s) will still make rational decisions about
whether the potential benefits are worth the risk before embarking on a robbery attempt. As
previous experience of success and failure is a key factor in decision making (Juliusson,
Karlsson, & Gärling, 2005) it is probable that the offender will seek similar robbery opportun-
ities to those that have resulted in successful robberies in the past, thus increasing the feasibil-
ity of the linkage task which is grounded in behavioural consistency.
Offender learning and offender adaptation could prove challenging to case linkage. Modus
operandi (MO) has been found to evolve over time (Yokota & Watanabe, 2002) as some
offenders learn what is effective (Keppel, 1995) and gain confidence (Douglas & Munn,
1992). Furthermore, offenders can adapt their MO in response to crime prevention measures
(Tilley et al., 2004) or as new opportunities for crime arise. However, research has found that
many robbery offenders develop a consistent method of committing their offences (Deakin
Linking personal robbery offences using offender behaviour
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
et al., 2007), suggesting that offender learning and adaptation might not impact crime scene
behaviours as much as initially thought.
CURRENT RESEARCH
The new research presented here tested the underlying assumptions of case linkage by compar-
ing the behavioural similarity of linked pairs of personal robbery offences (i.e. two offences
committed by the same offender) with the behavioural similarity of unlinked pairs of personal
robbery offences (i.e. two offences committed by different offenders). It was hypothesised that
linked pairs would be more behaviourally similar than unlinked pairs, thus providing evidence
for both of the theoretical assumptions. This approach has been successfully used by a number
of studies (e.g. Bennell & Canter, 2002; Bennell & Jones, 2005; Tonkin, et al., 2008;
Woodhams & Toye, 2007) and across a range of crime types (e.g. burglary, car theft, commer-
cial robbery). It was therefore anticipated that the approach would provide evidence that
offender behaviour can be used to distinguish between linked and unlinked pairs of personal
robbery.
The current research was novel in two ways. First, the research focused on personal
robbery, an offence type not previously explored by case linkage researchers. Personal
robbery is quite different to commercial robbery, particularly in relation to planning (as
discussed in the introduction), and the literature even warns that the two offence types should
not be analysed together because of their diverse nature (Matthews, 2002). As such, the
existing work on commercial robbery (Woodhams & Toye, 2007) is not necessarily applicable
for analysts and police officers working to link series of personal robbery offences. This
research, therefore, aimed to address this gap in the literature.
The research also considered the implications of the size of a study area (e.g. police force
area) on the linkage task. Previous research has utilised the whole study area when generating
unlinked pairs of offences. This is problematic for several reasons. First, offenders tend to
operate in a relatively small geographical area (Santtila, Laukkanen, & Zappalà, 2007), and
so, selecting random pairs of offences from anywhere in the police force area to act as unlinked
pairs is not reflective of known patterns of offending, thus potentially biasing the results.
Second, police analysts conduct case linkage at a local level (i.e. borough/policing district)
as well as a force-wide level (Burrell & Bull, 2011); and so, considering how the distance
between unlinked pairs might impact on linkage accuracy is very relevant to the practical
application of case linkage. Third, there is evidence that offenders learn from each other
(Clarke & Eck, 2005); and so, offenders working in the same local area might adopt similar
methods of operation, thus making the linkage task more challenging. Therefore, identifying
which behaviours can be used to reliably link crimes at a local level will help the analysts
working with a local remit.
The issues described earlier could impact on the similarity of unlinked pairs, and so, it is
possible that the case linkage performance of some behavioural domains would deteriorate
if the unlinked offences were geographically closer together. The current research was there-
fore conducted in two phases. Phase 1 compared the similarity of linked pairs of offences with
unlinked pairs of offences committed within the same police force area (which is quite a large
geographical area), allowing comparison of the results to previous research. Phase 2 reduced
the geographical area that the unlinked pairs were sourced from by ensuring that both offences
within the unlinked pair occurred in the same borough. This overcame the limitation of
generating unlinked pairs using a large geographical area, allowing the research to test whether
A. Burrell et al.
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
behaviour can be used to distinguish between linked and unlinked pairs on a more local level
as well as on a force-wide basis (as tested by phase 1). Previous research regarding burglary
has shown that although there are differences between policing districts, the performance of
individual domains as useful predictors can be demonstrated across multiple districts (Bennell
& Jones, 2005). It was therefore anticipated that some behavioural domains might be
identified as useful predictors by both phases of the research.
METHOD
Sample
The data sample was extracted from police records for solved personal robbery offences
recorded by Northamptonshire Police between 1st January 2005 and 31st December 2007.
The sample contained 166 offences committed by 83 offenders. The offenders were aged
between 10 and 44 years with an average age of 18years at the time of their offence.
Seventy-seven offenders were male, and five were female (the gender was recorded as
unknown for one offender). Over 70% (n=58) of the offenders were recorded as being White
people (including four of the women), 13 were Black people, and 12 (including 1 woman)
were of mixed heritage.
Procedure
It is common practice to include a constant number of offences (usually two) per offender in
case linkage analysis to remove the bias that might be presented by prolific offending (Bennell
& Canter, 2002; Bennell & Jones, 2005; Woodhams & Toye, 2007). The current research
replicates previous research (e.g. Bennell & Canter, 2002; Woodhams & Toye, 2007)
conducting the analysis using data from two offences per offender. Furthermore, a recent
publication from the Federal Bureau of Investigation in the US identifies serial murder as
‘the unlawful killing of two or more victims by the same offender(s) in separate events’
(Morton, 2008, p. 9), and so, there is precedent for defining a series as two or more offences.
Selecting linked pairs
This research uses the two most recent offences for each offender to create a linked offence
pair mirroring the approach used by other researchers (e.g. Woodhams & Toye, 2007). A total
of 135 serial personal robbers (i.e. those who had committed more than one offence in the
timeframe) were identified from the raw data. However, the Home Office Counting Rules
(Home Office, 2011) state that a separate crime should be recorded by the police for each
victim rather than each incident; and so, a single incident can result in multiple offences if
there is more than one victim. There were cases where the date, time, and location of offences
were identical and the MO information suggested that the two most recent offences were
actually part of the same incident. To include such pairs in the analysis would falsely inflate
the level of similarity in linked pairs. Nineteen offenders were excluded from the analysis
for this reason. In a similar vein, a further 21 offenders were omitted as one or both of their
offences already appeared in the dataset as part of the crime series of another offender (i.e.
their co-offender); and so,the inclusion of the pair would againcompromise the independence
of the linked pairs sample. A further 12 offenders were excluded as a result of missing data
Linking personal robbery offences using offender behaviour
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
about their offences. The two most recent offences (that were not part of the same incident)
were selected for each of the remaining 83 offenders, forming the linked sample for analysis.
Selecting unlinked pairs for phase 1
The current research mirrors previous case linkage research utilising an unlinked sample with
the same number of pairs as the linked sample (e.g. Markson et al., 2010, Tonkin, Woodhams,
Bull, Bond, & Palmer, 2011b; Tonkin, et al., 2008). The unlinked pairs were generated using
the =RAND() function in Microsoft Excel to randomly re-order the rows in the linked sample.
The unlinked pairs were created by using rows 1 and 2 as pair1, rows 3 and 4 as pair 2, and so
on. The data were then checked manually to ensure that all the unlinked pairs were in fact
unlinked as the random re-ordering of rows could result in linked offences being matched
together as an unlinked pair. A total of 83 unlinked pairs were created on the basis of the
166 offences contained within the linked sample. This dataset is labelled ‘unlinked1’through-
out this paper.
Selecting unlinked pairs for phase 2
A second unlinked sample (labelled ‘unlinked2’throughout the paper) was created for further
comparison. The random nature of allocating offences to an unlinked pair in phase 1 meant
that a single offence could be matched with an unrelated crime located anywhere in the police
force area.The police force area is geographically large (913 square miles), and so, there was a
high likelihood of unlinked pairs being located far apart. The police force area is broken down
into six boroughs, and further examination of the data revealed that the two offences for the
majority of linked pairs (82%) occurred in the same borough, whereas the two offences within
unlinked1 pairs typically occurred in different boroughs (75%). This difference between the
samples of linked and unlinked1 pairs could introduce bias into the analysis, potentially inflat-
ing the predictive ability of some of the behavioural domains.
The unlinked2 pairs were generated by randomly re-ordering therows in the linked sample
to create new pairs but this time controlling for borough to ensure the offences in each
unlinked2 pair occurred in the same local area. The borough for each offence was identified
using the crime reference number, which includes a reference to the borough. There were
slightly fewer unlinked pairs in the unlinked2 sample (n= 81) as two boroughs had an odd
number of offences associated with it, meaning that there was a single offence ‘left over’after
pairs had been created. In addition, one borough only had two offences associated with it—
these were both committed by the same offender and therefore could not be included as an
unlinked pair. This second unlinked sample (unlinked2) was then combined with the linked
sample from phase 1 and the statistical analyses re-run to determine if the same behaviours
emerged as useful linking factors.
Data coding
A description of the way in which each offence was committed (i.e. the MO) was included in
the police records (i.e. the dataset used in this study). Content analysis of these descriptions
was conducted as part of a wider study and a checklist of dichotomously coded behaviour
variables created. Binary coding—i.e. 1 denoting the presence of a behaviour, and 0 the
absence of a behaviour—was used because previous research has indicated that more complex
coding methods are difficult to apply to police data in a reliable way (Canter & Heritage,
A. Burrell et al.
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
1990). A total of 67 crime commission behaviour variables were identified; however, a large
proportion of these variables were not included in the current analysis. This was because they
were deemedto be more indicative of victim or bystander behaviour than offender behaviour,
they occurred in less than 1% of cases, and/or the variable had a poor inter-coder reliability
score. In addition, variables in relation to the approach used to commit the robbery were
excluded as preliminary analysis revealed there was insufficient information about the
approach used in offences available for analysis. Furthermore, where approach was possible
to identify, the coders did not always agree on how that approach should be categorised
(e.g. blitz, confidence trick, distraction).
Overall, 15 MO behavioural variables, which had a very good overall inter-coder reliability
score (k=0.95), were selected for inclusion in the current study. These variables were
combined with other variables extracted from the recorded crime data (e.g. time of day, day
of week, property stolen) to form a final ‘behaviour’checklist of 48 behaviours (Table 1).
Individual offence behaviours can be arranged into clusters, each thought to serve a differ-
ent purpose in the offence (Tonkin et al., 2008). For example, weapon use and threatening
language are both examples of how to control victims during an offence. Thus, the behaviours
were grouped into behavioural clusters or domains for analysis. Domains were originally
modelled on those used by Woodhams and Toye (2007) in their analysis of commercial
robbery, namely planning, target selection, control, and property. However, adjustments were
made because of available data and the differing nature of commercial and personal robbery.
First, it was not possible to create a domain for planning because of a lack of relevant beha-
viours recorded in relation to the personal robbery offences. The target selection domain
encompasses some behaviours used by Woodhams and Toye (2007), that is, day of week
and time of day. However, the time of day variables were structured differently to be more
representative of patternsin personal robberyand were based on the timebands used by Smith
(2003) in his research on personal robbery. Variables relating to whether the offender was
known to the victim, and whether the victim was at a cashpoint at the time of the attack were
also included in the target selection domain. The control domain included six variables in
relation to weapon use, variables relating to violent actions, offender commands, and whether
the victim and/or offender were alone or in a group when the offence occurred (n=15
behaviours in total). The property domain contained 14 types of property plus whether any
property was returned to the victim by the robber(s) during or following the offence. Temporal
proximity—i.e. the number of days between offences—and inter-crime distance (calculated
using Pythagoras’theorem to determine the number of metres between the grid references
[xand yco-ordinates] for the two crimes in each pair) were also included in the analysis. These
final two behaviours were included as they have been found to be useful predictors of linkage
by previous research (e.g. Tonkin, Santtila, & Bull, 2011a).
Measuring similarity
The similarity of pairs across each behavioural domain was measured using Jaccard’scoeffi-
cient. Jaccard’scoefficient does not take joint non-occurrences into account (Real & Vargas,
1996), so using this coefficient means that the level of similarity does not increase if the behav-
iour is not reported to have occurred within an offence pair (Woodhams, Grant, & Price,
2007b). This is an important issue when working with police data as the absence of abehaviour
does not necessarily mean that this behaviour did not occur, rather that it was not reported or
was not recorded (Tonkin et al., 2008). Some research findings have indicated that taxonomic
similarity might be a more appropriate measure of similarity than Jaccard’scoefficient
Linking personal robbery offences using offender behaviour
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
(Woodhams et al., 2007b); however, recent research failed to replicate these findings
particularly when large sample sizes were used (Melnyk, Bennell, Gauthier, & Gauthier,
2011). Furthermore, research has also demonstrated that Jaccard’scoefficient outperforms
Table 1. Behavioural domains
Behavioural domain Offence behaviours N(out of 166 offences) %
Target selection Monday 39 23.5
Tuesday 22 13.3
Wednesday 22 13.3
Thursday 23 13.9
Friday 24 14.5
Saturday 25 15.1
Sunday 11 6.6
22:00 to 01:59 34 20.5
02:00 to 05:59 5 3.0
06:00 to 09:59 5 3.0
10:00 to 13:59 22 13.3
14:00 to 17:59 46 27.7
18:00 to 21:59 54 32.5
Known offender 41 24.7
Unknown offender 64 38.6
Victim at cashpoint/bank 2 1.2
Control Weapon used 60 36.1
Knife 21 12.7
Firearm 4 2.4
Weapon other 11 6.6
Group of offenders versus group of victims 31 18.7
Group of offenders versus lone victim 72 43.4
Lone offender versus group of victims 9 5.4
Lone offender versus lone victim 47 28.3
Offender(s) searches victim/victims property 24 14.5
Violence—physical assault 55 33.1
Weapon threatened 36 21.7
Weapon shown/seen 29 17.5
Offender requests property 32 19.3
Offender demands property 54 32.5
Victim resists—met with threat 9 5.4
Property Cash 39 23.5
Mobile phone 51 30.7
Cards 8 4.8
Jewellery/watch 5 3.0
Wallet/ purse 11 6.6
Keys 6 3.6
Documents 8 4.8
Luggage 6 3.6
MP3 player 7 4.2
Clothing/ footwear 7 4.2
Food 3 1.8
Cigarettes 4 2.4
Pedal cycle 14 8.4
Miscellaneous 13 7.8
Property returned 3 1.8
Inter-crime distance Inter-crime distance (m)
Temporal proximity Temporal proximity (days)
Total variables 48
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Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
the taxonomic similarity measure across a range of conditions (Bennell, et al., 2010). Hence,
the decision was made to use the Jaccard’s similarity measure in this study.
Jaccard’s coefficients are expressed as a value of between 0 and 1, with 0 indicative of no
similarity and 1 denoting perfect similarity. Thus, higher Jaccard’scoefficients for linked pairs
compared with unlinked pairs would provide support for behavioural consistency and distinct-
iveness. Jaccard’s coefficients were calculated using the Statistical Package for the Social
Sciences (SPSS) version 18.0 © (IBM Corporation, Armonk, NY). SPSS calculates the similar-
ity of pairs of offences on the basis of the behaviours input into the analysis producing a matrix
containing the Jaccard’scoefficients for all possible combinations of offences. Coefficient
matrices were produced for each behavioural domain (i.e. target selection, control, and
property). In line with other research (e.g. Tonkin et al., 2011a), Jaccard’scoefficients were
also calculated for a combined domain. This contains all behaviours from the target selection,
control, and property domains.
The relevant Jaccard’scoefficients for each domain were extracted from each matrix for
each pair in the linked, unlinked1, and unlinked2 samples (i.e. the Jaccard’s coefficient for
target selection for linked pair 1, the Jaccard’s coefficient for target selection for linked pair
2, etc.). All other coefficients were excluded from the analysis. The coefficients for each
domain plus the variables temporal proximity and inter-crime distance formed the dataset
for the next stage of the analysis.
Comparing similarity of domains
The Kolmogorov–Smirnov (D) test of normality revealed that the distribution of Jaccard’s
scores, inter-crime distances, and temporal proximities were significantly different from
normal. This means that the median rather than the mean scores should be reported to compare
similarity(Field, 2005) and that a non-parametric test of significance should be used to assess
whether there is a statistically significant difference between similarity scores for linked and
unlinked pairs. Previous research (e.g. Markson et al., 2010; Tonkin et al., 2008; Woodhams
& Toye, 2007) has used Wilcoxon matched-pairs signed rank tests to test differences because
of concerns about the independence of the data. The present authors contend that the current
data can be considered to be independent. This is for two reasons: first, although the linked
and unlinked samples utilisethe same crime data, individual scores (i.e. Jaccard’s coefficients,
inter-crime distances, and temporal proximities) are generated using data points from two
separate offences. As such, no individual score impacts on the value of another. Second,
unlike some previous research (e.g. Bennell et al., 2009), the current research only compares
linked pairs with one possible combination of unlinked pairs at a time. Therefore, the under-
lying crime data are only used twice within a single set of analyses rather than to a large extent.
AMann–Whitney U-test was therefore selected to determine if there was a statistically signifi-
cant difference between linked and unlinked pairs for each domain in each phase of analysis.
Reporting effect size is good practice in statistics (Field, 2005) as this provides a measure of
the size of the difference between two populations (in this case linked versus unlinked pairs).
Effect sizes were calculated by converting z-scores from the Mann–Whitney Uanalysis into
the effect size estimate rusing the equation cited in Field (2005, p. 532).
Identifying predictive factors of linkage
A split-half validation method was introduced at this stage by dividing the data into experi-
mental samples (to build the predictive models) and test samples (to test the predictive
Linking personal robbery offences using offender behaviour
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
models). This mirrors the approach used by other researchers (e.g. Bennell & Canter, 2002;
Bennell & Jones, 2005; Tonkin et al., 2011a). The data were randomly split in half with 42
linked pairs, and 42 unlinked1 pairs forming the experimental sample, and 41 linked pairs
and 41 unlinked1 pairs forming the test sample for phase 1 of the research. The unlinked2
sample was also split into an experimental dataset (composing of 41 unlinked pairs of
offences) and a test sample (made up of 40 pairs). These were combined with the experimental
and test datasets for the linked sample to create the dataset for phase 2 of the analysis.
Single-factor logistic regression models, exploring whether any of the behavioural domains
could be used as accurate predictors of linkage, were calculated using the experimental data-
sets. Regression models consisting of multiple factors were also tested for each phase. This
was achieved through standard logistic regression with multiple factors or utilising stepwise
logistic regression. This determined whether the single factors could be combined to generate
optimal models with improved predictive performance.
The constant (a) and logit (b) values from the regression models were used to calculate the
estimated probabilities for each pair in the test samples using the process outlined in Bennell
and Canter (2002). To clarify, the aand bvalues from the experimental sample for phase 1
were used to calculate the probabilities for the test sample for phase 1, and the aand bvalues
from the experimental sample for phase 2 were used to calculate probabilities for the test
sample for phase 2.
The probabilities were used to perform receiver operating characteristic (ROC) analyses.
ROC analysis is becoming standard practice in case linkage research. ROC analysis is a meas-
ure of predictive accuracy and uses the area under the curve (AUC) to assess the linkage
accuracy of the data that gives rise to the ROC curve (Bennell et al., 2009). An AUC of 0.5
indicates chance level, and an AUC of 1.0 indicates perfect discrimination, meaning the larger
the AUC, the higher the predictive accuracy (Woodhams et al., 2007a). AUCs of between 0.5
and 0.7 are indicative of low levels of accuracy, 0.7 to 0.9 indicate moderate levels of
accuracy, and 0.9 to 1.0 high levels (Bennell & Jones, 2005; Swets, 1988). The approach
has many advantages and has been used to overcome concerns about statistical
independence and to set decision thresholds (e.g. Bennell & Jones, 2005; Bennell et al.,
2009). ROC analysis is also a useful method of calibrating the validity of linkage features
identified by regression models, and this is what it was used for in the current study.
The ROC analysis was conducted for each domain using SPSS version 18.0 © using the
probabilities as test variables and linkage status as the state variable. It was hypothesised that
the ROC analyses would mirror the trends found in the regression analyses, thus providing
validation for the regression models developed with the experimental sample.
RESULTS
Test of difference
Table 2 shows the median Jaccard’s scores, inter-crime distances, and temporal proximities for
each sample. The results of the Mann–Whitney U-tests and effect sizes are also listed.
Unlinked1 and unlinked2 pairs display lower Jaccard’s similarity scores across the target
selection, control, and combined behavioural domains than linked pairs. Both sets of unlinked
pairs have larger inter-crime distances and more days between offences than linked pairs.
Furthermore, the Mann–Whitney U-test indicates that these differences between linked and
unlinked samples are statistically significant for all observations except control in phase 1
A. Burrell et al.
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
although this is close to significance (p= 0.063). Interestingly, the effect sizes for the target
selection, control, and combined behavioural domains increase between phase 1 and phase 2,
indicating that these domains might be more useful for linking crimes within borough
compared with force-wide.
Conversely, inter-crime distance and temporal proximity are more useful on a force-wide
basis than on a local level with lower effect sizes reported in phase 2 than in phase 1.The
results for inter-crime distance are not surprising given the methodology for creating
unlinked2 pairs, which reduced the median distance between an unlinked pair from
12,990 to 2,314 m. It is promising, however, that linked pairs are still demonstratively
closer together on average than unlinked pairs and that this finding is statistically signifi-
cant. This indicates that inter-crime distance remains a useful linkage tool at a local level;
in fact, its effect size suggests it remains better than the other behavioural domains
examined. The reduction in the average number of days between offences and the effect
size from phase 1 to phase 2 is not as easily explained for temporal proximity. However,
a re-examination of the raw data (i.e. the 166 offences) revealed that offences within each
borough tended to be weighted towards either the start or the end of the timeframe
examined. In fact, no borough had offences from all three years represented within their
sample; thus, this anomalous finding is attributed to the distribution of date of offence
across the data.
There are no differences in the median Jaccard’s coefficients for the property domain.
Furthermore, the effect size is small, indicating that this domain is unhelpful for linkage
purposes. Overall, these analyses provide support for the assumptions, albeit not across
all behavioural domains.
Table 2. Comparison of similarity scores
Domain Median
Mann–Whitney U(z)* Effect size (r)
†
Phase 1 Phase 2 Phase 1 Phase 2
Target selection Linked = 0.250 2,380.000 1,975.000 0.28 0.38
Unlinked1 = 0.000 (3.590)* (4.840)*
Unlinked2 = 0.000
Control Linked = 0.250 2,884.000 2,670.500 0.14 0.18
Unlinked1 = 0.167 (1.859) (2.350)*
Unlinked2 = 0.125
Property Linked = 0.000 3,429.000 3,355.000 0.01 0.00
Unlinked1 = 0.000 (0.071) (0.031)
Unlinked2 = 0.000
Combined Linked = 0.200 2360.500 1,975.000 0.27 0.36
Unlinked1 = 0.143 (3.508)* (4.572)*
Unlinked2 = 0.091
Inter-crime
distance (metres)
Linked = 803.6 659.500 1,735.000 0.69 0.41
Unlinked1 = 12,989.8 (8.889)* (5.176)*
Unlinked2 = 2,313.5
Temporal
proximity (days)
Linked = 36 1,491.500 2,164.000 0.49 0.31
Unlinked1 = 292 (6.313)* (3.942)*
Unlinked2 = 144
†
Field (2005) indicates that r= 0.10 is a small effect size (explaining 1% of variance), r= 0.30 is a medium effect
size (explaining 9% of the variance), and r= 0.50 is a large effect size (explaining 25% of the variance).
*p<0.05.
Linking personal robbery offences using offender behaviour
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
Regression
The Mann–Whitney U-tests are a useful starting point for determining which behavioural
domains might be the most useful for differentiating between linked and unlinked samples.
However, additional analysis is needed to identify the predictive value of each domain.
Tables 3 and 4 outline the results of the regression analyses.
The single-factor model for target selection performed fairly well, explaining 12% of the
variance and improving predictive accuracy by 16% in phase 1 (Table 4). The positive result
was replicated in phase 2, this time explaining even more of the variance (20%) and improving
predictive accuracy by 17% beyond chance. This replicates the trends highlighted by the
Mann–Whitney U-test.
The results for the control domain are contrary to what would be expected given the Mann–
Whitney Uresults with phase 1 outperforming phase 2 in relation to R
2
and predictive accur-
acy. Furthermore, the chi-square was significant for phase 1 but not for phase 2. Overall, the
predictiveaccuracy of the models for phase 1 and phase 2 was low compared with chance, and
neither model explained much of the variance. Combined with low Wald statistics, this
analysis suggests control is of limited value to linkage analysis in cases of personal robbery.
Property was identified as a poor predictive factor for linkage in both phases of the research,
as non-significant chi-squares indicated that the models did not fit the data well, and the non-
significant Wald statistics indicated that this individual predictor should be removed from the
regression model. Furthermore, the single-factor model did not explain much of the variance,
and predictive accuracy did not improve much beyond chance. The results were not
unexpected given the findings of the Mann–Whitney U-tests and add weight to the argument
that the property stolen during a robbery is not particularly useful when predicting whether
two offences are linked or not.
The combined domain (which is composed of target selection, control, and property)
performed favourably compared with the single-factor models in phase 1. Although the
predictive accuracy of the target selection regression model was slightly better (16% compared
with 14%), the combined domain explained more of the variance (18% compared with 12%).
The combined regression model for phase 2 performed on par with the combined model for
phase 1, suggesting that this domain has some value for predicting whether offences are linked
at both a local and a force-wide level.
As expected from the Mann–Whitney U-tests, the regression model for inter-crime distance
in phase 1 performed very well, explaining 63% of the variance and improving predictive
accuracy by over 30%. Furthermore, the model in phase 2 was much weaker (explaining
17% of the variance and only improving predictive accuracy by 7%), replicating the trend
highlighted by the Mann–Whitney U-test. Similarly, the trends for temporal proximity repli-
cated those highlighted by the Mann–Whitney U-tests with a deterioration in how useful the
behaviour was between phase 1 and phase 2. The very poor performance of the phase 2 model
for temporal proximity (explaining less than 1% of the variance and not improving predictive
accuracy much beyond chance) is probably due to the distribution of date of offence in the data
(as stated previously). Furthermore, temporal proximity did explain 14% of the variance and
improved predictive accuracy by 27% in phase 1, indicating that it might still be useful in
certain circumstances.
A forward stepwise logistic regression produced optimal models consisting of target
selection and inter-crime distance for both phases of the research. Although there was some
deterioration in the predictive ability of the models between phase 1 and phase 2, the optimal
models performed the best in both conditions. The optimal model explained 69% of the
variance and improved predictive accuracy by 33% in phase 1 and explained 31% of the
A. Burrell et al.
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
Table 3. Regression models
Model
Constant a(SE) Logit b(SE) Wald (df) Model w
2
Nagelkerke R
2
Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2 Phase 1 Phase 2
Target selection 0.570 (0.307) 0.675 (0.305) 2.519 (0.969) 3.563 (1.121) 6.757 (1)** 10.097 (1)** 7.929** 13.440** 0.12 0.20
Control 0.387 (0.298) 0.112 (0.295) 1.832 (0.974) 0.553 (0.799) 3.535 (1) 0.480 (1) 3.899* 0.485 0.06 0.008
Property 0.103 (0.240) 0.098 (0.241) 0.806 (.788) 0.623 (0.848) 1.047 (1) 0.540 (1) 1.097 0.554 0.02 0.009
Combined 1.225 (0.465) 0.946 (0.404) 7.789 (2.319) 5.410 (0.946) 9.569 (1)** 7.717 (1)** 12.006** 10.148** 0.18 0.15
Inter-crime
distance
1.831 (0.431) 0.720 (0.328) 0.000 (0.000) 0.000 (0.000) 12.132 (1)** 6.426 (1)* 52.737** 11.313** 0.63 0.17
Temporal
proximity
0.736 (0.336) 0.117 (0.304) 0.003 (0.001) 0.001 (0.001) 8.161 (1)** 0.195 (1) 9.632** 0.196 0.14 0.003
Optimal target
selection
inter-crime
distance
1.044 (0.469) 0.005 (0.399) 4.647 (2.036)
0.000 (0.000)
3.451 (1.218)
0.000 (0.000)
5.209 (1)*
12.283 (1)**
8.032 (1)**
4.855 (1)*
60.267** 22.038** 0.69 0.31
SE, standard error.
*p<0.05,
**p<0.01.
Linking personal robbery offences using offender behaviour
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
variance and improved predictive accuracy by 18% in phase 2. Furthermore, the chi-square
values were significant to p<0.05 for both optimal models, indicating that they fitthe
data well.
Receiver operating characteristic analyses
The results of the ROC analyses (Table 5) were largely consistent with the logistic regression
analyses, with the optimal model (which combined target selection and inter-crime distance)
and the single-factor inter-crime distance model emerging as the best predictors of linkage in
phase 1, with AUCs of 0.90 and 0.92, respectively. AUCs of between 0.90 and 1.00 represent
a high measure of discrimination accuracy for the linkage feature(s) that gave rise to the curve
(Swets, 1988), indicating that the optimal model and, more particularly, inter-crime distance
were very useful for discriminating between linked and unlinked pairs of personal robbery.
Temporal proximity, target selection, and combined were not far behind with moderate
AUCs of 0.83, 0.64, and 0.64, respectively.
The value of inter-crime distance declined substantially in phase 2 (from 0.92 to 0.75). This
difference is statistically significant as the confidence intervals do not overlap (Melnyk et al.,
2011). The AUC for temporal proximity also declines in phase 2; however, this result is not
statistically significant. As with the regression analysis, the value of target selection improved
in phase 2 (the AUC increases to 0.69); however, as the confidence intervals overlap, this
difference is not statistically significant. Interestingly, the AUC for the combined domain
improves to 0.70 in phase 2 compared with 0.64 in phase 1. This is contrary to the regression
findings but is in line with the Mann–Whitney Uresults. Similarly, the AUC for control
improves from 0.56 to 0.66 when moving from phase 1 to phase 2, which is in line with the
Table 5. Receiver operating characteristic analysis
Model
AUC (SE) 95% confidence interval
Phase 1 Phase 2 Phase 1 Phase 2
Target selection 0.640 (0.061)* 0.691 (0.059)* 0.520–0.760 0.575–0.806
Control 0.563 (0.064) 0.657 (0.061)* 0.437–0.689 0.538–0.776
Property 0.448 (0.064) 0.451 (0.064) 0.323–0.573 0.325–0.577
Combined 0.640 (0.062)* 0.703 (0.059)* 0.519–0.761 0.588–0.818
Inter-crime distance (m) 0.918 (0.028)* 0.750 (0.055)* 0.862–0.974 0.643–0.857
Temporal proximity (days) 0.829 (0.045)* 0.717 (0.059)* 0.740–0.917 0.601–0.832
Optimal 0.904 (0.033)* 0.782 (0.050)* 0.840–0.969 0.684–0.881
Note: AUC, area under the curve; SE, standard error. An AUC value of 0.5 is non-informative, a value of 0.50–0.70 is
low, 0.70–0.90 is moderate, and 0.90–1.00 is high (Swets, 1988).
*p<0.05.
Table 4. Predictive accuracy of models
Target
selection Control Property Combined
Inter-crime
distance
Temporal
proximity Optimal
Phase 1 Random 50.0 50.0 50.0 50.0 50.6 50.0 50.0
Model 65.5 56.0 56.0 64.3 81.9 66.7 83.1
Phase 2 Random 50.6 50.6 50.6 50.6 50.6 50.6 50.6
Model 67.5 54.2 55.4 63.9 57.8 51.8 68.7
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Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
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Mann–Whitney U-test (and its associated effect size) but not the regression analyses.
Comparison of the confidence intervals indicates that the difference in AUCs between phase
1 and phase 2 is not significant for either the combined or control domains.
As would be expected on the basis of the Mann–Whitney Uresults and the regression
analyses, the property domain did not achieve a significant AUC in either phase. In fact, with
AUCs of less than 0.50, the domain is non-informative (Swets, 1988), performing at below the
threshold set by chance.
DISCUSSION
This research found that linked pairs were more similar than unlinked pairs across a range of
domains (i.e. behavioural consistency and distinctiveness was observed). Linked pairs had
larger similarity scores for target selection, control, and combined domains plus smaller
inter-crime distances and fewer days between offences than unlinked1 and unlinked2 pairs.
The current findings, although for the ‘new’crime of personal robbery, are somewhat simi-
lar to those of previous case linkage research that has consistently found inter-crime distance to
be one of the most useful single-factor models for linkage (Bennell & Canter, 2002; Bennell &
Jones, 2005; Markson et al., 2010; Tonkin et al., 2008; Tonkin et al., 2011a; Woodhams &
Toye, 2007). Furthermore, the AUCs attained are comparable with those found in the litera-
ture, as Bennell and Jones (2005) reported a range of 0.76 to 0.91 for inter-crime distance in
their research on burglary, with other researchers’AUCs for inter-crime distance falling within
this range. The inter-crime distance model also performed well in terms of predictive accuracy
when applied at the force level (31% improvement over the random model). However, the pre-
dictive accuracy of the regression model and the AUCs for inter-crime distance deteriorated
between phase 1 and phase 2. This suggests that caution needs to be applied when linking local
crimes using inter-crime distance alone as it can no longer be considered a strong predictor of
linkage. However, inter-crime distance did still achieve a moderate AUC (and in fact the high-
est AUC for a single-factor model) in phase 2, indicating it may still have some value when
working at a local level. The key message from this research would be that although inter-
crime distance remains valuable when working at a local level, it should be treated with more
caution than if working at a force-wide level. It is argued therefore thatit should not be used in
isolation to link crimes. This is particularly important as analysts may have successfully used
inter-crime distance to link other offence types locally and assumed that this could simply be
extended to personal robbery. Research exploring the thresholds for deciding whether crimes
are linked on the basis of inter-crime distance in different-sized geographical areas (i.e. force-
wide and borough) needs to be identified to assist analysts to make informed linkage
decisions.
There are a number of reasons why inter-crime distance might emerge as a useful linkage
factor. First, research consistently demonstrates that offenders tend to operate within a limited
geographical area or comfort zone. For example, Santtila et al. (2007) found the median
distance for committing a rape was 2.44 km from the offender’s home; this fell to 0.85 km
for homicide. Furthermore, as rational decision makers, offenders have a tendency to act on
the first or closest opportunity to commit crime (the least effort principle) (Rossmo &
Rombouts, 2008). As such, once offenders have found a good location to commit a robbery,
there is no immediate reason for them to travel far to commit the next robbery. This effect
would be more pronounced in rural areas such as Northamptonshire (which is 90% rural;
Office for National Statistics, 2004), where opportunities to commit crime are limited and/or
Linking personal robbery offences using offender behaviour
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
clustered geographically (e.g. robberies will cluster in the more urbanised parts of rural areas,
such as small market towns with surrounding villages, which are often located far from one
another). The clustering of targets maytherefore help the linkage task as the analystonly needs
to search a limited geographical area for other crimes in the series. However, it may also have
an adverse effect as multiple offenders will operate within any cluster of potential targets.
Thus, the frequency of offending in these areas may make it difficult to distinguish between
offenders (Bennell & Jones, 2005).
The target selection domain did not perform as well as in previous research. The AUCs
achieved (0.64 to 0.69) were lower than the AUC reported by Woodhams and Toye in their
2007 study oncommercial robbery (0.79). Furthermore, the predictive accuracy of the regres-
sion models were somewhat lower than that found by Woodhams and Toye (2007), an
increase of between 16% and 17% over chance compared with over 20% reported by
Woodhams and Toye (2007). However, the literature indicates that the performance of the
target selection domain can vary considerably, suggesting that this domain might be more
useful in some offences than others. For example, target selection behaviours have performed
better with samples of commercial robbery (Woodhams & Toye, 2007) and commercial
burglary (Bennell & Canter, 2002) compared with car theft (Tonkin et al., 2008). Also, its
performance seems to vary by country, for example, target selection performed well in the
study of Tonkin et al. (2011a) of residential burglary in Finland; however, it performed less
well in the study of Markson et al. (2010) of residential burglary in the UK. This could occur
for a number of reasons including which behaviours are included (or not included) in the tar-
get selection domain, the quality of data recording and coding, and/or because differing social
structures may present different opportunities to commit crime. Further research is needed to
explore the reasons for the differences and to identify the optimal combination of target
selection behaviours to use for linkage purposes.
The current study also found that the target selection domain performed slightly better at a
local level than on a force-wide basis (although the difference was not statistically significant).
This is perhaps unexpected because different areas present different opportunities (or targets)
for robbers, so some homology of targets might be anticipated when multiple offenders are
operating in the same area. Therefore, as active decision makers and risk assessors (Clarke
& Cornish, 1986), robbers operating in the same area would be expected to identify the same
or similar people to target, therefore making it difficult to distinguish between offenders. How-
ever, many offenders operate within a ‘patch’(Deakin et al., 2007); and if these patches do not
overlap, combined with the evidence that offenders do not travel far to commit their offences
(Santtila et al., 2007), this might be a reason why an individual offenders’crimes might be
easier to link using target selection at the more ‘local’level. Furthermore, it is probable that
there will be fewer active robbers operating in any single local area than force-wide, increas-
ing the chances of distinguishing between different offence series by using target selection.
Control has not been included as a behavioural domain in many studies, possibly because
this can be difficult to code or is not relevant in some crime types (e.g. burglary). Woodhams
and Toye (2007) found control to be the best predictor of linkage in their study on commercial
robbery, even performing better than inter-crime distance. The current research failed to
replicate this finding for personal robbery. This may be due to the differing variables included
in the domain. For example, Woodhams and Toye (2007) included information about the
manner in which the offence was committed (i.e. calm/confident, anxious/agitated, or loud/
aggressive), whereas the current study did not as it was not possible to code this behaviour
from the MO information available for the research. It is possible that replicating the present
study with a different set of variables within the control domain might increase predictive
A. Burrell et al.
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
accuracy. Despite the disappointing results, it is argued that there is potential for control to be a
useful predictor of robbery linkage, although more work is needed to identify which
behaviours are the most useful to include in the domain. More in-depth information about
the interaction between the victim and the offender(s) would be beneficial in terms of identi-
fying and coding control behaviours in a way that could be utilised for caselinkage. This could
be achieved through access to original victim and witness statements as these probably contain
more detailed information than the MO data accessed for this study.
The property domain performed poorly. The inability of the property domain to distinguish
between linked and unlinked pairs of robbery can perhaps be at least partially explained by
situation dependence as property stolen is largely determined by what that victim is carrying
at the time of the attack. Another reason why the propertydomain is unhelpful to link offences
is probably that many robbers typically target the same specific items such as mobile phones,
cash, and jewellery (Monk et al., 2010; Smith, 2003), that is, those items that can be easily
carried. Therefore, the type of property stolen is probably a characteristic of robbery generally,
thus not distinguishing between offenders. Overall, the results for property are not unexpected,
particularly when the fact that some researchers have reported low levels of predictive
accuracy and AUCs compared with other behavioural domains (e.g. Tonkin et al., 2011a) is
taken into account.
The combined domain (which is composed of target selection, control, and property)
performed favourably compared with the single-factor models in phase 1. Overall, the effect
size reported for the Mann–Whitney U-test and the AUCs in the ROC analysis increased
between phases 1 and 2 of the research. However, the difference in AUCs was not statistically
significant. Furthermore, the combined regression model for phase 2 performed on par with the
combined regression model for phase 1. Taken together, this suggests that the combined
domain has some value for predicting whether offences are linked at both local and force-wide
levels. The target selection regression model outperformed the combined model for phase 2.
Although this was not statistically significant, taking the performance of control and property
into account, this suggests that target selection behaviours are driving the success of the
combined domain. Further research, testing different combinations of behaviours in the com-
bined domain, would be useful to determine whether there is any evidence for this proposition.
The significant results from phase 1 suggest that temporal proximity might be a useful
linking factor in certain circumstances. However, given the methodology used to create the
linked pairs (i.e. selecting the two most recent offences), it is perhaps surprising that temporal
proximity did not perform better in phase 1, particularly when the poor results for phase 2 are
probably influenced by the distribution of data for date of offence. This highlights the impact
that the distribution of data can have on the outcome of statistical tests and emphasises the
importance of taking this into account when interpreting results. It is, however, important to
continue to explore the value of temporal proximity in case linkage as temporal behaviour
(combined with spatial behaviour) has been highlighted as a useful method of concentrating
investigative efforts in serial cases (Rossmo & Rombouts, 2008). It is compulsory for temporal
information to be recorded by the police for all offences in the UK, meaning that this resource
is readily available to analysts for case linkage work. Thus, it is important to establish whether
temporal proximity canbe used to link offences; and if so, how temporal data might be used in
the most effective way.
The optimal models are composed of target selection and inter-crime distance in both
phases, and these performed better than the single-factor models. However, in phase 1, these
differences were not large compared with inter-crime distance alone, accounting for a similar
proportion of the variance and comparable improvements to predictive accuracy recorded.
Linking personal robbery offences using offender behaviour
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
However, the optimal model was favourable compared with the inter-crime distance model in
phase 2, accounting for 31% of the variance compared with 17% and improving predictive
accuracy by 18% compared with 7%. This suggests that, although inter-crime distance is
the most useful linking factor if working at force level, it may be useful to combine this with
target selection if working locally. This is somewhat supported by the ROC analysis as inter-
crime distance recorded the highest AUC in phase 1 but the optimal model performed best in
phase 2, and although the difference between the AUCs did not quite reach significance, the
confidence intervals did not overlap by much (0.862–0.974 compared with 0.684–0.881).
Overall, the results provide some support for the theoretical assumptions of case linkage as
there were statistical differences between linked and unlinked pairs across a number of
behavioural domains. However, the results also suggest that the predictive ability of some
behavioural domains may be sensitive to whether the unlinked pairs have meaningful
constraints put on their inter-crime distance (i.e. ‘local’versus ‘force-wide’pairings).
Limitations and directions for future research
Much of the case linkage research has been conducted using police recorded crime data, and
this research is no exception. The limitations of working with police data are clearly outlined in
the case linkage literature, including the challenges presented by missing data (Tonkin et al.,
2008) and the inability to assess the reliability of data coding within police data systems
(Bennell & Canter, 2002). However, using police recorded crime data remains one of the most
ecologically valid methods of conducting linkage research for the people who perform case
linkage inan applied setting, typically police analysts (Woodhams & Toye, 2007). Given that,
overall, the research strongly suggests that case linkage with police data is indeed feasible,
then even better data recording and better victim interviewing (Milne & Bull, 1999) by the
police seem warranted. The data gaps and limitations highlighted within this (and other)
research provide a useful starting point for improving data quality. Access to more in-depth
data already held by the police (e.g. original victim and witness statements) would also be
beneficial.
Concerns have also been raised about the use of solved offences as the basis for the link-
age task (Bennell & Canter, 2002); not only is this not reflective of case linkage in an
applied setting, but it is also possible that one of the reasons cases are solved is that they
are behaviourally similar and/or geographically and temporally proximal (Bennell & Jones,
2005). Thus, using solved offences could inflate the similarity scores or artificially reduce
the geographical and temporal distances of linked offences compared with unsolved serial
crime. Future research needs to address this issue. This could be achieved by assessing
the behavioural consistency and distinctiveness of unsolved series of offences that have
been linked using another means (e.g. DNA or fingerprints) (Woodhams et al., 2007a) or
by comparing the similarity of linked pairs first identified through MO to offences
first linked through DNA (as Woodhams & Labuschagne [2011] have just explored with
interesting results).
This research compares a linked sample with unlinked samples of a comparable size. In an
applied setting, the analyst is looking for series of offences from within all recorded crime.
Thus, limiting the sample of unlinked pairs in this way is not reflective of the linkage task,
and this might have inflated or depressed the value of behavioural domains for linkage. This
limitation could be overcome by comparing the linked sample with all possible combinations
of unlinked pairs.
A. Burrell et al.
Copyright © 2012 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2012)
DOI: 10.1002/jip
It is important to consider whether findings can be generalised to other areas (e.g. other
police force areas or countries) (Bennell & Jones, 2005; Tonkin et al., 2008). For example,
the findings of this research might not be generalisable to other police force areas in the UK
because of the way different population densities, the level of urbanisation, and/or geograph-
ical layout might impact on crimeopportunities. It is therefore necessary to replicate the work
in a different police force area to determine whether the current findings hold true elsewhere.
Such a study (using the same coding dictionary and domains) is already underway using data
from a large, UK urban police force. Replicating the research using data from a comparable
police force in another country (such as New Zealand or Norway, which both have large rural
areas) would also be valuable to determine whether the same behavioural domains emerge as
strong linkage factors.
Finally, there is a clear gap in the literature in relation to personal robbery. There have been
a number of studies of commercial robbery (e.g. Gill, 2000), but not many studies that focus
on personal robbery. In some cases where studies of personal robbery are identified, it
becomes apparent that these are focused on ‘street crime’, which encompasses other crimes
(such as snatch thefts, low level violence, and sometimes gang activity), rather than personal
robbery specifically. Furthermore, definitions of personal robbery differ (particularly in differ-
ent countries), making comparisons between studies challenging. Future research on the scale
and nature of personal robbery would therefore be beneficial. It would also be useful to
conduct some research with robbery offenders to explore the factors that affect decision
making to add context to the existing literature.
CONCLUSION
This study found that the predictive accuracy of domains is subject to change if geographical
constraints for selecting the unlinked pairs are imposed, which more closely reflect how a
crime analyst might work at a local level. For some behavioural domains, predictive accuracy
improved with these constraints, most notably for target selection behaviours. Even where
performance deteriorated (as measured by the predictive accuracy of regression models, and
the AUC of ROC curves), statistical differences were still found between linked and unlinked
pairs of offences for some domains, most notably inter-crime distance, indicating that for such
domains, the assumptions of case linkage were still supported whether operating at a force-
wide or more local level.
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