Discuss the three items offenders apparently tend to pay attention to when evaluating a potential burglary target.


Discuss the three items offenders apparently tend to pay attention to when evaluating a potential burglary target. Also, discuss findings pertaining to burglary offender’s gender, age, and motivation, per (Santtila, Ritvanen,& Mokros,2004).


Predicting burglar characteristics from crime scene behaviour

Pekka Santtila,† Antti Ritvanen,‡ and Andreas Mokros§ †(Corresponding author) email: pekka.santtila@abo.fi or pekka.santtila@pakk.poliisi.fi ‡Department of Psychology at the University of Helsinki, PO Box 33, FIN 00014 University of Helsinki, Finland. Email: antti.ritvanen@helsinki.fi §WZFP Forensic Maximum Security Hospital, Lippstadt Westf. Zentrum für Forensische Psychiatrie, Eickelbornstrasse 21, D–59556 Lippstadt, Germany. Tel: +49 2945 981–2742; Fax: +49 2945 981–2749

Received 12 October 2003; accepted 12 December 2003

Pekka Santtila is a Senior Lecturer in Police Psychology at the Police College of Finland, Lecturer in Forensic Psychology, Department of Psychology, Åbo Akademi University and Lec- turer in Forensic and Investigative Psychology, Department of Psychology, University of Turku. His recearch interests include witness psychol- ogy, investigative psychology, legal psychology, sexual behaviour and evolutionary psychology. Antti Ritvanen MSc (psychology) is a school psychologist. The present article is partly based on his master’s thesis in psychology. Andreas Mokros MSc (psychology) is a PhD student at the University of Wuppertal, Germany. He studied psychology at the universities of Bochum, Germany, and Liverpool, UK. His doc- toral research focuses on an empirical test of the predictive validity of the Modus Operandi (MD) of sex offenders for their personality characteris- tics. His other interests include behavioural con- sistency in criminal conduct, offender profiling, and methodology.

ABSTRACT The aim of the present study was to predict offender characteristics on the basis of crime scene behaviour in urban burglaries. The police files of 633 burglaries in the Finnish Metropolitan Area between 1990 and 2001 were content analysed using a predetermined list of variables. The crime

scene behaviour variables were subjected to a principal component analysis. Fourteen factors indicative of different types of burglaries were identified and used to predict the characteristics of the 244 offenders using regression models. Statis- tically significant predictors of almost all offender characteristics were identified. From a practical point of view, the predictive models could be used in police investigations to narrow down the num- ber of suspects.

INTRODUCTION Approximately 10,000 burglaries are repor- ted to the Finnish police annually (Sirén, 2002). There have been no major changes in this number during the last ten years (Pietilä, 1992; Sirén, 1998, 2002). How- ever, victimological surveys suggest that the number of burglaries is much higher, with 1.2 per cent of the population (correspond- ing to 51,000 persons) reporting a burglary in the year 2000 (Aromaa & Heiskanen, 2000). In 1997, damage caused to house- holds by burglaries was estimated at h5.6 million (Heiskanen, Sirén, & Tallberg, 2001). The clearance rate of thefts is about 10 per cent and the clearance rate of aggra- vated theft, the category which most bur- glaries belong to, is about 20 per cent (Sirén, 2002). The aim of the present study

International Journal of Police Science and Management, Vol. 6 No. 3, 2004, pp. 136–154. © Vathek Publishing, 1461–3557

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was to investigate the feasibility of using automated decision-support systems to pre- dict burglar characteristics on the basis of their crime scene behaviour. Such systems have the potential of rendering burglary investigations more effective and improving the low clearance rates.

Most research on burglary has either concentrated on the characteristics of bur- glary targets or offenders. Mawby (2001) suggests that most burglaries are planned, rational acts and only rarely represent totally unplanned and opportunistic acts. The offenders apparently tend to pay attention to three different kinds of clues when evalu- ating a target: the risk of being seen, the presence of inhabitants and the ease of gaining access to a house or apartment (Taylor & Nee, 1988; Cromwell, Olson, & Avary, 1991, 1993; Mawby). The question whether the target seems to be wealthy represents an additional influence — the higher the estimated gain, the more attrac- tive the target (Buck, Hakim, & Rengert, 1993; Nee & Taylor). However, the evi- dence for rationality on the whole seems rather slim. More recently, criminal decision-making has been talked about in terms of bounded rationality (Cornish & Clarke, 1986). This means that the offender seldom has available to him all the informa- tion needed to make a rational choice. Instead, short cuts and heuristics based on limited information and experience are used in order to decide to offend or desist from offending.

Studies on the spatial behaviour of bur- glars have shown that the home locations of arrested offenders usually lie in areas with high burglary risk (Reiss & Farrington, 1991; Mawby, 2001). The observation of short journey-to-crime distances complies with routine activity theory according to which offenders choose their targets along regular, everyday routes that they use in connection with other activities (Felson, 1986).

Recently, studies of burglary have begun to emphasise the psychological meaning of home as part of people’s identity and sense of personal safety (Canter & Alison, 1999; Merry & Harsent, 1999; Kearon & Leach, 2000). Viewed from this perspective, bur- glars do not only violate the physical prop- erty of another person but also that person’s sense of safety and identity. As a result, burglaries are now increasingly viewed as having a distinct interpersonal dimension (Merry & Harsent).

Approximately 90 to 95 per cent of arrested burglars are male and 80 per cent are unmarried (Cassel & Bernstein, 2001; Farrington & Lambert, 1994; Mawby, 2001). Female burglars have often been reported as starting their criminal careers in burglary later and as being more likely to take on assisting roles than their male coun- terparts (Cassel & Bernstein). Burglars are usually relatively young (Pietilä, 1992; Cromwell, 1994; Cassel & Bernstein; Mawby), with a mean age of 21 to 25 years (Cromwell; Mawby).

Typically, inexperienced offenders (the median age at which offenders start their careers may be as low as 14) offend with and learn from more experienced burglars who may often be siblings of the beginners (Shover, 1972; Reiss & Farrington, 1991; Shover, 1991; Cromwell, 1994). As burglars get older and more experienced, they usually begin to find established ways of selling stolen goods, thus making it eco- nomically more profitable to offend on their own (Cromwell).

The most common reported motivation for burglary is financial gain. For example, an interview study with 105 active burglars found that more than 90 per cent of the offenders broke into apartments when they needed money to solve immediate problems (Wright & Decker). However, some young offenders may also be seeking excitement from burglaries (Cromwell, 1994; Mawby, 2001). Also, the immediate problem to be

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solved is oftentime one of acquiring more illicit drugs to be consumed (Wright & Decker). The rate of substance abusers among burglars is, in fact, high, with the most common estimates circling around two-thirds (Mawby).

With respect to specialisation, Farrington and Lambert (1994) found that more than half of the burglars but just one-quarter of violent offenders in their sample had a previous arrest for a burglary, suggesting that burglars tend to repeat burglary offen- ces at a more intensive level.

Psychological profiling Psychological profiling is the process of deducing offender characteristics from crime scene behaviour (Canter, 1995; Ains- worth, 2001). Lately, the reliability and validity of the profiling process has been questioned (Canter; Lee, 1999; Canter, 2000; Muller, 2000; Ainsworth; Alison, Bennell, Mokros, & Ormerod, 2002). It has been suggested that psychological profiling should analyse connections between crime scene behaviours and offender character- istics using statistical methods (Canter, 1995, 2000) which is the approach taken in the present study. When predicting offender characteristics on the basis of crime scene behaviour it has to be assumed that the characteristics are reflected in the behaviour of the offender and that there is variation in these characteristics (Canter, 1995; 2000). In addition, it is assumed that there is some stability in the way in which individual offenders commit their crimes. These assumptions may be problematic (Mokros & Alison, 2002; Alison et al.). For example, the effects of context (victim, physical envi- ronment) on crime scene behaviour have not been given considerable attention thus far.

In fact, the stability of human behaviour has been a much debated topic in the area of personality psychology from the 1930s until today (Krahé, 1992; Mischel, 1993;

Mischel & Shoda, 1995; Brody & Ehrlich- man, 1998; Mischel & Shoda, 1998). According to Mischel and Shoda (1995, 1998) behaviour can be expected to remain stable from one situation to another only to the extent that the psychologically impor- tant features of the situation remain similar. In burglaries, this would mean that the behaviour of an offender would be expec- ted to be the same at similar types of crime scenes. However, if the important psycho- logical features of the crime scenes differ (e.g. the level of risk), behavioural differ- ences would be expected.

The aim of the present study was to explore the existence and strength of asso- ciations between crime scene behaviour and offender characteristics in urban burglaries. On a more theoretical level, the search for such associations provides a test of assump- tions concerning the stability of behaviour that are routinely made in the fields of statistical profiling and personality psychol- ogy. The results give information on the feasibility of using automated decision- support systems to predict the character- istics of burglars from their crime scene behaviour. The findings have, therefore, potentially great practical utility.


Cases The cases of the study were drawn from among burglaries committed in the Finnish Metropolitan Area between 1990 and 2001. At the moment when the search was con- ducted, the electronic database of the police contained 9,625 cleared burglaries during the specified time-frame. A random sample of 633 cleared burglaries (6.8 per cent) committed by 244 different offenders was drawn from the total. Several offenders might be involved in a single burglary or, on the other hand, the same offender might have committed several burglaries, thus

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resulting in 913 burglary events in which a particular offender was connected to a par- ticular burglary.

Content analysis

The aim of the present study was to predict burglar characteristics on the basis of crime scene behaviour (see Table 1, discussed below). Both characteristics and behaviour variables were derived from a careful con- tent analysis of the police files (see Holsti, 1969; Krippendorff, 1980). Typically, the files contained reports by the investigating officers as well as statements of the victims and possible witnesses. Additionally, inter- rogations with the suspect(s) were included as well as forensic reports regarding evi- dence. A content dictionary was developed by reading police files and previous burglary research reports (e.g. Farrington & Lambert, 1994; Merry & Harsent, 1999) as well as by discussing preliminary lists of variables with experienced burglary inves- tigators. Variables with frequencies lower than 5 per cent (with two exceptions thought to be especially relevant) were deleted. Also, variables with more than 50 per cent of missing values were dropped.

Offender characteristics (see Table 2) were divided into those remaining stable from one offence to another and those which could potentially vary between two offences. This was necessary as otherwise predictions of stable characteristics (such as the gender of the offender) would have been unduly influenced by offenders with long burglary series. Missing values were not replaced by zeros for the offender char- acteristic variables. Therefore, the number of cases in the different regression models differs.

In addition to offender characteristics coded from the case files, the criminal his- tories of the offenders until the day of the first burglary included in the study were searched. This information, i.e. the cleared

offences for which the burglars were noted as suspects, was extracted from the elec- tronic police database. The specific crime titles contained in the database were com- bined into 13 dichotomous criminal history categories (e.g. manslaughter, murder, and infanticide were combined into a single homicide category; see Table 2).

The reliability of the content analysis

The interrater reliability of three independ- ent coders was assessed using Cohen’s esti- mate of effect size (κ) (Cohen, 1960; Brennan & Hays, 1992). Reliability was estimated both case by case and variable by variable using a random sample of 20 cases independently coded by each coder. Each case or variable received three different κ values which relate to the three possible pair-wise comparisons of the three raters: A with B, B with C, and A with C. The median κ for crime scene behaviours when analysed case by case was 0.78 and for offender characteristics 0.64. When com- puted variable by variable, the median κ for crime scene behaviours was 0.88 and for offender characteristics 0.64. The criminal history variables were not coded from police files and, therefore, it was not neces- sary to explore their reliability.

Statistical methods

Principal component analysis

This analysis was conducted on the crime scene behaviours in order to identify homogeneous sets of variables indicative of certain burglary ‘types’. For use in the anal- ysis, missing values in the remaining crime scene behaviour variables (see Table 1) were replaced by zeros. On average, the 85 crime scene behaviours had 3.3 per cent missing values (median 0.7 per cent). For some of the variables, replacing missing values with zeros may have underestimated the true

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Table 1: Crime scene behaviours

Variable Definition Overall %

Onecarry Items stolen could be carried by one person


Untidy Untidy search (a clear mention of such required)


Drawers Drawers searched 72.2 Tool A tool used to gain access 59.6 Jewelry Jewelry stolen 56.3 Cash Cash, lunch coupons stolen 51.2 Manual Manual force used to gain access 49.7 Away People away temporarily (less

than 24h) 48.5

Toolto Burglary tool brought to site 47.8 Door Access gained through a door 47.5 Smelectr Small size electronics stolen 47.3 1700>h Worth of burglarised items more

than h1,700 41.9

One A single burglar according to witness statements


Two Two perpetrators according to witness statements


Watch Watches, wristwatches stolen 36.1 Detached A detached house 35 Drawrem Drawers removed from their

place, possibly thrown on the floor


Bgelectr Big size (have to be carried with both hands)


Byfoot Burglar(s) leaves by foot (at least to start)


Purse Purses, backpacks, suitcases stolen 30.8 Alcohol Alcohol stolen 29.2 Multist Multi-storey block, other level 27.2 Window Access gained through a window 26 Winbreak Access gained by breaking glass 25 Clothes Clothes stolen 24.4 Tapes Tapes, CDs, LPs, video tapes

stolen 23.7

Allsearch Whole flat searched (a clear men- tion of such required)


Document Documents, e.g. passport, library card, driving licence


Backdoor Access gained through a back door


Seen Burglar(s) seen by an outsider or owner


Screwdr Screwdriver used to gain access 20.7 Away>3d People away over 3 days 19.6 Safetylock The flat had a safety-lock or

backlock on 18.9

Variable Definition Overall %

Away1–3d People away from 1 to 3 days 17.6 Terraced Terraced house 17.3 Threeor> Three or more perpetrators

according to witness statements 16.5

Keys Keys (home, car) stolen 15.6 Credit Credit cards, cheques, shares

stolen 15.6

Abandon Stolen items abandoned by the burglar(s)


Crowbar Crowbar used to gain access 14.3 Food Food including spices, coffee, tea

stolen 13.3

Bycar Burglar(s) leaves by car 12.8 Tidy Tidy search (an explicit mention/

owner unaware of burglary) 12.4

Porcelain Porcelain, crystals, silver stolen 12.3 Tobacco Tobacco products stolen 12.2 Wallet Wallet stolen 12 Toolfrom Burglary tool taken from site or

from the immediate vicinity 11.7

Streetlev Street level of a multi-storey block


Cosmetics Cosmetic, hygiene products stolen


Traces Fingerprints, footprints, or DNA left at the scene


Acckey Access gained by the use of a key 10.5 Open Access gained through an open

or unlocked door 10.1

8400h> Worth of burglarised items more than h8,400


Games Games stolen 9.7 Plates Plates, cups, cutlery stolen 9 1room Only room first entered searched 8.7 Swine Consumption of food, drinks,

alcohol, toilet/shower, defecate/ urinate


Urban Urban city area 8.3 Accsharp Access gained by using a sharp

weapon 8.1

Opticals Spectacles, sunglasses other opti- cals stolen


Acclock Access gained through lock 8 Sharpweap Sharp weapons (not cutlery)

stolen 7.9

Present People present during the burglary


<170h Worth of burglarised items less than h170


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occurrence rate of the crime scene behaviour.

The variables were dichotomous, a prop- erty that is sometimes said to preclude data from multivariate analysis based on correla- tion coefficients, such as pricipal compo- nent analysis. However, one of the classical texts on factor analysis (Harman, 1976) clearly states that this is not the case, simply by virtue of the fact that the correlation coefficient from a cross-tabulation of two dichotomous variables is nevertheless a product-moment correlation coefficient being suitable for factor analysis. Apart from that, Tabachnick and Fidell (2001) point out that assumptions concerning the distribu- tions of variables are not in force when a principal components analysis is used for descriptive purposes only.

Logistic regression

The aim of this analysis was to test the main hypothesis of the study, i.e. that it is possible

to predict burglar characteristics from their crime scene behaviour. Logistic regression was used in predicting offender character- istics as these were dichotomous (for exam- ples of the use of logistic regression in investigative psychology, see Aitken, Con- nolly, Gammerman, Zhang, & Oldfield, 1995; Davies, Wittebrood, & Jackson, 1998). Component scores calculated on the basis of the principal components analysis of the crime scene behaviours were used as predictors. Crime scene behaviours loading higher than 0.32 on one of the components were included in the calculation of the component scores. These summary scores were used directly for predicting offender characteristics that could vary from one offence to another (N = 913). Averaged summary scores were formed for those offenders who had committed more than one offence. These averaged scores were used in predicting offender characteristics which remained stable from one offence to another (N = 244).

Table 1: Continued

Variable Definition Overall %

Interrupted Burglary interrupted 7.4 Hook Hook used to gain access 7.2 Hidden Stolen items hidden close by 7.1 Fake Fake jewelry stolen 6.9 Climb Access gained through climbing

(above street level) 6.8

Brick A brick or a stone used to gain access


Balcony Access gained through a balcony door


Pile Property piled up to be carried away


Medicine Prescription medication stolen 6.1 Construct Construction tools or materials

stolen 5.9

Toolat Tools used in the burglary left at the scene


Variable Definition Overall %

Studio A studio flat 5.8 Gun Firearms, ammunition, explosives

stolen 5.5

Other Target other building type 5.4 Guard Burglary interrupted by a guard,

the inhabitants 5.4

Garden A gardening tool, axe, hammer, spade, drill used to gain access


Antique Antique or art objects stolen 4.8 Vehstol A vehicle stolen to leave the

scene 4.3

Innerdoor Inner doors opened using force 4.2 Mailbox Access gained through the

mailbox 3.9

Safeguard Alarm, dog, guard, high fence, automatic lights, lights on 24h


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Table 2: Burglar characteristics

Variable %

A. Burglar characteristics that may change from one offence to another (% of burglary cases) Offender lives in the city in which the crime was committed 60.1 Goods at least partly for own use 49.9 Crime committed alone 31.2 Under the influence of drugs or alcohol 23.1 Perpetrator had a fence prior to the burglary 11.2 Visited the target earlier 10.5 Perpetrator knew the owner of the target 9.7 Prior conflict with owner of target 2.8 Other suspects with same last name 2.1 Owner non-Finnish 2.1 Perpetrator hurt during the burglary 1.5 Target was perpetrator property 0.3

B. Stable burglar characteristics (% of offenders) B.1. Living arrangements characteristics No permanent residence (used night shelters, lived in somebody else’s flat, had no place to live) 29.9 Had no place to live 29.5 Had a permanent residence (lived with parents, cohabited with a partner, lived with a room

mate, lived alone, lived in an owned flat, lived in an institution) 24.6

Lived with parents 9.8 Lived in an institution 7.4 Cohabited with a partner 4.5 Lived in somebody else’s flat 2.5 Lived alone 2.0 Lived with a room mate 2.0 Lived in an owned flat 0.8 Used night shelters 0.4

B.2. Income and socio-economic characteristics Unemployed or in retirement/sickness pension 63.5 Worker (e.g. varied, construction, service, process, transport) 60.7 Income at least h336 during one of the burglaries 34.8 School pupil 16.4 Welfare benefit receiver 15.6 Student 10.2 Income at least h504 during one of the burglaries 9.0 Entrepreuner, civil servant, managerial position 6.6 Explicitly mentioned high debt, problems with payment of bills 6.6

B.3. Criminal history (attempts included) Theft 51.6 Assault 21.3 Dishonesty, deception 20.9 Traffic violation 14.8 Robbery 14.3 Drug 14.3 Firearm violation 9.4 Other 7.0 Vandalism 6.1 Stalking, deprivation of liberty 5.3

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Descriptive information on the burglaries Most of the burglaries were committed on weekdays (75 per cent) during daytime between 6 am and 6 pm (77 per cent). Over one-third were committed on Fridays or Saturdays. Time estimates were sometimes quite rough (e.g. if the owner of the flat had been away for a whole weekend sometime during which the burglary had taken place).

Identifying different types of burglaries The correlation matrix of the crime scene behaviour variables contained several statis- tically significant correlations exceeding 0.30. Further, Bartlett’s test indicated that as a whole the correlations were different from zero. Additionally, the KMO-test gave a value of 0.59 suggesting that the matrix was factorable (Tabachnick & Fidell, 2001).

The method devised by Horn (1965) for determining the number of extractable fac- tors was carried out. The results suggested that the first 21 factors from the principal

component analysis of the actual data matrix clarify an amount of variance that exceeds the amount that could be expected at chance level. The final number of factors was set at 14 with the aim of arriving at interpretable factors with more than a few variables loading highly on each of them. The eigenvalues of the components varied between 1.62 and 6.31 and the rotated solution clarified 45 per cent of the total variance. When a cut-off point of 0.32 was used, 13 out of the 85 variables (15 per cent) did not load on any of the factors.

After Varimax rotation (Kaiser, 1958), the prinicipal components were named based on the variables that had high loadings on them (see Table 1 for variable definitions):

1. Basic burglary: Untidy 0.71; Drawers 0.71; Drawrem 0.59; Tidy –0.56; All- search 0.49; Jewelry 0.43; Present –0.40; Watch 0.34.

2. Detached house burglary: Acckey –0.73; Two 0.65; Acclock –0.65; One –0.62; Manual 0.51; Tool 0.47; Multist –0.40; Detached 0.36; Screwdr 0.36; Tidy –0.32.

3. Terraced or semi-detached burglary: Bgelectr 0.64; Tapes 0.61; Onecarry

Table 2: Continued

Variable %

Sexual 1.6 Homicide 1.6 Arson 0.0

B.4. Other characteristics Male 91.4 Children 23.8 Prison experience 15.2 Drug dependence 12.7 Alcoholic 9.4 Married 8.6 Female 7.8 Foreigner 2.0 Open or closed psychiatric care 0.8

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–0.59; Smelectr 0.52; Purse 0.40; Clothes 0.39; Threeor> 0.36; 1700h> 0.35; Terraced 0.34.

4. Spontaneous burglary: Toolfrom 0.84; Brick 0.76; Winbreak 0.65; Toolat 0.58; Toolto –0.33.

5. Interrupted burglary: Interrupted 0.85; Guard 0.81; Seen 0.67; Pile 0.47; <170h 0.24; Present 0.36.

6. Suburban burglary: Multist –0.60; Door –0.57; Hook –0.52; Backdoor 0.51; Mailbox –0.47; Terraced 0.40; Away –0.39; Crowbar 0.37.

7. Tool-to-scene burglary: Open –0.61; Tool 0.57; Window –0.56; Toolto 0.52; Alcohol 0.31; Manual 0.41; Crowbar 0.36.

8. Opportunistic burglary: Document 0.70; Wallet 0.66; Credit 0.63; Keys 0.49; Abandon 0.36; Opticals 0.28; Present 0.33; Purse 0.34.

9. Inner city studio burglary: Studio 0.85; 1room 0.85; Urban 0.50; Streetlev 0.44.

10. High-value burglary: Porcelain 0.52; Antique 0.45; Threeor> –0.42; Plates 0.41; 8400h> 0.41; Safetylock 0.30; Jewelry 0.34; Watch 0.33.

11. Cottage/shed burglary: Other 0.60; Food 0.51; Swine 0.45; Garden 0.43; Inner- door 0.39.

12. Balcony burglary: Balcony 0.75; Climb 0.71; Streetlev 0.38.

13. Games and guns burglary: Hidden 0.53; Detached 0.49; Terraced –0.44; Byfoot 0.42; Gun. 0.41; Tobacco 0.38; By car –0.35; Away –0.32.

14. Protected house/escape car burglary: Vehstol 0.50; Safeguard 0.44; Away>3d 0.43; Screwdr 0.40; Bycar 0.38; Sharpweap 0.34.

As there was large variation in the num- ber of burglaries committed by the different burglars, Spearman’s rank-order correlation coefficients were computed between the number of burglaries in a series and the

different component scores. A greater num- ber of burglaries was positively associated with basic burglary scores (rs = 0.22, p < 0.001) and negatively associated with detached house burglary (rs = –0.14, p < 0.032), inner city studio burglary (rs = –0.17, p < 0.007), and balcony burglary scores (rs = –0.24, p < 0.001).

Descriptive information on the burglars Of the 244 offenders 224 (92 per cent) were male and 19 (8 per cent) female; the gender of one of the offenders had not been writ- ten down in the police file. From a casewise perspective, offender age varied from 12 to 64 years (mean = 27, median = 25). The length of the burglary series varied between 1 and 55 burglaries. There were 127 offen- ders who had only committed a single burglary, the rest (117 offenders) had com- mitted more than one burglary. More than half of the burglaries (69 per cent) had been committed by a group of offenders. In about one-tenth of the burglary cases, the offender did not have a permanent resi- dence noted down in the police files.

Predicting burglar characteristics that may change from one offence to another As there were no a priori reasons to prior- itise any of the components over others, all regression models were formed using a stepwise method (Forward Wald-method). The p-value for including a given variable into the model was set at 0.05. Tables 3 and 4 show the final steps of the regression models, respectively.

Offender age was predicted using a mul- tiple linear regression model (see Table 3). The model explained approximately half (R2 = 0.51) of the variance in offender age. The principal components detached house burglary, guns and games burglary and suburban burglary were associated with younger offen- ders, while the rest of the components in

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the model were associated with older offenders.

Other than age, all remaining burglar characteristics were scored dichotomously as present or non-present. These dichot- omous variables were predicted using logis- tic regression models. Missing characteristic values decrease the number of cases in the different analyses.

The effectiveness of the resulting logistic regression models was evaluated by compar- ing the correct classification rate of the model with a best-guess rate based on the frequency of the more likely category of the charac- teristic in question. For example, the cor- rect classification rate based on the regression model was 0.81 for the charac- teristic of the offender visiting the target prior to the offence. The best-guess rate based on the frequency of the characteristic was 0.53. If no other information was available, a rational guess would be to say that an offender has not visited the crime scene

previously, simply because the majority of the offenders (53 per cent) in the sample had not done so.

It is desirable that the correct classification rate of a model exceeds the best-guess rate. For this reason and due to space constraints, Table 4 presents the details from logistic regression analyses only in which this cri- terion is fulfilled.

This is the case for four burglar charac- teristics that may change from one offence to another: offender having visited the tar- get earlier; offender living in the city in which the crime was committed; crime committed alone; and perpetrator having had a fence prior to the burglary. Moreover, the correct classification rates from the logistic regression models surpass their best-guess counterparts for four stable burglar charac- teristic, three of which refer to previous convictions: unemployment or in retirement/sickness pension; assault; traffic violation; and theft.

Table 3: Predicting offender age at the time of the offence on the basis of crime scene behaviour using multiple linear regression (N = 912)


Offender age

B SE β Effectiveness

Detached house –2.19 0.30 –0.28*** High-value 4.47 0.54 0.25*** Inner city studio 3.48 0.56 0.19*** Tool to scene 1.83 0.43 0.15*** Cottage/shed 2.37 0.80 0.09** Residuals Opportunistic 1.34 0.37 0.11*** M = 0.01 Spontaneous 1.18 0.41 0.09** SD = 8.33 Games and guns –1.73 0.54 –0.10*** +/–1 year = 0.09 Interrupted 1.34 0.44 0.09** +/–5 years = 0.48 Suburban –1.35 0.43 –0.11** Balcony 2.08 0.81 .08** Constant 21.77 0.60 R2 = 0.51

Notes: At final step F(11,899) = 29.04, p < 0.001. Residuals refer to the difference between age predicted as the basis of the specified regression model and the real age of the offender at the time of the offence. *** p < 0.001, ** p < 0.01.

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Table 4: Selected results from logistic regression analyses exceeding best-guess rates

Burglary component

Burglar characteristic

B SE Wald Effectiveness

Visited the target earlier (present = 96, absent = 106)

Basic –0.87 0.22 16.33*** Detached house –0.69 0.18 15.07*** Terraced or semi-d 0.56 0.20 7.66** Opportunistic –1.30 0.37 12.35*** BG = 0.53 Cottage/shed 2.12 0.59 12.97*** CC = 0.81 Constant 1.47 0.39 14.09*** R2 = 0.53

Offender lives in the city in which the crime was committed (present = 549, absent = 364)

Suburban –0.42 0.10 18.03*** Tool-to-scene –0.71 0.11 45.54*** Games and guns 0.69 0.14 25.95*** BG = 0.60 Protected house 0.60 0.19 10.37*** CC = 0.65 Constant 0.25 0.13 3.64+ R2 = 0.11

Crime committed alone (present = 285, absent = 627)

Detached house –1.23 0.10 161.88*** Terraced or semi-d –0.38 0.10 16.29*** Suburbian 0.38 0.12 10.54*** BG = 0.69 Protected house 1.15 0.22 28.32*** CC = 0.76 Constant –0.51 0.15 11.51** R2 = 0.36

Perpetrator had a fence prior to the burglary (present = 25, absent = 77)

Opportunistic –2.40 1.11 4.67* High-value 2.49 0.73 11.76*** Cottage/shed –2.82 1.40 4.04* BG = 0.76 Balcony 3.09 1.08 7.81** CC = 0.85 Constant –1.15 0.40 8.22** R2 = 0.49

Unemployed or in retirement/sickness pension (present = 155, absent 89)

Inner city studio –0.77 0.26 8.71** BG = 0.64 Guns and games –0.76 0.27 8.09** CC = 0.70 Constant 0.98 0.18 28.69*** R2 = 0.08

Assault (present = 52, absent = 192)

Interrupted 0.65 0.22 8.76** BG = 0.79 Constant –1.64 0.20 65.35*** CC = 0.80

R2 = 0.05

Traffic violation (present = 36, absent = 208)

Detached house 0.74 0.29 6.66* Spontaneous –0.99 0.45 4.80* Suburban 0.67 0.32 4.35* High-value 0.93 0.36 6.83** BG = 0.85 Protected house –1.54 0.56 7.57** CC = 0.86 Constant –1.52 0.39 15.35*** R2 = 0.19

Theft (present = 126, absent = 118)

Basic 0.33 0.13 6.65* BG = 0.52 Guns and games –0.55 0.26 4.47* CC = 0.58 Constant –0.22 0.21 1.03+ R2 = 0.05

Notes: BG = Correct classification based on base rate frequency (best-guess rate). CC = Correct classification rate based on logistic regression model. *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.10. R2 = Nagelkerke’s R2.

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A summary of associations between crime scene behaviour and offender characteristics Table 5 yields a summary of the predictive relationships between burglary types and burglar characteristics. Only the direction of the relationship is shown, without an indication of its strength. For example, a basic burglary predicted that the offender had some kind of apartment, lived in a relation- ship, used some of the loot himself and had been arrested for theft previously (positive relationships). A basic burglary also predicted that the offender had not visited the target prior to the offence, did not know the owner and had not had conflicts with him or her. The owner was also unlikely to be a foreigner (negative relationships).

DISCUSSION There are some limitations that have to be considered before generalising from the present results. First, the predictive regres- sion models seldom exceed the classification rate attainable through base rate frequen- cies. Secondly, using police files as a data source poses several problems (Aitken et al., 1995; Ainsworth, 2001; Grubin, Kelly, & Brunsdon, 2001): The aim of the police is to gather evidence necessary for solving a particular crime; the extent and quality of the information gathered may, therefore, vary from case to case. Thirdly, only solved burglary cases were included in the study. It may not be possible to generalise from such a data-set to cases where the offender has not been caught.

In spite of these potential threats to gen- eralisability, the present study highlights that it is feasible to predict certain offender characteristics on the basis of the crime scene behaviour in urban burglaries. Thus, the theoretical assumptions underlying psy- chological profiling may be as valid for property crime as they are for violent crimes. The results may therefore be used to

aid burglary investigations, thereby reducing the psychological, social and economic costs these entail.

It was possible to find statistically sig- nificant predictors from among the crime scene behaviour components for almost all of the burglar characteristics studied. This is a potentially useful finding from the prac- tical investigative perspective as it can be used to prioritise suspects. The results also support the theoretical assumptions out- lined in the introduction. There are varia- tions in both the way burglaries are committed and in offender characteristics and there are meaningful associations between the two.

The time of commission of the studied burglaries differed somewhat from previous studies. According to Cassel and Bernstein (2001) 53 per cent of the burglaries from their US sample are committed during day- light hours, whereas two-thirds (77 per cent) of the urban burglaries from the pres- ent Finnish sample were committed between 6 am and 6 pm. However, differ- ences in the length of daylight vary radically according to the time of year in Finland and make it difficult to compare the results directly. Looking at the weekdays, the results corresponded to those of Farrington and Lambert (1994) who found that approximately one-third of burglaries are committed during Friday or Saturday.

Males were overrepresented (91 per cent) among the offenders in the present sample, thus corroborating results from previous studies (Farrington & Lambert, 1994; Cassel & Bernstein, 2001; Mawby, 2001). Their mean age was somewhat higher com- pared to earlier studies, but the offenders could nevertheless be considered to be youths (Pietilä, 1992). Increases in offender age have been associated with increases in the likelihood of committing burglaries alone (Eskridge, 1983; Reiss & Farrington, 1991; Shover, 1991; Mawby, 2001). There- fore, it was interesting that in spite of their

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Table 5: Summary of the relationships between burglary types and offender characteristics

Burglary Characteristics more likely to be present Characteristics less likely to be present

Basic + Had a permanent residence – Visited the target earlier + Cohabited with a partner – Perpetrator knew the owner of the target + Goods at least partly for own use – Prior conflict with owner of target + Theft – Owner non-Finnish

Detached house + Other suspects with the same last name – Age + Traffic violation – Visited the target earlier

– Crime committed alone – Perpetrator knew the owner of the target – Perpetrator hurt during the burglary – Lived in an owned flat – Income at least h504

Terraced or semi- detached

+ Visited the target earlier – Crime committed alone

+ Prior conflict with owner of target

Spontaneous + Age – Traffic violation + Perpetrator hurt during the burglary + Lived in an institution + School pupil + Open or closed psychiatric care

Interrupted + Age + Perpetrator knew the owner of the target + Income at least h336 + Income at least h504 + Homicide + Robbery + Firearm violation

Suburban + Crime committed alone – Age + Perpetrator hurt during the burglary – Offender lives in the city in which the

crime was committed + Traffic violation

Tool to scene + Age – Offender lives in the city in which the crime was committed

– Target was perpetrator’s property – Had a permanent residence – Lived with a room mate

Opportunistic + Age – Visited the target earlier + Goods at least partly for own use – Other suspects with the same last name + Homicide – Perpetrator had a fence prior to the

burglary + Stalking, deprivation of liberty – Perpetrator knew the owner of the target + Firearm violation – Prior conflict with owner of target + Other crime + Prison experience

Inner city studio + Age – Unemployed or in retirement/sickness pension

+ Owner non-Finnish + Perpetrator hurt during the burglary + No permanent residence + Lived in somebody else’s flat + School pupil + Alcoholic

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somewhat higher mean age, Finnish bur- glars are mostly (69 per cent) acting in groups.

Some burglary types were plausibly asso- ciated with offender characteristics (the results referred to here are based on the information contained in Table 3 (which

addresses the relationship between burglary type and offender age) as well as Table 5 (which summarises the significant relation- ships between burglary type and offender related characteristics)). High-value burglary was associated with older offenders, who apparently had developed some expertise in

Table 5: Continued

Burglary Characteristics more likely to be present Characteristics less likely to be present

High-value + Age – School pupil + Perpetrator had a fence prior to the

burglary + Lived in somebody else’s flat + Lived with a room mate + Income at least h336 + Welfare benefit receiver + Traffic violation + Drug arrest + Drug dependence

Cottage/shed + Age – Perpetrator had a fence prior to the burglary

+ Goods at least partly for own use – Welfare benefit receiver + Target was perpetrator property – Children + Perpetrator knew the owner of the target + Owner non-Finnish + Under the influence of drugs or alcohol + Other crime

Balcony + Age + Other suspects with the same last name + Perpetrator had a fence prior to the

burglary + Perpetrator knew the owner of the target + Perpetrator hurt during the burglary + Cohabited with a partner

Games and guns + Offender lives in the city in which the crime was committed

– Age

+ Male – Owner non-Finnish – No permanent residence – Had no place to live – Unemployed or in retirement/sickness

pension – Entrepreneur, civi servant, managerial

position – Children – Prison experience

Protected house/ escape car

+ Offender lives in the city in which the crime was committed

– Income at least h336

+ Crime committed alone – Traffic violation + Owner non-Finnish + Lived in an owned flat + Foreigner

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terms of evaluating profitable targets (Mac- Donald & Gifford, 1989; Wright, Logie, & Decker, 1995; Nee & Taylor, 2000; Cassel & Bernstein, 2001). High-value burglary also predicted the offender as having a fence in mind prior to the burglary — the develop- ment of nets of buyers has been seen to be associated with older offenders and the development of expertise (Cromwell, 1994). This burglary type was also indicative of a maximum of two offenders which fits well with the view of older and more experienced perpetrators — in larger groups the economic gain per burglary is lower (Cromwell, 1994).

Guns and games burglary, in contrast, was associated with younger offenders. The items stolen included computer games and other things which can be thought of as being particularly attractive to young offen- ders. In line with the offenders being younger, this burglary type was also asso- ciated with the burglar having no children, no prison experience and a permanent resi- dence. Guns and games burglary together with detached house burglary were the strong- est predictors of younger offenders. Detached house burglary also predicted a group of offenders, a feature seen typical of younger offenders (Eskridge, 1983; Reiss & Farrington, 1991; Shover, 1991). This burglary type was also associated with some of the offenders sharing the same surname. Typically, burglary offending begins in groups which include more experienced offenders (Shover, 1972; Reiss & Farrington, 1991; Cromwell, 1994). These experienced offenders may often be siblings of the beginner (Reiss & Farrington, 1991; Cromwell, 1994; Cassel & Bernstein, 2001). These conclusions were also sup- ported by those engaging in detached house burglary being less likely to have been sus- pects of a large number of burglaries overall.

Spontaneous burglary predicted the offen- der as having been hurt during the burglary.

This type of burglary was associated with characteristics describing a rather unbal- anced perpetrator and, in fact, it predicted him as having been subjected to psychiatric care or to having lived in an institution. Further, spontaneous burglaries were associ- ated with either older offenders or school pupils. Also, interrupted burglaries seemed to be associated with psychologically unbal- anced offenders. Crime scene behaviour in these offences was careless and the offenders had extensive criminal histories, also including homicides. In fact, in a Finnish context, homicides are often associated with marginalisation processes and sub- stance abuse problems (Kivivuori, 1999). More generally, findings regarding the criminal histories of the burglars in the present study were in line with earlier research on specialisation in burglaries (Farrington & Lambert, 1994; Mawby, 2001). Thefts were the most common crim- inal history category, over one-half of the offenders had committed these types of offences. The strongest predictor of thefts was interestingly basic burglary. Basic burglary was also associated with longer series. How- ever, the burglars often had quite extensive criminal histories suggesting that special- isation in burglaries only is relatively rare (Farrington & Lambert, 1994). None of the 244 offenders had a criminal history involv- ing an arson-related crime suggesting that some combinations of criminal activity are, nevertheless, uncommon.

Crime scene behaviour in opportunistic burglaries is suggestive of an impulsive deci- sion to carry out a burglary. The offenders did not know the owner and had no fence in mind beforehand. They had also not visited the target previously. Opportunistic burglary predicted that the offender had prison experience and a criminal history involving amongst others homicides and violations of firearm regulations. Also this offence type corresponds with the findings

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of Kivivuori (1999) cited in the previous paragraph, ie of Finnish homicides being related to processes of marginalisation and substance abuse. Another type of an oppor- tunistic burglary, balcony burglary, was asso- ciated with shorter burglary series. This is not surprising since such burglaries do not require any special skills on the part of the offender.

Research on burglaries has highlighted variations in the level of planning as an important feature of this crime category (Mawby, 2001). In the present study, high levels of planning seem to be associated with suburban burglaries and tool-to-scene bur- glaries. There were no signs of impulsiveness or carelessness in the crime scene behaviour of either of these burglary types. The offen- der may also have crossed city boundaries in order to get to the crime scene. This differs from the generally short distances that bur- glars tend to travel to their targets (Green, Booth & Biderman, 1976; Rengert & Wasilchick, 1985; Reiss & Farrington, 1991; Barker, 1999). Barker suggests that as burglars commit more offences, the dis- tances they travel tend to increase. Suburban burglaries and tool-to-scene burglaries may, therefore, be associated with more rational offenders with extensive burglary experi- ence and the needs and skills to move away from familiar areas to new targets.

Both terraced or semi-detached burglaries, cottage/shed burglaries and balcony burglaries predicted a prior relationship of some sort between the offender and either the owner of the target or the target itself. Terraced or semi-detached burglaries were associated with the offender having visited the target pre- viously and with the offender and the owner having had prior conflicts which may also have included violence. Lately, interpersonal aspects of burglaries have been emphasised (Merry & Harsent, 1999). It may be that these burglaries are motivated by a need for revenge. Also, balcony burglaries

as well as cottage/shed burglaries were asso- ciated with the offender knowing the owner. The latter type of burglary was also associated with the target being owned by the offender which may be indicative of, for example, an insurance-fraud attempt. Cottage/shed burglaries were also predictive of the offender having been under the influ- ence of alcohol or drugs. The offender being a downright alcoholic was predicted by inner city studio burglaries, however. Gen- erally speaking, there was less indication of substance abuse in the present sample in comparison to the ones from earlier studies (Cromwell et al., 1991; Pietilä, 1992; Cromwell, 1994; Mawby, 2001). It has to be remembered, however, that it is difficult to establish the extent of drug abuse from police files (Pietilä, 1992). The clearest pre- dictor of offender drug involvement was a high-value burglary.

Protected house burglaries predicted a single offender who lived in the same city in which the crime was committed. Security measures have, on one hand, been seen to attract burglars (MacDonald & Gifford, 1989; Shaw & Gifford, 1994) and, on the other hand, to lower the risk of burglary (Buck et al., 1993; Cromwell et al., 1993). The present results do not solve the ques- tion even if the offender acting alone may be indicative of more experience in evaluat- ing the target (MacDonald & Gifford; Wright et al., 1995; Nee & Taylor, 2000; Cassel & Bernstein, 2001). If this is true, security measures might, in fact, increase burglary risk. Protected house burglary was also the only burglary type predicting a foreign offender. Overall, only 2 per cent of the offenders were foreigners.

On the whole, it is interesting to note that the burglary types can be viewed as lying on a continuum that varies from more professional ones (a minority in the present study) over a medium range in terms of planning and carefulness of execution (the

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majority in the present study) to opportu- nistic ones (a large number of types). This kind of continuum accords well with pre- vious studies showing evidence for similar groupings (e.g. Nee & Taylor, 1988; 2000).

The results could be used by the police in several different ways. First of all, by adding illustrative examples, the results of the present study could be used in the teaching of new police officers about bur- glary offenders. Secondly, the results could be used in ongoing investigations to make predictions concerning the likely character- istics of the offender. Thirdly, decision sup- port systems could be developed that would use the crime scene behaviour in a partic- ular burglary in order to search a database of known offenders (the database would have to include information on the character- istics of these offenders) in order to create a list of offenders particularly likely to have committed the burglary in question.

Future studies should look at the prob- lems inherent in the method of the present study: pro formas should be used in order to code crime scene information more reli- ably. Also, the efficacy of experienced bur- glary investigators in making similar predictions should be compared to the sta- tistical methodology used in the present study.


This research was financially supported by the Finnish Ministry of the Interior and by Grant 54456 from the Academy of Finland.

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