Forensic Science International 157 (2006) 71–82 www.elsevier.com/locate/forsciint
Predicting the residential location of a serial commercial robber Manne Laukkanen *, Pekka Santtila Police College of Finland, P.O. Box 13, 02151 Espoo, Finland Received 11 August 2004; received in revised form 14 March 2005; accepted 15 March 2005 Available online 21 June 2005
Abstract Residential location of a serial offender can potentially be predicted by using models created from home to crime site journeys of solved crimes in the area [N. Levine, Journey-to-crime estimation, retrieved 23 October 2003 from http:// www.icpsr.umich.edu/NACJD/crimestat/CrimeStatChapter.9.pdf, last visited 1 February 2005]. Aims of this study were: (1) to examine the accuracy of this technique, (2) to explore relation of modus operandi (m.o.) to the distance the crime was committed from home and (3) to analyse whether the accuracy of prediction is enhanced by taking the m.o. into account. Data consisted of 76 commercial robbery series from the Greater Helsinki area. Accuracy of prediction was tested by using leave-one-out technique: the series which the predicting function was applied to was never part of the function used to predict. The functions allowed limiting the area to be searched to 4.7% (Mdn, IQR = 31.0%) of the study area generally, and to 1.0% (Mdn, IQR = 2.6%) when the suspect’s spatial behaviour conformed to the circle hypotheses presented by Canter and Larkin [D. Canter, P. Larkin, The environmental range of serial rapists, J. Environ. Psychol. 13 (1993) 63–69]. Significant correlations between m.o. and the length of the journey-to-crime were found, but this information did not enhance accuracy of prediction. Low percentage of marauder style perpetrators in the data gives support to the possible separation of hypotheses of underlying spatial behaviour in instrumental crimes versus crimes of interpersonal violence or arson. Suggestions for development of investigative tools are presented. # 2005 Elsevier Ireland Ltd. All rights reserved. Keywords: Distance decay; Journey-to-crime; JTC; Geographic Profiling; Marauder; Commuter; Circle hypothesis
1. Introduction Journeys from an offender’s home to crime site are often surprisingly short. A phenomenon called distance decay has been documented in several studies—activity generally diminishes as a function of distance from a person’s home whether that activity is general [1] or criminal [2–6]. The observed tendencies of criminal spatial behaviour have led to further studies charting the peculiarities of journeys-to-crime (JTCs). Existence of an area of lessened criminal activity near the offender’s * Corresponding author. Tel.: +358 44 56 03 047; fax: +358 9 8388 3500. E-mail address:
[email protected] (M. Laukkanen).
home, called buffer-zone has been hypothesized [7]. An important reason behind this phenomenon has been thought to be avoiding the risk of being identified. Buffer-zones have been found for different types of crimes [6]. There are also results pointing to the possibility that crimes having arguably more expressive or emotional internal drive from the offender’s part, such as rape, manslaughter or arson, tend to have shorter journeys-to-crime than those of a more instrumental nature [6]. It has also been found that differences in the way a crime is committed, for example, high degrees/indications of planning and amount of preparation often correlate with the length of the JTC even within a certain crime type [8,9]. But how important is home in the choosing of crime sites apart from crimes committed at home?
0379-0738/$ – see front matter # 2005 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.forsciint.2005.03.020
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The residential location of an offender is thought to play a special role in choosing crime sites [10], although several other factors influence these decisions as well (see, e.g. [11]). Canter and Larkin [10] found that 87% of serial rapists in the UK committed their crimes confirming the so-called circle hypothesis. That is, serial offenders’ residential locations resided within a circle defined by two of their crime sites that were situated farthest from each other. In agreement with Canter’s hypothesis, 82% of serial arsonists in Australia also acted according to this form of spatial behaviour [12]. Canter’s aforementioned study led to suggesting the existence of two groups of offenders: marauder and commuter. Marauders commit their crimes with their home base as a nexus of criminal activity, from which the offences spread into surrounding area. Commuters commute from their home into a separate area to offend. This area might, for example, have abundance in suitable targets [38]. It may well be that different crime types have a majority of either marauding or commuting as the predominant spatial behaviour type [13]. It is also possible that modus operandi (m.o.)—the way of committing the crime might reveal whether the villain is a marauder or a commuter. Could all this knowledge of the characteristics of JTC’s be used to locate the unknown villain’s residence in crimes that need some investigative effort? It has been suggested that the accumulated knowledge of crime trips in solved crimes could be used as an investigative tool when solving new offences of similar nature or type [14]. Underlying this approach is the assumption that the distribution of the length of these JTCs are representative and generalizable features internal to criminals, or characteristic of the mobility a certain area allows for or creates in individuals. It is possible, thus, to predict the home location of an offender by using the crime sites as a starting point. A general statistical model describing JTCs in solved cases in a particular area can be used to predict the probable location of offenders’ home locations. The model can be applied to the crime sites of a new serial offender to predict the location of offender’s home, as the distance from home to crime site is the same as the distance from crime site to home. How generalizable is such a statistical model? It is probably best to form a model for a certain area from data representing spatial behaviour for that area. This way some of the inherent properties affecting movement in the area are taken into account. For example, a grid-like structure of street network found in some American cities (e.g. Salt Lake City) may affect movement differently when compared to a more circular street network found in some European cities (e.g. Milan, Italy) [10,14]. Also, an empirical model has some relation to the actual spatial behaviour inherent to criminal activity in the area, since the model is formed from real incidents in the area. It is possible, of course, to use a general mathematical function such as a negative exponential function to describe the spatial behaviour of criminals in different cities. This would, however, demand proof that such a mathematical function really is
Fig. 1. Journeys-to-rob. Graph displays a frequency histogram and a kernel density estimation of the same distribution of journeys-tocrime used as data (n = 213). Bandwidth in the kernel density estimation in this example was 0.5 km. Kernel density estimation renders the discontinuous continuous (see, e.g. [15]).
generalizable as a depiction of inherent tendencies of spatial behaviour in criminals. Apart from the general distance decay phenomenon, justification for such a generalization in criminal spatial behaviour has not been presented. One means of constructing an empirical model of JTC behaviour is by transforming the discontinuous point distribution of observed JTCs into a continuous statistical model. This can be achieved, for example, by means of kernel density estimation (see, e.g. [15]). By using this approach for JTCs, each crime incident gets, instead of a single point on the distance axis, a continuous kernel density function placed on the distance point of its occurrence. A kernel used for each individual crime incident might, for example, be a Gaussian kernel, with the shape of the familiar bell-curve. Thus, the distance point of the actual observation is changed into a point having the highest probability density for that particular crime’s occurrence. From that point, the probability for its occurrence decreases in both directions on the distance axis. When these continuous kernels placed on individual point observations overlap each other, the model becomes continuous (Fig. 1), enabling its use for prediction for all points of the distance axis [14]. To use the resulting model for prediction based on a single crime incident, the origin of the model is placed on the location of the crime site and probability for the offender’s residential location is predicted in all directions of the geographical plane. When applied to a series of crime incidents, the density accumulation from each individual incident combined with those of the other incidents produces a cumulative density1 surface [14] (see Fig. 2). 1 Levine’s method, strictly speaking, when used to form a likelihood surface on a geographical plane actually seems to sum ‘‘likelihood densities’’ for centres of cells in a grid and not to count actual probabilities on a continuous probability surface (see [14], pp. 357, 400).
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Fig. 2. CrimeStat# II journey-to-crime prediction as displayed in Surfer1 for Windows. Displayed is a wire-frame map of the density surface formed after an empirical model has been applied to both crime sites (1 and 2) of the series. X-coordinate is longitude, Y-coordinate is latitude and Z (height) is density. Actual location of home has been added to the map (H).
The areas of higher probability may be used as a starting point for a search for the location of an offender’s residence (Fig. 3a and b). Predictions made in this manner are of course fairly crude, as they ignore the existence of naturally uninhabited areas such as parks, rivers or the sea. Future enhancements should take this into account as well as the distribution of suitable targets and the opportunities to commit crime [14]. Psycho-
logically speaking, it is of great interest whether using the observed differences of crime scene behaviour within and between crime types could aid the accuracy of prediction [6,13]. Modus operandi and JTC distance seem to have a relationship according to some studies [9,8]. As it is to be expected that these predictions will be better for marauders, it is of interest if persons could be classified into a marauder or a commuter group by m.o. or features of a crime series.
Fig. 3. (a) Two-dimensional contour map of the same density surface and crime series. High density areas are shown as lighter. Shown on the map are the areas of summed probability accumulation from models applied to crime sites (1 and 2) and the actual residential location (H). Lighter areas are areas of higher probability for an offenders residence. The rectangle contains the Greater Helsinki area consisting of the cities of Helsinki, Espoo and Vantaa (see Fig. 4). Axes show coordinates in decimal degrees. (b) Areas of lower probability have been removed until the remaining area contains the offenders dwelling. In this example, 6.3% of the total search area remains. If the police would have searched the area according to the prediction received, this would have been the maximal area to search until finding the home of the perpetrator.
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The aforementioned approach holds much promise, since a successful prediction of an unknown offender’s home location would truly invigorate a police investigation. True efficiency of the technique remains open, however, partly because of mixed results concerning the method’s level of accuracy [16,14]. It was in the researchers’ interest to evaluate the accuracy of this approach when using it to locate serial criminals’ home locations. A scientifically acceptable testing of this method requires a sizeable sample of crime series and an analytical approach that is free from the effect of the crime series the prediction is applied to. Since commercial robbery is more prevalent a crime in Finland than homicide or rape committed by a stranger [17], it was decided to use commercial robbery series as data. Also, compared to burglaries, which would offer a multitudinous amount of incidents to investigate even in Finland, commercial robberies offer interesting behavioural information, as the victims most often have direct interaction with the robber. The aims of this study were thus: (1) to evaluate the overall accuracy of the JTC empirical function-technique [14] with commercial serial robbery, (2) to explore the relation of modus operandi (method of committing the crime) to the distance the crime was committed from home and (3) to analyse whether the accuracy of prediction is enhanced by taking the m.o. into account.
2. Method 2.1. Data The data were all solved commercial robbery series from the Greater Helsinki area during the years 1992–2001. The
study area, consisting of the neighbouring cities of Helsinki, Espoo and Vantaa housed approximately a million inhabitants in the years under scrutiny (Fig. 4). A series was judged to be two or more commercial robberies by the same perpetrator. Both cases committed alone and cases committed in groups were included. Commercial robbery was defined for the needs of the present study as a robbery style crime against a business. Resistance robberies, i.e. cases where a theft changed into a robbery when the perpetrator turned violent after staff intervention, were included. Businesses included shops of all sizes, kiosks, grills, banks, currency exchange businesses, pharmacies and gas stations with sales personnel. Taxi robberies and money transfer-robberies were excluded, as they sometimes do not yield a steady target and hence offer no unambiguous coordinates for a JTC evaluation. Perpetrator was deemed to be the person who was a suspect for the crime when the Pre-Trial Investigation conducted by the police had ended. The data did not include cases where there was no information regarding the residential location of the perpetrator at the time of the crime. Data included, naturally, only solved crimes. The clearance rate of commercial robberies in the greater Helsinki area was 71.9% in the year 2001 (source, crime incident database of the Finnish police). Unreported crime with this particular crime type is probably small as the owners of the businesses are prone to prefer having their robbed money back from insurance companies [18]. The police case files used in the research were obtained from crime incident database of the Finnish Police containing all crime incidents from years 1991 to 2002. A database query was formulated so that persons suspected of participating in two or more robberies against a business following a concluded police investigation were returned
Fig. 4. The study area is approximately 23.3 km high by 35.0 km wide (roughly 815 km2). Shown on the map is Helsinki, with neighbouring cities of Vantaa and Espoo. The area housed close to a million inhabitants in the years under study. Dark areas in the southern shore of Helsinki and in Espoo are sea or lakes, light areas are forest and medium–dark areas (e.g. next to roads) are inhabited areas.
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from the query. Only incidents taking place within the Greater Helsinki area were included (Fig. 4). Aforementioned query returned, along with other information, the incident identification numbers of the robbery incidents. These identification numbers were used to collect the text summaries of the individual cases in electronic form from the database. Cases where the address information for the perpetrator consisted of ‘‘No address’’, ‘‘Without a steady address’’ or equivalent non-address information were excluded (15%). Geographical coordinates according to WGS-84 chart projection for the home and robbery sites were retrieved from the database of the Finnish National Population Registry Center (FNPRC). Mainly because of spelling errors and variations in the written form of the street addresses, some of the coordinate pairs for original home and crime sites were not returned from the FNPRC database. The acquired coordinates were transformed into decimal degrees to enable creation of a statistical model using the CrimeStat# II software [19]. After these actions, it was checked whether the cases still formed a series of two or more commercial robberies. This resulted in 213 JTCs from 168 different commercial robbery incidents forming 76 commercial robbery series. As the serial robberies were often committed by a group of offenders, a single incident could well yield several JTCs. A vast majority (93%) of the robbery incidents were from the years 1994 to 2001. A majority (58%) of the commercial robbery series consisted of two incidents (see Table 1). If the perpetrator’s residential address changed during the series, the address with more occurrences was chosen. If there were only two addresses, the prediction was made for both addresses. Series having one or the other of the mentioned conditions numbered 16 (21% of the data). It is noteworthy that the FNPRC database lists the coordinates for the centre points of buildings. Therefore, when getting a JTC of 0 m, i.e. the robbery is committed against a business in the same building as the dwelling of the perpetrator, the actual distance might be some meters vertically or sometimes some 10 m in the horizontal plane.
Table 1 Lengths of robbery series Robberies in series
f
%
2 3 4 5 6 7
44 18 7 2 2 3
58 24 9 3 3 4
Note: 76 commercial robbery series, 168 robbery incidents and 213 journeys-to-crime (JTCs).
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2.2. Forming the empirical function from the acquired coordinates According to the longitudinal and latitudinal coordinates in decimal form (eight-digit accuracy) a model of the Greater Helsinki area journey-to-crime (JTC) behaviour for commercial robberies was estimated. An estimated model of JTC behaviour depicts the probability of committing a crime as a function of distance. Estimation was made by kernel density interpolation [20] using CrimeStat# II spatial statistics programme [14]. The kernel function chosen to represent each individual JTC instance was the normal distribution. Bandwidth of a single kernel was 1.1 km. The kernel of an individual incident was placed into the JTC distance of its occurrence, with an accuracy of 20 m—i.e. distance bin width was 20 m. Density of JTC frequencies at different distances was output as the statistical probability of committing a crime incident at a certain distance from home. A home location search area was predicted for each robbery series with (1) a general function formed from all the JTCs contained in the data and (2) a sub-function, formed from cases matching the m.o. of the series (see later: Crime features and distance). Two sub-functions were formed, one from the incidents having an m.o. that correlated with short JTC, one from the incidents that had an m.o. that correlated with long JTC. For the testing of accuracy of the subfunctions, the cases were divided into two groups with an m.o. predicting either a short or a long JTC. In both settings, testing the accuracy of the general function as a predictor of the perpetrator’s home location, and testing of the subfunction as a predictor of the perpetrator’s home location, separate functions were estimated for each perpetrator excluding that particular perpetrator’s JTCs from the function that was used to predict the home location with (a.k.a. ‘‘leave-one-out’’ procedure). 2.3. Defining the target area Area to which the home location predictions were projected was a rectangular area enclosing the Greater Helsinki area (Fig. 4). To enable the calculation of probabilities for a certain location, CrimeStat# II programme divided the geographical area into a matrix of rectangular cells, a grid, into which CrimeStat# II counted the probability of the existence of the perpetrator’s home location. Amount of columns in the matrix was 100, CrimeStat# II was let to calculate the number of rows so as to conserve the cells of the area matrix as rectangles. Probability of the existence of the home location in a grid cell was calculated according to the location of crime sites. The programme applied the empirical JTC model for each crime site of the series and combined the overlapping models creating a cumulative incident density surface as described in [14]. The grid containing the probability accumulation information was saved to a form suitable for further analysis with Surfer1 Surface Mapping System—computer programme.
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2.4. Evaluating the size of the search area
Table 2 Businesses that fell victims to robberies in the data
To evaluate the accuracy of the prediction for a particular offender’s residential location, the geographical area that would need to be searched until finding the home location was calculated. First, the resulting probability surface for the series output by CrimeStat# was opened on the Surfer1 Surface Mapping System. Then, the actual residential location of the robber was marked on the map. Areas of lower probability were removed until the area left to search included the location of the offender’s residence (see Fig. 3a and b). The size of the planar area remaining was calculated with Surfer1 ‘‘Grid-Volume-Positive Planar Area’’-function, where the Zcut-off point was the probability value of the area containing the actual residential location of robber. Thus, the area containing offender’s residence as well as the areas having greater probability according to the prediction were calculated as the final search area (see Fig. 3a and b). When using the technique in practise, it would be possible to start from the points of highest probability and advance downwards towards areas of lower probability, hence the aforementioned way of evaluating the search area. The percentage of the final search area of the original total search area was calculated and taken as accuracy of prediction.
Target
%
Convenience store Shop Kiosk or grill with sales-hatch Gas station Bank or currency exchange Pharmacy Other
40 31 7 6 3 2 12
2.5. Modus operandi and other features of robbery incident In the 168 robbery incident reports of the 76 robbery series, there were 213 case rows containing a suspect’s JTC and crime features. In these 213 cases, there were 186 incidents of on-crime site behaviour of individual robbers.2 If the electronic Pre-Trial Investigation Record (PTIR) summary did not reveal which on-site behaviour belonged to which suspect, a full text version of the PTIR was obtained to preserve accuracy of information. There were 22 such robbery cases in the data. To clarify, the robberies were sometimes committed by a group of perpetrators, which produced several suspect case rows per robbery incident. The reason for the amount of coded on-site behaviours being smaller than the total amount of case rows was that sometimes a person participating in and committing a commercial robbery series might act as a driver or assist in the robbery outside the actual robbery site, thus yielding no on-crime site behaviour. Some crime features were still extracted for all 213 suspect case rows, e.g. time of offence, type of target, etc. These were deemed common to all participants in the robbery. As well as the home and crime site addresses, all viable data about the m.o. reported in the PTIR were coded into a statistical spreadsheet package (SPSS1 10.0–11.0). The m.o. variables were chosen according to previous research 2 If a person, for example, takes part in a robbery by waiting in an escape vehicle outside the robbery target, an m.o. is often not known and thus does not yield crime site behaviour to be coded.
[9,21–23], and suggestions by experienced police investigators. A number of variables were added after a careful reading of the material. It was decided to avoid premature narrowing of measurement and, therefore, variables coded by the authors ranged in the hundreds. Full list of variables can be obtained from the authors. Variables were coded dichotomously (1: present, 0: not present, empty: missing). Variables having over 10% missing values were dropped from further analysis. 50.5% of the variables coded had no missing values. Interrater agreement in coding of the m.o. variables was evaluated using the Kappa (K)-statistic [24,25]. In the inauguration phase, a research assistant was presented with five randomly chosen cases, which were coded together with the researchers. After this, 10 new cases were randomly chosen and the assistant coded these independently. The Kstatistic was calculated casewise. The average K for the 10 cases was 0.81 (S.E.M. = 0.01). The interrater agreement could be deemed as almost perfect [39].
3. Results 3.1. Commercial robbers The 76 commercial robbery series were committed by 71 males and 5 females, whose median (Mdn) age was 21 years (IQR = 8.74). Majority (58%) of the series had two robbery incidents3 in them (see Table 1). Of the 168 robbery incidents in the data, most were targeted at convenience store outlets (R-kiosk) or small shops, and very rarely at banks or currency exchanges (Table 2). Instead, it seemed that even robbers who had to some extent prepared for the robbery4 targeted consumer 3 It is possible that the perpetrator has been guilty of more robbery incidents than what the cases of the final data display. 4 Several factor analyses and a Mokken scaling analysis [31] were done on the dichotomous m.o. variables after a thorough elimination of variables without covariance or variance (resulting no. of variables = 43, no. of cases = 213). All analyses indicated three major latent, orthogonal structures underlying the observed variation in the studied features of the robbery incidents and behaviour of individuals when committing a robbery. We labelled these structures: preparedness/professionalism, desperate theft and displayed violence/aggression.
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those that had covered more than half of their facial area, i.e. were disguised, only a third (33.3%) used a single mask like a balaclava or helmet. More common was a disguise consisting of a combination of the following: sunglasses, knit cap, scarf, hood of a sports jacket or a collar of a polo shirt (72.7%). Robbery intent was stated verbally (64.0%), e.g. ‘‘Give me the money.’’ (translated from Finnish) or ‘‘This is a robbery.’’ (translated from Finnish), without much cursing, verbal threatening or yelling (85.9%). The loot consisted mainly of cash (76.6%) with a Mdn value of s231.4 (IQR = 605.5). Large loots were a notable exception.
goods oriented businesses which might be easier targets than the well protected banks and money offices [18]. It is of note that the frequency of bank robberies in Finland decreased during the 1990s as the countermeasures taken by banks became more thorough [17]. There were no statistical differences between robbery frequencies during days of the week or seasons of the year: x2(6, n = 168) = 6.17 and x2(3, n = 168) = 1.05. According to data (n = 168), a typical incident would have been a robbery that occurred in the suburbs (67.7%) into a convenience store type kiosk with the sales area indoors (40.1%) or to a shop (31.1%). At the moment of the robbery, there were no clients (66.1%) and only a single member of personnel (69.9%) present. A saleswoman working alone was quite often the victim of the robbery (44.2%). The crime was perpetrated by one (50.6%) or two (36.1%) males (94.3%). Robbers were at least in half of the cases without any disguise (52.5%), but were often armed (66.8%). Those armed favoured a sharp weapon like a knife (44.5%) or a real or a replica hand-gun (39.5%). Of
3.2. Journey-to-robbery distances Fig. 1 displays the frequency histogram and a kernel density estimate of the data as a function of frequency to distance. Distance decay [1] is apparent. The single journeys to rob in the data ranged from about 0 to 29.63 km. Half of the journeys were under 3.53 km (Mdn,
Table 3 Variables correlating with distance and the formed regression model Crime feature variable
n
rs
Unstandardized coefficients B
Constant Robber was deemed by the victim(s) to be under influence of alcohol or drugs when committing robbery Robber does not touch the staff physically Robber acted alone at the crime site Robber does not react to actions taken by staff or bystanders, continues aiming for cash Robber gropes the money over the counter or through sales hatch Robber does not enter staff area Robber cuts phone lines or prevents staff from calling help There is an escape vehicle with a driver waiting in vehicle Target is at the city center Victims receive bruises, scratches or scalp injuries during the robbery As a reaction to actions taken by staff or bystanders robber flees or leaves the scene Loot has valuables like lottery tickets or jewelry Staff consists of a lone female Escape vehicle within 100 m of target Robber holds and/or controls and/or guides the staff with physical contact
Standardized coefficients, b
t
p
S.E.
0.17*
183
3.87 0.09
1.19 1.41
0.01
3.25 0.07
0.00 0.95
0.18* 0.20** 0.21**
178 186 186
1.08 1.91 0.51
1.02 0.79 0.69
0.10 0.19 0.06
1.06 2.43 0.75
0.29 0.02 0.46
0.22**
178
2.10
1.21
0.13
1.74
0.08
0.15* 0.16*
186 186
0.88 2.70
0.87 1.83
0.09 0.12
1.02 1.47
0.31 0.14
0.18*
174
0.54
1.20
0.04
0.45
0.65
0.18* 0.19**
185 186
1.83 1.88
0.81 1.19
0.17 0.14
2.26 1.58
0.03 0.12
0.21**
186
0.73
0.83
0.07
0.88
0.38
0.22** 0.23** 0.24** 0.27**
186 186 173 183
1.65 1.54 0.42 5.61
1.61 0.78 1.22 1.63
0.09 0.16 0.03 0.29
1.03 1.98 0.34 3.45
0.31 0.05 0.73 0.00
Note: Table has been organized by the correlation of single variables to distance. rs: strength of correlation between a variable and distance, n: amount of observations of variables in data, B: unstandardized regression coefficient and its standard error, b: standardized regression coefficient, t: test for the significance of the contribution of a single variable to the model. According to Tabachnick and Fidell [26], the t-test results do not necessarily give a full picture of the variables significance for the model (see [26], p. 144). * p < 0.05. ** p < 0.01.
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IQR = 8.76) being even shorter (Mdn = 2.22 km, IQR = 6.29) for perpetrators acting alone at the crime site. Skewness value of 1.18 is larger than its standard error 0.18 and thus points to a strongly skewed distribution, which is apparent from Fig. 1. Distances from home to crime site did not increase significantly as the series progressed (rs = 0.01, p = 0.84). It was not known for the researchers how far the criminal career of the robber had proceeded when the incidents took place, so the result should be interpreted with this in mind. JTC distances in series, where incidents were spread over a longer time period did not differ significantly from those where crimes were committed during a shorter time period (rs = 0.08, p = 0.46). This was evaluated by investigating the correlation of average distance in the series to the standard deviation in years of the dates of the incidents in the series. 3.3. Crime features and distance A number of statistically significant ( p < 0.05) correlations5 between crime features and distance were found. A regression model was formed from these variables. Variables having statistically significant intercorrelations exceeding rs > 0.50 were handled so that the variable in the pair having a weaker correlation with distance was dropped. This practise was followed in order to avoid multicollinearity [26]. The resulting regression model can be seen in Table 3. A summary variable was formed from the m.o. variables in the regression model so that each variable was represented in the summary variable by its correlation (rs) with distance (Table 3). An occurrence of an m.o. variable increased or decreased the summary variable value according to the positive/negative correlation with distance the m.o. variable had. For example, if the robber acted alone at the crime site (rs with distance = 0.20), does not react in any way when staff starts, e.g. to visibly call the police but continues (rs with distance = 0.21) groping the money through sales hatch (rs with distance = 0.22) and the crime features do not fill criteria for any other listed variables correlating with distance, the robber would get a 0.63 score on the summary variable, suggesting a probable short journey-to-crime. As a consequence, high values on the summary variable indicated that the perpetrator’s behaviour contained many features that were associated with long JTC distances. In a similar manner, low values indicated that the behaviour contained elements associated with short JTC distances. Correlation (rs) of the summary variable values with distance was 0.46 (n = 186) for perpetrators who had actually taken part in the act of robbery at the crime site, and 0.44 (n = 213) for suspects taking part in the robbery in some form (the previous group, plus, e.g. drivers waiting outside the robbery target in an escape vehicle). As mentioned before, there were 5 All correlations reported are non-parametric and significances two-tailed except where otherwise noted.
some crime features that could be known for all participants. Both correlations were statistically significant at the p < 0.01 level. The regression model was statistically significant (ANOVA, F = 3.731, d.f. = 15, p < 0.001) and explained 28% of the variation in distance (R = 28%). The average of the summary variable values for the individual robberies in a perpetrators series was computed in order to get a description of the behaviour at the level of a series. 3.4. Using crime features in predicting home location As the police investigators often have some crime features at their disposal in the beginning of a robbery investigation, interest arose in classifying the robbery series with the features of crime to test if the accuracy of prediction could be improved in this manner. Calculating the summary variable value for each robbery incident enabled the categorizing of single robbery cases as having crime features predicting a short or a long JTC. The categorization was made so that if the summary variable value for a perpetrator in an incident was positive, that JTC was used for the long JTC model and if it was negative, it was used for the short JTC model. From these cases, two sub-models were formed for testing purposes (see Fig. 5). One model was made from the crime incidents with the crime features of a short JTC and one model from the ones with the features of a long journey-to-crime. These sub-models were then used to predict the home location of the offender for the series in addition of the general model presented earlier. The average of the summary variable values obtained for a robbery series was used to categorize a series as predicting long JTCs or predicting short JTCs, according to the positive or negative average a series had. Sixty-four percent of the individual cases and 59% of the series were classified as predicting a short JTC by their m.o. Predictions of the home location were made using the leave-one-out technique, i.e. the JTCs of the series were removed from the model used to predict. Using of these sub-functions did not, as a rule generate more accurate predictions in this study. The three models used when predicting the home location can be seen in Fig. 5. 3.5. Home location predictions and the circle hypothesis Used JTC method was beneficial when predicting perpetrators home address. Mdn search area when using the general model was 4.69% (IQR = 31.0%) of the aforementioned area containing the Greater Helsinki. Smallest area to be searched was 0.14% with the largest being 100% of the area (i.e. the perpetrator lived outside of the study area). When the perpetrators committed their robbery series according to the circle hypothesis of Canter and Larkin [10] the predictions were more accurate: Mdn size of search area was 1.07% (IQR = 2.61). In series of the commuter type, the predictions were much more imprecise, Mdn search
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Fig. 5. Figure displays the kernel density estimation of the probability of an incident happening as function of distance. The overlap of subfunctions is evident. Bandwidth when estimating was 1.1 km, absolute density values (y-axis) are not reported due to CrimeStat# II programmes way of counting probabilities. Frequency histogram presented earlier displays the relative frequencies correctly. Shown here are the general function, i.e. JTC function from all of the journeys-to-crime in data (n = 213), function for incidents with an m.o. predicting a short JTC (n = 136) and the function for incidents with an m.o. predicting a long JTC (n = 77).
area for these types of series was 24.06% (IQR = 30.34) of the total search area. The difference of the size of these search areas was statistically highly significant (Mann– Whitney U: z = 5.16, p < 0.000). This suggests that the method is useful mainly when applied to marauder type robbers. There were only 30 series, 39% of the data that conformed to the circle hypothesis. The sub-group functions created according to the knowledge of the m.o. did not improve the accuracy of prediction in a significant manner. There was some correlation with strong manifestation of an m.o. of a certain type in the series with smaller search areas, i.e. more accurate prediction. The correlation between the absolute value of the average m.o. scores for the series and the size of the search area was 0.24 ( p < 0.05). Also, having a larger number of incidents in the series improved the accuracy of prediction, i.e. number of incidents correlated negatively with the size of the search area with the general model (rs = 0.36, p < 0.001). Having a greater average number of perpetrators in the incidents of the series correlated with a greater search area, i.e. worse prediction when using the by m.o. defined sub-group functions to predict (rs = 0.37, p < 0.001). As the results pointed to the possibility of the JTC method being at its most useful when predicting the home locations of marauder type robbers, an attempt was made to predict this type of spatial behaviour from features of the robbery series. A logistic regression analysis was conducted with the marauder/commuter dichotomy as the dependent variable and with the number of robberies in the series and the Mean Interpoint Distance (MID) of the series as predictors. Mean Interpoint Distance is the average of the distances between the crime incident locations in a series. The best guess rate in this instance was 63% (this was the
percentage of commuters in the data). By using logistic regression, it was possible to improve this prediction to an overall correct classification rate of 77.6% (see Table 4). This model was statistically significant (x22 ¼ 21:01, p < 0.001), and explained 0.33 (Nagelkerke R2) of the variation. Amount of robberies in a series was more useful a predictor than MID in the study (see Table 4). A ROC curve of the classification confirms what the logistic regression shows: classifying a villain successfully as marauder seems to be more successful when using the length of the robbery series as the main classification aid (Fig. 6). MID does not seem to be a strong aid in this practise in this study. Using the presented classification tools as aid when classifying commuters and marauders, it may be possible to use the JTC function as a home location predictor Table 4 Using a logistic regression model to recognize marauders and commuters Observed
Commuter Marauder Overall
Predicted
% Correct
Commuter
Marauder
40 11
6 19
87.0 63.3 77.6
Table shows the success when classifying a series (n = 76) as conforming to the marauder/commuter classification. Classification was made with the logistic regression model formed from the number of robberies in series and Mean Interpoint Distance (MID) as independent variables. MID had a B of 0.122 (S.E. = 0.05) and the amount of robberies in a series had a B of 0.84 (S.E. = 0.28), making the amount of robberies in a series a more useful predictor of the marauder commuter classification than MID.
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Fig. 6. Figure displays the ROC curve depicting accuracy of classifying robbery series as conforming to that of the marauder type. A ROC curve displays sensitivity (vertical axis) and 1-specificity (horizontal axis) of a given indicator or test score used to classify data as belonging to a certain group. An ideal indicator would manifest a ROC ‘‘curve’’ that would be connected to the vertical axis—i.e. would be able to correctly identify all marauders in data (be sensitive) while never making a false classification (be accurate). A worthless indicator would manifest a line like the reference line in figure—increase in sensitivity would correspond to an equal decrease in specificity. Area under the curve is 0.75 (S.E. = 0.06) with n of robbery incidents and 0.66 (S.E. = 0.06) with Mean Interpoint Distance (MID), making the n of robberies a better classification aid of a series conforming to the marauder classification in this study.
when this is most likely to be accurate. As there is no full certainty that the shortest series in data contain all of the commercial robberies made by these serial robbers during their criminal career, it is possible that some of the commuters in data would be revealed as marauders if all of their criminal spatial behaviour were available.
4. Conclusions and discussion 4.1. Conclusions Using an empirical JTC function to predict an unknown perpetrator’s residential location seems promising, if the possibilities for error are kept in mind. Use of the function limited, as a general rule, the search area to a fraction of the whole search area. This knowledge may well be used by the police to prioritise search areas and/or suspects, or to decide which areas to put under surveillance, if the need arises. The evaluation of accuracy is also on the conservative side, as the predicted search areas contained several uninhabited areas such as parks and sea (e.g. compare Figs. 3b and 4). Often, these do not contain the residence of the villain. Winters in Finland are cold. There were several statistically significant but weak correlations between distance and m.o. Using these did not increase the accuracy of prediction in a significant manner in the present study.
Limitations for the generalizability of the results are brought by the losses of coordinate pairs received due to differences in the writing form of the addresses in the police files and the FNRC address database. However, it is highly unlikely that this is connected in any systematic way to the variables researched. Also, although the clearance percentage for commercial robberies in the capital area of Helsinki was relatively high during the years 1992–2002, the data are from solved crimes. It is also of note that perpetrators with no reported address were left out due to the design of this study. Reasons that the use of m.o. variables did not increase the accuracy of the prediction remain partly unclear. Due to relatively limited numbers of data this research problem was not unambiguosly addressed—creating the sub-group models from incidents embodying the extreme ends of a certain type of m.o. correlating with distance might well yield more accurate predictions (see earlier, correlation between absolute values of summary variable scores and search area— strength of m.o. is correlated with a smaller search area, i.e. accurate prediction). Although it is not always possible for the police to know whether a crime is committed by single or multiple perpetrators, this might also well affect the results: the roles robbers might take such as driver, money collector or armed guard chosen or distributed before the robbery by a group of robbers might well limit the manifestation of individual crime site behaviour [27]. 4.2. Uses of the approach This report was a feasibility study for a potential residence location prediction system. Although the results do seem promising, a number of restrictions have to be mentioned. Results of this study should not be considered to apply for all crime types and all types of offenders. Also, as previously noted, the data studied did not contain ‘‘drifters’’ without a permanent residence; it also presented only one crime type and produced worse results when applied to criminals whose spatial behaviour was in accordance with the commuter type presented by Canter and Larkin [10]. Correct linking of crimes to their respective series [37] is a definitive prerequisite for the method to work. Failing to do so will cause an erroneous model: it is highly likely to cause the overlapping activity zones of different offenders or offender teams to form a probability distribution worthless for residence prediction of the perpetrators of any of the series. There are already automated tools for linking in use by the police (see, e.g. the West Midlands Police FLINTS system [34]). Using these should be strongly considered as an option, especially considering that human-made linking of crime series is likely to be prone to human error, particularly if strong expertise with the crime type under investigation has not been acquired [35]. The method presented as a Geographic Profiling tool can be used in several ways. It can be used to locate the unknown perpetrator; it is also possible to use the method for con-
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necting unsolved crimes in an area of known offender’s spatial activity to the offender, although this is not a proactive tool for police investigation. Much stronger linking ability is clearly offered by DNA, but in situations where that is not an option, the use of Geographic Profiling as presented in this manuscript is also a possibility. Despite the promising results, the use of this method is likely to be most beneficial when combined with other leads and forensic information of the particular crime series at hand. For example, if both DNA and an empirical model suitable for the study area are possible, prioritizing possible suspects who match the spatial profile in the perhaps long list of DNA testees might well save resources and hasten the success of the police investigation (e.g. [36]). If resources are abundant and time is not a serious factor, it could also be possible to use the prioritized search area as the area of taking the DNA samples from. 4.3. Future prospects of the approach From the viewpoint of Forensic Science as a whole, this study can seem reductionistic due to its focus on just one method (the use of empirical JTC functions) from just one approach—that of Geographic Profiling [32]. However, regardless of how a system or approach is used, on its own or as a modular part of a larger system, it is valuable in its own right to do strict empirical testing of the validity of an approach. This enables developing it for the purpose of crime investigation, Forensic Science, as well as for the understanding of criminal spatial behaviour as a phenomenon. Better understanding of the spatial processes involved will improve the strength of managerial decision making based on the results using an implemented system based on JTC functions, let the system be used as stand-alone system, or as a module of a larger system. There are currently several systems for Geographic Profiling offered for use of the police (see, e.g. [32]), yet the authors have yet to see widespread strict empirical testing of the effectiveness of those methods with real data. Also of importance in this study was the object of combining m.o. features of an act of crime to the distance from home to crime site—empirical fact based theoretical understanding of the psychological phenomena involved between a journey-to-crime and modus operandi is likely to help the crime investigation as a whole. Results of this study are promising indicators for the use of the method. Next step for evaluating a system using the technology discussed in this manuscript would be to test the effectiveness of the method as one source of information in the course of a police investigation of a suspected crime series. The future of Geographic Profiling, or profiling for that matter, might arguably be seen to reside as modules in larger automated investigation decision support systems. The practical function of such a Geographic Profiling module would be in statistical predictions extracted from self-calibrating,
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automated statistical models created for a particular geographical area. This is possible with the programming tools offered for use today. An information system delivering the statistical prediction would calibrate its statistical model for the crime (series) at hand, searching automatically through the police files in electronic form for cases with similar features. From these cases, the system would create the statistical data from which to draw the prediction from. System would then deliver the predictions with the expected level of error resident in the prediction. Also to be taken into account in the future are: the opportunity of committing a crime, the penetrability of different routes or areas surrounding the target and areas for which it is not necessary to calculate probability at all, such as sea areas. Public transport stops, nexuses for traffic and obstacles formed by nature or by man should also be taken into account. Individual experience of the time spent when travelling might also well be a factor affecting travel— same physical distance travelled by subway or on foot might surely be quite differingly attractive for a travelling person [1]. Incorporating this information to models of travelling behaviour is not at all impossible, Dijkstra’s algorithm [28] and other algorithms handling weighted graphs (a data structure, see, e.g. [29]) make it possible to quickly compare the arduousness of complex routes with several connection points. In a Geographic Profiling application, the edges of the data structure would be the routes, the edge weights the probabilities/demandingnesses of taking different routes and the vertexes the connection points of these routes. This might enable better representation of the individually formed mental representations, ‘‘cognitive maps’’ of the environment human individuals have of actual geographical distances and areas (see, e.g. [30]). Travel behavioural models could be made to ‘‘fold’’ at routes offering easier or faster travel. Tentative research for substitution of the use of Euclidean distance metrics with functional distance measures is already underway (see, e.g. [33]). Commuter type spatial behaviour was in majority (63%) in this data of serial commercial robbery, giving support to results by Meaney [13]. It could well be that the optimal models used to predict an offenders residential location might vary with respect to type of crime (ibid.). It might well be that center of minimum distance perceived in some studies to be the best predictor for an offenders residential location as well as the technique scrutinized here are practicable mainly with a marauder style offender. If this is the case, number of crimes in series and/or MID could be used as predictors of whether the perpetrator is one, with keeping the aforementioned considerations of length of series in mind.
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