1. Geoprofiling in commercial robbery series. Is it useful? Pekka Santtila, PhD Professor of Forensic Psychology Department of Psychology Åbo Akademi University Turku, Finland pekka.santtila@abo.fi

2. Background 1 ● Geoprofiling: using knowledge of crime trips in solved crimes as an investigative tool when solving new offences. ● Journeys from an offender’s home to crime site often surprisingly short. ● The residential location of an offender plays a role in choosing crime sites (Canter & Larkin, 1993): – 87% of serial rapists marauding (as opposed to commuting). ● Distance-decay documented in several studies (Turner, 1969; Capone & Nichols, 1975; LeBeau, 1987a; Rengert, Piquero & Jones, 1999; Santtila et al., 2004).

3. Decay function

4. Examples of distance decay functions (Santtila et al., 2004) Stranger rapes: Mdn = 2.44 km (N = 100) Burglaries Mdn = 4.03 km (N = 300)

5. Background 2 ● Expressive crimes (e.g. homicide and rape) have shorter crime trips than instrumental crimes (e.g. burglary and robbery) (Santtila et al., 2004). ● Differences in the way a crime is committed (e.g. indications of planning) correlate with crime trips within a crime type (White, 1932; van Koppen & Jansen, 1998).

6. Aims of the present study The aims of the present study were to evaluate the overall accuracy of using an empirical function to predict home location (Levine, 2003) in commercial robbery series to explore the relation of modus operandi (m.o.) to the distance the crime was committed from home to analyse whether the accuracy of prediction is enhanced by taking the m.o. into account to analyse whether the accuracy of prediction is better for marauding offenders and whether marauders can be identified using m.o.

7. Search area

8. Cases – All solved commercial robbery series from the Greater Helsinki area during the years 1992-2001. ● clearance rate was 72% ● 15 % of cases without address excluded

9. Targets

10. Distance decay – Distance decay was apparent: ● Range 0-29.63 km ● Mdn = 3.53 km (IQR =8.76)

11. Kernel density estimation ● Functions formed using kernel density estimation: – Each crime incident gets a continuous kernel density function placed on the distance point of its occurrence. – The model becomes continuous enabling its use for prediction for all points of the distance axis (Levine, 2003).

12. Prediction Origin of model placed on location of crime site. Probability for offender home computed in all directions. When applied to a series, the density from each incident sums with those of the other incidents, producing a cumulative density surface.

13. Accuracy of prediction Size of area that had to be searched: Mdn = 4.7% (IQR = 31.0%). Leaveone- out –classification: the series which the predicting function was applied to was never part of the function used.

14. Effects of modus operandi ● The m.o. variables were chosen according to previous research and suggestions by experienced police investigators. ● Correlations between crime features and distance were found.

15. Variables associated with shorter distances

16. Variables associated with longer distances

17. Summary m.o. variable indicative of distance ● Each variable was weighted by its correlation with distance. – An occurrence of a m.o. variable increased or decreased the summary variable value according to the positive/negative correlation with distance the m.o. variable had. ● Correlation of the summary variable with distance r =.46

18. Distance functions adjusted for m.o. ● The cases were divided into two groups with an m.o. predicting either short or a long journey-to-crime. – Separate distance decay functions were formed. ● The average of the summary variable values obtained for a robbery series was used to categorize the series as one for which either – A function describing shorter distances or – A function describing longer distances was appropriate

19. Results from adjusted functions ● Predictions were not more accurate – number of m.o. features present in the case descriptions correlated with smaller search areas r = -.24 (p <.05).

20. Marauders vs. commuters ● Predictions were more accurate for – marauders ● Mdn = 1.07% (IQR =2.61) – commuters ● Mdn = 24.06% (IQR =30.34) p < 0.001 ● A logistic regression was conducted to predict marauding – Predictors: ● number of robberies in the series ● Mean Interpoint Distance (MID)

21. Predicting marauding ● 77.6% of the cases could be correctly classified – percentage of commuters 63% ● Number of robberies in a series was more useful a predictor than MID

22. Conclusions ● Use of the function limited,as a general rule, the search area to a fraction of the whole search area. ● Reasons that the use of m.o. variables did not increase the accuracy of the prediction remain partly unclear - 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. ● 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.