Geographic Profiling in Australia –

1. Geographic Profiling in Australia – An examination of the predictive potential of serial armed robberies in the Australian Environment The Seventh Annual International Crime Mapping Research Conference March 31- April 3, 2004: Boston , Massachusetts By Peter Branca

2. Introduction • Outline of research in Australia • Serial Armed Robbery Research • Environment • Data analysis • Results • Conclusions

3. Research In Australia Few studies have been reported in relation to the geography of serial crime in Australia. • Kocsis & Irwin (1997) examined serial rape, arson and burglary - support Canter’s ‘Circle Theory’ • Catalano (2001) examined the spatial behaviour patterns of serial robbery in Perth - criminological theories were ‘helpful’ in predictions • Kocsis et al (2002) assessed the ‘Circle Theory’ for Geographic Psychological Profiling - rural town burglaries 50/50 Commuter and Marauder • Spencer - (Unpublished) PhD research into spatial patterns of serial sex offences - both Commuter and Marauder behaviour exhibited by same offenders, utility of Dragnet and CrimeStat II investigated

4. International Research - Focus on the home being central to serial crime locations. Centrography Journey to Crime Routine Activity Theory Circle Theory

5. Centrographic Analysis Refers to the single location that is the shortest distance to each crime site in the series. Centroid can be easily calculated using a GIS

6. gym friends home work shops Routine Activity and Journey to Crime (JTC) Crime Activity

7. Prof. David Canter’s ‘Circle Theory’ - Criminal Range Marauder Commuter 87% of serial sexual offenders were found to be Marauders

8. Computer Programs - JTC Rigel Dragnet Predator CrimeStat

9. Research Aims - Serial Armed Robbery in Victoria To explore the predictive potential of Geographic Profiling in relation to serial armed robbery in Victoria, Australia. Utilising Journey to Crime (JTC) and Centroid calculations to predict the home location of serial offenders.

10. Victoria Sydney Melbourne Australia - (a Quick Geography Lesson) 20 Million People (25%) 4.8 Million

11. Greater Melbourne 3.2 Million

12. Data Source: Victoria Police 28 serial armed robbers Total of 240 offences (Mean of 8.6 crimes per series).

13. Data Analysis The vast majority of offenders were males 92.9%. Average offenders’ age 31.5 years. The youngest offender was 20 and the oldest was 47. Knife was the most common weapon used (34.6%). Syringes exceeded firearms.

14. Travel to Crime Rewards Distance Data Analysis Opportunity Theory of Travel Distance v $ Amount Stolen

15. Data Analysis Marauder or Commuter ? 12 of the 28 series, or 43%, fitted the description of the Marauder model (Group most effective for Geo Profiling)

16. Data Analysis Commuter - Street Offences 26% Marauder - Milkbars (Convenience Store) 18%, Other Shops 25%, Service (GAS) Stations 17%

17. Data Analysis Commuter - Syringe 25% Marauder - Knife 47%

18. Data Analysis Other less significant comparisons were: Offender Average Age (Mar - 33yrs, Com - 31yrs) Average Number of Offences (Mar - 11, Com - 7) Day of the Week (Mar - Sundays, Com - early in the week) Average Value Stolen (Mar - $1600, Com - $1200) Mode of Transport (Mar - Bicycle) *Largely unknown

19. ? Marauder Commuter Conclusion It may be possible to differentiate between the Marauder and Commuter behaviour by examining the Offender and Offence Characteristics

20. Software used for analysis • MapInfo • CrimeStat • MCi

21. Analysis Tools - MCi MCi - MapInfo/CrimeStat Interface Specially created for this research project MapInfo CrimeStat CrimeStat Analysis View Results

22. Visual Analysis - Output

23. Ned Levine. CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Locations (v 2.0). JTC Model Development CrimeStat - Journey To Crime (JTC) Models can be based on either : • Mathematical function, or • Empirically derived function

24. (5 Models Available) JTC Model Development Mathematical function The following methodology was used : 1. Calculate the Euclidean JTC distances using the research data . 2. Group the distances into appropriate distance intervals (ie range bins). 3. Graphically display the data to assess the central tendency and spread. 4. Generate probability distributions using functions to determine the model that best represents the data. 5. Fit the frequency distribution model(s) to calculate the appropriate parameters required by CrimeStat. 6. Compare models to the original data and select the best fit.

25. Culling the data for modeling To remove: • Commuters • Outliers

26. Best Fit - Mathematical Models Linear Truncated @ 30ks (Model 1) Adj R2 0.83 Negative Exponential *excluding outliers (Model 2) Adj R2 0.68

27. Empirically Derived (Calibrated) Model Development Developed with the research dataset

28. Prediction Analysis JTC Predictions: Models 1 & 2 (Mathematical functions) Model 3 (Empirical Model - Calibrated) Model 4 (Centrography)

29. Prediction Analysis JTC Predictions (Model 1, 2 & 3)

30. Prediction Analysis Model 4 (Centrography)

31. Prediction Analysis - Evaluation Percentage of Activity Space (PAS): (Predicted Area / Activity Space) * 100 = PAS (2.27/ 23.27)* 100 = 9.75%

32. Predicted Area

33. Analysis Results Entire Dataset Marauder Model

34. Conclusions - Research Research Indicates: • Australian findings are consistent with international research • The spatial behaviour of serial armed robbers is consistent to findings of other types of serial violent offenders • It may be possible to identify a Marauder based upon offender/offence characteristics

35. Analysis Results - Further Investigation • Identify a predictive relationship between offence characteristics and Marauder/Commuter behaviour patterns • Research utilising larger data samples and other offence types should be investigated • Need for JTC software programs with greater flexibility in relation to the mathematical models available

36. Peter Branca psb8@bigpond.com +61 (0) 419427997 Thank You