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

Introduction Outline of research in Australia Serial Armed Robbery Research –Environment –Data analysis –Results –Conclusions

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‘Circle Theory’ for Geographic Psychological Profiling - rural town burglaries 50/50 Commuter and Marauder Spencer - (Unpublished) PhD research into spatial patterns of serial sex offences - both Commuter and Marauder behaviour exhibited by same offenders, utility of Dragnet and CrimeStat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 Research In Australia Few studies have been reported in relation to the geography of serial crime in Australia.

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

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

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

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

Computer Programs - JTC  Rigel  Dragnet  Predator  CrimeStat

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.

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

Greater Melbourne 3.2 Million

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

 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. Data Analysis

Opportunity Theory of Travel Travel to Crime Distance v $ Amount Stolen

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)

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

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

Data Analysis  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 Other less significant comparisons were:

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

Software used for analysis MapInfo CrimeStat MCi

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

Visual Analysis - Output

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

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. (5 Models Available)

Culling the data for modeling To remove:  Commuters  Outliers

Best Fit - Mathematical Models Linear 30ks (Model 1) Adj R Negative Exponential *excluding outliers (Model 2) Adj R

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

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

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

Prediction Analysis Model 4 (Centrography)

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

Predicted Area

Analysis Results Marauder ModelEntire Dataset

Conclusions - Research 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 Research Indicates:

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

Peter Branca +61 (0) Thank You