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
Introduction
•Outline of research in Australia
•Serial Armed Robbery Research
–Environment
–Data analysis
–Results
–Conclusions
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•Spencer - (Unpublished) PhD research into spatial patterns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
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
Routine Activity
gym
and Journey to
Crime (JTC)
friends
Crime Activity
home
work
shops
Prof. David Canter’s
‘Circle Theory’ - Criminal Range
Marauder
Commuter
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.
Australia - (a Quick Geography Lesson)
20 Million People
Victoria
(25%) 4.8 Million
Sydney
Melbourne
Greater Melbourne
3.2 Million
Data
Source: Victoria Police
28 serial armed robbers
Total of 240 offences
(Mean of 8.6 crimes per
series).
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.
Data Analysis
Opportunity Theory of Travel
Distance v $ Amount Stolen
Travel to Crime
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
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
Conclusion
It may be possible to differentiate between the
Marauder and Commuter behaviour by examining
the Offender and Offence Characteristics
?
Marauder
Commuter
Software used for analysis
•MapInfo
•CrimeStat
•MCi
Analysis Tools - MCi
MCi - MapInfo/CrimeStat Interface
Specially created for this research project
MapInfo
View Results
CrimeStat
Analysis
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 .
(5 Models Available)
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.
Culling the data for modeling
To remove:
Commuters
Outliers
Best Fit - Mathematical Models
Linear Truncated @ 30ks
(Model 1)
Adj R2 0.83
Negative Exponential
*excluding outliers
(Model 2)
Adj R2 0.68
Distance Km
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
Entire Dataset
JTC Mathematical
Model 1
Marauder Model
JTC Mathematical
Model 2
JTC Calibrated
Model 3
Centrography
Model 4
Percentage
Percentage
Percentage Percentage
JTC Prediction of Activity JTC Prediction of Activity
JTC Prediction of Activity
of Activity
Level (1-10) Space
Level (1-10)
Level (1-10) Space
Space
Space
Mean
2.83
Standard Deviation
Best
Worst
No. times PAS of
less than 25%
Mean
(Entire Dataset)
3.81
20.73%
2.58
25.51%
3.67
30.73%
26.97%
19.09%
15.95%
26.83%
25.16%
1.80%
5.43%
4.28%
0.59%
67.07%
56.99%
88.05%
92.88%
(92%) 11
(58%) 7
(67%) 8
(58%) 7
67.48%
68.43%
61.90%
3.38
64.13%
4.56
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
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
Thank You
Peter Branca
[email protected]
+61 (0) 419427997