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Geographic Profiling in AustraliaGeographic Profiling in Australia – –An examination of the predictive potential of serial armed An examination of the predictive potential of serial armed robberies in the Australian Environmentrobberies in the Australian Environment
ByPeter Branca
The Seventh Annual International The Seventh Annual International Crime Mapping Research ConferenceCrime Mapping Research ConferenceMarch 31- April 3, 2004: Boston , MassachusettsMarch 31- April 3, 2004: Boston , Massachusetts
IntroductionIntroduction
•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•Spencer - (Unpublished) PhD research into spatial patterns of serial sex offences - both Commuter and Marauder behaviour exhibited by same offenders, utility of Dragnet and CrimeStat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 CrimeRoutine Activity TheoryCircle 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 ActivityRoutine Activity
and Journey to and Journey to Crime (JTC) Crime (JTC)
Crime ActivityCrime 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 - Research Aims - Serial Armed Robbery Serial Armed Robbery in Victoriain 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 - Australia - (a Quick Geography Lesson)(a Quick Geography Lesson)
Victoria(25%) 4.8 Million
Greater Melbourne3.2 Million
DataData
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
Data AnalysisOpportunity 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 - MCiMCi - MapInfo/CrimeStat Interface
Specially created for this research project
MapInfo CrimeStatCrimeStatAnalysisCrimeStatCrimeStat
View ResultsView Results
Visual Analysis - Output
JTC Model DevelopmentCrimeStat - 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
Distance Km
Linear Truncated @ 30ks
(Model 1)
Adj R2 0.83
Negative Exponential*excluding outliers(Model 2)
Adj R2 0.68
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%
PredictedArea
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
