geographic profiling lecture
TRANSCRIPT
8/11/2019 Geographic Profiling Lecture
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Geographic Profiling: Hype
or Hope?Preliminary Results into the Accuracy of Geographic
Profiling Software
Presented by
Dr. Derek J. Paulsen Assistant Professor Eastern Kentucky University
Institute for the Spatial Analysis of Crime
UK Crime Mapping Conference
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What is Geographic Profiling
Strategic information management systemused to assist in investigations into serial
crimes
First commercial software created by
Kim D. Rossmo
Analyzes crime locations to determine themost probable area of offender residence.
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How Geographic Profiling worksInfluenced by Routine Activities Theory,
Rationale Choice, and research into mentalmaps, awareness space and Journey to
Crime
Brantingham & Brantingham
Used information about a criminals activity
space to predict where an offender willcommit crimes
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How Geographic Profiling works
Geographic profiling inverts the
Brantingham research
Using information about where an offenderhas chosen to commit crimes, geographic
profiling attempts to determine where an
offender is most likely to reside
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Geographic Profiling Models
There are three main geographic profilingmodels currently used.
RIGEL: Developed by Kim D. Rossmo
DRAGNET: Developed by David Canter
Crimestat: Developed by Ned Levine
Main differences: Calculations, Cost,
Interface, and Output.
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Use of Geographic Profiling
Extensive Media Coverage after the DC
Sniper
Increasingly being used by Law
Enforcement.RCMP, ATF, Local Law Enforcement
Increased funding for development andtraining
NLECTC-SE & NIJ
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Issues with Geographic
Profiling1. Lack of Independent Research
2. More anecdotal support than empirical
3. Data Issues:Small Samples: Rossmo, Canter &
Levine
Serial Murder cases only: Rossmo &
Canter
Non-random case selection: Levine4. Determining Accuracy:
Better than centrographic measures or
other methods?
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Purpose of the research
1. Independently determine the relative
accuracy of the different Geographic
Profiling software packages.
2. Assess whether the various softwarepackages are significantly more accurate
than simple centrographic measures.
3. Determine areas of potential improvement
for software
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Data Used in AnalysisBaltimore County, MD
Offenders arrested multiple times from
1994-1997.
270 crime series: Reporting on only 150
series todayThree or more crimes
All the same crime: Rape, Robbery,Theft, Burglary, Auto Theft & Arson
Stable home address
Continuous period of time
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Analysis MeasuresDistance Measure: Distance from top point
in profile to home location.
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Distance Measure
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Distance Measure
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Distance Measure
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Analysis Measures
Profile Distance Measure: Distance fromclosest part of top profile region to home
location.
Distance Measure: Distance from top point in
profile to home location.
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Profile Distance Measure
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Profile Distance Measure
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Profile Distance Measure
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Analysis Measures
Profile Area: Total area of top profile
region.
Search Area: Percent of search area
represented by top profile region.
Success: Home location within top profileregion.
Logistic Regression: What impacts
success or failure.
Distance Measure: Distance from top point in profile
to home location.
Profile Distance Measure: Distance from closest partof top profile region to home location.
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Methods Analyzed
RIGEL: Default
DRAGNET: Default, Euclidian distance;
Mean Interpoint Distance; Probability map.
Crimestat: Mathematical Formula; Negative
exponential.Center of Minimum Distance: 1.6 km radius
circle.
Median Center: 1.6 km radius circle.
Mean Center: 1.6 km radius circle.
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Results are preliminaryThese are NOT the final results of the
research project.
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Success of the ProfileMethodNumber Correct
N=150Percentage
Correct
RIGEL 30 20%
DRAGNET 25 17%
Crimestat 30 20%
CMD 50 33%
Median Center 51 34%
Mean 42 28%
Centrographic measures are significantly better
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Success by Search Area
Method0-16.09
n=70
16.10-32.18
n=12
32.2-64.36
n=13
64.4-136.76
n=25<137
n=20
RIGEL 13 (19%) 2 (17%) 2 (15%) 4 (16%) 9 (30%)
Dragnet 11 (16%) 3 (25%) 2 (15%) 5 (20%) 4 (13%)
Crimestat 16 (23%) 2 (17%) 4 (31%) 3 (12%) 5 (17%)
CMD 39 (56%) 2 (17%) 2 (15%) 3 (12%) 4 (13%)
Median
Center 39 (56%) 3 (25%) 2 (15%) 3 (12%) 4 (13%)
Mean 38 (54%) 0 1 (8%) 0 (0%) 3 (10%)
Centrographic are far better in small areas, equal in large areas.RIGEL is much better in largest search areas.
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Success by number of Offenses
Method 3Crimesn=55
4-5Crimesn= 58
6-7Crimesn=22
8-9Crimesn=9
10-11Crimesn=4
12+Crimesn=2
RIGEL 14 (25%) 10 (17%) 3 (14%) 2 (22%) 0 (0%) 1 (50%)
Dragnet 9 (16%) 11 (19%) 2 (9%) 2 (22%) 0 (0%) 1 (50%)
Crimestat 10 (17%) 14 (24%) 4 (18%) 1 (11%) 0 (0%) 1 (50%)
CMD 17 (31%) 20 (35%) 7 (32%) 4 (44%) 1 (25%) 1 (50%)
Median
Center 17 (31%) 22 (38%) 5 (23%) 4 (44%) 2 (50%) 1 (50%)
Mean 17 (31%) 17 (29%) 3 (14%) 2 (22%) 2 (50%) 1 (50%)
Centrographic measures are better with smaller series.
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Distance Measure: Distance from top point
in profile to home location
Measure Average Distance Variance
RIGEL 5.869 27.832
DRAGNET 5.766 28.474
Crimestat 6.176 28.319
CMD 5.916 27.861
Median Center 6.016 28.413
Mean 5.940 27.583
Differences are very small: .41 km total range
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Profile Distance Measure: Distance from
closest part of top profile region to home location
Measure Average Distance Variance
RIGEL 3.835 24.760
DRAGNET 4.638 26.700
Crimestat 4.601 26.752
CMD 4.370 26.052
Median Center 4.476 26.485
Mean 4.316 26.092
RIGEL is better in both distance and variance.
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Profile Area: Total area of top profile region
Measure Average Top Profile
AreaVariance
RIGEL 14.379 256.784
DRAGNET 6.875 72.283
Crimestat 3.383 4.796
Centrographic 5.052 NA
This may explain why RIGEL is the lowest on Profile Distance
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Search Area: Percent of search area represented
by top profile region.
Measure Average % of
Search AreaVariance
RIGEL 20.642 99.511
DRAGNET 12.4559 301.760
Crimestat 15.9670 364.021
Centrographic 1150.44 Very high
While RIGEL is a larger percentage of the search area it has far less variance than DRAGNET or Crimestat.
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Search Area: Percent of search area represented by
top profile region.
Method0-16.09
n=70
16.10-32.18
n=1232.2-64.36
n=13
64.4-136.76
n=25
>137
n=20
RIGEL 22.84 19.46 17.21 19.50 18.40
Dragnet 15.80 9.04 10.96 10.23 8.6
Crimestat 27.49* 11.69 8.85 5.02 3.7
Centrographic
Measures 2474.31 22.23* 11.3* 5.46* 2.3*
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Logistic Regression: What factors most impact
success or failure of the profile.
Factors RIGEL DRAGNET Crimestat
Number ofoffenses .616(-.484)** .744(-.296) .613(-.490)*
JTC Avgerage .119(-2.21)** .701(-.356) .490(-.714)
JTC Minimum 1.1667(.154) .133(-2.01)** .000(-8.018)**
JTC Maximum 1.642(.496) .840(-.175) .178(-1.728)
Dispersion 1.345(.296) 2.275(.822)* 13.01(2.57)*
Search Area 1.018(.018)* .916(-.035)* .970(-.031)
Constant 3.976(1.38) .915(-.089) 7.714(2.043)
*p <.05 **p <.01
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Conclusions:
Factors SuccessTop
PointProfile
Distance
Profile
Area
% ofSearch
Area
Ease ofuse
RIGEL √ - √ √
DRAGNET - √*
Crimestat √ - √
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Profile Software vs. Centrographic Measures
Factors Success TopPoint
ProfileDistance Profile Area % ofSearch Area
Ease ofUse
RIGEL
- √ √
DRAGNET - √
Crimestat - √CMD √ - √* √* √
Median √ - √* √* √
Mean - √* √* √
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Overall FindingsPRELIMINARY FINDINGS ONLY
RIGEL is slightly better overall than otherGeographic Profiling software, but not by a
large amount.
Centrographic measures are equally as
good as Geographic Profiling software.
Dispersion of crimes and size of the searchare have more impact on accuracy of
profiles than number of crimes in the series.
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Future Issues
More Cases: Approximately 120 more
series.
Other Measures:
Crimestat: Other routines
RIGEL Expert System
Human predictions
Other Data: Looking for more cities.
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Suggestions or Data?Contact Information:
Dr. Derek J. Paulsen Assistant Professor
Director, Institute for the Spatial Analysis of Crime
Eastern Kentucky UniversityRichmond, KY USA 40507-3102
859-622-2906