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
What is Geographic Profiling
Strategic information management system
used to assist in investigations into serial
crimes
First commercial software created by
Kim D. Rossmo
Analyzes crime locations to determine the
most probable area of offender residence.
How Geographic Profiling works
Influenced by Routine Activities Theory,
Rationale Choice, and research into mental
maps, awareness space and Journey to
Crime
Brantingham & Brantingham
Used information about a criminals activity
space to predict where an offender will
commit crimes
How Geographic Profiling works
Geographic profiling inverts the
Brantingham research
Using information about where an offender
has chosen to commit crimes, geographic
profiling attempts to determine where an
offender is most likely to reside
Geographic Profiling Models
There are three main geographic profiling
models 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.
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 and
training
NLECTC-SE & NIJ
Issues with Geographic
Profiling
1. 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: Levine
4. Determining Accuracy:
Better than centrographic measures or
other methods?
Purpose of the research
1. Independently determine the relative
accuracy of the different Geographic
Profiling software packages.
2. Assess whether the various software
packages are significantly more accurate
than simple centrographic measures.
3. Determine areas of potential improvement
for software
Data Used in Analysis
Baltimore County, MD
Offenders arrested multiple times from
1994-1997.
270 crime series: Reporting on only 150
series today
Three or more crimes
All the same crime: Rape, Robbery,
Theft, Burglary, Auto Theft & Arson
Stable home address
Continuous period of time
Analysis Measures
Distance Measure: Distance from top point
in profile to home location.
Distance Measure
Distance Measure
Distance Measure
Analysis Measures
Distance Measure: Distance from top point in
profile to home location.
Profile Distance Measure: Distance from
closest part of top profile region to home
location.
Profile Distance Measure
Profile Distance Measure
Profile Distance Measure
Analysis Measures
Distance Measure: Distance from top point in profile
to home location.
Profile Distance Measure: Distance from closest part
Profile
Area:
area
of top profile
of top
profile
regionTotal
to home
location.
region.
Search Area: Percent of search area
represented by top profile region.
Success: Home location within top profile
region.
Logistic Regression: What impacts
success or failure.
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.
Results are preliminary
These are NOT the final results of the
research project.
Success of the Profile
Method
Number Correct
N=150
Percentage
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
Success by Search Area
Method
0-16.09
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.
Success by number of Offenses
Method
3
Crimes
n=55
4-5
Crimes
n= 58
6-7
Crimes
n=22
8-9
Crimes
n=9
10-11
Crimes
n=4
12+
Crimes
n=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.
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
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.
Profile Area: Total area of top profile region
Measure
Average Top Profile
Area
Variance
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
Search Area: Percent of search area represented
by top profile region.
Measure
Average % of
Search Area
Variance
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.
Search Area: Percent of search area represented by
top profile region.
Method
0-16.09
n=70
16.10-32.18
RIGEL
n=12
32.2-64.36
n=13
64.4-136.76
n=25
>137
n=20
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*
Logistic Regression: What factors most impact
success or failure of the profile.
Factors
RIGEL
DRAGNET
Crimestat
Number of
offenses
.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
Conclusions:
Factors Success
RIGEL
√
Profile
Distance
-
√
Profile
Area
√
-
% of
Ease of
Search
use
Area
√
-
DRAGNET
Crimestat
Top
Point
√*
√
Profile Software vs. Centrographic Measures
Factors Success
RIGEL
DRAGNET
Crimestat
CMD
Median
Mean
√
√
Top
Point
Profile
Distance
-
√
Profile
Area
% of Ease of
Search
Use
Area
√
√
√
√*
√*
√*
√*
√*
√*
√
√
√
Overall Findings
PRELIMINARY FINDINGS ONLY
RIGEL is slightly better overall than other
Geographic 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 search
are have more impact on accuracy of
profiles than number of crimes in the series.
Future Issues
More Cases: Approximately 120 more
series.
Other Measures:
Crimestat: Other routines
RIGEL Expert System
Human predictions
Other Data: Looking for more cities.
Suggestions or Data?
Contact Information:
Dr. Derek J. Paulsen
Assistant Professor
Director, Institute for the Spatial Analysis of Crime
Eastern Kentucky University
Richmond, KY USA 40507-3102
Derek.Paulsen@eku.edu
859-622-2906