Geographic Profiling: Hype or Hope?

Preliminary Results into the Accuracy of Geographic Profiling Software

Presented by

Dr. Derek J. Paulsen Assistant Professor

Eastern Kentucky UniversityInstitute 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 worksInfluenced 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 researchUsing 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. RossmoDRAGNET: Developed by David CanterCrimestat: Developed by Ned Levine

Main differences: Calculations, Cost, Interface, and Output.

Use of Geographic Profiling

Extensive Media Coverage after the DC SniperIncreasingly being used by Law Enforcement.

RCMP, ATF, Local Law EnforcementIncreased funding for development and training

NLECTC-SE & NIJ

Issues with Geographic Profiling

1. Lack of Independent Research2. More anecdotal support than empirical3. Data Issues:

Small Samples: Rossmo, Canter & LevineSerial Murder cases only: Rossmo & CanterNon-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 AnalysisBaltimore County, MDOffenders 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 & ArsonStable home address Continuous period of time

Analysis MeasuresDistance Measure: Distance from top point in profile to home location.

Distance Measure

Distance Measure

Distance Measure

Analysis Measures

Profile Distance Measure: Distance from closest part of top profile region to home location.

Distance Measure: Distance from top point in profile to home location.

Profile Distance Measure

Profile Distance Measure

Profile Distance Measure

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 profile region.Logistic Regression: What impacts success or failure.

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.

Methods AnalyzedRIGEL: DefaultDRAGNET: 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 preliminaryThese are NOT the final results of the

research project.

Success of the ProfileMethod Number 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

Success by Search AreaMethod 0-16.09

n=7016.10-32.18

n=1232.2-64.36

n=1364.4-136.76

n=25<137n=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 OffensesMethod

3 Crimesn=55

4-5 Crimesn= 58

6-7 Crimesn=22

8-9Crimes

n=9

10-11Crimes

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 farless variance than DRAGNET or Crimestat.

Search Area: Percent of search area represented by top profile region.

Method 0-16.09n=70

16.10-32.18n=12

32.2-64.36n=13

64.4-136.76n=25

>137n=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

CentrographicMeasures 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 CrimestatNumber 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 Top Point

Profile Distance

Profile Area

% of Search Area

Ease of use

RIGEL √ - √ √

DRAGNET - √*

Crimestat √ - √

Profile Software vs. Centrographic MeasuresFactors Success Top

PointProfile

DistanceProfile Area

% of Search Area

Ease of Use

RIGEL - √ √DRAGNET - √

Crimestat - √CMD √ - √* √* √

Median √ - √* √* √Mean - √* √* √

Overall FindingsPRELIMINARY FINDINGS ONLYRIGEL 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 IssuesMore Cases: Approximately 120 more series.Other Measures:

Crimestat: Other routinesRIGEL Expert SystemHuman predictions

Other Data: Looking for more cities.

Suggestions or Data?Contact Information:

Dr. Derek J. PaulsenAssistant Professor

Director, Institute for the Spatial Analysis of CrimeEastern Kentucky University

Richmond, KY USA 40507-3102Derek.Paulsen@eku.edu

859-622-2906