Geographic Profiling Lecture

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



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.



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



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



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.



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



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?



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



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



Analysis MeasuresDistance Measure: Distance from top point

in profile to home location.



Distance Measure



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.



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 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.



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 preliminaryThese are NOT the final results of the

research project.



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



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.



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.



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


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

AreaVariance

RIGEL 14.379 256.784

DRAGNET 6.875 72.283


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 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.



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*



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



Conclusions:

Factors SuccessTop

PointProfile

Distance

Profile

Area

% ofSearch

Area

Ease ofuse

RIGEL √ - √ √

DRAGNET - √*

Crimestat √ - √



Profile Software vs. Centrographic Measures

Factors Success TopPoint

ProfileDistance Profile Area % ofSearch Area

Ease ofUse

RIGEL

- √ √

DRAGNET - √

Crimestat - √CMD √ - √* √* √

Median √ - √* √* √

Mean - √* √* √



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.



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 UniversityRichmond, KY USA 40507-3102

[email protected]

859-622-2906

Geographic Profiling Lecture

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