Geographic Profiling: Hype or Hope?

Transcription

1 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

2 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.

3 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

4 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

5 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.

6 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

7 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?

8 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

9 Data Used in Analysis Baltimore County, MD Offenders arrested multiple times from 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

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

11 Distance Measure

12 Distance Measure

13 Distance Measure

14 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.

15 Profile Distance Measure

16 Profile Distance Measure

17 Profile Distance Measure

18 Analysis Measures Distance Measure: Distance from top point in profile to home location. Profile Distance Measure: Distance from closest part Profile Area: Total area of top profile region. of top profile region to home location. 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.

19 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.

20 Results are preliminary These are NOT the final results of the research project.

21 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

22 Success by Search Area Method n= n= n= 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.

23 Success by number of Offenses Method 3 Crimes n= Crimes n= Crimes n= Crimes n= 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.

24 Distance Measure: Distance from top point in profile to home location Measure Average Distance Variance RIGEL DRAGNET Crimestat CMD Median Center Mean Differences are very small:.41 km total range

25 Profile Distance Measure: Distance from closest part of top profile region to home location Measure Average Distance Variance RIGEL DRAGNET Crimestat CMD Median Center Mean RIGEL is better in both distance and variance.

26 Profile Area: Total area of top profile region Measure Average Top Profile Area Variance RIGEL DRAGNET Crimestat Centrographic NA This may explain why RIGEL is the lowest on Profile Distance

27 Search Area: Percent of search area represented by top profile region. Measure Average % of Search Area Variance RIGEL DRAGNET Crimestat Centrographic Very high While RIGEL is a larger percentage of the search area it has far less variance than DRAGNET or Crimestat.

28 Search Area: Percent of search area represented by top profile region. Method n= n= n= n=25 >137 n=20 RIGEL Dragnet Crimestat 27.49* Centrographic Measures * 11.3* 5.46* 2.3*

29 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 (.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

30 Conclusions: Factors Success Top Point Profile Distance Profile Area % of Search Area Ease of use RIGEL - DRAGNET - * Crimestat -

31 Profile Software vs. Centrographic Measures Factors Success Top Point Profile Distance Profile Area % of Search Area Ease of Use RIGEL - DRAGNET - Crimestat - CMD - * * Median - * * Mean - * *

32 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.

33 Future Issues More Cases: Approximately 120 more series. Other Measures: Crimestat: Other routines RIGEL Expert System Human predictions Other Data: Looking for more cities.

34 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