Transcript Predicting Next Event Locations in a Crime Series using
Catching Lightning in a Bottle: Forescasting Next Events
Presented by Dr. Derek J. Paulsen Director, Institute for the Spatial Analysis of Crime Assistant Professor Eastern Kentucky University 2005 iPSY Conference
Spatial Forecasting and Crime Analysis
Evolution of Crime Analysis in the U.S.
Increasing focus on Tactical Analysis and assistance in major crime investigations.
Increasing use of advanced technology Geographic profiling Crime Series Identification software Forecasting/Prediction Great potential to assist in investigations, but research has been limited.
Developing Crime Series Analysis tools and training as part of a NIJ grant.
Main Research Questions
How accurate are traditional strategies in comparison to TWKDI at predicting the location of a future crime event in an active crime series?
Under what circumstances do forecasting techniques work?
Are there crime types that are better for forecasting than others?
What case specifics best predict success?
Forecasting Strategies Studied Traditional Methods
Standard Deviation Rectangles: “Gottleib Rectangles” Jennrich/Turner Ellipse Minimum-Convex-Hull Polygon
New Methods
Modified Correlated Walk Analysis Time-Weighted Kernel Density Interpolation
Control Method
Modified Center of Minimum Distance
Standard Deviation Rectangle 2 Standard Deviation rectangle around the mean center of the incident locations in the series
Jennrich-Turner Ellipse 2 Standard Deviation ellipse based around the mean center of the incident locations in the series and drawn around a least squares trend line
Minimum Convex-Hull Polygon Creates a minimum bounding polygon around all of the incident locations in the series
Modified Correlated Walk Analysis Uses the CWA as a seed point and creates a search area by drawing a circle with a radius of the average distance between crime events in the series.
Time-Weighted Kernel Density Interpolation Kernel Density Interpolation of crime incident locations using time as a weighting variable
Modified Center of Minimum Distance Uses the CMD as a seed point and creates a search area by drawing a circle with a radius of the average distance between crime events in the series.
Data Used in Study
247 serial crime events that occurred in Baltimore County, MD between 1994-1997.
Random sample of 45 cases in which there were 6 or more incidents.
Series ranged from 6-14 events Burglary, Robbery, Arson, Auto theft, Rape, Theft Last Crime was removed from series and remaining crimes were used to predict the final event.
Analysis was conducted using: Arcview 3.3 and 9.0
Crimestat 2.0
Animal Movement Extension/CASE Program
Measuring Accuracy of Predictions
How do you measure accuracy in predicting next events in a crime series?
Accuracy in prediction needs to encompass both correctness and the precision of the prediction in order to maintain practical utility.
A prediction may be accurate, but the predicted area may so large as to provide little practical benefit.
Methods
1.
Correct
: Was the final event location within predicted area.
2.
Search Area
: Average size of the predicted area.
3.
Search Cost
: Percent of base search area covered by the final predicted area.
4.
Accuracy Precision
: % of correct forecasts divided by the average predicted area.
Search Area, Search Cost, and Accuracy Precision
Method SDR % Correct 80% Avg. Search Area 151.68
Avg. Search Cost 170% Accuracy Precision .5274
JTE MCP CWA TWKDI 73% 42% 24% 52% 122.10
23.21
59.82
19.35
134% 26% 85% 21% .5978
1.8095
.4012
2.6873
CMD 80% 59.82
85% Average base search area was 92 sq. miles 1.3373
Success by crimes in series 120 100 80 60 40 20 0 4 5 6 7 8 9 10 Number of Crimes 11 12 13 Success Poly. (Success)
Average: 57%
Average distance between crimes 1 0 3 2 5 4 7 6 4 5 6 7 8 9 10 Number of Crimes 11 12 13 Success Failure
Dispersion by Crime in series
20 18 16 14 12 10 8 6 4 2 0 4 5 6 7 8 9 10 Number of Crimes 11 12 13 Success Failure
Search Area size by number of crimes in series 6 5 4 3 2 1 0 4 5 6 7 8 9 10 Number of crimes 11 12 13 Success Failure
Accuracy/Precision by crime number 60 50 40 30 20 10 0 4 5 6 7 8 9 10 11 12 13 Number of Crimes AP Ratio Poly. (AP Ratio)
Commercial Burglary Series -5 crimes within 6 days.
-Stealing cigarettes from gas stations -Crime area of approximately 10 square miles -Over 409 businesses within the area.
Commercial Burglary Series -8 gas stations within initial crime area -22 gas stations within area and 1/2 miles surrounding it.
Commercial Burglary Series -Prioritized search into two main areas of .9 square miles -Top area contained 3 gas stations -Second tier area contained 3 gas stations
Commercial Burglary Series -Last station burglarized was within top priority search area.
Overall Findings
Time-Weighted is the best at reducing the search area while remaining accurate.
Success most influenced by number of incidents in series and the distribution of the crimes.
Convex-Hull Polygon and modified CMD also produced good results, whereas other traditional strategies were poor performers.
While average predicted areas are rather large, practical use could reduce them to smaller area.
Future Issues
More research, more data.
Determine impact of other factors such as crime type, city type, and road network.
Determine case variables that may indicate predictive success.
Develop and analyze other new strategies.
Temporal as well as spatial forecasting/prediction More research on serial offender spatial and temporal behavior.
Data or Suggestions?
Contact Information: Dr . Derek J. Paulsen Assistant Professor Director, Institute for the Spatial Analysis of Crime Eastern Kentucky University Richmond, KY USA 40507-3102 [email protected]
859-622-2906