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Howsand whys of aPGM tactical analysis
Citation preview
Fundamentals of Crime Mapping
Tactical Analysis Elements
Tactical Analysis
Time period is even shorter and you are often just waiting for a new hit to see if your predictions were correct
Predictive purpose Geographic area defined by crime series,
trend, cluster, pattern or spree you may be following
Goals of A Tactical Analysis
Predict the next date, time, DOW of the next offense in a series
Predict the probable location for the next offenses in a series
Identify additional suspect and Investigative Lead Information from databases for assigned units
Limit potential offender data obtained in the step above, using Journey to Crime Analysis or another method
Date, DOW and Time Predictions
Midpoint Weighted averaging Other
◦ Correlated walk analysis in Crime Stat Not important to get the exact
minute Stick with the best probability no
matter which method you use
Spatial Predictive Themes Standard Deviation Rectangles Standard Deviation Ellipses Convex Hull Polygon Distance between hits buffer Distance from mean center buffer Animated path Correlated walk analysis (Crime Stat) Victimology (what targets is this
offender hitting?)
Standard Deviation Rectangles Steve Gottlieb Take the mean of X and the Mean of Y to
find the center of occurrence Calculate the standard deviation of X & Y
and create at least the lower left and upper right corners points to draw a box around
Crime Stat III and Spatial StatisticsTools that come with Arc Map 9x
both can do this
Standard Deviation Ellipses Take the mean of X and the Mean of Y to
find the center of occurrence Calculate the standard deviation of X & Y. Find the theta angle of rotation and a few
other statistics and create ellipses.
Crime Stat III and Spatial StatisticsTools that come with Arc Map 9x
both can do this
Last Hit Buffer Calculate the distances between each hit
in the series in sequence of occurrence Calculate the mean and standard
deviation distance Draw one or more buffers around the last
hit in the series you know about using the mean and/or the mean plus/minus the standard deviation distance, etc.
So far no tool in ArcMap 9xTo do this – Manual Process
Buffer Around Mean Center This is the same idea as the last hit buffer,
except the distances are calculated from the mean center of all the hits to each hit
Mean and standard deviation calculated Buffer(s) drawn around the mean center
So far no tool in ArcMap 9xTo do this – Manual Process
Animated path Create a line theme between each hit in
sequence Flash each line to see patterns in the
travel behavior of the suspect Create a polygon theme which depicts our
best guess on which direction the offender will travel based on watching the path animation (if possible)
So far no tool in ArcMap 9xTo do this – Well….there is the animation
utility and Crime Stat III…
Crime Stat’s Correlated Walk Analysis
This Crime Stat II routine attempts to calculate the location of a next hit in a crime series based on statistical calculations of time, distance and bearing
The analyst can choose between using the mean, median, or regression for each of the three variables; time, distance, and bearing.
The ideal situation would be that the CWA routine accurately pinpoints the location where the next hit in a series will be
Victimology If your offender is hitting only
convenience stores, why not put all the convenience stores on the map which are within your SD rectangles or ellipses and list them in your prediction as potential targets?
You can greatly reduce the number of officer involved in “stake outs” by using the victim data available to you in your crime series.
The Probability Grid Method in Tactical Analysis
A process of combining commonly used spatial methods to create a
prediction of a new hit location in a crime series
A Note on Practioner Research
Whatever the excuse, do it anyway and make the time
You will learn and help others to learn right along with you
It can only increase the professionalism in this profession
THE COMMON PROBLEMIn this example from an actual series, there are about 56 stores of the typethe suspect is hitting within the 95%
rectangle.
SAME PROBLEM WITH THE ELLIPSES
Potential Elements in a PGM Standard Deviation (SD) Rectangles SD Ellipses (Crime Stat II or CA TOOLS Extension)
Minimum convex Hull polygon (CA Tools)
Crime Path analysis - Directionality◦ Correlated Walk Analysis (Crime Stat II)◦ Circular Point Statistics (Animal Movement Extension )◦ Visual observation of movement between hits (Animal
Movement or CA Tools) Census and Land use geography Target (victimization) analysis
◦ Repeats and type of establishment Average distance between hits analysis Average distance from mean center to
hits Intuitive logic based on experience
Probability Grid Assumptions
If one method works well, a combination of methods may work better
No single method is any better than another when a large geographic area is covered by the suspect
Typical spatial models provide an operationally limited product when used by themselves in some cases
An analysts intuition and experience are valuable resources when making predictions
Why make these
assumptions?
Element Performance in Series Cases from Glendale, AZ
Total of 24 Series Analyzed (2 burglary, 15 robbery series, 7 Test series with very observable path)
54.2% had an observable pattern in the path animation, and another 25% was a “maybe.” (7 were test series)
54% of the predicted “next hits” were within the one standard deviation rectangle
91.7% of the predicted “next hits” were within the two standard deviation rectangle
71% of the predicted “next hits” were within the one standard deviation ellipse
95.8% of the predicted “next hits” were within the two standard deviation ellipse
Element Performance in Series Cases from Glendale, AZ Continued...
50% of the predicted “next hits” fell within the average distance between hits buffer from the last hit◦ 83.3% fell in the mean + two standard deviations buffer
83% of the predicted “next hits” fell within the convex Hull polygon area
Other spatial statistical elements scored at about the same level
Actual Case Study “Video Bandit”
11 robberies, 1 murder Consistent target selection (video stores) Observable travel pattern to targets 2 cities involved (Karen Kontak and me) Red Saturn seen in several robberies Large geographic area (40-65 square
miles) Vague suspect description JTC data to calibrate CrimeStat Person databases available to query NEW: Just plead guilty, got 17 years, no
parole possible
Very large prediction areas
27 potential “next” targets
Not operationally useful to investigators
(they laughed)
The Ellipse, Rectangle, and Convex Hull Models Alone
The Basic Idea for PGM Layering of
Data Elements to get an overall score for each “grid.”
A compilation of methods and processes that work well together and are already being used by crime analysts individually
What elements should I include?
SD Ellipses and Rectangles Convex Hull Polygon Crime Path Observations and calculations Distance From Last Hit Analysis and mean
center calculations Where Are My Targets Located? Or What Kind of Targets Are They? Any Stores Have Repeat Victimization
Problems? Direction or Bearing Analysis (Circular Point
Stats or Correlated Walk Analysis) Anything Else You Feel May Be Important
No target analysis completed yet,
however the probability area is already significantly reduced!
(from 27 stores to 14)
Target analysis completed more reduction on probable target area (from 14 stores to
2)
JTC analysis reduced possible offenders in a Red Saturn from 355 to 54, which were further
reduced to 8 individuals by investigators and the
crime analyst.
The suspect had a felony warrant and was
arrested. Evidence found at his house linking him to the
robberies and homicide.
Other Elements You Can Use
Observation of Crime Path Travel
Animal Movement-Circular Point Stats
CrimeStat II’s Correlated Walk Routine
MAKING THE FINAL PRODUCT
THINGS TO CONSIDER IN THAT DOCUMENT
Create a Bulletin or Product for the Investigators
Data Range
Analyzed
M.O. Summarized
Suspect, Vehicle, and
Weapon Summarized
Next day, hour, date, and day of
week prediction
Next Location Prediction
List Possible Target Stores
List All Events in The Series
List Possible Suspects, FI’s, Etc...
Journey to Crime Analysis Map created Using Crime
Stat II
A Who Created This, and Who to Contact Note
Where in the heck is this document if I ever want to
find it again!
Use CrimeStat III or Spatial Stats Tools in Arc 9x