Howsand whys of aPGM tactical analysis

2. 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

3. 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

4. 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

5. 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?)

6. 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

7. Crime Stat III and Spatial Statistics
Tools that come with Arc Map 9x
both can do this

8. 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.

9. Crime Stat III and Spatial Statistics
Tools that come with Arc Map 9x
both can do this

10. 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.

11. So far no tool in ArcMap 9x
To do this – Manual Process

12. 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


13. So far no tool in ArcMap 9x
To do this – Manual Process

14. 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)

15. So far no tool in ArcMap 9x
To do this – Well….there is the animation
utility and Crime Stat III…

16. 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

17. 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.

19. 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

20. THE COMMON PROBLEM
In this example from an actual series,
there are about 56 stores of the type
the suspect is hitting within the 95%
rectangle.

21. SAME PROBLEM WITH THE ELLIPSES

22. 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


23. 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

25. 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

26. 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

27. 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

28. Very large

prediction areas
27 potential

“next” targets
Not operationally

useful to
investigators
(they laughed)

29. 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

30. 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


31. No target analysis completed
yet,
however the probability area is
already significantly reduced!
(from 27 stores to 14)

32. Target analysis completed
more reduction on probable
target area (from 14 stores to 2)

33. 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.

34. Other Elements You Can Use
CrimeStat II’s Correlated Walk Routine
Animal Movement-Circular Point Stats
Observation of Crime Path Travel

36. Create a Bulletin or Product for the Investigators
Data
Range
Analyzed
Next Location
Prediction
Next
day, hour, dat
M.O. e, and day of
Summarized week
prediction
Suspect, Vehi
cle, and
Weapon
Summarized

37. List Possible
Target Stores
List All List Possible
Suspects, FI’
Events in The
Series s, Etc...

38. 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!