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