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Fundamentalsof Crime Mapping Tactical Analysis Concepts
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Fundamentalsof Crime Mapping Tactical Analysis Concepts

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Howsand whys of aPGM tactical analysis

Howsand whys of aPGM tactical analysis


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  • 1. 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
  • 2. 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
  • 3. 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
  • 4. 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?)
  • 5. 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
  • 6. Crime Stat III and Spatial Statistics Tools that come with Arc Map 9x both can do this
  • 7. 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.
  • 8. Crime Stat III and Spatial Statistics Tools that come with Arc Map 9x both can do this
  • 9. 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.
  • 10. So far no tool in ArcMap 9x To do this – Manual Process
  • 11. 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 
  • 12. So far no tool in ArcMap 9x To do this – Manual Process
  • 13. 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)
  • 14. So far no tool in ArcMap 9x To do this – Well….there is the animation utility and Crime Stat III…
  • 15. 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
  • 16. 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.
  • 17. 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
  • 18. 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.
  • 19. SAME PROBLEM WITH THE ELLIPSES
  • 20. 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 
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. 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
  • 25. Very large  prediction areas 27 potential  “next” targets Not operationally  useful to investigators (they laughed)
  • 26. 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
  • 27. 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 
  • 28. No target analysis completed yet, however the probability area is already significantly reduced! (from 27 stores to 14)
  • 29. Target analysis completed more reduction on probable target area (from 14 stores to 2)
  • 30. 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.
  • 31. Other Elements You Can Use CrimeStat II’s Correlated Walk Routine Animal Movement-Circular Point Stats Observation of Crime Path Travel
  • 32. 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
  • 33. List Possible Target Stores List All List Possible Suspects, FI’ Events in The Series s, Etc...
  • 34. 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!

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