2. Crime Forecasting Sergeant Tim Podlin Hanover Park Police Department

3. Why forecast crime? Catch Criminals Officer and Citizen Safety Allocation of Resources Long Term Work Reduction

4. Why does crime forecasting work? Past behavior is the best predictor of future behavior.

5. Crime Forecasting Guarantee If you never use crime forecasting, you will never forecast a crime.

6. Golden Rule of Crime Forecasting NEVER let numbers over rule common sense or sound professional judgment.

7. Who What When Where Why How “Statistical Elimination”

8. Gathering Data – Quality Timely Accurate Complete Reliable Valid

9. Gathering Data – Where Police Reports Dispatch Download (be careful with times)

10. Gathering Data – What Location Date(s) Time(s) Target Entry Tool/Weapon Suspect Other MO Reference Data

11. Matrix Format

12. Matrix Completed

13. Matrix Incident Relationship / Pattern vs. Series

14. Matrix Suspect Descriptions

15. Matrix Final

16. Date Analysis Mean The sum of all data divided by the number of data points. “Commonly called Average”

17. Date Analysis Standard Deviation A method of showing how far the data range above and below the mean. “Average Deviation from the Mean”

18. Date Analysis Confidence Interval Statistically accepted level of chance a data point will be above or below the mean. “Levels of Likely Deviation from the Mean”

19. Date Analysis Date Comparison

20. Date Analysis Days Between Hits (DBH)

21. Date Analysis Mean – Standard Deviation

22. Date Analysis Confidence Intervals

23. Date Analysis Confidence Intervals - Rounded

24. Date Analysis Forecasting Results

25. Time Analysis Format

26. Time Analysis Equal Opportunity Method

28. Time Analysis Equal Opportunity Method with Frequency Distribution

29. Time Analysis Weighted Method

34. Time Analysis Time Range Peaks 58% Chance 0000-0400 hours 27% Chance 2000-2300 hours 90% Chance 2000-0400 hours

35. Complete Analysis Who: M/W, tall, thin, dark coat What: Residential Burglaries Where: Area of “Tree Streets” How: Prying side or rear doors and removing jewelry and USC from bedrooms.

36. Date and Time Predictions Dates 68% Chance Eves of 2/13-2/18 95% Chance Eves of 2/10-2/20 99% Chance before Eve of 2/24 Times 58% Chance 0000-0400 hours 27% Chance 2000-2300 hours 90% Chance 2000-0400 hours

37. Day of Week Analysis Determine Days of Week Sun, Sun, Sat, Sat, Fri, Mon, Sun, Mon, Tue, Wed, Thu, Thu, Sun, Mon, Mon, Tue, Fri, and Sun.

38. Day of Week Analysis Determine Daily and Total Frequency Sun, Sun, Sat, Sat, Fri, Mon, Sun, Mon, Tue, Wed, Thu, Thu, Sun, Mon, Sun, Mon, Mon, Tue, Fri, and Sun.

39. Day of Week Analysis Determine Percentage per Day

40. Day of Week Analysis Optional – Graph Results

41. Spatial Analysis Visual (Common Sense) Grid STAC CrimeSTAT STandard deviation Around Mean center Point (STAMP)

42. Spatial Analysis (STAMP) Plot Incidents – Determine Longitude and Latitude

43. Spatial Analysis (STAMP) Calculate Mean Center Point

44. Spatial Analysis (STAMP) Plot Mean Point

45. Spatial Analysis (STAMP) Determine Distances from Mean Point

46. Spatial Analysis (STAMP) Calculate Mean Distance

47. Spatial Analysis (STAMP) Calculate Standard Deviation

48. Spatial Analysis (STAMP) Calculate Confidence Intervals

49. Spatial Analysis (STAMP) Plot Buffers Using Confidence Intervals

50. Spatial Analysis (STAMP) Shade Buffers for Thematic Map

51. Spatial Analysis (STAMP) Next Incident and Offender Location

52. Spatial Analysis (STAMP) CLEAR Arrest Data

53. Dissemination Intelligence Bulletins Monthly Report Crime Report (Public) Bi-Weekly Staff Meetings Intelligence Newsletters Roll Call Discussions

54. ?

55. Crime Forecasting Sergeant Tim Podlin Hanover Park Police Department 630.372.4423 tpodlin@hanoverparkillinois.org