2. REAL WORLD DATA Overall Long Beach Data burglaries: Total number of burglaries (2000-2005)

3. Multiple Robberies distinguished by color

4. LEGEND Blue – 1 Cyan – 2 Red – 3 Yellow – 4 Long Beach Burglaries from the Year 2000, Number of Times Victimized

5. AVERAGE FREQUENCY OF ROBBERIES The following Excel file shows Frequency of the houses burgled with respect to weeks between robberiesExcel file

6. OVERALL DATA ANALYSIS The following Frequency graph plotted using the average data More burglaries occur in rapid succession rather than long intervals.

7. AVERAGE NUMBER OF ROBBERIES The following Excel File shows the Average number of robberies for all five years.Excel File

8. AVERAGE NUMBER OF ROBBERIES PLOT

9. MY MODEL  Virtual robbers are placed on a line of length L meant to represent houses  robber can do 4 things at each step, essentially: stay put where he/she is, move left or right, or rob the house where he/she is at  The probability of moving to a neighboring location is calculated based on the “attractiveness” of the neighbor houses as follows:  After all the robbers have robbed and moved, the houses will update their b values according to this formula:

10. COMPARISON OF MY MODEL AND REAL WORLD DATA Robbers=800;Blue  Model Houses=13000;Black  Data Time=One year; η=.5 =0.5; =.01; b0=.01

11. COMPARISON OF MY MODEL AND REAL WORLD DATA Number of Robberies per site for the same parameters as before Binning number time robbed: 1  2046, 2  296, 3  75, 4  19, 5  2, 6  1

12. What is a Hot Spot?

13. Hot Spot Definition 1: Areas with high percentages of multiple burglaries

14. Hot Spot 1  100x100 grid  Each cell is approximately 485x500 ft  Each cell represents the number of multiple burglaries divided by the total number of burglaries

15. Hot Spot Definition 2: Clusters of burglaries where one burglary is within 500 ft of another

16. Clustering Algorithm

17. 1.Chooses a random point 2.Finds all points within 500 ft

18. Clustering Algorithm

19. 1.Chooses a random point NOT already within a cluster 2.Finds all points within 500 ft

20. Clustering Algorithm

21. Eventually, all points are in clustered in some groups

22. Clustering Algorithm Re-checks that points within 500 ft are in the same cluster

23. Clustering Algorithm Some different clusters might be within 500 ft of each other

24. Clustering Algorithm Clusters within 500 ft of each other are combined into one cluster

25. Total of 521 Clusters, top 10 are displayed The biggest cluster consisted of 76 housing units 500 ft is about the size of a block Are these hot spots? Long Beach Burglary Clusters for the year 2000

26. Apologies….

27. Possible Correlations?

28. Possible Correlations: 2000: Total Housing Units

29. Possible Correlations: 2000: Total People

30. Possible Correlations: 2000: Mean Earnings Based on sampling

31. Possible Correlations: 2000: Median Age

32. Possible Correlations: 2000: Percentage of Those 65 and Older

33. Possible Uncorrelations: 2000: Percentage of Households with 1 Person

34. Possible Correlations: 2000: Percentage of those with at most a 9 th grade education level Based on Sampling

35. Possible Correlations: 2000: Percentage of those 25 years or older with at least a Bachelors Based on Sampling

36. Possible Correlations: 2000: Race Percentage of those who are …. ?

37. Possible Correlations: 2000: Race Percentage of those who are Caucasian

38. Possible Correlations: 2000: Race Percentage of those who are African-American

39. Possible Correlations: 2000: Race Percentage of those who are Asian American

40. Possible Correlations: 2000: Race Percentage of those who are Hispanic/Latino