1. Measuring Repeat and Near- Repeat Burglary Effects

2. Repeat and Near-Repeat Victimization Criminals likely to revisit crime scene Likely to rob neighbors of previous victims

3. Why? Knowledge of entry modes and security Easy access to site Abundance of material possessions Knowledge of neighbor’s daily routines

4. Data analysis Measured the distribution of wait times between successive burglaries Rapidly decaying function Conclusion: houses likely to be robbed again within a short period of time of a burglary Thus repeat victimization hypothesis is true?

5. Sliding Window Method After a house is robbed, we watch it for the next 727 days. If it is robbed again, we record the elapsed time, Find that data follows this model:

6. Sliding Window Method Long Beach Data Set 3

7. Each house can be in one of three states gives the rate of robbery in state i gives the fraction of houses in state I Long Beach Results:

8. Neighborhoods Split events into sections Examine repeat crime dynamics of different neighborhoods Partition data into 1-day bins, scale, and then fit with exponential sum For fit, we equally weight each bin and use equation of form:

12. Near Neighbor Effects Repeat victimization hypothesis also applies to near repeats: repeated robberies within a short distance of original crime Compute time interval for repeat within a range of Euclidean radii –e.g. 0-100 m

13. Near Neighbor Effects We then fit histogram to: If we assume housing density is uniform and scale the lambdas according, we find that decays with increasing radius in the form of a power law:

15. Manhattan Distance We also analyze near repeats according to Manhattan rather than Euclidean distance Manhattan distance:

16. Manhattan Distance Euclidean Distance

18. Near Repeat Neighborhoods Perform same analysis, but only consider near repeats within the same neighborhoods Use Euclidian distance

21. Current Work Programmed C++ simulation based on repeat model with parameters from LB Currently adding different dynamics for different neighborhoods

22. Application to Disaster LA Can use analysis on LA crime data to figure out LA parameters Neighborhood dynamics more applicable in LA than LB Can use simulation to predict the spread of mayhem in a disaster