measuring repeat and near- repeat burglary effects
TRANSCRIPT
Measuring Repeat and Near-Repeat Burglary Effects
Repeat and Near-Repeat Victimization
• Criminals likely to revisit crime scene
• Likely to rob neighbors of previous victims
Why?
• Knowledge of entry modes and security
• Easy access to site
• Abundance of material possessions
• Knowledge of neighbor’s daily routines
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?
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:
Sliding Window Method
• Long Beach Data Set
3
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:
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:
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
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:
Manhattan Distance
• We also analyze near repeats according to Manhattan rather than Euclidean distance
• Manhattan distance:
Manhattan Distance Euclidean Distance
Near Repeat Neighborhoods
• Perform same analysis, but only consider near repeats within the same neighborhoods
• Use Euclidian distance
Current Work
• Programmed C++ simulation based on repeat model with parameters from LB
• Currently adding different dynamics for different neighborhoods
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