1. Predictive Policing Professor Shane D Johnson
(Kate Bowers, Toby Davies, Ken Pease)
UCL Department of Security and Crime Science

2. Overview Some basic findings
Background theory – optimal foraging theory
Prospective crime mapping
Optimizing predictions
Influence of the street network
Resources

3. Crime Concentration - Burglary

4. Crime Concentration - Burglary
If we look at the distribution of burglary we find that if we consider the areas most at risk, about 40% of burglaries occur in about 20% of the area. As we look at smaller areas, in this case street segments, we see that it is even more clustered.
Johnson, S.D. (2010). A Brief History of the Analysis of Crime Concentration. European Journal of Applied Mathematics, 21,

5. Concentration at places: Repeat Victimization

6. Is Victimization Risk Time-Stable? Timing of repeat victimization
Johnson, S.D., Bowers, K.J., and Hirschfield, A.F. (1997). New insights into the spatial and temporal distribution of repeat victimization. British Journal of Criminology, 37(2):

7. Explaining Repeat Victimisation
Boost Account
Repeat victimisation is the work of a returning offender
Optimal foraging Theory (Johnson & Bowers, 2004) - maximising benefit, minimising risk and keeping search time to a minimum-
repeat victimisation as an example of this
burglaries on the same street in short spaces of time would also be an example of this
Consider what happens in the wake of a burglary
To what extent is risk to non-victimised homes shaped by an initial event?
Black-browed albatross
Johnson, S.D., and Bowers, K.J. (2004).The Stability of Space-Time Clusters of Burglary. British Journal of Criminology, 44(1),

8. An analogy with disease Communicability
Communicability - inferred from closeness in space and time of manifestations of the disease in different people.
area
burglaries + + + + + + + + + + + + + + + +

9. Neighbour effects at the street level
Bowers, K.J., and Johnson, S.D. (2005). Domestic burglary repeats and space-time clusters: the dimensions of risk. European Journal of Criminology, 2(1),
Johnson, S.D. et al. (2007). Space-time patterns of risk: A cross national assessment of residential burglary victimization. Journal of Quantitative Criminology, 23:

10. Patterns in detection data?
For pairs of crimes:
Those that occur within 100m and 14 days of each other, 76% are cleared to the same offender
Those that occur within 100m and 112 days or more of each other, only 2% are cleared to the same offender
Johnson, S.D., Summers, L., Pease, K. (2009). Offender as Forager? A Direct Test of the Boost Account of Victimization. Journal of Quantitative Criminology, 25,

11. “If this area I didn’t get caught in, I earned enough money to see me through the day then I’d go back the following day to the same place. If I was in, say, that place and it came on top, and by it came on top I mean I was seen, I was confronted, I didn’t feel right, I’d move areas straight away …” (P02)
Summers, Johnson, & Rengert (2010) The Use of Maps in Offender Interviewing. In W. Bernasco (Ed.) Offenders on Offending. Willan.

12. “The police certainly see a pattern, don’t they, so even a week’s a bit too long. Basically two or three days is ideal, you just smash it and then move on … find somewhere else and then just repeat it, and then the next area …” (RC02)
Summers, Johnson, & Rengert (2010) The Use of Maps in Offender Interviewing. In W. Bernasco (Ed.) Offenders on Offending. Willan.

13. Forecasting - ProMap Risk High Low
Bowers, K.J., Johnson, S.D., and Pease, K. (2004). Prospective Hot-spotting: The Future of Crime Mapping? The British J. of Criminology, 44,

14. Event driven and Time Stable factors

15. Event driven and Long-term factors (7- day forecast)
Here I use only roads, but elsewhere we use sociodemographic factors of the environment and other variables.
Johnson, S.D., Bowers, K.J., Birks, D. and Pease, K. (2009). Predictive Mapping of Crime by ProMap: Accuracy, Units of Analysis and the Environmental Backcloth, Weisburd, D. , W. Bernasco and G. Bruinsma (Eds) Putting Crime in its Place: Units of Analysis in Spatial Crime Research, New York: Springer.

16. Our most recent work looks at risk at the street segment level and we have shown that the risk of burglary is systematically higher on certain types of segment – types that can be identified through a pure mathematical analysis of the street network. We are developing the forecasting approach to generate street segment predictions.

17. Resources
Fielding & Jones (2012) – Disrupting the optimal forager…. Journal of Police Science and Management
38% reduction in residential burglary!
29% reduction in TFMV!
JDi Briefs (
POP guide (
Vigilance Modeller (
Risk Terrain Modelling (