Predicting Burglary Hotspots

1. Predicting Burglary Hotspots Shane Johnson, Kate Bowers, Ken Pease

2. Repeat Victimization Prior victimisation is an excellent predictor of future risk (Burglary, DV, CIT, hotel theft……) Repeat burglary victimization occurs swiftly (e.g. Polvi et al., 1991)

3. Theories of rv • A sports team loses the first two matches of the season. Why did it lose the second one? Was it because the first result reflected the fact that it was a poor team, and it was still a poor team at the time of the second match? This is a flag account. Alternatively, did the first result destroy its confidence so that it played tentatively in the second match? This is a boost account

4. Explaining Repeat Victimisation Risk heterogeneity/Flag hypothesis Some households are always at more risk than others Flag accounts alone encounters problems explaining the time-course of repeat victimisation (Johnson, 2008) Johnson, S.D. (2008). Repeat burglary victimisation: A Tale of Two Theories. J Exp Criminol, 4: 215-240.

5. Explaining Repeat Victimisation • Boost Account • Repeat victimisation is the work of a returning offender • Optimal foraging Theory - 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 victim and nearby homes shaped by an initial event?

6. Ashton Brown and Senior • “The house would be targeted again ‘a few weeks later’ when the stuff had been replaced and because the first time had been easy...”“It was a chance to get things which you had seen the first time and now had a buyer for”.“Once you have been into a place it is easier to burgle because you are then familiar with the layout, and you can get out much quicker”

7. Gill and Pease (and Everson) • repeat robbers of the same target were more determined, more likely to carry a loaded gun, and more likely to have committed a robbery where someone had been injured. They had longer criminal records, were more likely to have been in prison before, and for a sentence upwards of five years. They planned their robberies more, and were more likely to have worn a disguise.

8. Repeat Victimisation Makes Time Central • Surprising how often time is neglected in police mapping. • Repeat victimisation is a special case of risk communication

9. Fortnightly variation Burglary Concentration High Low

10. Morning shift

11. Afternoon shift

12. Overnight shift

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

14. Neighbour effects for all housing: Near repeats Not all on same day Burgled home 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), 67-92.

16. Communicability of Risk Johnson, S.D., and Bowers, K.J. (2004). The burglary as clue to the future: the beginnings of prospective hot-Spotting. European Journal of Criminology, 1(2), 237-255.

17. International comparison (burglary) Using a modified technique to test for disease contagion Demonstrated pattern is statistically reliable in five different countries: USA, UK, Netherlands, Australia, New Zealand Johnson, S.D. et al. (2007). Space-time patterns of risk: A cross national assessment of residential burglary victimization. J Quant Criminol 23: 201-219.

18. Near Repeats: 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, Summers & Pease (2009) Offender as Forager: A Test of the Boost Account of Victimization, Journal of Quantitative Criminology, in press.

19. 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, 641-658.

20. Forecast Accuracy – Next 7 days Johnson, S.D., Bowers, K.J., Birks, D. and Pease, K. (2008).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. New York: Springer.

21. ProMap – Next 7 days (Merseyside, UK) Johnson et al. (2008)

22. ProMap*Backcloth – Next 7 days (Merseyside, UK) Johnson et al. (2008)

23. Retrospective KDE – Next 7 days (Merseyside, UK) Johnson et al. (2008)

24. Thematic map – Next 7 days (Merseyside, UK) Johnson et al. (2008)

25. Confidence versus Accuracy Pearson’s correlation (r) = non significant Spearman’s rho (rs) = non significant Confidence does not predict accuracy

26. Towards an Operational Tool Ensure police geocoding accuracy Check spatio-temporal patterns for each offence type, initially and periodically thereafter Obtain senior police prevention preferences by crime type and crime mix Optimise patrol routes Identify stable and emergent spates and hotspots for bespoke action

27. Presumptive routing

28. Publications Johnson, S.D., & Bowers, K.J. (2004). The Burglary as Clue to the Future: The beginnings of Prospective Hot-Spotting. Bowers, K.J., & Johnson, S.D. (2005). Domestic Burglary Repeats and Space-time Clusters: the Dimensions of Risk. Johnson, S.D., & Bowers, K.J. (2004). The Stability of Space-time Clusters of Burglary. Bowers, K.J., Johnson, S.D., & Pease, K. (2004). Prospective Hot-spotting: The Future of Crime Mapping? Bowers, K.J., & Johnson, S.D. (2004). A Test of the Boost explanation of Near Repeats. Western Criminology Review. Johnson, S.D., Bowers, K.J., Pease, K. (2005). Predicting the Future or Summarising the Past? Crime Mapping as Anticipation. Launching Crime Science.

30. Thank you