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Predictive Policing

Predictive Policing. Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk. Overview. Some basic findings Background theory – optimal foraging theory Prospective crime mapping Optimizing predictions

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Predictive Policing

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

  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 Johnson, S.D. (2010). A Brief History of the Analysis of Crime Concentration. European Journal of Applied Mathematics, 21, 349-370.

  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): 224-241.

  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? Johnson, S.D., and Bowers, K.J. (2004).The Stability of Space-Time Clusters of Burglary. British Journal of Criminology, 44(1), 55-65.

  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), 67-92. Johnson, S.D. et al. (2007). Space-time patterns of risk: A cross national assessment of residential burglary victimization. Journal of Quantitative Criminology,23: 201-219.

  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, 25,181-200.

  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, 641-658.

  14. Event driven and Time Stable factors

  15. Event driven and Long-term factors(7- day forecast) 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. 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 (http://www.ucl.ac.uk/jdibrief/analysis) • POP guide (http://www.popcenter.org/tools/repeat_victimization/) • Vigilance Modeller(https://www.vigilancemodeller.net/) • Risk Terrain Modelling(http://www.rutgerscps.org/rtm/)

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