predictive policing professor shane d johnson (kate bowers, toby davies, ken pease) ucl department...

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Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science [email protected]

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Page 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

Predictive Policing

Professor Shane D Johnson

(Kate Bowers, Toby Davies, Ken Pease)

UCL Department of Security and Crime Science

[email protected]

Page 2: 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

• Influence of the street network

• Resources

Page 3: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

Crime Concentration - Burglary

Page 4: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

Crime Concentration - Burglary

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

Page 5: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

Concentration at places: Repeat Victimization

Page 6: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

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.

Page 7: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

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.

Page 8: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

• Communicability - inferred from closeness in space and time of manifestations of the disease in different people.

An analogy with disease Communicability

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area

burglaries

Page 9: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

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.

Page 10: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

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.

 

Page 11: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

“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.

Page 12: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

“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.

Page 13: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

High

Low

Risk

Forecasting - ProMap

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.

Page 14: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

Event driven and Time Stable factors

Page 15: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

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.

Page 16: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk
Page 17: Predictive Policing Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease) UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

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/)