shane johnson

Space-time dynamics of crime

Professor Shane D Johnson (Kate Bowers, Toby Davies, Ken Pease)

UCL Department of Security and Crime Science shane.johnson@ucl.ac.uk

Overview

•  Clustering – temporal and spatial

•  Some basic findings

•  Background theory –  Target heterogeneity –  Contagion/boost

•  Space-time clustering of urban crime

•  Space-time clustering of extreme events

Temporal patterns of burglary (hourly)

•  Actual time of burglaries is usually unknown •  Earliest and latest times used

–  Compute the probability of each burglary occurring in each hour –  Compute the error of this estimate (vertical bars in graph)

Temporal burglary patterns – (daily)

Crime Concentration - Burglary

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

Baudains, P., Braithwaite, A., & Johnson, S. D. (2013). Spatial patterns in the 2011 London riots. Policing, 7(1), 21-31.

Crime Concentration Burglary Riots

Repeat Victimization: Concentration and Timing

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.

Weisel, D. L. (2005). Analyzing repeat victimization. US Department of Justice, Office of Community Oriented Policing Services.

Explaining Repeat Victimisation

1.  Target heterogeneity (Burglary)

2.  Contagion or boost

Johnson, S.D., and Bowers, K.J. (2010). Permeability and Crime Risk: Are Cul-de-sacs Safer? Journal of Quantitative Criminology, 26, 113-138.

Pease, K. (1998). Repeat victimisation: Taking stock. Home Office Police Research Group.

Theoretical explanations for repeat victimization

Risk heterogeneity (e.g. Nagin and Paternoster, 1991; 2000)

•  Even if the risk of burglary were homogeneous some repeat victimization would be expected on a chance basis, but risk is heterogeneous

•  Different offenders target the same property due to time-stable differences in target attractiveness or accessibility

–  Stability in the variation of risk drives the correlation between past and future risk

•  Aggregate patterns may thus be a ruse generated by the heterogeneity or victimization risk

•  Loaded dice

The time course: Heterogeneity’s ruse?

Elapsed time

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e to

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Micro-simulation study

•  Bottom-up approach

•  Recorded burglary data 1999-2003 (50,691 events) –  Date, time, location (address and x and y coordinates)

•  2001 Census output area geography –  In simple terms, the system created Output Areas with around 125

households and populations which tended towards homogeneity (http://www.statistics.gov.uk/census2001/op12.asp)

–  Housing type and various other data

•  Ordnance survey address point data (590,856 homes)

High

Medium

Low

Household Risk

0 17.5 35km

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Medium

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Household Risk

Victim selection (weekly patterns)

Heterogeneous risk models

•  Area level risk

•  Area AND within area variation models –  Homes within each area randomly allocated to a particular type

with the model calibrated using styalized facts

•  Seasonal variation

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Number of times victimized (n )

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Heterogeneous risk models

Johnson, S.D. (2008). Repeat burglary victimisation: A Tale of Two Theories. J Exp Criminol, 4: 215-240.

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Weeks between events

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Heterogeneous risk models

Johnson, S.D. (2008). Repeat burglary victimisation: A Tale of Two Theories. J Exp Criminol, 4: 215-240.

Explaining Repeat Victimisation and extending the concept

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 (near repeats)

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

•  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

Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries. A test of the near repeat hypothesis. British Journal of Criminology, 43(3), 615-633.

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.

Knox Analyses Previous analysis does not take account of patterns across streets

The degree to which clustering occurs in Euclidian space can be measured using: - Monte Carlo simulation and Knox ratios (Knox, 1964)

Distance between events in pair

0-100m 101-200m 201-300m

7 days

421

221

189

14 days 246 209 091

Time between events in pair

21 days 102 237 144

 Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries. A test of the near repeat hypothesis. British Journal of Criminology, 43(3), 615-633. 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.

Other Offence Types

Burglary – 5+ studies (Aus, UK, NDL, USA, China, ..)

Bicycle theft – 2+ studies (UK, China)

Theft of and from vehicles – 2+ studies (UK, USA)

Shootings – 1+ studies (USA)

IED attacks – 4+ studies (Iraq, Spain)

Maritime Piracy – 2+ studies (Somalia, World)

Chains of events

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area burglaries

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Isolated pairs

Length  =  9  

Length  =  7  

Johnson,  S.D.  &  Bowers,  K.J.  (2004).  The  stability  of  space-­‐Fme  clusters  of  burglary.  Brit  J  Criminol  44:  55-­‐65.  

Length of clusters

Period of day consistency

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Days between events

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100m

1000m

Johnson, S. D., Birks, D., Mcloughlin, L., Bowers, K., Pease, K. (2007). Prospective Mapping in Operational Context. Home Office Online Report London: Home Office.

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.

Patterns in detection data?

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Johnson, S.D. (2014). How do offenders choose where to offend? Perspectives from animal foraging. Legal and Criminological Psychology, in press.

“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. Cullompton: Willan.

“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.

Extreme Events (2011 London riots)

Contagion (Markov process)

Baudains, P., Johnson, S. D., & Braithwaite, A. M. (2013). Geographic patterns of diffusion in the 2011 London riots. Applied Geography, 45, 211-219.

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Contagion

•  Modifiable Unit Problem •  Spatial and temporal scales

Police deployments

First half (Z scores)

Baudains, P., Johnson, S. D., & Braithwaite, A. M. (2013). Geographic patterns of diffusion in the 2011 London riots. Applied Geography, 45, 211-219.

Second half (Z scores)

Baudains, P., Johnson, S. D., & Braithwaite, A. M. (2013). Geographic patterns of diffusion in the 2011 London riots. Applied Geography, 45, 211-219.

Conclusions

•  Crime clusters in space, time and in space and time –  Urban crime (e.g. burglary, theft from vehicles) –  Insurgency –  Maritime piracy –  Riots

•  Burglars return to previous targets swiftly, exhibiting foraging patterns observed across species

•  Informs crime analysis and responses