Predicting Burglary Hotspots Shane Johnson, Kate Bowers, Ken Pease

Predicting Burglary HotspotsShane Johnson, Kate Bowers, Ken Pease

Conference’s name here 00.00.00

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)


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


Explaining Repeat Victimisation

Risk heterogeneity/Flag hypothesisSome 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.


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?


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”


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.


Repeat Victimisation Makes Time Central

• Surprising how often time is neglected in police mapping.

• Repeat victimisation is a special case of risk communication


Fortnightly variation

High

Low

Burglary

Concentration


Morning shift


Afternoon shift


Overnight shift


• 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


Neighbour effects for all housing: Near repeats

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.

0

500

1000

1500

2000

2500

5 4 3 2 1 0 1 2 3 4 5

Doors apart

Num

ber

of b

urgl

arie

s

1-week

2-week

Burgled home

Not all on same day


2000

2500

3000

3500

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4500

1 2 3 4 5 6 7 8 9 10

house numbers apart (opposite side of street)

num

ber o

f bur

glar

ies

same side equivalent

opposite side


1 2 3 4 5 6

repe

ats

100

200

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500

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900

1000

-6

-4

-2

0

2

4

6

8

residual

time(months)distance (m)

6-8

4-6

2-4

0-2

-2-0

-4--2

-6--4

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.


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.


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.


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.


Forecast Accuracy – Next 7 days

Accuracy (%)

Thematic 30

KDE 50

ProMap 63

ProMap (Backcloth) 71Johnson, 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.


ProMap – Next 7 days (Merseyside, UK)

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Percentage of cells searched

Pe

rce

nta

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of

bu

rga

ry id

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tifi

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Promap

Simulation 95th percentile

Simulation mean

Johnson et al. (2008)


ProMap*Backcloth – Next 7 days (Merseyside, UK)

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100


Pe

rce

nta

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tifi

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Promap*Rds*Houses


Simulation mean



Retrospective KDE – Next 7 days (Merseyside, UK)

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100


Pe

rce

nta

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tifi

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Retrospective KDE


Simulation mean



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- 5

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Percentage of area

Pe

rce

nta

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of

bu

rgla

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de

nti

fie

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Beats by rate per HHSimulation 95th PercentileSimulation Mean (N=99)

Thematic map – Next 7 days (Merseyside, UK)



Confidence versus Accuracy

Low Confidence High Confidence

Lo

w A

ccu

racy

Hig

h A

ccura

cy

Pearson’s correlation (r) = non significant

Spearman’s rho (rs) = non significant

Confidence does

not predict accuracy


Towards an Operational Tool

1. Ensure police geocoding accuracy2. Check spatio-temporal patterns for each offence

type, initially and periodically thereafter3. Obtain senior police prevention preferences by

crime type and crime mix4. Optimise patrol routes5. Identify stable and emergent spates and hotspots

for bespoke action


Presumptive routing


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.

Publications


Thank you

Documents

Predicting Burglary Hotspots Shane Johnson, Kate Bowers, Ken Pease