Professor Shane D Johnson UCL Department of Security and Crime Science shanejohnsonuclacuk Predicting future patterns Questions we might ask How many burglaries are expected in the next few days ID: 179978
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Slide1
Moving Towards Predictive Policing
Professor Shane D JohnsonUCL Department of Security and Crime Scienceshane.johnson@ucl.ac.ukSlide2
Predicting future patterns
Questions we might askHow many burglaries are expected in the next few days?Bursty
analysis (Johnson et al., 2012)?
Self-exciting Point Process?
What is likely to be stolen (Bowers & Johnson, 2012
)?
Where will burglaries most likely next occur?
What is the relative risk within an area for (say) the next seven days
By day/nightSlide3
Overview
Spatial patterns (risk heterogeneity)Patterns and predictors at the street segment levelSpace-time clustering (event dependency)What happens in the wake of an offense?Point level analysis
Collaboration with West Midlands Police
Combining the approaches to analysisDisplacement?Slide4
First things first: Spatial clustering of Burglary?
Ordnance Survey © Crown Copyright. All Rights reserved
Johnson, S.D., and Bowers, K.J. (2010). Permeability and Crime Risk: Are Cul-de-sacs Safer?
Journal of Quantitative Criminology
, 26, 113-138.Slide5
Spatial Clustering at the Street Level?
Johnson, S.D. (2010). A Brief History of the Analysis of Crime Concentration. European Journal of Applied Mathematics, 21, 349-370.Highest risk segments:5% of homes
40% of burglarySlide6
Crime Pattern Theory
Offender search patterns and personal activity space
Home to work to recreation – nodes and paths, and mental maps
Looking for opportunities
Paths
people take and the nodes they inhabit explain their risks to
victimisationSlide7
Hypotheses
H1 – the risk of burglary will be greater on Major roads and those intended to be most frequently used H2 – the risk of burglary will be highest on the most connected streets, particularly those connected to major roads
H3 - the risk of burglary will be lower in cul-de-sacs and, in particular, in those that are non-linear
Johnson, S.D., and Bowers, K.J. (2010). Permeability and Crime Risk: Are Cul-de-sacs Safer?
Journal of Quantitative Criminology
, 26, 113-138.Slide8
Road classification
OS classificationMajorMinorLocalPrivate
Manual classification (~11k street segments)LinearNon-linear cul-de-sacs (Sinuous)Slide9
Cul-de-Sacs
Mostly Linear
Mostly Sinuous
Ordnance Survey © Crown Copyright. All Rights reservedSlide10
Aggregate Results by Segment TypeSlide11
Concentration at places: Repeat VictimizationSlide12
Is Victimization Risk Time-Stable?
Timing of repeat victimization
Johnson, S.D.,
Bowers
, K.J
., & Hirschfield (1997)
.
New Insights into the Spatial and Temporal Distribution of Repeat Victimization.
British Journal of Criminology
,
37(2), 224-241.Slide13
Explaining Repeat Victimisation
Boost Account
Repeat victimisation is the work of a returning offender
O
ptimal 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?Slide14
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. Slide15
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
burglariesSlide16
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; Johnson et al.,
2007)
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.Slide17
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
.Slide18
Near Repeats – Foraging
What do offenders say?“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)
“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,
L., Johnson
,
S.D., &
Rengert
, G.
(2010) The Use of Maps in Offender Interviewing. In W.
Bernasco
(Ed.)
Offenders on Offending.
Willan
.Slide19
High
Low
Risk
Forecasting burglary
risk
Bowers, K.J., Johnson, S.D., and Pease, K. (2004). Prospective
Hotspotting
: The Future of Crime mapping?
British Journal of Criminology
, 44(5), 641-658.Slide20
Computer Simulation
Pitcher, A., & Johnson, S.D. (2011). Exploring Theories of Victimization Using a Mathematical Model of Burglary.
Journal of Research in Crime and Delinquency
, 48(1), 83-109.Slide21
Forecast Accuracy
Grid (50m X 50m cells)Slide22
One
BCU – Night (8pm to 8am)
Model parameters may need updating:
Changes in offenders at liberty
Changes due to police strategy
Other factorsSlide23
But what’s the point of prediction, targeted policing will only displace the problem right?Slide24
Summary and Combining the Approaches
Triangulation across methodsBurglary more likely at more connected segments Analyses ignore patterns over timeRisk of crime temporarily elevated around victimized homes (predictable in space-time)Topology of the street network ignoredUnits of analysis “cells” not street segments
West Midlands Police and UCL Dept
SCS Collaboration (Toby Davies)Does risk diffuse along the street network in predictable ways?Is risk more likely to be diffused along certain types of segment?
Other offence types
Randomized Controlled TrialSlide25