The Predictive Policing Challenges of
Near Repeat Armed Street Robbery
CORY HABERMAN
Research Associate
Temple University
JERRY RATCLIFFE
BSC PHD FRGS
Professor & Chair
Temple University
2011 International Crime & Intelligence Analysis Conference
Manchester, UK
The near repeat phenomenon
•
Communicability of risk
•
•
After a previous crime event, nearby targets have an
increased risk of victimization for a short period of time.
Space and time clustering
See: Townsley et al., 2003; Bowers & Johnson, 2004; Ratcliffe & Rengert, 2008.
Evidence of near repeat patterns
Location
Observed
Event
Temporal
Parameter
Spatial
Parameter
Townsley, Homel, &
Chaseling (2003)
Queensland, AUS
Burglary
2 months
200m
≈650 ft
Johnson and Bowers
(2004)
Merseyside, UK
Burglary
1 month
100m
≈330 ft
10 Locations
5 Countries
Burglary
14 days
100m
≈330 ft
Ratcliffe & Rengert
(2008)
Philadelphia, USA
Shootings
14 days
≈122 m
400 ft
Townsley, Johnson,
& Ratcliffe (2008)
Iraq
IED
Attacks
2 days
1km
≈3281 ft
Researchers
Johnson et al.
(2007)
Identifying near repeat patterns
The Knox Method (Knox, 1964; Townsley et al., 2003; Bowers & Johnson, 2004, Johnson et al., 2007)
•
•
•
First measure spatial and temporal distance between all point pairs
Construct a contingency table using hypothesized parameters
Calculate frequency of point pairs in each cell
Spatial parameter
Te m p o r a l p a r a m e t e r
Distance/Time
0 to 7 days
8 to 14 days
15 to 21 days
More than
21 days
1 to 400m
260
236
222
281
401 to 800m
190
210
232
362
801 to 1200m
164
155
175
477
1201 to 1600m
133
188
201
588
Identifying near repeat patterns
•
Monte Carlo simulation used to create an expected
distribution of space-time distance pairs
•
•
•
Randomize dates
Re-measure spatial & temporal distances & then
Re-calculate cell frequencies in the contingency table
Jan 7
May 9
Oct 19
Oct 21
March 4
May 14
June 28
Oct 15
Nov 22
Dec 4
Feb 22
Identifying near repeat patterns
Simulations are repeated enough times to ensure
observed pattern did not occur on the basis of chance
Nov 22
Oct 15
Dec 4
March 4
May 9
Jan 7
Oct 19
May 14
Oct 21
Feb 22
June 28
Identifying near repeat patterns
For each cell in the contingency table, the observed
frequency of point pairs is compared to the distribution
of expected frequencies from the simulations.
Frequency
1/1000
Observations
p = .001
150
Number of point pairs
350
The current study
•
We know very little about the extent to which the near
repeat process occurs (see Johnson et al., 2007; Townsley, 2007 for
exceptions looking at burglary).
•
Research Questions
•
•
Does the near repeat process occur in a chain-like manner?
What impact do near repeat chains have on the formation of
hot spots?
Data
•
2009 Philadelphia PD armed street robbery events
•
UCR Robbery Rates (FBI, 2009)
•
Philadelphia: 584 per 100,000
•
USA Total: 133 per 100,000
•
XY-coordinates
•
Date of occurrence
•
98% geocoding hit rate, n=3,556
Near Repeat Calculator (Ratcliffe, 2007)
Available for download: www.temple.edu/cj/misc/nr/
Near repeat armed street robbery pattern
Distance / Time
0 to 7 days 8 to 14 days 15 to 21 days 22 to 28 days
More than
28 days
Same location
2.49**
1.89**
1.18
0.89
0.90
1 to 400 ft
1.80**
0.89
0.85
1.11
0.97
401 to 800 ft
1.31**
1.01
0.86
0.99
0.99
801 to 1200 ft
1.16*
1.03
0.95
1.00
0.99
1201 to 1600 ft
1.09
1.02
1.01
0.94
1.00
More than 1600 ft
1.00
1.00
1.00
1.00
1.00**
*p<.01; **p<.001
Identifying near repeat chains
Events occurring within 7 days and 1200 ft were
considered within the same near repeat chain.
3-Event Near Repeat
Oct. 15 Chain
450 ft.
450 ft.
Oct. 10
625 ft.
Oct. 19
1550 ft.
Oct. 17
1250 ft.
Nov. 10
Near repeat armed street robbery chains
Total
Events
Total
Chains
Mean
Risk Time
7
1
-----
18
3
11.0 days
40
8
10.8 days
84
21
8.3 days
237
79
5.5 days
502
251
3.1 days
888
363
4.2 days
n=3,556
Armed street robbery hot spots
•
Hierarchical Nearest Neighbor
Clustering
•
52 first-order clusters
•
Only 2.3% of all intersections
•
But roughly 21% of all ASRs
•
Avg. of 11.9 intersections
•
Avg. of 14.19 ASRs (max=29)
•
Avg. of 6 NR ASRs (max=17)
Proportion of NR events within hotspots
Min: 0.00
Frequency
Max: 0.80
Mean: 0.41
St. Dev: 0.20
Proportion of near repeat events within hot spots
Temporal stability of ASR hot spots
•
Homogeneity Index (Gibbs & Martin, 1962; Blau, 1977)
•
Describes the dispersion of data across nominal categories
h=1-∑pi2
•
•
•
•
•
pi = the proportion data in each category (i)
Bounded statistic from 0 to 1-(1/# of categories)
Value of 0 indicates data are concentrated within one group
Values close to maximum indicate data are dispersed across
categories
Used the 13 28-day intervals for 2009 as the categories
to compute temporal stability for each hot spot
Te m p o r a l s t a b i l i t y s t a t i s t i c
Temporal stability & NR composition of hot spots
Proportion of near repeat robberies
Implications
•
Past ASRs predict future ASRs
•
But chasing robbery in real time is likely to be difficult
•
•
•
•
Retrospective micro-level hot spots were stable over time
•
•
Sophisticated (& likely automated) surveillance system
Decision-making framework allowing quick & appropriate responses
Operational flexibility to re-allocate resources quickly
HSP using retrospective analyses empirically shown to be effective
Hot spot strategies should consider the NR composition of
targeted hot spots to more effectively design strategies
The Predictive Policing Challenges of Near
Repeat Armed Street Robbery
CORY HABERMAN
Temple University
cory.haberman@temple.edu
JERRY RATCLIFFE
Temple University
2011 International Crime & Intelligence Analysis Conference
Manchester, UK