Innovation in Burglary Analysis:
Combining Intelligence on the Geography and
Timing of Burglary & Target Hardening to Inform
Resource Allocation
4th National Crime Mapping Conference
24th – 25th May 2006, London
Professor Alex Hirschfield & Dr. Andrew Newton
Applied Criminology Centre (ACC),
University of Huddersfield
Structure of Presentation
Initial Questions
•
•
•
•
•
Why analyse crime prevention ?
What questions can we ask ?
What data do we need ?
What types of new indicator can we derive ?
How can we interpret them ?
Examples from Manchester CDRP
Conclusions and Issues for Discussion
Questions we ask and those we don’t
Typical Questions on Crime
• Where does crime occur ?
• When does crime occur ?
• Which areas have significantly
high crime ?
• Which areas have significantly
low crime ?
• How geographically
concentrated is crime ?
• Do areas with high levels of one
crime type have high levels of
another ?
• What else can we say about high
crime areas
Questions on Prevention
•
•
•
•
•
•
•
Where are we putting our resources
for crime prevention ?
How does this vary seasonally,
monthly, by day of week and by time
of day ?
Which areas have significantly high
crime prevention investment ?
Which areas have significantly low
crime prevention investment ?
How geographically concentrated is
crime prevention ?
Do areas with high levels of one type
of crime prevention have high levels
of another ?
How do crime prevention measures
correspond to the location and timing
of different types of crime ?
Analysing Crime Prevention:
The Challenge
• How can we categorise crime prevention ?
• How can we identify systematically what
interventions are being implemented, where and
when ?
• How can we quantify how much crime prevention
is being delivered (the dosage) ?
• How can we develop new measures, indices and
tools to analyse the dispensation of crime
prevention and compare it with crime ?
Data we have and data we don’t
DO HAVE: Data on Crime
Offence (location, crime
code, time, MO)
Victim (location, age,
gender, ethnicity)
Calls or Service (location,
time, incident code)
Offenders (Offence, age,
gender, residential
location)
DON’T:Data on Crime Prevention
Type of Intervention
Location
Date (dd,mm,yy)
Intensity (No of locks, bolts, gates)
Coverage (No of properties
protected)
Cost (policy design, policy
targeting, equipment,
implementation)
Data analysis tools we have and data
analysis tools we don’t
Crime
Crime Prevention (CP)
Crime rates
crime mix
crime concentration
Spatial overlap analysis
Composite Crime Indices
Expenditure on CP per household
Intensity of measures by time & location
Cumulative intensity by location
Expenditure per 1000 crimes
Intensity of implementation per 1000
crimes
Crime Prevention Policy Profiler:
Scarcity, overlap, synergy
Crime/ crime prevention gap detectors
Crime prevention investment hot spots
Point distribution maps
Spatial/ Temporal Clustering & Hot
Spots
Repeat crimes
Time course of repeat victimisation
Additional Considerations
Relationships between crime prevention and
crime
• Is there a variation in Crime Prevention
investment across communities with similar
levels and patterns of crime ?
• How far is there an inverse prevention law (i.e.
where areas with lower crime receive more
attention) ?
• How can we better align crime prevention &
crime risk ?
(a) Crime Patterns
(b) Crime Prevention Evidence Base
(d) Targeting Crime
Prevention Measure
( c) Selection of Crime Prevention
Measure
(a) Crime Patterns
(b) Crime Prevention Evidence Base
(e) Existing
Distribution of
Crime Prevention
( c) Selection of Crime Prevention
Measure
(d) Targeting
Crime Prevention
Measure
A REFINED MODEL ?
Manchester Project I
• Burglary performance in Manchester in 2003
highlighted that considerable reduction was required
to meet national and local PSA targets
• Manchester selected for ODPM PLACES initiative
(2004) to improve performance of selected areas in
key domains including crime
• Although burglary fell in 2004/05 not all areas within
the city registered the same reductions –disparities
remained between the wards.
Manchester Project II
• GONW commissioned researchers to provide
analysis, evaluation and training for the CDRP
•
•
•
•
Target hardening as a case study of this approach
Focus on 2 Specific Wards
Relate change in target areas to the rest of city
Manchester CDRP:
– Approx 200,000 Domestic dwellings
– Population just under 400,000
– 2004 Burglary Rate 40/1000
What do we need to know about:
(a) Burglary patterns and trends ?
(b) Policy Interventions ?
(c) The relationship between Policy
Interventions and Burglary ?
Burglary Patterns and Trends I
•
•
•
•
•
•
•
•
•
Where are the areas with the highest burglary rates ?
How many burglaries do they have ?
How are burglaries distributed with them ?
Where are the ‘hot spots’ ?
Which streets have the highest concentration of burglaries?
What proportion are attempted burglaries ?
How many burglaries are repeat victimizations ?
Where are the repeats ?
How prominent is burglary as a proportion of all crime ?
Burglary Patterns and Trends II
• Have tools to analyse
• Historical and current trends
– Spatial analysis
– Temporal analysis (aoristic analysis)
– Spatio-temporal analysis
• Move towards predictive
– (Bowers and Johnson 2004)
Ward1 – Burglary
RTT1 by Street, Ward 1
Num ber of
Incidents
Num ber of
Streets
Cum Num
Incidents
Cum Num
Streets
%
incidents
%
streets
cum %
incidents
cum %
streets
31
23
21
20
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
1
1
3
1
1
1
1
3
3
2
1
4
3
1
10
11
14
16
24
24
42
86
143
31
54
117
137
155
172
188
233
275
301
313
357
387
396
476
553
637
717
813
885
969
1055
1055
1
2
5
6
7
8
9
12
15
17
18
22
25
26
36
47
61
77
101
125
167
253
396
2.94
2.18
5.97
1.90
1.71
1.61
1.52
4.27
3.98
2.46
1.14
4.17
2.84
0.85
7.58
7.30
7.96
7.58
9.10
6.82
7.96
8.15
0.00
100.00
0.3
0.3
0.8
0.3
0.3
0.3
0.3
0.8
0.8
0.5
0.3
1.0
0.8
0.3
2.5
2.8
3.5
4.0
6.1
6.1
10.6
21.7
36.1
100.0
2.94
5.12
11.09
12.99
14.69
16.30
17.82
22.09
26.07
28.53
29.67
33.84
36.69
37.54
45.12
52.42
60.38
67.96
77.06
83.89
91.85
100.00
100.00
n/a
0.3
0.5
1.3
1.5
1.8
2.0
2.3
3.0
3.8
4.3
4.5
5.6
6.3
6.6
9.1
11.9
15.4
19.4
25.5
31.6
42.2
63.9
100.0
n/a
Burglary Rate Per Quarter
Burglary Rate Per 1000
Households
Burlary Rate
20.0
15.0
Wards 1 + 2
10.0
Rest Manchester
5.0
0.0
Q1
Q2
Q3
Q4
Q5
Quarter (2003 to 2004)
Q6
Q7
Q8
Burglary Ratio Per Quarter
Burglary Ratio (Ward 1+2 compared rest Manchester)
R a ti o
2.0
1.5
Divergence
1.0
Burglary Ratio
0.5
0.0
Q1
Q2
Q3
Q4
Q5
Quater (2003-2004)
Q6
Q7
Q8
Manchester Target Hardening Data
• Have
–
–
–
–
–
–
4 years
All of Manchester
17,000 records
Date
Postcode and Address – Geocoded
Proactive/Reactive
• Don’t have
– Details of actual work done/breakdown of cost
– Work brought to a TH standard
Data Sets Generated from Burglary
and Target Hardening Records
1.
2.
3.
4.
5.
6.
All Burglaries: Each Record = one burglary incident
All Properties Burgled: Each record = one address + no. of burglaries
All Target Hardening Activities: Each record = one target hardening job
All Properties Target Hardened: Each record = one address + no of TH jobs
All Properties Target Hardened: previously and/or subsequently burgled
All properties Burgled: not in the Target Hardening Database
Geo-coding:
Address, Postcode, Easting, Northing, Ward Code, Police Beat
Burglary: Apr 2002 – Mar 2005
Target Hardening: Jan 2001 – Mar 2005
Burglary/ Target Hardening
Relationship
How far was target hardening taking place in wards with
the highest burglary rates ?
To what extent did target hardening take place within burglary hot
spots ?
How similar was the spatial distribution of target hardening to that of
Burglary ? (Index of Dissimilarity)
How did the spatial relationship between the two change over time ?
How concentrated was target hardening at street level ?
How did this relate to the concentration of burglary ?
What was the gap between the two wards and the rest of Manchester
in burglary and in target hardening activity ?
Was there convergence or divergence between them over time ?
What else could/should we examine ?
Target Hardening per 1,000 Hlds
Target Hardening Rate by Burglary Level
70
60
50
Top 4 Burglary
Rest of Manchester
Bottom 3 Burglary
40
30
20
10
0
2001/02
2002/03
2003/04
2004/05
Notes:
Burglary rates exceeding 1sd above the mean in:
2002/03 >65.9 burglaries per 1,000 households
2003/04 >64.6 burglaries per 1,000 households
2004/05 >49.9 burglaries per 1,000 households
N
N
N
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1.2
1.2
Target Hardening
HardeningYear
Year3
21
Target
Burglary Hot
Hot Spots
Spots Year 1
Burglary
Ward Boundary
Boundary
Ward
1.8 Kilometers
Kilometers
1.8
The Index of Dissimilarity
IOD= 0.5 ∑ | bi/B - ti/T |
• Where bi is the number of burglaries in Output Area i
• B is the number of burglaries in Manchester
• Where ti is the number of target hardened properties in Output
Area i and
• T is the number of target hardened properties in Manchester
IOD = 0.1 minimum dissimilarity
1.0 maximum dissimilarity
Year Manchester Ward 1 Ward 2
2002/3
0.53 0.51 0.31
2003/4
0.44 0.18 0.44
2004/5
0.39 0.14 0.29
INDEX OF DISSIMILARITY
BURGLARY AND TARGET HARDENING
Io D 1 = M a x im u m D is s im ila rity
1.00
1
2
3
Manchester
0.10
Ward 1
Financial Year
Ward 2
Target Hardening
RTT2 by Street, Ward 1
Number of Number of Cum Num Cum Num %
Incidents Streets
Incidents Streets
incidents
31
23
21
20
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
1
1
3
1
1
1
1
3
3
2
1
4
3
1
10
11
14
16
24
24
42
86
143
31
54
117
137
155
172
188
233
275
301
313
357
387
396
476
553
637
717
813
885
969
1055
1055
1
2
5
6
7
8
9
12
15
17
18
22
25
26
36
47
61
77
101
125
167
253
396
2.9
2.2
6.0
1.9
1.7
1.6
1.5
4.3
4.0
2.5
1.1
4.2
2.8
0.9
7.6
7.3
8.0
7.6
9.1
6.8
8.0
8.2
0.0
cum %
% streets incidents
0.3
0.3
0.8
0.3
0.3
0.3
0.3
0.8
0.8
0.5
0.3
1.0
0.8
0.3
2.5
2.8
3.5
4.0
6.1
6.1
10.6
21.7
36.1
2.9
5.1
11.1
13.0
14.7
16.3
17.8
22.1
26.1
28.5
29.7
33.8
36.7
37.5
45.1
52.4
60.4
68.0
77.1
83.9
91.9
100.0
100.0
Cum %
TH
2.6
4.1
9.0
9.7
10.7
11.4
12.5
15.4
17.7
19.5
20.2
27.5
29.0
29.4
35.8
45.9
51.4
60.0
65.0
69.6
77.9
91.7
100.0
cum %
streets
0.3
0.5
1.3
1.5
1.8
2.0
2.3
3.0
3.8
4.3
4.5
5.6
6.3
6.6
9.1
11.9
15.4
19.4
25.5
31.6
42.2
63.9
100.0
Burglary Ratio Per Quarter
Burglary ratio and Cum % TH properties, wards 1 + 2
7.00
6.00
5.00
4.00
Burglary Ratio
3.00
2.00
1.00
0.00
Cum % TH Properites
1
2
3
4
5
Quarter
6
7
8
Burglary Ratio; TH Ratio, Cm%TH
Ratios & Cumulative TH
100
10
1
1
2
3
4
5
0.1
Quarterly Period
6
7
8
Further Analysis
(a)
(b)
(c)
Households target hardened since 2000
Never, once, twice, three or more times by Ward
Households Proactively Target Hardened
Households Reactively Target Hardened
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
Burglaries per 1,000 (a,b,c)
Repeat Burglaries per 1,000 (a,b,c)
Survival Time (interval between burglaries) for a,b,c
Attempted Burglaries per 1,000 (a,b,c)
Successful Burglaries per 1,000 (a,b,c)
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
The two targeted wards as a ratio to the rest of Manchester for each of
the above
Wards by Index of Deprivation; Anayses by Geodemographics
Food for Thought I
• Is it sufficient to implement intervention
policies based upon crime data and
environmental data alone?
• What new policy analysis tools can be
developed to assist us?
• How useful are such tools and what are their
implications?
Food for Thought II
• How can we identify systematically what
interventions are being implemented, where and
when ?
• How can we quantify how much crime prevention is
being delivered (the dosage) ?
• How far is its capture fundamentally different from
that of recording crime ?
• Who will collect such data ?
• How much will it cost to do this well ?
(systematically and accurately)
• How can we develop new measures, indices and tools
to analyse the dispensation of crime prevention and
compare it with crime ?
Contact Details
Professor Alex Hirschfield,
Applied Criminology Centre,
University of Huddersfield,
Floor 14, Central Services Building,
Quuensgate Huddersfield HD1 3DH
Tel: 01484 473676
Email: a.hirschfield@hud.ac.uk