Understanding the Link Between
‘Bus Related Crime’ and Other
Crimes
The 2nd UK National Crime Mapping Conference
9th/10th March 2004
Dr Andrew Newton
Environmental Criminology Research Unit (ECRU),
University of Liverpool
adnewton@liv.ac.uk
Tel 0151 794 3116
Overview of Presentation
•
•
•
•
•
•
Research Context
What Mean ‘Bus Related Crime’
Results of Baseline Questionnaire
Methodology Used
Preliminary Findings (Case Study Area)
Summary – Key Implications
Links Between ‘Bus Related
Crime’ and Other Crimes
•
•
DfT commissioned research
Believe ‘links’ evident
– paucity of studies specifically examine
relationships between
1) area bus traverses and crime levels
2) those who offend on buses (eg fraud, non
payment fare and those with outstanding
warrants/other offences)
the premise of offender self selection
3) both offences and offenders
Joint Research: ECRU/Crime Concern
• ECRU
–
–
–
–
Baseline Questionnaire of Agencies
Establish Data Collect – ‘Bus Related Crime’
Crime Analysis (of available data)
‘Examine links’ (4 case study areas)
• Crime Concern
– Literature Review (Research and Interventions)
– CDRP Audits & Strategies Round 2
(minority refer to bus incidents <10%)
– Review of existing prevention schemes
– ‘Identify best practice’ (3 case study areas)
• Findings Autumn 2004
‘Bus Related Crime’
• What definition do we use?
• How do we record and monitor extent of ?
• How develop baseline information/
'evidence’ to inform policy/target resources?
• What prevention measures can we
implement/ which likely successful?
• How do we evaluate relative success/failure
of measures?
• How do we disseminate findings?
Bus Related Crime (UK)
• Unique set of problems for analysis
• Police don’t record incidents on ‘bus journey’ as
category in own right
• No dedicated force policing buses (unlike BTP
for rail)
• Bus Journey – ‘Whole Journey’ Approach
–
–
–
–
–
include walking to and from stops
waiting at stops
travelling on buses (en route)
all contribute to fear of crime (“bus journey”)
which reported/extent of under-reporting?
• Infrastructure of bus industry (after 1986
deregulation outside of London)
• Difficult to report location (moving incident)
• How encourage staff and passengers to report
• Ownership/Responsibility/Regulation
– London (TfL + Operators)
– 7 PTE areas (+ Operators)
– County Councils (+ Operators)
• All collect information for different purposes and
in different formats
– Consistency
– Minimum recording requirements
Links Between ‘Bus Related
Crime’ and Other Crimes
•
•
Baseline questionnaire of agencies
Establish data collect on ‘bus related crime’
– London (TOCU) + 7 PTE areas (PTEG)
–
South Yorkshire, Merseytravel, West Midlands (CENTRO),
Greater Manchester, Tyne + Wear (NEXUS), West Yorkshire
(METRO), Strathclyde.
– Non PTE areas – County Councils (randomly
selected ~25)
•
Reporting procedure, information collected,
what use made of this
Findings of Questionnaire
• Lack of detailed information collected
• Some collected aggregated statistics
• Directed to operators for individual records
– Number of bus operators run in area
– Deregulation
– Consistency/ How Standardise
• 6 areas -individual route data
• 2 areas -location specific (GIS)
• Limited offender related data
Analysis Restricted by Data
•
Examine relationship
1) between area bus traverses and crime levels
2) offenders (eg fraud, non payment fare and
outstanding warrants- premise of offender self
selection)
3) both offences and offenders
•
‘Bus Journey’
1) En Route Incidents Reported
2) Shelter Damage
Research Approach
•
•
•
•
4 Case Study Areas
‘Bus Related Crime’ Data
Recorded Crime Statistics (Police)
Other Contextual Information
Data Collection
• Bus Related Crime
– PTEs, TOCU (London), Bus Operators, Private
Security Firms
• Location of ‘Bus Incidents’ by
– entire route
– road name
– captured in GIS (x,y)
• Incident Type
– (e.g. Assault, Damage, Fraud, Graffiti, Theft,
Public Order, School Pupil Disorder)
• Date, Time, Cost (damage)
• Other
Case Study Areas
• Police Recorded Crime Records
– Selected Crime Types
– Burglary, Robbery, Assault, Theft of/from
Vehicle, Simple Theft, Criminal Damage,
The identification
of residential
neighbourhoods
Fraud, Possession
of Controlled
Drugs, Public
Offences
(eg threatening
behaviour,
whichOrder
are similar
in terms
of their demographic,
other 'low level
incivilities'),
socio-economic,
ethnic
and housing composition
(eg
ACORN, MOSAIC,
• Contextual
Data SuperProfile Lifestyle)
– Census, Geodemographics, Passenger
Volumes, Route Frequencies
Analysis
• Framework for Analysis
• Location Specific Data (x,y)
– route not captured
• Route Specific Data (to entire route)
• Grid Based (within 250m grids)
• Different Spatial Areas / Units of Analysis
• Different Levels of Aggregation
Ideal
Unit of
World
Analysis?
Buffer Zones
50m
50m
50m
Predefined Boundaries
(eg Police Beats, Census EDs/Output
Areas, other user defined)
Non Specified
Analysis Data Dependent
• If data is not at disaggregate individual (point
level)
• Unit of analysis some extent predetermined
• Example using one of case study areas
• 2000-2002 data (still collecting data)
• Police Data (aggregated to ED level)
• Route Data (specific to entire route, not x,y)
• Methodology Fairly Complicated to Explain
• Series of diagrams to simplify
Ideal World
Police data (aggregated to ED)
5
25
51
3
8
Police Recorded Crime
5
25
51
3
8
Route
Ideal World
data (entire route)
10
10 Incidents
know which area occur in
Information Provided
10
5
25
51
3
For 1 bus route
Do for all bus routes and EDs (in Merseyside)
8
For each ED
3
12
10
25
5
For each ED
• Total number incidents recorded crime
– Subdivide by crime type
• Total number of bus incidents
– for all buses that traverse route
– Don’t know if incident happened on the part of
route within ED area
Crime Risk
• How does the crime risk vary dependent
upon the areas a bus route traverses?
• Police recorded crime (all crime) in each
ED split into 10 deciles (10% of all EDs in
each)
• Lowest Number of Crimes = 1
• Highest Number of Crimes = 10
5
25
51
3
8
Decile 5
25
5
Decile 1
51
Decile 10
3
8
Decile 1
Each ED area
• Total number of incidents on all buses traverse
ED
• ‘Risk’ (measured by all recorded crime)
– Decile
• 1 (lowest)
• 10 (highest)
• Crime Rates
• Denominator for crime rate
– Domestic Burglary per 1000 households
– Robbery per 1000 persons (residential population)
– Devised 3 denominators for research
12
10
25
5
Length of Route (m)
Number of bus stops (exit and
entry points)
Frequency of Services (per week)
100
1000
5
55
Crime Denominator
• Number of incidents by
– Length of Bus Route in each ED
– Frequency of Services (per week for example)
along each ED
– Number of bus stops (exit and entry points)
– Related to number of houses / resident
population
Compare
• Police recorded crime
– each ED
– crime deciles 1 to 10.
• Construct Relative value of total bus
incidents in each ED (assign total for route
to all segments along route)
10
5
25
51
3
8
Compare Relative Risk Rates
• Police recorded crime
– each ED
– crime deciles 1 to 10.
• Relative value of total bus incidents in each
ED (used total for route for each segment)
• Test if risk relates to EDs a bus traverses
(high and low crime)
• Denominators for rates
• Number of journeys
• Length of routes
• Number of bus stops
Police - All Recorded Crime
Relative Risk (per 10 journeys)
2.5
2
1.5
Incidents
by
frequency
of service
1
0.5
0
1
2
3
4
5
6
Decile
7
8
9
10
Police - All Recorded Crime
Relative Risk (per 100 metres)
7
6
5
4
Incidents
by length
of route
3
2
1
0
1
2
3
4
5
6
Decile
7
8
9
10
Police - All Recorded Crime
Relative Risk (per bus stop)
12
10
8
6
Incidents
by
number
of bus
stops
4
2
0
1
2
3
4
5
6
Decile
7
8
9
10
• Suggests routes which traverse high crime
deciles at greater risk of crime
• Influence of
– length of route (per 100m)
– number of journeys (per 10)
– number of stops
Police Recorded Crime
12
Relative Risk
10
8
Frequency Services
Length Route
Number bus stops
6
4
2
0
1
2
3
4
5
6
Decile
7
8
9
10
Youths Calling Annoyance Calls
10
9
8
Relative Risk
7
6
Frequency
Services
5
Length Route
4
Number bus
stops
3
2
1
0
1
2
3
4
5
Decile
6
7
8
9
10
Criminal Damage Recorded by Police
12
Relative Risk
10
8
Frequency Services
6
Length Route
Number bus stops
4
2
0
1
2
3
4
5
6
Decile
7
8
9
10
Indicative Findings
• Risk of crime (to passengers, staff and
vehicles) on those routes that traverse high
crime areas greater than others
• Risk of crime on routes go through high
crime areas with relatively high numbers of
stops are at greatest risk (multiple entry and
exit points)
• Importance of more (+ more detailed) data
Contextual data
• Census Data
• Geodemographic (SuperProfile Lifestyle)
• Deprivation Indices
Index of Local Conditons (ILC)
16
14
Relative Risk
12
10
Frequency Services
Length Route
Number bus stops
8
6
4
2
0
1
2
3
4
5
6
Decile
7
8
9
10
SuperProfile Lifestyle
'Most
Deprived'
12
'Urban
Venturers'
Hard
Pressed
Families
Relative Risk
10
8
Frequency Services
Length Route
Number bus stops
6
4
'Affluent
Acheivers'
2
0
1
2
3
4
5
6
Decile
7
8
9
10
Number of Youths 5-19 (Census)
12
Relative Risk
10
8
Frequency Services
Length Route
Number bus stops
6
4
2
0
1
2
3
4
5
6
Decile
7
8
9
10
Importance of Route Information
•
•
•
•
Even if have no location information
Other methods of analysis available
Resource Target Table
Number of incidents of crime on bus routes
compared with total number of bus routes
• Jan 2000 to Dec 2002
Num ber of
Incidents
352
333
288
273
266
210
183
172
158
156
144
132
131
129
91
89
87
28
25
24
23
20 –10
9–1
0
Num ber of
Routes
Affected
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
3
2
1
4
38
134
479
Cum ulative
Num ber of
Routes
1
2
3
4
5
6
7
8
9
10
11
12
13
14
16
17
18
49
51
52
56
94
228
707
Cum ulative
Num ber of
Incidents
352
685
973
1246
1512
1722
1905
2077
2235
2391
2535
2667
2798
2927
3018
3107
3194
4440
4465
4489
4512
4640
4685
n/a
Cum ulative % of Cum ulative % of
Routes
Incidents
0.14
7.51
0.28
14.62
0.42
20.77
0.57
26.6
0.71
32.27
0.85
36.76
0.99
40.66
1.13
44.33
1.27
47.71
1.41
51.04
1.56
54.11
1.7
56.93
1.84
59.72
1.98
62.48
2.26
64.42
2.4
66.32
2.55
68.18
6.93
7.21
7.36
7.92
13.3
32.25
100
94.77
95.3
95.82
96.31
99.04
100
n./a
Target resources by incident type and
time of day
Time of Day
Crime Type
1 Assault / VAP /
Offensive
Weap
2 Theft
3 Criminal
Damage
4 Missile
Projected
5 Drugs/Alcohol
6 Disorder (all)
7 Disorder
(youth)
8 Fraud / Forgery
9 Other
Not
Recorded
0600- 1000- 14000959 1359 1759
18002159
22000159
02000559
Total %
Total
Number
0.1
0.0
0.3
0.1
0.7
0.8
1.9
1.6
2.7
1.6
1.1
0.5
0.8
0.0
7.5
4.7
491
307
0.1
0.3
0.4
2.0
2.8
1.2
0.8
7.5
490
0.1
0.4
2.8
9.5
26.2
6.3
0.3
45.6
2991
0.0
0.2
0.5
1.1
1.3
1.3
0.4
4.8
314
0.1
0.2
0.5
1.1
1.0
1.2
1.7
5.8
382
0.1
0.2
0.7
3.8
7.1
2.5
0.6
15.0
983
0.0
0.0
0.4
0.0
0.9
0.0
2.2
0.1
2.1
0.3
1.2
0.1
1.5
0.1
8.5
0.7
555
49
Total %
0.6
2.2
7.2
23.2
45.2
15.4
6.2
100.0
Total Number
39
142
473
1524
2966
1012
406
6562
Target resources by incident type, time
of day and route
Time of Day
Crime Type
Not
Recorded
06000959
10001359
1400- 1800- 2200- 0200Total
1759 2159 0159 0559 Total % Number
Top 15 ranked routes for incidents of crime (out of 707 routes)
1
2
Assault / VAP /
Offensive Weap
Theft
0.08
0.02
0.12
0.05
0.30
0.27
0.67
0.55
0.99
0.82
0.38
0.20
0.18
0.00
2.73
1.90
179
125
3
4
Criminal Damage
Missile Projected
0.06
0.06
0.12
0.20
0.15
1.20
0.91
4.60
1.39 0.53
11.38 2.86
0.21
0.11
3.38
20.42
222
1340
5
6
Drugs/Alcohol
Disorder (all)
0.02
0.00
0.08
0.15
0.27
0.21
0.72
0.47
0.78
0.32
0.66
0.53
0.17
0.64
2.68
2.33
176
153
7
8
Disorder (youth)
Fraud / Forgery
0.00
0.05
0.11
0.26
0.41
0.49
1.69
1.30
3.29
1.49
1.17
0.56
0.12
0.72
6.80
4.86
446
319
9
Other
Total %
0.00
0.03
0.02
0.03
0.17
0.03
0.03
0.30
20
0.27
1.11
3.34
10.94 20.63 6.93
2.18
45.41
18
73
219
Total Number
718
1354
455
143
2980
Shelter Damage by Route
Number
of Shelters NumberExpected
of Incidents
Observed
Top 15 routes (incidents)
667 (20%)
6507 (40%)
Top
15
routes
6507
3393
Other routes
2405 (80%)
9121 (60%)
Otherroutes
routes
9121
12235
All
3072
15628
Chi Square Analysis
Significant at p<0.001
Good information / evidence
• What practitioners need to collect
• Skills need to analyse
• Implications for policy makers
• Establish Baseline Statistics
• Monitor Intervention
Wish List of Data
•
•
•
•
•
•
Incident Number
Time and Date
Route Number
Location (x,y)
Police crime number
Incident type
– code and description
• Other
• cost of damage
• details of perpetrators
• details of victims
• number of persons
• action taken
• description (free text)
• Disaggregate Police Data
• Essential
• Digitised bus routes
• Desirable
• Passenger volume/ route
• Resource Dependent
frequency
• Contextual Information
Part 2
How evaluate police intervention
Newton, A.D., Johnson, S.D., & Bowers, K.J. (2004).Crime
on Bus Routes: An Evaluation of a Safer Travel Initiative.
Policing, an International Journal of Police Strategies and
Management. Forthcoming
Key Implications
• Bus route crime suggest related to crime in areas
traverses
• Importance of entry/exit points (bus stops)
• Limited data available for analysis
• Evidence of police operation impacting on some
types of crime
• Suggest bus crime incidents highly concentrated
• Need data for baseline statistics, resource
targeting, formulating policy and evaluating
prevention measures
• Why collect data? Skills necessary to analyse?
Importance of multi-agency data sharing
Mr Andrew Newton,
Research Associate,
Environmental Criminology Research Unit (ECRU),
Department of Civic Design,
University of Liverpool,
England,
L69 7ZQ.
tel : +44 (0)151 794 3116
fax : +44 (0)151 794 3125
email: adnewton@liv.ac.uk
http://www.liv.ac.uk/civdes/staff/newton.htm