Neighbourhoods
matter: spill-over effects
in the fear of crime
Ian Brunton-Smith
Department of Sociology, University of Surrey
1
Motivation

Increasing interest in influence of neighbourhood
on crime and disorder (and public concerns)

Academic – social disorganisation; collective
efficacy, neighbourhood disorder, subcultural
diversity

Policy – community policing, safer
neighbourhoods, reassurance policing, CSOs

But limited understanding of ‘neighbourhood’
and methodological weaknesses
2
Our study

The role of neighbourhoods in shaping
individual fear
 Key

mechanisms, limitations of existing work
Detailed neighbourhood analysis
 Defining
neighbourhoods,
 Composition and dependency
 Spillover effects
3
Fear of crime
Important component of subjective wellbeing and community health
 Frequently employed as performance target
for police/government

 More
important than crime itself?
Safer neighbourhoods scheme
 Neighbourhood mechanisms shaping fear

 Research
inconclusive – ‘paradoxical’ nature of
fear
4
Neighbourhood mechanisms
5
1. Incidence of crime

For several reasons neighbourhoods experience
widely different levels of crime
 If
individuals respond rationally to objective risk,
expressed fear should be higher in areas where crime
is higher (Lewis and Maxfield, 1980)


But evidence for this relationship is surprisingly
thin/inconsistent
Limitations of existing evidence – spatial scale,
crime measure, metropolitan focus
6
2. Visible signs of disorder

Hunter (1978) – low level disorder serves as
important symbol of victimization risk
 Graffiti,



litter, teenage gangs, drug-taking
Can be more important than actual incidence of
crime – visibility and scope
‘Broken windows’ theory (Wilson and Kelling
1982); Signal crimes (Innes, 2004)
Existing evidence relies on perception measures
to capture disorder
 Systematic
social observation finds no clear link
7
3. Social-structural characteristics

Social disorganisation theory (Shaw and Mckay
(1942)
 Collective





efficacy – (Sampson et al.,)
Residential mobility, ethnic diversity, and
economic disadvantage reduce community
cohesion
which weakens mechanisms of informal control
which leads to an increase in criminal and
disorderly behaviour
which in turn reduces community cohesion
…and so on
8
Key limitations of existing studies

Failure to account for non-independence of
individuals within neighbourhoods
 More
recent studies using multilevel provide clearer
evidence



Reliance on respondent assessments of
disorder, crime and structural characteristics
(often examined in isolation)
Theoretically weak neighbourhood definitions –
wards, census tracts, regions
Insufficient compositional controls
9
Our analysis

Neighbourhood effects on fear across England
 Full


range of urban, rural and metropolitan areas
Adjust for dependency using multilevel models
Detailed characterisation of local
neighbourhoods using full range of census and
administrative data
 Independent
of sample
 Spillover effects
10
Data

British Crime Survey 2002-2005

Victimization survey of adults 16+ in
private households

Response rate = 74%
11
Defining neighbourhoods

Studies generally rely on available boundaries –
wards, census tracts, PSU, region
 Vary
widely in size and not very meaningful in terms
of ‘neighbourhood’ (Lupton, 2003)

BCS sample point? = postcode sector

We use Middle Super Output Area (MSOA)
geography created in 2001 by ONS
 Still
large, but stable and closer to ‘neighbourhood’
12
Defining neighbourhoods - MSOA
Middle Layer Super
Output Areas
• 2,000 households
• 7,200 individuals
• Boundaries
determined in
collaboration with
community to
represent ‘local area’
• Sufficient sample
clustering for analysis
(n=20)
13
The national picture


6,781 MSOA across England
Census and other
administrative data available on
all residents
Multi-level Model
yij = β0ij + β1x1ij + α1w1j + α2w1jx1ij
β0ij = β0 + u0j + e0ij
15
Spatial autocorrelation
Individual assessments of fear also
influenced by surrounding neighbourhoods
 May draw on environmental cues from
surrounding areas
 Residents from a number of spatially
proximal areas may all be influenced by a
single crime hotspot
 Routine activities

Including neighbouring neighbourhoods
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
17
Including neighbouring neighbourhoods
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
18
Including neighbouring neighbourhoods
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
19
Including neighbouring neighbourhoods
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
20
Including neighbouring neighbourhoods
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
21
Including neighbouring neighbourhoods
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
22
Including neighbouring neighbourhoods
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
23
The national picture



Generates ‘adjacency matrix’ detailing
surrounding neighbourhoods for each
sampled MSOA
Each surrounding area given equal
weight
Attach area information (crime and
disorder) as ‘weighted average’ across
neighbours
The spatially adjusted multilevel
model
yijk = β0ijk + β1x1ijk + α1w1jk + α2w1jkx1ijk + α3w3k
β0ijk = β0 +



∑z
j≠k
*
v
jk k + ujk + eijk
vk* is the effect of each neighbourhood on its neighbours
zjk is a weight term, equal to 1/nj when neighourhood k is on
the boundary of neighbourhood j, and 0 otherwise
α3w3k is surrounding measure of crime/disorder (spatially
lagged variable – weighted sum of all neighbours)
Fear of crime measure

First principal component of:
 How
worried are you about being mugged or robbed?
 How worried are you about being physically attacked
by strangers?
 How worried are you about being insulted or pestered
by anybody, while in the street or any other public
place?
 ‘not at all worried’ (1), to ‘very worried’ (4)
26
Measuring neighbourhood difference – Social structural
variables
Neighbourhood Measure
Working population on income support
Lone parent families
Local authority housing
Working population unemployed
Non-Car owning households
Working in professional/managerial
role
Owner occupied housing
Domestic property
Green-space
Population density (per square KM)
Working in agriculture
In migration
Out migration
Single person, non-pensioner
households
Commercial property
More than 1.5 people per room
Resident population over 65
Resident population under 16
Terraced housing
Vacant property
Flats


Range of neighbourhood
measures identified to capture
social and organisational
structure
Factorial ecology approach
used to identify key dimensions
of neighbourhood difference
Measuring neighbourhood difference – Social structural
variables
Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Working population on income support
Lone parent families
Local authority housing
Working population unemployed
Non-Car owning households
Working in professional/managerial
role
Owner occupied housing
Domestic property
Green-space
Population density (per square KM)
Working in agriculture
In migration
Out migration
Single person, non-pensioner
households
Commercial property
More than 1.5 people per room
Resident population over 65
Resident population under 16
Terraced housing
Vacant property
Flats
Eigen Value
0.89
0.847
0.846
0.843
0.798
0.245
0.222
0.064
0.293
0.417
0.191
0.002
-0.009
0.173
0.363
0.138
0.263
0.146
0.118
-0.01
0.092
0.153
-0.168
0.125
0.057
-0.787
-0.608
0.104
-0.214
0.245
-0.126
-0.074
-0.019
0.002
-0.249
0.921
-0.902
0.824
-0.663
0.102
0.162
0.153
-0.349
0.165
-0.18
0.262
-0.006
0.916
0.903
0.146
-0.572
0.052
-0.011
0.15
-0.183
0.069
0.119
-0.368
0.053
0.112
-0.043
-0.135
-0.03
0.071
0.134
0.355
0.378
0.428
-0.052
0.427
0.323
0.319
0.453
0.364
0.432
0.472
-0.21
0.04
0.263
-0.118
0.359
0.743
0.529
0.507
-0.271
-0.464
0.102
0.485
0.489
0.134
0.019
0.197
-0.892
0.635
0.274
-0.173
0.008
-0.092
-0.093
-0.326
-0.021
0.19
0.689
0.53
-0.524
9.3
3.3
1.9
1.4
1.3
Measuring neighbourhood difference – Social structural
variables
Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Working population on income support
Lone parent families
Local authority housing
Working population unemployed
Non-Car owning households
Working in professional/managerial
role
Owner occupied housing
Domestic property
Green-space
Population density (per square KM)
Working in agriculture
In migration
Out migration
Single person, non-pensioner
households
Commercial property
More than 1.5 people per room
Resident population over 65
Resident population under 16
Terraced housing
Vacant property
Flats
Eigen Value
Socio-economic
disadvantage
0.89
0.847
0.846
0.843
0.798
0.245
0.222
0.064
0.293
0.417
0.191
0.002
-0.009
0.173
0.363
0.138
0.263
0.146
0.118
-0.01
0.092
0.153
-0.168
0.125
0.057
-0.787
-0.608
0.104
-0.214
0.245
-0.126
-0.074
-0.019
0.002
-0.249
0.921
-0.902
0.824
-0.663
0.102
0.162
0.153
-0.349
0.165
-0.18
0.262
-0.006
0.916
0.903
0.146
-0.572
0.052
-0.011
0.15
-0.183
0.069
0.119
-0.368
0.053
0.112
-0.043
-0.135
-0.03
0.071
0.134
0.355
0.378
0.428
-0.052
0.427
0.323
0.319
0.453
0.364
0.432
0.472
-0.21
0.04
0.263
-0.118
0.359
0.743
0.529
0.507
-0.271
-0.464
0.102
0.485
0.489
0.134
0.019
0.197
-0.892
0.635
0.274
-0.173
0.008
-0.092
-0.093
-0.326
-0.021
0.19
0.689
0.53
-0.524
9.3
3.3
1.9
1.4
1.3
Measuring neighbourhood difference – Social structural
variables
Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Working population on income support
Lone parent families
Local authority housing
Working population unemployed
Non-Car owning households
Working in professional/managerial
role
Owner occupied housing
Domestic property
Green-space
Population density (per square KM)
Working in agriculture
In migration
Out migration
Single person, non-pensioner
households
Commercial property
More than 1.5 people per room
Resident population over 65
Resident population under 16
Terraced housing
Vacant property
Flats
Eigen Value
Socio-economic
disadvantage
Urbanicity
0.89
0.847
0.846
0.843
0.798
0.245
0.222
0.064
0.293
0.417
0.191
0.002
-0.009
0.173
0.363
0.138
0.263
0.146
0.118
-0.01
0.092
0.153
-0.168
0.125
0.057
-0.787
-0.608
0.104
-0.214
0.245
-0.126
-0.074
-0.019
0.002
-0.249
0.921
-0.902
0.824
-0.663
0.102
0.162
0.153
-0.349
0.165
-0.18
0.262
-0.006
0.916
0.903
0.146
-0.572
0.052
-0.011
0.15
-0.183
0.069
0.119
-0.368
0.053
0.112
-0.043
-0.135
-0.03
0.071
0.134
0.355
0.378
0.428
-0.052
0.427
0.323
0.319
0.453
0.364
0.432
0.472
-0.21
0.04
0.263
-0.118
0.359
0.743
0.529
0.507
-0.271
-0.464
0.102
0.485
0.489
0.134
0.019
0.197
-0.892
0.635
0.274
-0.173
0.008
-0.092
-0.093
-0.326
-0.021
0.19
0.689
0.53
-0.524
9.3
3.3
1.9
1.4
1.3
Measuring neighbourhood difference – Social structural
variables
Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Working population on income support
Lone parent families
Local authority housing
Working population unemployed
Non-Car owning households
Working in professional/managerial
role
Owner occupied housing
Domestic property
Green-space
Population density (per square KM)
Working in agriculture
In migration
Out migration
Single person, non-pensioner
households
Commercial property
More than 1.5 people per room
Resident population over 65
Resident population under 16
Terraced housing
Vacant property
Flats
Eigen Value
Socio-economic
disadvantage
Urbanicity
Population
Mobility
0.89
0.847
0.846
0.843
0.798
0.245
0.222
0.064
0.293
0.417
0.191
0.002
-0.009
0.173
0.363
0.138
0.263
0.146
0.118
-0.01
0.092
0.153
-0.168
0.125
0.057
-0.787
-0.608
0.104
-0.214
0.245
-0.126
-0.074
-0.019
0.002
-0.249
0.921
-0.902
0.824
-0.663
0.102
0.162
0.153
-0.349
0.165
-0.18
0.262
-0.006
0.916
0.903
0.146
-0.572
0.052
-0.011
0.15
-0.183
0.069
0.119
-0.368
0.053
0.112
-0.043
-0.135
-0.03
0.071
0.134
0.355
0.378
0.428
-0.052
0.427
0.323
0.319
0.453
0.364
0.432
0.472
-0.21
0.04
0.263
-0.118
0.359
0.743
0.529
0.507
-0.271
-0.464
0.102
0.485
0.489
0.134
0.019
0.197
-0.892
0.635
0.274
-0.173
0.008
-0.092
-0.093
-0.326
-0.021
0.19
0.689
0.53
-0.524
9.3
3.3
1.9
1.4
1.3
Measuring neighbourhood difference – Social structural
variables
Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Working population on income support
Lone parent families
Local authority housing
Working population unemployed
Non-Car owning households
Working in professional/managerial
role
Owner occupied housing
Domestic property
Green-space
Population density (per square KM)
Working in agriculture
In migration
Out migration
Single person, non-pensioner
households
Commercial property
More than 1.5 people per room
Resident population over 65
Resident population under 16
Terraced housing
Vacant property
Flats
Eigen Value
Socio-economic
disadvantage
Urbanicity
Population
Mobility
Age Profile
0.89
0.847
0.846
0.843
0.798
0.245
0.222
0.064
0.293
0.417
0.191
0.002
-0.009
0.173
0.363
0.138
0.263
0.146
0.118
-0.01
0.092
0.153
-0.168
0.125
0.057
-0.787
-0.608
0.104
-0.214
0.245
-0.126
-0.074
-0.019
0.002
-0.249
0.921
-0.902
0.824
-0.663
0.102
0.162
0.153
-0.349
0.165
-0.18
0.262
-0.006
0.916
0.903
0.146
-0.572
0.052
-0.011
0.15
-0.183
0.069
0.119
-0.368
0.053
0.112
-0.043
-0.135
-0.03
0.071
0.134
0.355
0.378
0.428
-0.052
0.427
0.323
0.319
0.453
0.364
0.432
0.472
-0.21
0.04
0.263
-0.118
0.359
0.743
0.529
0.507
-0.271
-0.464
0.102
0.485
0.489
0.134
0.019
0.197
-0.892
0.635
0.274
-0.173
0.008
-0.092
-0.093
-0.326
-0.021
0.19
0.689
0.53
-0.524
9.3
3.3
1.9
1.4
1.3
Measuring neighbourhood difference – Social structural
variables
Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Working population on income support
Lone parent families
Local authority housing
Working population unemployed
Non-Car owning households
Working in professional/managerial
role
Owner occupied housing
Domestic property
Green-space
Population density (per square KM)
Working in agriculture
In migration
Out migration
Single person, non-pensioner
households
Commercial property
More than 1.5 people per room
Resident population over 65
Resident population under 16
Terraced housing
Vacant property
Flats
Eigen Value
Socio-economic
disadvantage
Urbanicity
Population
Mobility
Age Profile
Housing Profile
0.89
0.847
0.846
0.843
0.798
0.245
0.222
0.064
0.293
0.417
0.191
0.002
-0.009
0.173
0.363
0.138
0.263
0.146
0.118
-0.01
0.092
0.153
-0.168
0.125
0.057
-0.787
-0.608
0.104
-0.214
0.245
-0.126
-0.074
-0.019
0.002
-0.249
0.921
-0.902
0.824
-0.663
0.102
0.162
0.153
-0.349
0.165
-0.18
0.262
-0.006
0.916
0.903
0.146
-0.572
0.052
-0.011
0.15
-0.183
0.069
0.119
-0.368
0.053
0.112
-0.043
-0.135
-0.03
0.071
0.134
0.355
0.378
0.428
-0.052
0.427
0.323
0.319
0.453
0.364
0.432
0.472
-0.21
0.04
0.263
-0.118
0.359
0.743
0.529
0.507
-0.271
-0.464
0.102
0.485
0.489
0.134
0.019
0.197
-0.892
0.635
0.274
-0.173
0.008
-0.092
-0.093
-0.326
-0.021
0.19
0.689
0.53
-0.524
9.3
3.3
1.9
1.4
1.3
Measuring neighbourhood difference – Social structural
variables
Neighbourhood Measure
Working population on income support
Lone parent families
Local authority housing
Working population unemployed
Non-Car owning households
Working in professional/managerial
role
Owner occupied housing
Domestic property
Green-space
Population density (per square KM)
Working in agriculture
In migration
Out migration
Single person, non-pensioner
households
Commercial property
More than 1.5 people per room
Resident population over 65
Resident population under 16
Terraced housing
Vacant property
Flats

We also include a measure of
ethnic diversity

White, black, asian, or other
n
ELF = 1-
∑S
2
i
i=1

Capturing the degree of
neighbourhood homogeneity
Visual signs of disorder



Usually derived from survey respondents
Some have used pictures and video recording
which is later coded
We use principal component of interviewer
assessments of level of:
 1.
litter
 2. graffiti & vandalism
 3. run-down property


measured on a 4-point scale from ‘not at all
common’ to ‘very common’
High scale reliability (0.93)
35
Recorded crime
Police recorded crime aggregated to
MSOA level
 Composite index of 33 different offences in
4 major categories:

 Burglary
 Theft
 Criminal
damage
 Violence
36
Results
37
Individual fixed effects

More fearful groups:
 Women,
younger people, ethnic minorities,
less educated, previous victimization
experience, tabloid readers, students, those in
poorer health, being married, longer term
residents

Neighbourhood (and surrounding area)
effects – 7.5% of total variation
38
Neighbourhood effects
Table 2. Fear of Crime Across neighbourhoods - adjusting for spatial autocorrelation1
Model I
NEIGHBOURHOOD FIXED EFFECTS
Neighborhood disadvantage
Urbanicity
Population mobility
Age profile
Housing structure
Ethnic diversity
BCS interviewer rating of disorder
Recorded crime (IMD 2004)
*Personal crime (once)
*Personal crime (multiple)
Spatial autocorrelation
Neighborhood variance
Individual
variance
1
Model II
0.01
0.06**
0.00
0.01**
-0.02**
0.27**
0.06**
0.07**
0.01
0.06 **
0.00
0.01 **
-0.02 **
0.27 **
0.06 **
0.07 **
0.05 **
0.01
0.027**
0.016**
0.811**
0.027 **
0.015 **
0.811 **
Unweighted data. Base n for all models 102,133
** P < (0.01) * P < (0.05)
Neighbourhood levels of crime and disorder significantly related to
individual fear
Recorded crime & victimisation
experience
0.8
Fear of Criminal Victimisation
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-3
-0.1
-2
-1
0
1
2
3
Neighbourhood Recorded Crime Level
Non-Victim
Victim
Repeat Victim
40
Spillover effects?
41
Table 3. Fear of Crime Across neighbourhoods - adjusting for spatial autocorrelation1
Model III
NEIGHBOURHOOD FIXED EFFECTS
Neighborhood disadvantage
Urbanicity
Population mobility
Age profile
Housing structure
Ethnic diversity
BCS interviewer rating of disorder
Recorded crime (IMD 2004)
*Personal crime (once)
*Personal crime (multiple)
SPATIALLY LAGGED EFFECTS
BCS interviewer rating of disorder
Recorded crime (IMD 2004)
Spatial autocorrelation
Neighborhood variance
Individual
variance
1
0.01
0.05 **
0.00
0.01 **
-0.02 **
0.20 **
0.06 **
0.05 **
0.05 **
0.01
0.06 **
0.04 *
0.026 **
0.015 **
0.811**
Unweighted data. Base n for all models 102,133
** P < (0.01) * P < (0.05)
Individuals also influenced by the levels of crime and disorder in the
surrounding area
42
Conclusions

Neighbourhoods matter
 Fear
of crime survey questions sensitive to variation in
objective risk
 Visual signs of disorder magnify crime-related anxiety
 Neighbourhood characteristics accentuate the effects
of individual level causes of fear (Brunton-Smith &
Sturgis, 2011)

Residents influenced by surrounding areas (in
addition to their own neighbourhood)
 Crime
and disorder in surrounding areas important to
assessments of victimisation risk

But MSOA still spatially large – LSOA?
43
Defining neighbourhoods – LSOA?
Lower Layer Super
Output Areas
• 400 households
(minimum)
• 1,500 individuals
• Suitable individual
level data only
available for London
(Metpas)
44
Defining neighbourhoods – LSOA?
Lower Layer Super
Output Areas
• 400 households
(minimum)
• 1,500 individuals
• Suitable individual
level data only
available for London
(Metpas)
45
Defining neighbourhoods – LSOA?
Lower Layer Super
Output Areas
• 400 households
(minimum)
• 1,500 individuals
• Suitable individual
level data only
available for London
(Metpas)
46
Defining neighbourhoods – LSOA?
Lower Layer Super
Output Areas
• 400 households
(minimum)
• 1,500 individuals
• Suitable individual
level data only
available for London
(Metpas)
47
Defining neighbourhoods – LSOA?
Lower Layer Super
Output Areas
• 400 households
(minimum)
• 1,500 individuals
• Suitable individual
level data only
available for London
(Metpas)
48
Defining neighbourhoods – LSOA?
Lower Layer Super
Output Areas
• 400 households
(minimum)
• 1,500 individuals
• Suitable individual
level data only
available for London
(Metpas)
49