2. 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
3. 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
4. 4
Fear of crime
Important component of subjective well-
being 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
6. 6
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
7. 7
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
8. 8
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
9. 9
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
10. 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
11. 11
Data
British Crime Survey 2002-2005
Victimization survey of adults 16+ in
private households
Response rate = 74%
12. 12
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’
13. 13
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)
Defining neighbourhoods - MSOA
14. The national picture
6,781 MSOA across England
Census and other
administrative data available on
all residents
16. 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
17. 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. 18
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
Including neighbouring neighbourhoods
19. 19
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
Including neighbouring neighbourhoods
20. 20
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
Including neighbouring neighbourhoods
21. 21
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
Including neighbouring neighbourhoods
22. 22
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
Including neighbouring neighbourhoods
23. 23
• Allow for possibility
that neighbouring
areas also influence
fear
o Spillover effects
o Saliency effects
• Identify all areas that
touch neighbourhood
boundaries
Including neighbouring neighbourhoods
24. 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
25. The spatially adjusted multilevel
model
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
α3
w3k
is surrounding measure of crime/disorder (spatially
lagged variable – weighted sum of all neighbours)
yijk
= β0ijk
+ β1
x1ijk
+ α1
w1jk
+ α2
w1jk
x1ijk
+ α3
w3k
β0ijk
= β0
+ ∑zjkvk + ujk
+ eijk
j≠k
*
*
26. 26
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)
27. 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
Measuring neighbourhood difference – Social structural
variables
Range of neighbourhood
measures identified to capture
social and organisational
structure
Factorial ecology approach
used to identify key dimensions
of neighbourhood difference
28. Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Working population on income support 0.89 0.245 0.191 0.138 0.092
Lone parent families 0.847 0.222 0.002 0.263 0.153
Local authority housing 0.846 0.064 -0.009 0.146 -0.168
Working population unemployed 0.843 0.293 0.173 0.118 0.125
Non-Car owning households 0.798 0.417 0.363 -0.01 0.057
Working in professional/managerial role -0.787 0.002 0.153 0.146 -0.368
Owner occupied housing -0.608 -0.249 -0.349 -0.572 0.053
Domestic property 0.104 0.921 0.165 0.052 0.112
Green-space -0.214 -0.902 -0.18 -0.011 -0.043
Population density (per square KM) 0.245 0.824 0.262 0.15 -0.135
Working in agriculture -0.126 -0.663 -0.006 -0.183 -0.03
In migration -0.074 0.102 0.916 0.069 0.071
Out migration -0.019 0.162 0.903 0.119 0.134
Single person, non-pensioner households 0.355 0.364 0.743 0.134 -0.092
Commercial property 0.378 0.432 0.529 0.019 -0.093
More than 1.5 people per room 0.428 0.472 0.507 0.197 -0.326
Resident population over 65 -0.052 -0.21 -0.271 -0.892 -0.021
Resident population under 16 0.427 0.04 -0.464 0.635 0.19
Terraced housing 0.323 0.263 0.102 0.274 0.689
Vacant property 0.319 -0.118 0.485 -0.173 0.53
Flats 0.453 0.359 0.489 0.008 -0.524
Eigen Value 9.3 3.3 1.9 1.4 1.3
Measuring neighbourhood difference – Social structural
variables
29. Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Socio-economic
disadvantage
Working population on income support 0.89 0.245 0.191 0.138 0.092
Lone parent families 0.847 0.222 0.002 0.263 0.153
Local authority housing 0.846 0.064 -0.009 0.146 -0.168
Working population unemployed 0.843 0.293 0.173 0.118 0.125
Non-Car owning households 0.798 0.417 0.363 -0.01 0.057
Working in professional/managerial role -0.787 0.002 0.153 0.146 -0.368
Owner occupied housing -0.608 -0.249 -0.349 -0.572 0.053
Domestic property 0.104 0.921 0.165 0.052 0.112
Green-space -0.214 -0.902 -0.18 -0.011 -0.043
Population density (per square KM) 0.245 0.824 0.262 0.15 -0.135
Working in agriculture -0.126 -0.663 -0.006 -0.183 -0.03
In migration -0.074 0.102 0.916 0.069 0.071
Out migration -0.019 0.162 0.903 0.119 0.134
Single person, non-pensioner households 0.355 0.364 0.743 0.134 -0.092
Commercial property 0.378 0.432 0.529 0.019 -0.093
More than 1.5 people per room 0.428 0.472 0.507 0.197 -0.326
Resident population over 65 -0.052 -0.21 -0.271 -0.892 -0.021
Resident population under 16 0.427 0.04 -0.464 0.635 0.19
Terraced housing 0.323 0.263 0.102 0.274 0.689
Vacant property 0.319 -0.118 0.485 -0.173 0.53
Flats 0.453 0.359 0.489 0.008 -0.524
Eigen Value 9.3 3.3 1.9 1.4 1.3
Measuring neighbourhood difference – Social structural
variables
30. Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Socio-economic
disadvantage
Urbanicity
Working population on income support 0.89 0.245 0.191 0.138 0.092
Lone parent families 0.847 0.222 0.002 0.263 0.153
Local authority housing 0.846 0.064 -0.009 0.146 -0.168
Working population unemployed 0.843 0.293 0.173 0.118 0.125
Non-Car owning households 0.798 0.417 0.363 -0.01 0.057
Working in professional/managerial role -0.787 0.002 0.153 0.146 -0.368
Owner occupied housing -0.608 -0.249 -0.349 -0.572 0.053
Domestic property 0.104 0.921 0.165 0.052 0.112
Green-space -0.214 -0.902 -0.18 -0.011 -0.043
Population density (per square KM) 0.245 0.824 0.262 0.15 -0.135
Working in agriculture -0.126 -0.663 -0.006 -0.183 -0.03
In migration -0.074 0.102 0.916 0.069 0.071
Out migration -0.019 0.162 0.903 0.119 0.134
Single person, non-pensioner households 0.355 0.364 0.743 0.134 -0.092
Commercial property 0.378 0.432 0.529 0.019 -0.093
More than 1.5 people per room 0.428 0.472 0.507 0.197 -0.326
Resident population over 65 -0.052 -0.21 -0.271 -0.892 -0.021
Resident population under 16 0.427 0.04 -0.464 0.635 0.19
Terraced housing 0.323 0.263 0.102 0.274 0.689
Vacant property 0.319 -0.118 0.485 -0.173 0.53
Flats 0.453 0.359 0.489 0.008 -0.524
Eigen Value 9.3 3.3 1.9 1.4 1.3
Measuring neighbourhood difference – Social structural
variables
31. Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Socio-economic
disadvantage
Urbanicity Population
Mobility
Working population on income support 0.89 0.245 0.191 0.138 0.092
Lone parent families 0.847 0.222 0.002 0.263 0.153
Local authority housing 0.846 0.064 -0.009 0.146 -0.168
Working population unemployed 0.843 0.293 0.173 0.118 0.125
Non-Car owning households 0.798 0.417 0.363 -0.01 0.057
Working in professional/managerial role -0.787 0.002 0.153 0.146 -0.368
Owner occupied housing -0.608 -0.249 -0.349 -0.572 0.053
Domestic property 0.104 0.921 0.165 0.052 0.112
Green-space -0.214 -0.902 -0.18 -0.011 -0.043
Population density (per square KM) 0.245 0.824 0.262 0.15 -0.135
Working in agriculture -0.126 -0.663 -0.006 -0.183 -0.03
In migration -0.074 0.102 0.916 0.069 0.071
Out migration -0.019 0.162 0.903 0.119 0.134
Single person, non-pensioner households 0.355 0.364 0.743 0.134 -0.092
Commercial property 0.378 0.432 0.529 0.019 -0.093
More than 1.5 people per room 0.428 0.472 0.507 0.197 -0.326
Resident population over 65 -0.052 -0.21 -0.271 -0.892 -0.021
Resident population under 16 0.427 0.04 -0.464 0.635 0.19
Terraced housing 0.323 0.263 0.102 0.274 0.689
Vacant property 0.319 -0.118 0.485 -0.173 0.53
Flats 0.453 0.359 0.489 0.008 -0.524
Eigen Value 9.3 3.3 1.9 1.4 1.3
Measuring neighbourhood difference – Social structural
variables
32. Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Socio-economic
disadvantage
Urbanicity Population
Mobility
Age Profile
Working population on income support 0.89 0.245 0.191 0.138 0.092
Lone parent families 0.847 0.222 0.002 0.263 0.153
Local authority housing 0.846 0.064 -0.009 0.146 -0.168
Working population unemployed 0.843 0.293 0.173 0.118 0.125
Non-Car owning households 0.798 0.417 0.363 -0.01 0.057
Working in professional/managerial role -0.787 0.002 0.153 0.146 -0.368
Owner occupied housing -0.608 -0.249 -0.349 -0.572 0.053
Domestic property 0.104 0.921 0.165 0.052 0.112
Green-space -0.214 -0.902 -0.18 -0.011 -0.043
Population density (per square KM) 0.245 0.824 0.262 0.15 -0.135
Working in agriculture -0.126 -0.663 -0.006 -0.183 -0.03
In migration -0.074 0.102 0.916 0.069 0.071
Out migration -0.019 0.162 0.903 0.119 0.134
Single person, non-pensioner households 0.355 0.364 0.743 0.134 -0.092
Commercial property 0.378 0.432 0.529 0.019 -0.093
More than 1.5 people per room 0.428 0.472 0.507 0.197 -0.326
Resident population over 65 -0.052 -0.21 -0.271 -0.892 -0.021
Resident population under 16 0.427 0.04 -0.464 0.635 0.19
Terraced housing 0.323 0.263 0.102 0.274 0.689
Vacant property 0.319 -0.118 0.485 -0.173 0.53
Flats 0.453 0.359 0.489 0.008 -0.524
Eigen Value 9.3 3.3 1.9 1.4 1.3
Measuring neighbourhood difference – Social structural
variables
33. Table 1. Rotated Component Loadings from Factorial Ecology
Neighbourhood Measure
Socio-economic
disadvantage
Urbanicity Population
Mobility
Age Profile Housing
Profile
Working population on income support 0.89 0.245 0.191 0.138 0.092
Lone parent families 0.847 0.222 0.002 0.263 0.153
Local authority housing 0.846 0.064 -0.009 0.146 -0.168
Working population unemployed 0.843 0.293 0.173 0.118 0.125
Non-Car owning households 0.798 0.417 0.363 -0.01 0.057
Working in professional/managerial role -0.787 0.002 0.153 0.146 -0.368
Owner occupied housing -0.608 -0.249 -0.349 -0.572 0.053
Domestic property 0.104 0.921 0.165 0.052 0.112
Green-space -0.214 -0.902 -0.18 -0.011 -0.043
Population density (per square KM) 0.245 0.824 0.262 0.15 -0.135
Working in agriculture -0.126 -0.663 -0.006 -0.183 -0.03
In migration -0.074 0.102 0.916 0.069 0.071
Out migration -0.019 0.162 0.903 0.119 0.134
Single person, non-pensioner households 0.355 0.364 0.743 0.134 -0.092
Commercial property 0.378 0.432 0.529 0.019 -0.093
More than 1.5 people per room 0.428 0.472 0.507 0.197 -0.326
Resident population over 65 -0.052 -0.21 -0.271 -0.892 -0.021
Resident population under 16 0.427 0.04 -0.464 0.635 0.19
Terraced housing 0.323 0.263 0.102 0.274 0.689
Vacant property 0.319 -0.118 0.485 -0.173 0.53
Flats 0.453 0.359 0.489 0.008 -0.524
Eigen Value 9.3 3.3 1.9 1.4 1.3
Measuring neighbourhood difference – Social structural
variables
34. 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
Measuring neighbourhood difference – Social structural
variables
We also include a measure of
ethnic diversity
White, black, asian, or other
Capturing the degree of
neighbourhood homogeneity
ELF = 1-∑Si
i=1
n
2
35. 35
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)
36. 36
Recorded crime
Police recorded crime aggregated to
MSOA level
Composite index of 33 different offences
in 4 major categories:
Burglary
Theft
Criminal damage
Violence
38. 38
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
39. Neighbourhood effects
Table 2. Fear of Crime Across neighbourhoods - adjusting for spatial
autocorrelation1
Model I Model II
NEIGHBOURHOOD FIXED EFFECTS
Neighborhood disadvantage 0.01 0.01
Urbanicity 0.06** 0.06**
Population mobility 0.00 0.00
Age profile 0.01** 0.01**
Housing structure -0.02** -0.02**
Ethnic diversity 0.27** 0.27**
BCS interviewer rating of disorder 0.06** 0.06**
Recorded crime (IMD 2004) 0.07** 0.07**
*Personal crime (once) 0.05**
*Personal crime (multiple) 0.01
Spatial autocorrelation 0.027** 0.027**
Neighborhood variance 0.016** 0.015**
Individual variance 0.811** 0.811**
1
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
42. 42
Table 3. Fear of Crime Across neighbourhoods - adjusting for spatial autocorrelation1
Model III
NEIGHBOURHOOD FIXED EFFECTS
Neighborhood disadvantage 0.01
Urbanicity 0.05**
Population mobility 0.00
Age profile 0.01**
Housing structure -0.02**
Ethnic diversity 0.20**
BCS interviewer rating of disorder 0.06**
Recorded crime (IMD 2004) 0.05**
*Personal crime (once) 0.05**
*Personal crime (multiple) 0.01
SPATIALLY LAGGED EFFECTS
BCS interviewer rating of disorder 0.06**
Recorded crime (IMD 2004) 0.04*
Spatial autocorrelation 0.026**
Neighborhood variance 0.015**
Individual variance 0.811**
1 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
43. 43
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?
44. 44
Lower Layer Super
Output Areas
• 400 households
(minimum)
• 1,500 individuals
• Suitable individual
level data only
available for London
(Metpas)
Defining neighbourhoods – LSOA?
45. 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. 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. 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. 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. 49
Defining neighbourhoods – LSOA?
Lower Layer Super
Output Areas
• 400 households
(minimum)
• 1,500 individuals
• Suitable individual
level data only
available for London
(Metpas)