This document summarizes a study examining the influence of neighbourhood characteristics on individual fear of crime. The study uses a multi-level model to account for the nested nature of individuals within neighbourhoods. Neighbourhoods are defined using Middle Super Output Areas from census data. Factorial ecology is used to characterize neighbourhood differences across multiple social structural variables, which are then included in the model. The model also considers "spillover effects" by including crime and disorder measures from surrounding neighbourhoods. This approach aims to address limitations of previous studies that did not fully capture neighbourhood definitions or spillover influences.

1. 1
Neighbourhoods
matter: spill-over effects
in the fear of crime
Ian Brunton-Smith
Department of Sociology, University of Surrey

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

5. 5
Neighbourhood mechanisms

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

15. 15
Multi-level Model
yij
= β0ij
+ β1
x1ij
+ α1
w1j
+ α2
w1j
x1ij
β0ij
= β0
+ u0j
+ e0ij

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

37. 37
Results

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

40. 40
Recorded crime & victimisation
experience

41. 41
Spillover effects?

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)