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Transcript of 1 Neighbourhoods matter: spill-over effects in the fear of crime Ian Brunton-Smith Department of...
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Neighbourhoods matter: spill-over effects in the fear of crime
Ian Brunton-Smith
Department of Sociology, University of Surrey
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 fearKey mechanisms, limitations of existing work
Detailed neighbourhood analysisDefining neighbourhoods, Composition and dependencySpillover effects
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Fear of crime
Important component of subjective well-being and community health
Frequently employed as performance target for police/governmentMore important than crime itself?
Safer neighbourhoods scheme Neighbourhood mechanisms shaping fear
Research inconclusive – ‘paradoxical’ nature of fear
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Neighbourhood mechanisms
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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
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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
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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
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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
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
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Data
British Crime Survey 2002-2005
Victimization survey of adults 16+ in private households
Response rate = 74%
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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’
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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
The national picture
6,781 MSOA across England Census and other administrative
data available on all residents
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Multi-level Model
yij = β0ij + β1x1ij + α1w1j + α2w1jx1ij
β0ij = β0 + u0j + e0ij
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
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Including neighbouring neighbourhoods
• Allow for possibility that neighbouring areas also influence fearo Spillover effectso Saliency effects
• Identify all areas that touch neighbourhood boundaries
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• Allow for possibility that neighbouring areas also influence fearo Spillover effectso Saliency effects
• Identify all areas that touch neighbourhood boundaries
Including neighbouring neighbourhoods
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• Allow for possibility that neighbouring areas also influence fearo Spillover effectso Saliency effects
• Identify all areas that touch neighbourhood boundaries
Including neighbouring neighbourhoods
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• Allow for possibility that neighbouring areas also influence fearo Spillover effectso Saliency effects
• Identify all areas that touch neighbourhood boundaries
Including neighbouring neighbourhoods
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• Allow for possibility that neighbouring areas also influence fearo Spillover effectso Saliency effects
• Identify all areas that touch neighbourhood boundaries
Including neighbouring neighbourhoods
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• Allow for possibility that neighbouring areas also influence fearo Spillover effectso Saliency effects
• Identify all areas that touch neighbourhood boundaries
Including neighbouring neighbourhoods
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• Allow for possibility that neighbouring areas also influence fearo Spillover effectso Saliency effects
• Identify all areas that touch neighbourhood boundaries
Including neighbouring neighbourhoods
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
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)
yijk = β0ijk + β1x1ijk + α1w1jk + α2w1jkx1ijk + α3w3k
β0ijk = β0 + ∑zjkvk + ujk + eijkj≠k
*
*
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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)
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
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
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
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
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
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
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
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-∑Sii=1
n
2
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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)
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Recorded crime
Police recorded crime aggregated to MSOA level
Composite index of 33 different offences in 4 major categories:BurglaryTheftCriminal damageViolence
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Results
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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
Neighbourhood effectsTable 2. Fear of Crime Across neighbourhoods - adjusting for spatial autocorrelation1
Model I Model IINEIGHBOURHOOD 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
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Recorded crime & victimisation experience
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Spillover effects?
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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
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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?
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Lower Layer Super Output Areas
• 400 households (minimum)
• 1,500 individuals• Suitable individual
level data only available for London (Metpas)
Defining neighbourhoods – LSOA?
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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
Defining neighbourhoods – LSOA?
Lower Layer Super Output Areas
• 400 households (minimum)
• 1,500 individuals• Suitable individual
level data only available for London (Metpas)