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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)

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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)

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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)

48

Defining neighbourhoods – LSOA?

Lower Layer Super Output Areas

• 400 households (minimum)

• 1,500 individuals• Suitable individual

level data only available for London (Metpas)

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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)