A model for spatially varying crime
rates in English districts: the effects
of social capital, fragmentation,
deprivation and urbanicity
Peter Congdon, Queen Mary University of London
p.congdon@qmul.ac.uk
http://www.geog.qmul.ac.uk/staff/congdonp.html
http://webspace.qmul.ac.uk/pcongdon/
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Crime variations & urban structure
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Geographic variations in crime are increasingly
linked to aspects of urban social structure.
However, relatively limited synoptic evidence on
geographic crime differences & potentially
relevant urban structural characteristics.
Many studies partial, considering particular
associations, e.g. crime-poverty or crimeinequality links, or have restricted spatial focus
Potentially relevant influences considered here:
deprivation, urbanicity, social capital, social
fragmentation, income inequality, and with
England-wide focus
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Social capital
 Social
capital: norms of reciprocity & trust
that promote civic participation, activity in
social organizations or voluntary activity
(Putnam, 1995).
 Social disorganisation theory stresses
neighbourhood effects on crime, and role of
social capital in informal control, but main
focus is crime variations within urban areas.
 Seek here to consider more complete
spectrum of urban-rural contexts, albeit at
aggregated spatial scale
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Methodological aspects: latent variables
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Some important methodological issues
Typically relevant aspects of urban socioeconomic structure are latent constructs
The constructs are not directly observed, but
instead proxied by set of observed indicators.
Examples: area deprivation “measured” by
variables such as unemployment rate, level of
welfare dependency, poverty rate; social
capital measured by perceptions of local
neighbourhood, participation in voluntary
activity, etc.
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Methodological aspects: spatial units of
analysis
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Assume an area focus using area crime rates and
area variables – this means that comprehensive
administrative data can be used. Here, use
notifiable offences recorded by police in 2009/10
for 324 English local authorities.
Need to allow for spatial structure/correlation in
regression model (e.g. spatially correlated
residuals) to obtain valid effect measures
Kubrin & Weitzer (2003) mention spatial
dependencies “how adjacent neighborhoods may
affect each other’s level of disorganization and
crime”.
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Methodological aspects: effect mediation
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Social capital may affect crime rates (negative
effect expected).
However, social capital itself may be affected
by other urban dimensions: deprivation,
urbanicity and fragmentation.
So in a spatial crime regression, social capital
may mediate effects on crime of deprivation,
urbanicity and fragmentation
Quote from Kubrin/Weitzer: “Social ties and
informal control are…mediating the effects of
exogenous sources of social disorganization
(e.g., poverty, residential instability, ethnic
heterogeneity) on neighborhood crime”
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Study Data: Measuring Social Capital
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Six indicators of neighbourhood perception &
volunteering activity from 2008 UK Place Survey used to
measure social capital.
For example, respondents asked whether
“they belong to their immediate neighbourhood”,
“satisfied with their local area as a place to live”,
“given unpaid help at least once per month over the
last 12 months”.
Principal component analysis shows leading
eigenvalue of 4.54, accounting for 76% of original
variation. Supports concept of single latent variable
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Map of
component
scores
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Social Capital by English Region
Average Social Capital Scores by Region
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
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Study Data: Measuring Other Constructs
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Measuring urbanicity: pop’n density, % land that is
greenspace, access to services (primary health,
schools, post offices, retail stores),% working in
agriculture, flatted housing. Leading component
explains 79.0% of variation
Measuring social fragmentation (summarises
residential stability/family structure): migrant
turnover, one person households, private renting,
% adults married. Leading component also
explains 79% of variation in these indicators.
Measuring area deprivation: receiving income
support, unemployment rate, professional and
managerial, % adults with higher education.
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How social capital
varies with the
other urban
dimensions
Average social capital
according to quintile
groupings of local
authorities on
deprivation,
fragmentation, urbanicity
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Study Model:
Geographic Crime Variation via Spatial Regression
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The response variables are crime rates (total,
violent, property)
Crime rates are spatially correlated, unmeasured
influences likely to remain. Regression residuals
assumed spatially correlated (Conditional
Autoregressive or CAR spatial)
Poisson log link regression is adopted (Osgood,
2000), adjusting for population at risk→ response
is log relative risk of crime.
Winbugs package used (Bayesian MCMC
estimation)
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Geographic Crime Variation: Spatial Regression
 Area
crime predictors: four constructs as
above and income inequality
 Income inequality is coefficient of variation
within each local authority of middle level
super output area income estimates, 2007-08
 Modelling sequence: no predictors;
predictors excluding social capital; all
predictors
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Model Sequence
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Crime Variation Regression: Findings
 If
social capital not included as predictor
(regression 2), deprivation is strongest
influence on crime responses, whether βcoefficients or risk ratios between 5th and
95th percentiles considered.
 Strongest effect of urbanicity is on violent
crime.
 Effects of income inequality in model 2
insignificant: inequality effect entirely
mediated by deprivation, urbanicity and
fragmentation
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Crime Variation Regression: Findings
 Impacts
of urbanicity and deprivation
considerably reduced in regression 3, in line
with their effects being partially or completely
mediated by social capital.
 In fact, deprivation no longer has a
significant impact on property crime – so
providing an example of complete
mediation
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Crime gradient (rates per 1000) by decile of social capital
score, controlling for other urban dimensions (deprivation,
fragmentation, urbanity set to zero)
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References
 Kubrin
C, Weitzer R(2003)New Directions in
Social Disorganization Theory. J Research
Crime Delinquency, 40
 Osgood D (2000) Poisson-based regression
analysis of aggregate crime rates. J. Quant
Criminology, 16.
 Putnam R (1995)Bowling Alone: America's
Declining Social Capital. J of Democracy,
6:65-78.