A model for spatially varying crime rates in English districts: the effects of social capital, fragmentation, deprivatio

1. 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/

2. Crime variations & urban structure • 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-povertyor crime-inequality links, or have restricted spatial focus • Potentially relevant influences considered here: deprivation, urbanicity, social capital, social fragmentation, income inequality, and with England-wide focus

3. 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

4. Methodological aspects: latent variables • Some important methodological issues • Typically relevant aspects of urban socio-economic 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.

5. Methodological aspects: spatial units of analysis • 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 neighborhoodsmay affect each other’s level of disorganization and crime”.

6. Methodological aspects: effect mediation • 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 neighborhoodcrime”

7. Study Data: Measuring Social Capital • 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

8. Map of component scores

9. Social Capital by English Region

10. Study Data: Measuring Other Constructs • 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.

11. How social capital varies with the other urban dimensions Average social capital according to quintile groupings of local authorities on deprivation, fragmentation, urbanicity

12. Study Model:Geographic Crime Variation via Spatial Regression • 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)

13. 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

14. Model Sequence

15. Crime Variation Regression: Findings • If social capital not included as predictor (regression 2), deprivation is strongest influence on crime responses, whether β-coefficients or risk ratios between 5th and 95th percentiles considered. • Strongest effect of urbanicityis on violent crime. • Effects of income inequality in model 2 insignificant: inequality effect entirely mediated by deprivation, urbanicity and fragmentation

16. 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

17. Crime gradient (rates per 1000) by decile of social capital score, controlling for other urban dimensions (deprivation, fragmentation, urbanity set to zero)

18. References • KubrinC, 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.