Crime Victimisation Risk – data and modelling requirements

Tim Hope and Alan Trickett

Keele University

© 5 December 2006

Access to Data and Analytic Capacity

• Secondary use part of original BCS justification• Limited secondary analysis capacity in UK (both

in-house and external)• Complexity and unfriendliness of data• Restrictions and limitations on data access

– e.g. Harder now to link to other national statistics (Census, Neighbourhood Statistics) than in 1982.

• Lack of Government interest in adding value to BCS or commercialisation?

• Is the BCS a public statistic?

The Importance of Area Contextual Data

• Why is area contextual data necessary?

• What kind of contextual data?

• What kinds of models/explanations are suggested?

BCS Sample Design

• Primary Sampling Unit (PSU) of the BCS is a nested cluster sample of respondents living in close, spatial proximity to each other.

• Geo-coded spatial referencing (postal geography, Census geography)

• Aggregation of individual responses to form pseudo-neighbourhoods

• Attachment of Census data to individual cases to form area-context variables

Pseudo-neighbourhood analyses

Sampson, R.J. and B. W. Groves (1989). ‘Community structure and crime: testing social disorganisation theory’, American Journal of Sociology, 94, 774-802.

 Osborn, D.R., A. Trickett, and R. Elder (1992). ‘Area characteristics and regional variates as determinants of area property crime levels.’ Journal of Quantitative Criminology, 8, 265-285

Trickett, A., D.R. Osborn, J. Seymour, and K. Pease (1992). ‘What is different about high crime areas?’, British Journal of Criminology, 32, 81-9.

Osborn, D.R., A. Trickett, and R. Elder (1992). ‘Area characteristics and regional variates as determinants of area property crime levels.’ Journal of Quantitative Criminology, 8, 265-285

Trickett, A., D. Ellingworth, T. Hope and K. Pease (1995). 'Crime victimisation in the eighties: changes in area and regional inequality'. British Journal of Criminology, 35 (3), 343-359.

Kershaw, C. and A. Tseloni (2005). ‘Predicting crime rates, fear and disorder based on area information: evidence from the 2000 British Crime Survey’. International Review of Victimology, 12, 293-312.

 

Pseudo-neighbourhood analyses• Sampson and Groves (1989) Aggregate individual-level responses

in 1982/1984 BCS to produce Ward-level variables– Victimisation rate (dependent variable)– Measures of ‘social disorganisation’ (mediating variables)– Structural variables (low SES, mobility, heterogeneity)

• Widely-cited ‘test’ of social disorganisation theory• But aggregation of sampled observations produces biased

estimates – i.e. aggregates sampling error!• Osborn et al., (1992) model victimisation rates (1984 BCS and

1981 Census data)• Kershaw and Tseloni (2005) model crime, fear and disorder rates

(2000 BCS, 1991 Census)• Reduction of error vs. loss of explanation

What is different about high crime neighbourhoods?

• Better predictions of prevalence than incidence (Osborn et al. (1992); Kershaw and Tseloni, 2005). Why?

• Answer: Difference between expected and observed prevalence rates (Trickett et al.,1992; Trickett et al., 1995):– Too few victims: (more repeat victims or more

‘immune’ residents?)

– Too much crime concentrated in high crime areas

Area concentration of crime

• Distribution replicated using individual-level data (negative binomial regression model) Osborn, D.R. and A. Tseloni (1998). The distribution of household

property crimes. Journal of Quantitative Criminology, 14, 307-330.

• Distribution mirrored by area deprivation level

Hope, T. (2001). ‘Crime victimisation and inequality in risk society’. In R. Matthews and J. Pitts (Eds.) Crime Prevention, Disorder and Community Safety. London: Routledge.

 

The Role of Individual and Area Influences

The role of individual and area differencesTrickett, A., D. R. Osborn and D. Ellingworth (1995b). ‘ Property crime victimisation: the roles of individual and area influences’. International

Review of Victimology, 3, 273-295.

Logistic Regression

Goodness-of-Fit

Test Statistic p.

Overall model χ2 440.38 .0000

Decrease in χ2: individual variables omitted

173.64 .0000

Decrease in χ2:

Region/area variables omitted

230.06 .0000

Property crime victimisation likelihood by dwelling and area type: bivariate and multivariate odds

   Bivariate (sample) odds

Multivariate odds (p. )

Detached house 1.000 1.000

Semi-detached house 1.254 0.845 (.054)

End-terrace (row) house 1.726 0.986 (.901)

Mid-terrace house 1.505 0.763 (.005)

Flat (apartment) 1.569 0.664 (.000)

Affluence of area 0.803 0.846 (.000)

 

Source: 1992 British Crime Survey/1991 Census. Univariate odds calculated from weighted data. Multivariate odds estimate from a logistic regression model Hope, T. (2000). ‘Inequality and the clubbing of private security’. In T. Hope and R. Sparks (Eds.) Crime,

Risk and Insecurity. London: Routledge. 

Estimated Probability of being a Victim of Property Crime

Household Type

NW Inner City

NW Suburbia

East

Anglia Urban

East

Anglia Rural

Young Professional

0.5489

0.3373

0.3159

0.0892

Young Non-Professional

0.4418

0.2495

0.2318

0.0601

Mid-Aged Professional

0.4319

0.2413

0.2240

0.0576

Mid-Aged Non-Professional

0.3469

0.1818

0.1678

0.0406

Retired Professional

0.3355

0.1744

0.1608

0.0390

Retired Non-Professional

0.2478

0.1211

0.1112

0.0258

Source: Trickett et al (1995), Property Crime Victimisation: The Roles of

Individual and Area Influences, International Review of Victimology

 

HOUSEHOLD RISK FACTORS FOR HOUSEHOLD PROPERTY CRIME

(Hope, 2001) 

the worse off?

       council tenant (+)       young head of household (+)       children in household (+)       lone-parent household (+)

       Asian sub-continent origin (+)       low level of household security (+)

 

the better off? 

      non-manual occupation (+)      house-dweller (+)      number of cars (+)      detached house (+)

      income (+) 

AREA RISK FACTORS FOR HOUSEHOLD PROPERTY CRIME

(Hope, 2001) 

Deprivation?

        Single parents (+)

     Children aged 5-15 (+)

       Private rented housing (+)

       Inner city (+)

       ‘Rich’ ACORN groups (+)

  Affluence?        Cars per household (-)       Deprivation Index (-)

(over-crowded householdslarge families

housing association rentalmale unemployment)

       ‘Poor’ ACORN groups (+)

What Kind of Area (Census) Data?• Individual variables versus Geodemographic classifications

(MOSAIC, ACORN, etc.)?• Horses for courses – explanation versus prediction• Explanation – individual variables better

– Explanatory (which area characteristics matter?)– Less collinearity– Ecological fallacy– More explanatory power (more degrees of freedom)– ‘Purer’ (less non-Census data, post 1991)

• Prediction – Geo-demographics better– Operational targeting/profiling systems– Typologies and norms– Good at predicting the ‘normal’? (deviants? Hard-to-reach?)