Living near to burglars: estimating the small area level risk of burglary in Cambridgeshire Robert Haining Department of Geography University of Cambridge ESRC Research Methods Festival, Oxford, July 2010

Outline: 1.The nature of ecological analysis. 2.Geographical variation in numbers of burglaries by Census Output Area (COA) in Cambridgeshire: description and explanation. 3. The challenges presented by ecological analysis. 4. Concluding remarks.

1. The nature of ecological analysis. Ecological: study of groups or aggregates using data grouped by: social class; socio-economic status; demographics (sex, age cohorts).... geography (and time). Geography scale

UK Census: Census Output Areas (COAs) – c. 220,000. Design criteria for COAs: - aggregations of postcodes; - recommended to contain approx. 125 households; - socially homogeneous based on housing tenure and dwelling type.

(ONS website)

Police recorded crime database, data quality issues: [1] Accuracy (geocoding; time of event) [2] Completeness (e.g. offender not known; not all offences reported; extent of personal details) [3] Consistency. [4] Resolution (COA level).

Forms of ecological analysis: Descriptive: maps (presentation graphics; visualization tools); graphical and numerical summaries (e.g. hotspot locations). Confirmatory: model fitting for parameter estimation and hypothesis testing.

Why are ecological analyses of crime data useful and important? [a] Police are “territorial” and one aspect of resourceallocation is by geographical area. PFAs, BCUs and beats/ neighbourhoods.

[b] Many theories about the location of offences have an ecological level, but: - what is the appropriate spatial framework? - what is relationship between the appropriate framework and data availability?

2. Geographical variation in numbers of burglaries by COA in Cambridgeshire: description and explanation

Map of burglary counts at COA level in Cambridgeshire, 2002.

Conceptualising the problem: Burglary as the outcome of a rational choice “two stage process”. Stage 1: Select area Stage 2: Select target within the chosen area. Each stage involves a distinct set of factors

Area selection factors: (1) area attractiveness (reward): likely gains from a burglary => affluent areas might be favoured over less affluent and deprived areas. also areas where households are expected to have high value and easy to steal goods.

(2) area opportunity (risk): likelihood of not getting caught => areas with fewer formal and/or informal “capable guardians” offer a greater likelihood of success. (Routine activity theory; Cohen and Felson, 1979.) - affluent neighbourhoods where residents are absent for extended periods during a day/at weekends. - areas with low levels of collective efficacy (social cohesion + willingness to act for common good).

(3) area accessibility (familiarity + least effort principle): - areas which are known to the offender perhaps because they are near to where they live (or work etc) but where (s)he will not be recognized.

Summary: At stage 1, for the motivated offender, choice of area is a balance of risk against reward whilst taking into account the effort involved. At stage 2, choice of target may be opportunistic.

Questions: [1] What is the statistical significance of each of these three sets of factors and how far do they help us to explain area differences in burglary rates. {Modelling for the purpose of hypothesis testing} [2] By how much, on average, do area level rates of burglary increase for unit increases in each of the different factors. {Modelling for the purpose of parameter estimation}

Problem: We are dependent on the UK National Census but there is often no clear or unambiguous link between Census variables and the attractiveness or opportunity factors. However we are mainly interested in estimating the importance of the accessibility factor whilst controlling for these two other factors. We collected census data on 20 variables covering: - household composition (e.g. Prop. lone parent households) - living arrangements (e.g. Prop. single people in households) - household tenure (e.g. Prop private rented) - accommodation type (e.g. Prop. detached housing) - population turnover - social and ethnic composition (e.g. index of ethnicity)

f(d(i,j)): a function of the distance, d(i,j), between the centroids of COAs i and j. z(j) denotes the number of burglaries committed by residents of COA j in u(i) denotes the number of dwellings in i. After Bernasco and Luykx (2003).

Distance from centroid of COA where offender resides to centroid of COA where offence was committed: (a) offenders resident in urban COAs (b) offenders resident in rural COAs (c) Gamma functions fitted to the distances travelled by offenders resident in urban and rural COAs: Dashed line: Gamma (1.12, ) for offenders resident in urban COAs. Solid line: Gamma (0.7, ) for offenders resident in the rural COAs.

Burglary counts by COA: 2002 Histogram of burglary counts: Cambridgeshire COAs, Mean: 2.28 Variance: 9.56

(2) Poisson model with spatial (S) and non-spatial (U) random effects (1) Negative binomial GLM

Negative binomial GLM Poisson with spatial random effects Prop. detached hhlds [Att] Prop. private rented hhlds [Att] Prop. lone parent hhlds [Op] Prop. Economically inactive Prop. multi-person hhlds (not students) [Op] Prop. single people in hhlds [Op] Access index quantile 3 [Ac] Access index quantile 4 [Ac] Peterborough c DIC Psi [sd(S)/sd(U+S)]0.670 (.577,.850) Dispersion parameter (r)2.160 (1.9, 2.47) Moran test (Pearson residuals) (prob=0.001) Multiplicative change in the expected number of burglaries in a COA, due to a unit increase in the corresponding variable: Cambridgeshire COAs 2002.

Remarks: 1.The British Crime Survey (BCS) reveals the types of ACORN areas with the highest burglary rates. These areas include: (1)areas with council flats, high unemployment and persons living alone (3.1 times the national average (x3.1)) or many lone parent families (x 2.8). (2)areas with furnished flats and bedsits housing young single people (x2.4). Many of these ACORN areas seem to be characterised by providing “opportunity” rather than being “attractive”. 2. This study gives a Cambridgeshire county level perspective on these factors: (1) a similar emphasis but with increases in risk that appear modest by comparison with findings from the BCS. (2) Comparable increases in risk are found in the case of Peterborough (x1.45) and when COAs are close to the homes of motivated offenders (x1.88). A COA in Peterborough close to concentrations of motivated offenders has a raised risk of x2.74. (3) Mapping the residual relative risk reveals high levels of risk in north/northwest Cambridge unaccounted for by these factors.

3.Some challenges presented by ecological analysis. (a) modifiable areal units problem (MAUP): (i) scale effect (different results at different resolutions). (ii) grouping effect (different results from different aggregations) The MAUP has implications for: - mapping (pattern detection); - hot spot detection; - results of modelling (regression) and correlation):

(b) Selection of “appropriate” spatial units: neighbourhoods. (c) Incompatible spatial frameworks. (d) Areas with small populations – populations tend to be more homogeneous but statistics suffer from the small number problem. Areas with large populations – statistics more robust (with smaller standard errors) but populations tend to be more heterogeneous

(e) Classical statistical analyses need to contend with the problems created by (inter-area) spatial autocorrelation: (i) in dependent variable (ii) in model residuals. inference problems

4. Concluding remarks. -Spatial ecological analyses have a place in research into crime and disorder and are also relevant to the way police forces operate. - Spatial ecological analyses present a number of challenges to data analysts. - Spatial ecological analysis continues to be a rapidly developing area of methodological research that crime analysts ought to keep an eye on.