Small Area Estimation of Public Safety Indicators in the Netherlands

Bart BuelensStatistics Netherlands

Conference on Indicators and Survey MethodologyVienna, Feb. 2010

National Safety Monitor (NSM)

Crime and victimization, satisfaction with police, feelings of unsafety Annual survey conducted in 1st quarter among people aged 15+ living in NL Mixed mode telephone – personal interviews Target response 750 per Police District (PD) Equal fractions per municipality in each PD 25 PDs, target pop size approx. 13 mln. sample size approx. 19,000

From NSM to ISM

NSM: 2005 (pilot), 2006 – 2008 (production)NSM successor: ISM (2008 Q4)“parallel NSM” (pNSM): in parallel with ISMTo quantify discontinuities in time seriespNMS: reduced size, approx. 6000 respondentsDiscontinuities at PD level? pNMS sample too smallConsider SAE methods

NSM estimation

Generalized regression estimator (GREG) Age, gender, ethnicity, marital status, income, household size, urbanisation

Some 200 target variablesincluding nine for the VBBV programthree of these are indicators

NSM Indicators

Anti-social behaviour (ASB); scale 1-7drunk people, harassment, drug relatedproblems, groups of youngsters

Degradation (DEG); scale 1-7graffiti, rubbish, litter, vandalism

Opinion on police performance (POL); scale 1-10contact with public, protection,responsive, dedicated, efficient

NSM 2006, 2007, 2008, pNSM Survey variable: ABS, DEG and POL indicators PDs are small areas Use models to borrow strength from other PDs Area level linear mixed model linking of register and survey data problematic so

no unit level models possible at this stage

Small area estimation

Linear Mixed Model (Fay-Herriot)

Estimation using EBLUP (Rao 2003)

Estimation of model variance

standard methods ML, REML, methods of moments, lead to zero-estimates of model variance Bayesian approach use posterior mean as plug-in in EBLUP (Bell, 1999)

Covariates

Known for all PDs (from registers) Police Register of Reported Offences

Violent crimes, property crimes, vandalism, traffic offences (N/A for 2008, pNSM!!)

Municipal Administration Age, ethnicity, (gender) Address density

Principal Component Analysis Reduction of dimension 2 PCs explain > 98% of variance

Model selection

criteria to select the best model

Best models

ASB1st principal component

DEG

registered vandalism, urbanization

POLregistered violent crimes, registeredvandalism, traffic offences

Reduction in coefficient of variation

NSM 2006

NSM 2007

NSM 2008

pNSM

ASB 6.2 8.8 16.7 34.5

DEG 3.5 5.5 5.5 16.9

POL 9.5 7.3 13.4 22.7

Weight of the direct estimate in the EBLUP

NSM 2006

NSM 2007

NSM 2008

pNSM

ASB 0.87 0.82 0.67 0.43

DEG 0.92 0.88 0.88 0.69

POL 0.79 0.83 0.70 0.57

Coefficient estimates and st.err.

NSM 2006 NSM 2007 NSM 2008 pNSM

ASB

Intercept 1.214 (0.083) 1.231 (0.075) 1.22 (0.054) 0.399 (0.193)

Princ. comp. -0.217 (0.033) -0.213 (0.03) -0.215 (0.022) -0.563 (0.078)

DEG

Intercept 2.029 (0.318) 1.694 (0.269) 1.723 (0.255) 1.599 (0.529)

Vandalism 0.312 (0.214) 0.529 (0.181) 0.51 (0.168) 0.459 (0.352)

Urbanization 0.009 (0.002) 0.008 (0.001) 0.008 (0.001) 0.013 (0.003)

POL

Intercept 6.841 (0.345) 7.043 (0.411) 6.857 (0.284) 7.044 (0.468)

Violent crim. 0.26 (0.217) 0.752 (0.274) 0.639 (0.222) 0.854 (0.366)

Vandalism -0.519 (0.243) -0.921 (0.284) -0.488 (0.197) -1.007 (0.328)

Traffic off. -0.387 (0.169) -0.229 (0.212) -0.593 (0.154) -0.178 (0.274)

ASB

DEG

POL

Results

pNSM benefits from SAE, NSM not most gains in precision for ASB, least for DEG; POL in between

Earlier results (SAE conf. Elche) – NSM only SAE works well for violent crimes not for attitudes/opinions about e.g. public safety

Future work

ESSnet on Small Area Estimation this preliminary work to be extended as a case study, e.g: unit level models (when possible) other covariates (socio-economic characteristics) consider lower regional levels consider temporal aspects

ESSnet: presentation by S. Falorsi earlier today