Small Area Estimation of Public Safety Indicators in the Netherlands Bart Buelens Statistics Netherlands Conference on Indicators and Survey Methodology Vienna, Feb. 2010

National Safety Monitor (NSM)  Crime and victimization, satisfaction with police, feelings of unsafety  Annual survey conducted in 1 st 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 ISM  To quantify discontinuities in time series  pNMS: reduced size, approx respondents  Discontinuities at PD level? pNMS sample too small  Consider SAE methods

NSM estimation  Generalized regression estimator (GREG)  Age, gender, ethnicity, marital status, income, household size, urbanisation Some 200 target variables  including nine for the VBBV program  three of these are indicators

NSM Indicators  Anti-social behaviour (ASB); scale 1-7 drunk people, harassment, drug related problems, groups of youngsters  Degradation (DEG); scale 1-7 graffiti, rubbish, litter, vandalism  Opinion on police performance (POL); scale 1-10 contact 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 ASB 1st principal component DEG registered vandalism, urbanization POL registered violent crimes, registered vandalism, traffic offences

Reduction in coefficient of variation NSM 2006 NSM 2007 NSM 2008 pNSM ASB DEG POL

Weight of the direct estimate in the EBLUP NSM 2006 NSM 2007 NSM 2008 pNSM ASB DEG POL

Coefficient estimates and st.err. NSM 2006NSM 2007NSM 2008pNSM ASB Intercept1.214 (0.083)1.231 (0.075)1.22 (0.054)0.399 (0.193) Princ. comp (0.033) (0.03) (0.022) (0.078) DEG Intercept2.029 (0.318)1.694 (0.269)1.723 (0.255)1.599 (0.529) Vandalism0.312 (0.214)0.529 (0.181)0.51 (0.168)0.459 (0.352) Urbanization0.009 (0.002)0.008 (0.001) (0.003) POL Intercept6.841 (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 (0.243) (0.284) (0.197) (0.328) Traffic off (0.169) (0.212) (0.154) (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