small area estimation of public safety indicators in the netherlands bart buelens statistics...
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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