model {

for (k in 1:N) {

Y[k] ∼ dpois(mu[k])

log(mu[k]) <- log(E[k]) + beta0 + gamma0*yr_ctr[k] +

beta1*blkpct_ctr[k] +

beta2*hispct_ctr[k] + beta3*asianpct_ctr[k] +

beta4*popm15_30_ctr[k] +

beta5*blpovz90_ctr[k] + beta6*dam_psqm_ctr[k] +

gamma1*totalc_prdwym_ctr[k] + gamma2*pctoff-

salesurr91[k] +

gamma3*pctoffsalesurr91[k]*yearind[k] +

phi[year[k],C[k]] }

for (j in 1:10){

phi[j, 1:290] ∼ car.normal(adj[], weights[], num[], tau[j])

tau[j] ∼ dgamma (0.1, 0.1)

}

for (k in 1:SumNumNeigh) {weights [ k ] <- 1 }

beta0 ∼ dnorm ( 0.0, 1.0E-5 )

beta1 ∼ dnorm ( 0.0, 1.0E-5 )

beta2 ∼ dnorm ( 0.0, 1.0E-5 )

beta3 ∼ dnorm ( 0.0, 1.0E-5 )

beta4 ∼ dnorm ( 0.0, 1.0E-5 )

beta5 ∼ dnorm ( 0.0, 1.0E-5 )

beta6 ∼ dnorm ( 0.0, 1.0E-5 )

gamma0 ∼ dnorm ( 0.0, 1.0E-5 )

gamma1 ∼ dnorm ( 0.0, 1.0E-5 )

gamma2 ∼ dnorm ( 0.0, 1.0E-5 )

gamma3 ∼ dnorm ( 0.0, 1.0E-5 )

}