model
{

  for(i in 1:N)
  {
     observed[i] ~ dpois(mu[i])
     log(theta[i]) <-  alpha + beta*nonwhite[i] + u[i] + v[i] 
     mu[i] <- expected[i]*theta[i]

     u[i] ~ dnorm(0, precu)
  }

  v[1:N] ~ car.normal(adj[], weights[], num[], precv)

  alpha ~ dflat()
  beta ~ dnorm(0,1.0E-5)
  precu ~ dgamma(0.001, 0.001)
  precv ~ dgamma(0.1, 0.1)

  sigmau<-1/precu
  sigmav<-1/precv
}
