Table 2.
Bayesian conditional autoregressive Poisson models of counts of alcohol outlets within towns in rural Victoria (n = 353)*
| Model 1 (Bars) | Model 2 (Restaurants) | Model 3 (Off Premise) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Variable | median | 2.5% | 97.5% | median | 2.5% | 97.5% | median | 2.5% | 97.5% |
| Demand | |||||||||
| Resident population (×10,000) | 1.288 | 1.213 | 1.372 | 1.538 | 1.349 | 1.757 | 1.339 | 1.263 | 1.424 |
| Commuters (×10%) | 1.191 | 1.103 | 1.289 | 1.261 | 1.074 | 1.487 | 1.151 | 1.063 | 1.247 |
| Visitors (×10%) | 1.172 | 1.047 | 1.314 | 1.468 | 1.181 | 1.835 | 1.032 | 0.883 | 1.187 |
| Spatial interaction (×10,000 units) | 0.833 | 0.727 | 0.950 | 0.752 | 0.589 | 0.953 | 0.929 | 0.823 | 1.048 |
| Median Household Income | |||||||||
| Local (× $10,000) | 0.798 | 0.716 | 0.887 | 0.751 | 0.603 | 0.927 | 0.811 | 0.717 | 0.907 |
| Lagged (× $10,000) | 1.233 | 1.082 | 1.412 | 1.697 | 1.355 | 2.198 | 1.304 | 1.135 | 1.499 |
| Proportion of SA1 variance explained by CAR random effect | 0.025 | 0.001 | 0.345 | 0.001 | 0.000 | 0.068 | 0.003 | 0.000 | 0.043 |
| SD of CAR random effect | 0.109 | 0.017 | 0.428 | 0.037 | 0.014 | 0.408 | 0.035 | 0.014 | 0.141 |
| SD of noise random effect | 0.669 | 0.545 | 0.787 | 1.546 | 1.315 | 1.825 | 0.681 | 0.562 | 0.814 |
| Global Moran’s I for CAR random effect | 0.855 | 0.805 | 0.725 | ||||||
*
Intercept suppressed from table; bolded odds ratios denote a 95% credible interval that does not include 1.000, thereby indicating support for an association between the corresponding independent variable and counts of alcohol outlets.