We develop a spatial Poisson hurdle model to explore geographic variation in emergency department (ED) visits while accounting for zero inflation. The model consists of two components: a Bernoulli component that models the probability of any ED use (i.e. at least one ED visit per year), and a truncated Poisson component that models the number of ED visits given use. Together, these components address both the abundance of 0s and the right-skewed nature of the non-zero counts. The model has a hierarchical structure that incorporates patient and area level covariates, as well as spatially correlated random effects for each areal unit. Because regions with high rates of ED use are likely to have high expected counts among users, we model the spatial random effects via a bivariate conditionally auto-regressive prior, which introduces dependence between the components and provides spatial smoothing and sharing of information across neighbouring regions. Using a simulation study, we show that modelling the between-component correlation reduces bias in parameter estimates. We adopt a Bayesian estimation approach, and the model can be fitted by using standard Bayesian software. We apply the model to a study of patient and neighbourhood factors influencing ED use in Durham County, North Carolina.