  In disease mapping, the aim is to estimate the spatial pattern in disease
risk over an extended geographical region, so that areas with elevated risks
can be identified. A Bayesian hierarchical approach is typically used to
produce such maps, which models the risk surface with a set of spatially smooth
random effects. However, in complex urban settings there are likely to be
boundaries in the risk surface, which separate populations that are
geographically adjacent but have very different risk profiles. Therefore this
paper proposes an approach for detecting such risk boundaries, and tests its
effectiveness by simulation. Finally, the model is applied to lung cancer
incidence data in Greater Glasgow, Scotland, between 2001 and 2005.
