We consider the problem of mapping the risk from a disease using a series o
f regional counts of observed and expected cases, and information on potent
ial risk factors. To analyse this problem from a Bayesian viewpoint, we pro
pose a methodology which extends a spatial partition model by including cat
egorical covariate information. Such an extension allows detection of clust
ers in the residual variation, reflecting further, possibly unobserved, cov
ariates. The methodology is implemented by means of reversible jump Markov
chain Monte Carlo sampling. An application is presented in order to illustr
ate and compare our proposed extensions with a purely spatial partition mod
el. Here we analyse a well-known data set on lip cancer incidence in Scotla
nd. Copyright (C) 2000 John Wiley & Sons, Ltd.