This paper discusses joint outcome modeling of multivariate spatial data, where outcomes include count as well as zero-inflated count data. The framework utilized for the joint spatial count outcome analysis reflects that which is now commonly used for the joint analysis of longitudinal and survival data, termed shared frailty models, in which the outcomes are linked through a shared latent spatial random risk term. We discuss these types of joint mapping models and consider the benefits achieved through such joint modeling in the disease mapping context. We also consider the power of tests for common spatial structure in the context of two spatial maps and develop recommendations on the sort of power achievable in some contexts, as well as overall recommendations on the utility of joint mapping. We illustrate the approaches in an analysis of lung cancer mortality as well as an ecological study of Comandra blister rust infection of lodgepole pine trees. Copyright © 2012 John Wiley & Sons, Ltd.