  Many models for spatial and spatio-temporal data assume that "near things are
more related than distant things," which is known as the first law of
geography. While geography may be important, it may not be all-important, for
at least two reasons. First, technology helps bridge distance, so that regions
separated by large distances may be more similar than would be expected based
on geographical distance. Second, geographical, political, and social divisions
can make neighboring regions dissimilar. We develop a flexible Bayesian
approach for learning from spatial data which units are close in an unobserved
socio-demographic space and hence which units are similar. As a by-product, the
Bayesian approach helps quantify the relative importance of socio-demographic
space relative to geographical space. To demonstrate the proposed approach, we
present simulations along with an application to county-level data on median
household income in the U.S. state of Florida.
