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

A Socio-Demographic Latent Space Approach to Spatial Data When Geography is Important but Not All-Important