Historically, catalytic research and many areas of surface science have used phenomenological rate equations to build kinetic models for surface processes. The models treat surfaces as a lattice of sites, track the probability of finding a site in a particular state, and use maximal-entropy/well-mixed assumption to reconstruct spatially correlated information. This well-mixed assumption, however, often fails. This talk will develop a hierarchy of models that are able to take into account short range spatial correlations. The hierarchy is developed in the context of averaging an underlying master equation. The talk will continue with some simple examples, an example in catalysis, and conclude with ideas on several other applications for this framework.