The broad objective of this work is to improve upon zonal approaches to recreation demand modeling. A standard zonal model ignores important aspects of spatial heterogeneity that are inherent in recreation demand contexts. The standard approach aggregates across groups of heterogeneous agents, and models them as homogenous points of origin for demand estimation. The standard approach also ignores that these heterogeneous agents deliberately choose their points of origin, which introduces a source of bias into the estimation. This work has produced a peer-reviewed article that addresses these shortcomings. This article, “A Latent Class Approach to Modeling Endogenous Spatial Sorting in Zonal Recreation Demand Models” (Land Economics86(4):800-816), demonstrates how a latent class count data model can control for unobserved heterogeneity that may lead to spatial sorting of recreationists. Results show that welfare estimates from this model for a southern California wilderness site are substantially smaller than for the standard approach.