This study assesses the value of enhanced spatial resolution in the agriculture and land use component of an integrated assessment (IA) model. IA models typically represent land use decisions at finer resolution than the energy and economic components, to account for spatial heterogeneity of land productivity and use. However, increasing spatial resolution incurs costs, from additional input data processing, run time, and complexity of results. This study uses the Global Change Assessment Model (GCAM) to analyze land use in the Midwestern United States in three levels of spatial aggregation, and three climate change mitigation scenarios. For visualization and simplification of higher resolution model output, we use non-metric multidimensional scaling. We find that the level of spatial aggregation influences the magnitude but not the direction of land use change in response to the modeled drivers, and in the examples analyzed, increasing spatial resolution reduces the extent of land use change.