Landscape features may serve as either barriers or gateways to the spread of certain infectious diseases, and understanding the way geographic structure impacts disease spread could lead to improved containment strategies. Here, we focus on modeling the space-time diffusion process of a raccoon rabies outbreak across several states in the Eastern United States. Specifically, we measure the impact that landscape features, such as mountains and rivers, have on the speed of infectious disease diffusion. This work combines statistical modeling with spatial operations in a geographic information system (GIS) to study disease diffusion. We use a GIS to create landscape feature variables and explore three analytic approaches. First, we use spatial prediction (kriging) to provide a descriptive pattern of the spread of the virus. Second, we use Bayesian areal wombling to detect barriers for infectious disease transmission and examine spatial coincidence with potential features. Finally, we input landscape variables into a hierarchical Bayesian model with spatially varying coefficients to obtain model-based estimates of their local impacts on transmission time in counties.