In spatial epidemiology, disease incidence and demographic data are commonly summarized within larger regions such as administrative units because of privacy concerns. As a consequence, analyses using these aggregated data are subject to the Modifiable Areal Unit Problem (MAUP) as the geographical manifestation of ecological fallacy. In this study, we create small area disease estimates through dasymetric refinement, and investigate the effects on predictive epidemiological models. We perform a binary dasymetric refinement of municipality-aggregated dog tumor incidence counts in Switzerland for the year 2008 using residential land as a limiting ancillary variable. This refinement is expected to improve the quality of spatial data originally aggregated within arbitrary administrative units by deconstructing them into discontinuous subregions that better reflect the underlying population distribution. To shed light on effects of this refinement, we compare a predictive statistical model that uses unrefined administrative units with one that uses dasymetrically refined spatial units. Model diagnostics and spatial distributions of model residuals are assessed to evaluate the model performances in different regions. In particular, we explore changes in the spatial autocorrelation of the model residuals due to spatial refinement of the enumeration units in a selected mountainous region, where the rugged topography induces great shifts of the analytical units i.e., residential land. Such spatial data quality refinement results in a more realistic estimation of the population distribution within administrative units, and thus, in a more accurate modeling of dog tumor incidence patterns. Our results emphasize the benefits of implementing a dasymetric modeling framework in veterinary spatial epidemiology.