With the advent of high-resolution forecast models, new verification methods, referred to collectively as spatial verification methods, were rapidly proposed in part to deal with double penalties and over accumulation of small-scale errors that tended to favor coarser models for the wrong reasons. Other methods aimed more at diagnostic approaches. Regardless, some methods focused more on spatial pattern errors, referred to here as distance-based methods. Examples include the centroid distance, Baddeley's delta, Hausdorff, etc. With so many to choose from, it is important to understand their properties and how they inform about particular situations. This talk will introduce a new set of test comparisons designed to elicit a better understanding of these properties.