This work explores potential distortions created by race and ethnicity on the visualization, interpretation, and understanding of the spatial distribution of suicide in the United States. Due to radically different suicide rates among racial/ethnic groups, traditional crude or age-adjusted rates may introduce statistical confounding in both linear and spatial models. Using correlation, choropleth mapping, hot spot analysis, and location-allocation modeling, this work shows how traditional methods of health system planning may unintentionally overlook elevated risk in minority-dominated areas like inner cities, the Texas/Mexico border region, and the Deep South. The final chapter introduces a simulation protocol for examining potential distortions in datasets to identify spatial and non-spatial distortions created by the underlying population composition. Methodologically, this dissertation contributes to the discourse on place context versus population composition. More generally, this research points to potential hazards to creating a more inclusive and equitable healthcare system.