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Modeling spatial variables involves uncertainty. Uncertainty is affected by the degree to which a spatial variable has been sampled: decreased spacing between samples leads to decreased uncertainty. The reduction in uncertainty due to increased sampling is dependent on the properties of the variable being modeled. A densely sampled erratic variable may have a level of uncertainty similar to a sparsely sampled continuous variable. A simulation based approach is developed to quantify the relationship between uncertainty and data spacing. Reference realizations are simulated and sampled at different spacings. The samples are used to condition additional realizations from which uncertainty is quantified. A number of factors complicate the relationship between uncertainty and data spacing including the proportional effect, nonstationary variogram, classification threshold, number of realizations, data quality and modeling scale. A case study of the relationship between uncertainty and data density for bitumen thickness data from northern Alberta is presented. / Mining Engineering