This document presents a method for extracting high-order spatial statistics from categorical attribute data. It introduces indicator cumulants and multiple-point transition probabilities that capture relationships between categories at different locations. These statistics are calculated from both exhaustive and scattered hard data sets in 2D channel and 3D deposit case studies. The results demonstrate the ability to model complex spatial patterns and are useful for building conditional simulations.