We created a computational workflow which calculates element-wise atomic planar densities using crystallographic information files (CIFs) as inputs and applied this to a large database of compounds. We tested the usefulness of this dataset for machine learning applications and found that in a simple test case, the addition of max elementwise atomic planar density as a feature improves the predictive accuracy relative to learning on elemental presence only using the target property of bulk modulus.