First, we demonstrate successful machine-learned analysis of XANES data for coordination, in agreement with prior work , and to our knowledge, the first models for mean nearest-neighbor distance and Bader charge (the partial charge distribution on atoms, which is correlated with the oxidation state -see Methods and Figures S4-S11 in the SI). Secondly, we represent XANES spectra using two types of featurization and two choices of normalization, and find that models using post-edge normalized data and multiscale polynomial featurization are readily interpretable and conform with multiple known trends. Finally, we use the multiscale polynomial featurization to uncover high-level spectrum-property relationships for experimentally relevant material parameters, comparing the relative importance of white line energy, absorption magnitude, and linear / quadratic / cubic character of spectra across elements and properties. The results of this study highlight which energy regions are important, which could accelerate experimental structural characterization depending on individual elements and use cases. While most heuristics apply to a smaller subset of materials, these trends are based on thousands of spectra, and so may be applicable to a wide range of chemistries and structures.