Fundamentally, the rigor of models developed for bulk and surface processes on planets hinge upon the suitability and accuracy of underlying compositional analyses. The limited compositional data of most planetary surfaces, compared to Earth, magnifies the importance of analytical methods, especially in synthesizing different datasets.
Two projects on numerical methods and two others on photoanalysis relate to my interest in this promising area of planetary data analyses. In one , we focus on the spatial statistical methods that are best used with global datasets. Mars serves as the case study. Specifically, this identifies the primary statistical methods to use in answered five general queries that arise in planetary research: (1) Can we reject, to a desired degree of confidence, the null hypothesis that the data within one region are from the same distribution as the data within another? (2) How does the distribution of the attribute in one region compare qualitatively with that in another? (3) Can we reject, to a desired degree of confidence, the null hypothesis that the mean value of an attribute within one region is identical to that within another? (4) Is an attribute heterogeneous within a given region? (5) How are the solutions to the preceding questions altered when the ratio of two attributes is being investigated? By using a case-study approach, the associated project also provides the a recipe for future analyses with martian and other planetary data. The second project  targets the specific case of applying hierarchical multivariate regression with spatially autocorrelated data. This is supported by method and fit diagnostics. Possible applications include chemical and mienralogic data from the MESSENGER missiona t Mercury, and Dawn at Vesta and Ceres. Data collected so far at Gale Crater form the Curiosity Mission could also be analyzed with these methods, complementing principal and independent component analyses.
The two projects on photonalyses approach the challenges unique to characterizing soil on other planets. In contrast to Earth, where soils are often physically accessible, soil analyses on Mars and other planets must often rely on images for granulometry. Our work, first developed a semi-automated algorithm , coded in Mathematica as a template to expedite the laborious alternatives available from adaptations such as ImageJ. Importantly, the algorithm acquires the most intensive part of delineating the grains in an image, freeing sedimentologists to focus on qualitative adjustments to yield a realistic outcome. We subsequently quantified  the accuracy and precision of the algorithm -- as implemented in Mathematica software platform -- so that future work using the software would have reliable estimates of associated uncertainty.