NASA's Mars 2020 Mission is to study Mars' habitability and seek signs of past microbial life. The mission uses an X-ray fluorescence spectrometer to identify chemical elements at sub-millimeter scales of the Mars surface. The instrument captures high spatial resolution observations comprised of several thousand individual measured points by raster-scanning an area of the rock surface. This paper will show how different methods, including linear regression, k-means clustering, image segmentation, similarity functions, and euclidean distances, perform when analyzing datasets provided by the X-ray fluorescence spectrometer to assist scientists in understanding the distribution and abundance variations of chemical elements making up the scanned surface. We also created an interactive map to correlate the x-ray spectrum data with a visual image acquired by an RBG camera.