Methodology: The analytical tools used for this process included simple tiling of images by running GDAL merge, adjusting the parameters for the hillshade raster to obtain a visual representation of the bedrock layer, and understanding of coordinate systems and projections. The image extents when plotting the image and figuring out how to run the input from a jupyter notebook all presented challenges. The important part of this analysis lies in the tiling of the data. When using GDAL merge as long as the coordinate systems match, the images will line up perfectly. If one image has higher resolution than another, for example, 1 arc minute vs. 1 arc second, the image with the highest resolution needs to be placed first in the order of listing images on the command line. Python offered some incredible packages such as cartopy to add visual references such as coastlines and plotting points to the raster.   For this project the tools I discovered were useful and I would have liked to continue working with basemap. I hit some limits with the merging of shapefiles with the raster image but basemap may be able to establish that relationship. Addtitionally tools such as bokeh look very promising for incorporating interactivity into the jupyter notebook and beyond. This would be excellent if it were able to be embedded onto a webpage. Additional image processing using histograms would be another step in the methodology allowing for unique signatures to be made from the raster data.