This project applied raster-based spatial analysis to assess contamination risk to surface and groundwater in Webster Township, Michigan. Using soil texture (percent sand), elevation variability, and proximity to water, I created three normalized raster layers and combined them into a weighted contamination risk index. Tools like Raster Calculator, Euclidean Distance, Focal Statistics, and Reclassification were used to transform and standardize the data. The final output visualized high-risk, moderate-risk, and low-risk zones. This exercise demonstrates how raster overlay and environmental weighting can be used for suitability modeling and spatial decision support.