Identity theft is a prevalent and growing crime and spatial analysis of identity theft is scarce. Spatial analysis of identity theft can provide valuable insights into the nature of the crime and help reduce victimization. This thesis explores the suitability of using overlay mapping, a type of geospatial analysis algorithm, to predict identity theft victimization. Two types of overlay mapping were examined, fuzzy overlays and weighted overlays. As input to the overlay algorithms, correlations of identity theft victimization rates were determined using federal data sources of U.S. state-level identity theft victimization rates and demographics from the U.S. Census Bureau and other federal agencies. A total of 21 fuzzy and 6 weighted overlay maps were generated and evaluated. The results found that several overlay maps represented accurate predictions of identity theft victimization rates, although most of the overlay maps represented poor predictions due to poor correlations. Based on the results, overlay mapping holds promise as a tool of identity theft spatial pattern analysis.