Animal home range estimations are important for conservation planning and protecting the habitat of threatened species. The accuracy of home range calculations is influenced by the map projection chosen in a geographic information system (GIS) for data analysis. Different methods of projection will distort spatial data in different ways, so it is important to choose a projection that meets the needs of the research. The large number of projections in use today and the lack of distortion comparison between the various types make selecting the most appropriate projection a difficult decision. The purpose of this study is to quantify and compare the amount of area distortion in animal home range estimations when projected into a number of projected coordinate systems in order to understand how the chosen projection influences analysis. The objectives of this research are accomplished by analyzing the tracking data of four species from different regions in North and South America. The home range of each individual from the four species datasets is calculated using the Characteristic Hull Polygon method for home range estimation and then projected into eight projected coordinate systems of various scales and projection type, including equal area, conformal, equidistant, and compromise projections. A continental Albers Equal Area projection is then used as a baseline area for the calculation of a distortion measurement ratio and magnitude of distortion statistic. The distortion measurement ratio and magnitude calculations provide a measurement of the quantity of area distortion caused by a projection. Results show the amount distortion associated with each type of projection method and how the amount of distortion changes for a projection based on geographic location. These findings show how the choice of map projection can have a large influence on data analysis and illustrate the importance of using an appropriate PCS for the needs of a given study. Distorted perceptions can influence decision-making, so it is important to recognize how a map projection can influence the analysis and interpretation of spatial data.