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Enrollment projections are used by school administrators to predict the number of students expected to attend a school district within a defined period of time. This dissertation examines methods used for making enrollment projections and seeks to improve these methods through the application of geographic principles. The presented thesis challenges the existing aspatial framework used to calculate grade progression rates, arguing that a spatial framework improves projection accuracy. Grade progression rates are the critical element in enrollment projections and this dissertation's major contribution is the analysis of four different grade progression rate calculations at the school district level. This dissertation also argues that grade progression rates represent spatial relationships of migration that exist between adjacent school districts and uses these spatial relationships to create a new spatial Bayesian approach. This dissertation demonstrates that geographic methods can be successfully integrated to improve enrollment project accuracy through the reduction of the small number problem. In addition, this research identifies the importance of smoothing effects of the modified cohort progression method when compared to Bayesian approaches.