2004
DOI: 10.1177/19367244042100102
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Georeferencing School-Age Population Projections: Methodology for a Rapidly Growing District

Abstract: Schools and other public service providers often require detailed information about local populations to accommodate geographically specific population growth and decline with necessary resources. Faced with relentless middle-class population growth, the Ascension Parish School District requires fine-grained population projections of its school enrollment to ensure sufficient facilities and balanced attendance zones in coming years. In this paper we outline the method we created to integrate standard projected… Show more

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Cited by 2 publications
(1 citation statement)
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“…Finally, with the development of GIS, georeferenced population projections are combined with statistical analysis to systematically infer the spatial distribution of student households (Miller, 2008; Trouteaud et al, 2004; Tsai & Miller, 2005). For example, Trouteaud et al (2004) used traditional CSR to predict aggregate level (e.g., parish) school enrolment, and these enrolments are then allocated to each georeferenced point based on the calculated probabilities that a student will occupy these points/parcels in the predicted year. This method can predict both the growth of student enrolment by incorporating land use or residential development data (e.g., new building permits) and allow officials to modify enrolment boundaries quickly and easily.…”
Section: Introductionmentioning
confidence: 99%
“…Finally, with the development of GIS, georeferenced population projections are combined with statistical analysis to systematically infer the spatial distribution of student households (Miller, 2008; Trouteaud et al, 2004; Tsai & Miller, 2005). For example, Trouteaud et al (2004) used traditional CSR to predict aggregate level (e.g., parish) school enrolment, and these enrolments are then allocated to each georeferenced point based on the calculated probabilities that a student will occupy these points/parcels in the predicted year. This method can predict both the growth of student enrolment by incorporating land use or residential development data (e.g., new building permits) and allow officials to modify enrolment boundaries quickly and easily.…”
Section: Introductionmentioning
confidence: 99%