With prior NSF funding (SES 0921794, 0921279), the PIs at the College of William and Mary and the University of Minnesota created the School Attendance Boundary Information System (SABINS) data infrastructure project. It is designed to disseminate a rich, new data source of elementary, middle and high school attendance boundaries in a GIS framework via www.sabinsdata.org. The database currently contains 2009-2010 school year data on K-12th grade school attendance boundaries for three states (Minnesota, Delaware and Oregon), over 500 districts embedded in 13 metropolitan areas across the country, and over 5,000 unified school districts located in mostly rural areas. Each school attendance boundary in the SABINS database is integrated with population and housing data from the 2010 Decennial Census and the American Community Survey (ACS), allowing users to analyze an extensive set of the social and economic characteristics describing populations residing within each school attendance zone. SABINS data are also integrated with school-level information from the Common Core of Data (CCD) so that users can identify the characteristics of schools that supply services to each school attendance boundary.

This second phase of the SABINS project builds on the initial investment social science data infrastructure in two ways. First, the database adds annual, longitudinal updates to the existing geographic and tabular files so that users can explore school attendance boundary information for the 2009-2010 through the 2011-2012 school years. Since the ACS and CCD data are updated yearly, users will be able to explore the dynamic interplay between shifting educational geography, demographic change, and educational outcomes across schools and their corresponding catchment areas. Second, new activities will support an improved data retrieval system, enhance outreach activities and provide robust user support.

Broader Impacts This new phase involves a significant contribution to national cyberinfrastructure by making the SABINS data freely available to all users, including schools and school districts. Moreover, the project leverages NSF's existing investment into the National Historical GIS (NHGIS) housed at the University of Minnesota by improving existing software tools that optimize data dissemination and analysis of SABINS and associated data (ACS, CCD). The database is already being used widely for social science training, for policy research at the local, state and federal levels, and in the private sector. The SABINS project will be increasingly accessible for basic social science research, broadening the scope of local and regional analyses to explore variations across time and space simultaneously. The addition of longitudinal data combined with dissemination via the NHGIS at the University of Minnesota seeks to enable local, state, and federal administrators to deliver educational services more efficiently.

Project Report

Normal 0 false false false EN-US X-NONE X-NONE Within funding from NSF, scholars at the College of William and Mary and the Minnesota Population Center developed the School Attendance Boundary Information System (SABINS), a social science data infrastructure project that collects, harmonizes and disseminates spatial and tabular data of elementary, middle and high school attendance boundaries. Spatial Data, in the form of Geographic Information System (GIS) "shapefiles" are available to researchers and the public free-of-charge at www.sabinsdata.org. Data exist for the 2009-2010 through the 2011-2012 school years. The 2009-2010 data contain almost all of the largest 600 school districts, roughly 5,000 smaller districts and the every school district in Delaware, Minnesota, and Oregon. These school districts contain over half of the school-aged children in the U.S. The primary contribution made by the SABINS project team is implementing a GIS data structure that allows researchers, policy-makers and members of the general public the ability to conduct research that incorporates data describing the educational contexts in which children learn. By gathering school attendance boundary geography—and structuring this geography using the correct data model—the SABINS team created demographic estimates that describe the family background of children who live in school attendance boundaries. Scholars and policy-makers are currently using these demographic characteristics to plan the location of new schools, create bus routes, produce enrollment projections, and allocate resources across schools within school districts. Integrating demographic data with school attendance boundary geographies also allows scholars to investigate the effects racial and economic segregation on school performance and the relationship between school attendance boundary demographic characteristics on housing prices and school choice behavior. Researchers are currently using the data to study issues related to the concentration of school poverty, public health and epidemiology and gerrymandering. Another achievement of the SABINS data project was developing and documenting a set of data collection processes and data integration procedures designed to institutionalize the project so that school attendance boundary information becomes a permanent part of the Nation’s data infrastructure. This was an objective set by NSF and the NFS-appointed members of the SABINS Advisory Board. The Lead Principal Investigator of SABINS, Salvatore Saporito, met with Jack Buckley, Commissioner of the National Center for Education Statistics in 2011. Saporito persuaded Commissioner Buckley to move the SABINS project to the U.S. Department of Education (DOE). Since that time, the U.S. DOE and its contractors have collected spatial data of school attendance boundaries for the 2010-2011, and 2011-2012 school years for most of the 600 largest school districts in the United States. DOE and its contractors will attempt to collect data for the entire country for the 2013-2014 and 2015-2016 school years. By this metric, SABINS has been institutionalized and this is a major accomplishment—one that signifies that the project met all of its major data collection, dissemination and outreach efforts. Although the U.S. Department of Education will collect future years of school attendance boundary geography, it has neglected to adopt the innovative and useful data model created by the SABINS team. Nor has it documented its own data model (likely because it does not have a data model to document). Without a data model that organizes the data correctly, the school attendance boundary data collected by the U.S. Department of Education will be useless. One example illustrates this fact. According to popuation counts generated by the U.S. Department of Education, the School District of Philadelphia contains 1,504,950 people. But the popuation count for Philadephia's elementary school attendance boundaries is 1,478,525; the middle school boundarise cointain 636,280 people; the high school boundaries 1,699,185 people. All four of these layers should contain the same number of people (in this case, 1,504,950 people). There is an important reason why the collection of school attendance boundary geography was initially undertaken by scholars who were funded by the NSF—scholars within an academic environment have the expertise to create a GIS model that produces accurate and consistent results. These accuarate results can used by researchers and policy-makers. If the SABINS data model is not adopted by the Department of Education, the investment NSF made to create an attendance boundary information system will be wasted. I recommend that taxpayers who read this document contact the U.S. Department of Education and urge its representatives to empanel an advisory board consisting of GIS engineers and statisticians. Such experts can ensure that the U.S. Department of Education’s data collection and dissemination efforts—efforts funded with taxpayer dollars—will be money that is spent well.

National Science Foundation (NSF)
Division of Social and Economic Sciences (SES)
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Saylor Breckenridge
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College of William and Mary
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