Using Geospatial Data to Build 

Better Information at NCES 

Doug Geverdt

National Center for Education Statistics

Education Demographic and Geographic Estimates (EDGE) Program

This presentation is intended to encourage discussion and to inform interested 

parties of current research. The views expressed are solely those of the authors and 

are not necessarily those of the National Center for Education Statistics or the U.S. 

Census Bureau.

FCSM

October 27, 2022

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This presentation is intended to encourage discussion and inform interested parties of current research. 

The views expressed are solely those of the authors and are not necessarily those of the National Center 

for Education Statistics or the U.S. Census Bureau.
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Ex.1: School District Boundaries

• SAIPE and the School District Review Program (SDRP)

• Annual reimbursable collection/review of school district boundaries 

• About 13,000 geographically-defined districts; mostly special purpose local 

governments

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• School District Review Program (SDRP)

• Annual reimbursable collection/review of school district boundaries 

• About 13,000 geographically-defined districts; mostly special purpose local governments

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Ex.1: School District Boundaries

• Necessary input for a variety of products/processes:

• SAIPE poverty estimates for Title I allocations ($17B) and other statutory grants

• ACS address sampling operations (small area stratification/weighting)

• ACS school district estimates and ACS – Education Custom Tabulation (ACS-ED) 

• Decennial Census data products (PL, DHC, etc.) 

• District associations with other geographic areas (e.g., Congressional Districts)

• Geoprocessing options to aggregate/disaggregate spatial features by school district

• Demographic data products depend on the creation and application of geospatial data

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Ex. 2: School Locations and Locale Boundaries

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• Locale – 12-category general geographic indicator

• Basic types: City, Suburban, Town, Rural

• City, Suburban – Large, Midsize, Small

• Town, Rural – Fringe, Distant, Remote

• Consistent with Census Urban/Rural 

• Urban = City + Suburban + Town

• Rural = Rural
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https://nces.ed.gov/programs/maped/LocaleLookup/
Ex. 2: School Locations and Locale Boundaries

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• Locations – Public schools; Private Schools; Postsecondary schools; School district offices

• Necessary input for a variety of products/processes:

• NCES survey sample stratification and data disaggregation (e.g., NAEP, NTPS)

• Statutory program eligibility (e.g., Rural Education Achievement Program (REAP))

• Statutory program priorities (e.g., Clean School Bus, Bipartisan Infrastructure Bill)

• Geoprocessing options to associate schools with other geographic features

• Demographic data products depend on the creation and application of geospatial data
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Ex. 3: Point-optimized poverty estimates

• Create Census-based indicator of student and school poverty to supplement 

traditional administrative data from the National School Lunch Program

• Apply geostatistical methods to ACS household income at household locations to 

construct an estimation surface of the income-to-poverty ratio (IPR) for the U.S.

• Test #1: Connect school point locations to IPR prediction surface to produce 

school neighborhood poverty estimates for all public schools in the U.S. 

• Test #2: Connect student address geocodes to IPR prediction surface to produce 

poverty (IPR) estimates for students and schools (based on student enrollment)

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SIDE IPR for school locations 

(school neighborhood poverty)
Conclusions

• Geospatial data infrastructure is essential for supporting ED statistical and 

statutory programs

• Even more value expected in the future

• Challenges from the Geographic-Demographic collision:

• Different data cultures and drivers (FCSM vs. FGDC; Evidence Act vs. GeoData Act)

• Bias/preferences of statistical organizations

• New geographic areas (or no geographic areas)

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Questions?

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National Center for Education Statistics

Education Demographic and Geographic Estimates (EDGE) Program