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The NCES Education Demographic and Geographic Estimates (EDGE) program designs and develops information resources to help understand the social and spatial context of education in the U.S. It uses data from the U.S. Census Bureau’s American Community Survey to create custom indicators of social, economic, and housing conditions for school-age children and their parents. It also uses spatial data collected by NCES and the Census Bureau to create geographic locale indicators, school point locations, school district boundaries, and other types of data to support spatial analysis.

School Neighborhood Poverty

The EDGE School Neighborhood Poverty Estimates rely on household economic data from the Census Bureau’s American Community Survey (ACS) and public school point locations developed by NCES to estimate the income-to-poverty ratio for neighborhoods around school buildings. Unlike neighborhood poverty estimates created from survey responses collected for predefined geographic areas like census tracts, Spatially Interpolated Demographic Estimates (SIDE) predict conditions at specific point locations based on the survey responses nearest to those locations. This approach allows SIDE estimates to extract new value from existing data sources to provide indicators of neighborhood conditions. The economic conditions of school neighborhoods may be different from the economic conditions in neighborhoods where students live. However, the economic condition of the neighborhood around a school may impact schools, just as the condition of neighborhood schools may impact local neighborhoods. The school neighborhood poverty estimates provide an additional indicator to help identify these local conditions.

School Neighborhood Poverty Estimates (410 KB)

Additional Resources

Geverdt, D. and Nixon, L. (2018). Sidestepping the box: Designing a supplemental poverty indicator for school neighborhoods (NCES 2017-039) (1.54 MB). U.S. Department of Education. Washington, DC: National Center for Education Statistics.

Gribov, A. & Krivoruchko, K. (2020). Empirical Bayesian kriging implementation and usage. Science of the Total Environment. 722 (2020), 137290.

Krivoruchko, K. & Gribov, A. (2019). Evaluation of empirical Bayesian kriging. Spatial Statistics. 32 (2019), 100368.