Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization

By Seth E. Spielman, David C. Folch. 2015

96 citations recorded by the source corpus.

The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold.

Subfields: Economics and Econometrics, Management Science and Operations Research, Sociology and Political Science

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Citations in the corpus

Fair decision making using privacy-protected data (2019, background): “We are not aware of systematic studies of potential bias in the statistics currently released by statistical agencies, however Spielman observed that margins-of-error can be correlated with income levels in some Census products, leading to greater inaccuracies for low-income persons [29, 30].”