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

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  • Seth E Spielman
  • David C Folch

Abstract

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.

Suggested Citation

  • Seth E Spielman & David C Folch, 2015. "Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-21, February.
    • Handle: RePEc:plo:pone00:0115626
    DOI: 10.1371/journal.pone.0115626
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    Citations

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    Cited by:

    1. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.
    2. James Gaboardi, 2020. "Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance," Working Papers 20-5, Center for Economic Studies, U.S. Census Bureau.
    3. Levi J Wolf & Elijah Knaap & Sergio Rey, 2021. "Geosilhouettes: Geographical measures of cluster fit," Environment and Planning B, , vol. 48(3), pages 521-539, March.
    4. Daniel J. Hicks, 2020. "Census Demographics and Chlorpyrifos Use in California’s Central Valley, 2011–15: A Distributional Environmental Justice Analysis," IJERPH, MDPI, vol. 17(7), pages 1-20, April.
    5. Matthew H. E. M. Browning & Alessandro Rigolon, 2018. "Do Income, Race and Ethnicity, and Sprawl Influence the Greenspace-Human Health Link in City-Level Analyses? Findings from 496 Cities in the United States," IJERPH, MDPI, vol. 15(7), pages 1-22, July.
    6. Christopher S Fowler & Leif Jensen, 2020. "Bridging the gap between geographic concept and the data we have: The case of labor markets in the USA," Environment and Planning A, , vol. 52(7), pages 1395-1414, October.
    7. David C. Folch & Seth Spielman & Molly Graber, 2023. "The Impact of Covariance on American Community Survey Margins of Error: Computational Alternatives," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(4), pages 1-23, August.
    8. James Gaboardi, 2020. "Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance," Working Papers 20-05, Center for Economic Studies, U.S. Census Bureau.
    9. Raoul S. Liévanos & Amy Lubitow & Julius Alexander McGee, 2019. "Misrecognition in a Sustainability Capital: Race, Representation, and Transportation Survey Response Rates in the Portland Metropolitan Area," Sustainability, MDPI, vol. 11(16), pages 1-33, August.
    10. David C. Folch & Daniel Arribas-Bel & Julia Koschinsky & Seth E. Spielman, 2016. "Spatial Variation in the Quality of American Community Survey Estimates," Demography, Springer;Population Association of America (PAA), vol. 53(5), pages 1535-1554, October.

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