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Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach

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  • Seth E. Spielman
  • Alex Singleton

Abstract

In 2010 the American Community Survey (ACS) replaced the long form of the decennial census as the sole national source of demographic and economic data for small geographic areas such as census tracts. These small area estimates suffer from large margins of error, however, which makes the data difficult to use for many purposes. The value of a large and comprehensive survey like the ACS is that it provides a richly detailed, multivariate, composite picture of small areas. This article argues that one solution to the problem of large margins of error in the ACS is to shift from a variable-based mode of inquiry to one that emphasizes a composite multivariate picture of census tracts. Because the margin of error in a single ACS estimate, like household income, is assumed to be a symmetrically distributed random variable, positive and negative errors are equally likely. Because the variable-specific estimates are largely independent from each other, when looking at a large collection of variables these random errors average to zero. This means that although single variables can be methodologically problematic at the census tract scale, a large collection of such variables provides utility as a contextual descriptor of the place(s) under investigation. This idea is demonstrated by developing a geodemographic typology of all U.S. census tracts. The typology is firmly rooted in the social scientific literature and is organized around a framework of concepts, domains, and measures. The typology is validated using public domain data from the City of Chicago and the U.S. Federal Election Commission. The typology, as well as the data and methods used to create it, is open source and published freely online.

Suggested Citation

  • Seth E. Spielman & Alex Singleton, 2015. "Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(5), pages 1003-1025, September.
  • Handle: RePEc:taf:raagxx:v:105:y:2015:i:5:p:1003-1025
    DOI: 10.1080/00045608.2015.1052335
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    Citations

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    Cited by:

    1. Neil Lee & Andrés Rodríguez-Pose, 2021. "Entrepreneurship and the fight against poverty in US cities," Environment and Planning A, , vol. 53(1), pages 31-52, February.
    2. Patrick Ballantyne & Alex Singleton & Les Dolega & Kevin Credit, 2022. "A framework for delineating the scale, extent and characteristics of American retail centre agglomerations," Environment and Planning B, , vol. 49(3), pages 1112-1128, March.
    3. Neil Lee & Andrés Rodríguez-Pose, 2016. "Is There Trickle-Down from Tech? Poverty, Employment, and the High-Technology Multiplier in U.S. Cities," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(5), pages 1114-1134, September.
    4. Lekkas, Peter & Howard, Natasha J & Stankov, Ivana & daniel, mark & Paquet, Catherine, 2019. "A Longitudinal Typology of Neighbourhood-level Social Fragmentation: A Finite Mixture Model Approach," SocArXiv 56x9c, Center for Open Science.
    5. Joe Darden & Ron Malega & Rebecca Stallings, 2019. "Social and economic consequences of black residential segregation by neighbourhood socioeconomic characteristics: The case of Metropolitan Detroit," Urban Studies, Urban Studies Journal Limited, vol. 56(1), pages 115-130, January.
    6. Andrés Rodríguez-Pose & Neil Lee, 2020. "Hipsters vs. Geeks? Creative workers, STEM and innovation in US cities," Papers in Evolutionary Economic Geography (PEEG) 2021, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Apr 2020.
    7. 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.
    8. 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.
    9. 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.
    10. Lun Liu & Elisabete A Silva & Ying Long, 2019. "Block-level changes in the socio-spatial landscape in Beijing: Trends and processes," Urban Studies, Urban Studies Journal Limited, vol. 56(6), pages 1198-1214, May.

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