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Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance

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  • James Gaboardi

Abstract

This paper furthers a research agenda for modeling populations along spatial networks and expands upon an empirical analysis to a full U.S. county (Gaboardi, 2019, Ch. 1,2). Specific foci are the necessity of, and methods for, validating and benchmarking spatial data when conducting social science research with aggregated and ambiguous population representations. In order to promote the validation of publicly-available data, access to highly-restricted census microdata was requested, and granted, in order to determine the levels of accuracy and error associated with a network-based population modeling framework. Primary findings reinforce the utility of a novel network allocation method�populated polygons to networks (pp2n) in terms of accuracy, computational complexity, and real runtime (Gaboardi, 2019, Ch. 2). Also, a pseudo-benchmark dataset�s performance against the true census microdata shows promise in modeling populations along networks.

Suggested Citation

  • 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.
    • Handle: RePEc:cen:wpaper:20-05
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    File URL: https://www2.census.gov/ces/wp/2020/CES-WP-20-05.pdf
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    References listed on IDEAS

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    6. 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.
    7. Derek S. Young & Andrew M. Raim & Nancy R. Johnson, 2017. "Zero-inflated modelling for characterizing coverage errors of extracts from the US Census Bureau's Master Address File," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 73-97, January.
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    Keywords

    network allocation; Master Address File; population representation;
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