Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance

James Gaboardi

Working Papers from U.S. Census Bureau, Center for Economic Studies

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.

Keywords: network allocation; Master Address File; population representation (search for similar items in EconPapers)
Pages: 48 pages
Date: 2020-02
New Economics Papers: this item is included in nep-net
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https://www2.census.gov/ces/wp/2020/CES-WP-20-05.pdf First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:20-05

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