Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance
February 2020
Working Paper Number:
CES-20-05
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.
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information census,
data census,
census data,
census research,
geographically,
population,
geography,
research census,
network,
geographic,
community
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Center for Economic Studies,
Decennial Census,
Research Data Center,
Geographic Information Systems,
American Community Survey,
Social Security Number,
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Master Address File,
Census Bureau Disclosure Review Board,
Disclosure Review Board,
Federal Statistical Research Data Center
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