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Synthetic population data for small area estimation in the United States

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  • Yue Lin

Abstract

Small area estimation is critical for a wide range of applications, including urban planning, funding distribution, and policy formulation. Individual-level population data, which typically include each individual’s socio-demographic characteristics and small area location, are a rich source of information for small area estimation. However, individual-level population data are often not made public due to confidentiality concerns. This paper describes the development of a public-use synthetic individual-level population dataset in the United States that can be useful for small area estimation. This dataset contains characteristics of housing type, age, sex, race, and Hispanic or Latino origin for all 308,745,538 individuals in the United States at the census block group level, based on publicly available aggregated data from the 2010 Census. Experimental results suggest the validity of the synthetic data by comparing it to different data sources, and we show examples of how this dataset can be used in small area estimation.

Suggested Citation

  • Yue Lin, 2024. "Synthetic population data for small area estimation in the United States," Environment and Planning B, , vol. 51(2), pages 553-562, February.
    • Handle: RePEc:sae:envirb:v:51:y:2024:i:2:p:553-562
    DOI: 10.1177/23998083231215825
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    References listed on IDEAS

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    1. Yue Lin & Ningchuan Xiao, 2023. "Assessing the Impact of Differential Privacy on Population Uniques in Geographically Aggregated Data: The Case of the 2020 U.S. Census," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(5), pages 1-20, October.
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    5. Robin Lovelace & Mark Birkin & Dimitris Ballas & Eveline van Leeuwen, 2015. "Evaluating the Performance of Iterative Proportional Fitting for Spatial Microsimulation: New Tests for an Established Technique," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-21.
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