Full Citation

Title: Generating Small Areal Synthetic Microdata from Public Aggregated Data Using an Optimization Method

Citation Type: Journal Article

Forthcoming?: Yes

ISBN:

ISSN: 0033-0124

DOI: 10.1080/00330124.2023.2207640

NSFID:

PMCID:

PMID:

Abstract: Small area microdata contain attributes and locations of individual members of a population in small census geographies.This type of data is critical in research and policymaking, but it is often not publicly available due to confidentiality con-cerns. The limited access to small area microdata can result in insufficient data for certain research (data scarcity). Evenfor researchers qualified to access the small area microdata, their research can hardly be reproduced by others (methodirreproducibility). To address these issues, we develop a method to generate small area synthetic microdata (SASM) that issuitable for public use. Specifically, an optimization approach is proposed to minimize the difference between publishedcensus tables and the SASM. Two counties in Ohio are used as case studies to test the efficacy of the proposed methodand the validity of the resulting SASM. The results show that the SASM aligns not only with the census tables, but alsowith an external data source that contains a sample of the small area microdata. We also illustrate how the SASM can beused to address data scarcity and method irreproducibility in demographic research.

Url: https://www.tandfonline.com/doi/full/10.1080/00330124.2023.2207640

User Submitted?: No

Authors: Lin, Yue; Xiao, Ningchuan

Periodical (Full): The Professional Geographer

Issue:

Volume:

Pages: 1-11

Data Collections: IPUMS International

Topics: Methodology and Data Collection

Countries: Japan