Imputation of confidential data sets with spatial locations using disease mapping models.

Journal Article (Journal Article)

Data that include fine geographic information, such as census tract or street block identifiers, can be difficult to release as public use files. Fine geography provides information that ill-intentioned data users can use to identify individuals. We propose to release data with simulated geographies, so as to enable spatial analyses while reducing disclosure risks. We fit disease mapping models that predict areal-level counts from attributes in the file and sample new locations based on the estimated models. We illustrate this approach using data on causes of death in North Carolina, including evaluations of the disclosure risks and analytic validity that can result from releasing synthetic geographies.

Full Text

Duke Authors

Cited Authors

  • Paiva, T; Chakraborty, A; Reiter, J; Gelfand, A

Published Date

  • May 2014

Published In

Volume / Issue

  • 33 / 11

Start / End Page

  • 1928 - 1945

PubMed ID

  • 24395116

Pubmed Central ID

  • PMC4008679

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

International Standard Serial Number (ISSN)

  • 0277-6715

Digital Object Identifier (DOI)

  • 10.1002/sim.6078

Language

  • eng