Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography

Journal Article (Journal Article)

Many data stewards collect confidential data that include fine geography. When sharing these data with others, data stewards strive to disseminate data that are informative for a wide range of spatial and non-spatial analyses while simultaneously protecting the confidentiality of data subjects' identities and attributes. Typically, data stewards meet this challenge by coarsening the resolution of the released geography and, as needed, perturbing the confidential attributes. When done with high intensity, these redaction strategies can result in released data with poor analytic quality. We propose an alternative dissemination approach based on fully synthetic data. We generate data using marked point process models that can maintain both the statistical properties and the spatial dependence structure of the confidential data. We illustrate the approach using data consisting of mortality records from Durham, North Carolina.

Full Text

Duke Authors

Cited Authors

  • Quick, H; Holan, SH; Wikle, CK; Reiter, JP

Published Date

  • November 1, 2015

Published In

Volume / Issue

  • 14 /

Start / End Page

  • 439 - 451

International Standard Serial Number (ISSN)

  • 2211-6753

Digital Object Identifier (DOI)

  • 10.1016/j.spasta.2015.07.008

Citation Source

  • Scopus