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

Abstract

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.