Journal article
Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography
Spatial statistics, v 14, pp 439-451
Nov 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography
- Creators
- Harrison Quick - Centers for Disease Control and PreventionScott H. Holan - University of MissouriChristopher K. Wikle - University of MissouriJerome P. Reiter - Duke University
- Publication Details
- Spatial statistics, v 14, pp 439-451
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000368913400013
- Scopus ID
- 2-s2.0-84948948141
- Other Identifier
- 991020099949104721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Geosciences, Multidisciplinary
- Mathematics, Interdisciplinary Applications
- Remote Sensing
- Statistics & Probability