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Journal article
Generating partially synthetic geocoded public use data with decreased disclosure risk by using differential smoothing
Journal of the Royal Statistical Society. Series A, Statistics in society, v 181(3), pp 649-661
01 Jun 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
When collecting geocoded confidential data with the intent to disseminate, agencies often resort to altering the geographies before making data publicly available. An alternative to releasing aggregated and/or perturbed data is to release synthetic data, where sensitive values are replaced with draws from models designed to capture distributional features in the data collected. The issues associated with spatially outlying observations in the data, however, have received relatively little attention. Our goal here is to shed light on this problem, to propose a solution-referred to as 'differential smoothing'-and to illustrate our approach by using sale prices of homes in San Francisco.
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Details
- Title
- Generating partially synthetic geocoded public use data with decreased disclosure risk by using differential smoothing
- Creators
- Harrison Quick - Drexel UniversityScott H. Holan - University of MissouriChristopher K. Wikle - University of Missouri
- Publication Details
- Journal of the Royal Statistical Society. Series A, Statistics in society, v 181(3), pp 649-661
- Publisher
- Wiley
- Number of pages
- 13
- Grant note
- US National Science Foundation (NSF); National Science Foundation (NSF) SES-1132031 / US Census Bureau under NSF - NSF-Census Research Network programme
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000434143700006
- Scopus ID
- 2-s2.0-85043493649
- Other Identifier
- 991019168345404721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Social Sciences, Mathematical Methods
- Statistics & Probability