Linking Individuals to Areas: Protecting Confidentiality While Preserving Research Utility

IF 1.1 Q3 DEMOGRAPHY Spatial Demography Pub Date : 2023-11-28 DOI:10.1007/s40980-023-00121-9
Paul Norman, Jessie Colbert, Daniel J. Exeter
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Abstract

Modern computational capabilities have brought about concerns about risks associated with the level of information disclosed in public datasets. A tension exists between making data available that protects the confidentiality of individuals while containing sufficiently detailed geographic information to underpin the utility of research. Our aim is to inform data collectors and suppliers about geographic choices for confidentiality protection and to balance this with reassurance to the research community that data will still be fit-for-purpose. We test this using simple logistic regression models, by investigating the interplay between two geographical entities (points for the observations and polygons for area attributes) at a variety of scales, using a synthetic population of 22,000 people. In an England and Wales setting, we do this for individuals located by postcodes and by postal sector and postal district centroids and link these to a variety of census geographies. We also ‘jitter’ postcode coordinates to test the effect of moving people away from their original location. We find a smoothing of relationships up the geographical hierarchy. However, if postal sector centroids are used to locate individuals, linkages to Lower/Medium Super Output Area scales and subsequent results are very similar to the more detailed unit postcodes. Postcode locations jittered by 500–750 m in any direction are likely to allow the same conclusions to be drawn as for the original locations. Within these geographic scenarios, there is likely to be a sufficient level of confidentiality protection while statistical relationships are very similar to those obtained using the most detailed geographic locators.

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将个人与领域联系起来:在保持研究效用的同时保护机密
现代计算能力引起了人们对公共数据集中披露的信息水平相关风险的担忧。提供数据既要保护个人的机密性,又要包含足够详细的地理信息以支持研究的实用性,这两者之间存在着紧张关系。我们的目标是告知数据收集者和供应商关于保密保护的地理选择,并平衡这一点,向研究社区保证数据仍将适合目的。我们使用简单的逻辑回归模型来检验这一点,通过调查两个地理实体(观测点和区域属性多边形)在各种尺度上的相互作用,使用22,000人的合成人口。在英格兰和威尔士的设置中,我们对按邮政编码、邮政部门和邮政区质心定位的个人进行此操作,并将其链接到各种人口普查地理位置。我们还“抖动”邮政编码坐标,以测试将人们从原来的位置移开的效果。我们发现地理层次上的关系趋于平滑。但是,如果使用邮政部门的质心来定位个人,则与中低超级输出区域尺度的联系以及随后的结果与更详细的单位邮政编码非常相似。邮政编码的位置在任何方向上抖动500-750米,都可能得出与原始位置相同的结论。在这些地理场景中,可能有足够的机密保护,而统计关系与使用最详细的地理定位器获得的统计关系非常相似。
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Spatial Demography
Spatial Demography DEMOGRAPHY-
自引率
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期刊介绍: Spatial Demography focuses on understanding the spatial and spatiotemporal dimension of demographic processes.  More specifically, the journal is interested in submissions that include the innovative use and adoption of spatial concepts, geospatial data, spatial technologies, and spatial analytic methods that further our understanding of demographic and policy-related related questions. The journal publishes both substantive and methodological papers from across the discipline of demography and its related fields (including economics, geography, sociology, anthropology, environmental science) and in applications ranging from local to global scale. In addition to research articles the journal will consider for publication review essays, book reviews, and reports/reviews on data, software, and instructional resources.
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