Confidentiality and spatially explicit data: Concerns and challenges
Author(s) -
Leah K. VanWey,
Ronald R. Rindfuss,
Myron P. Gutmann,
Barbara Entwisle,
Deborah Balk
Publication year - 2005
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.0507804102
Subject(s) - confidentiality , data science , data sharing , computer science , context (archaeology) , respondent , data anonymization , data quality , dilemma , internet privacy , information privacy , computer security , political science , geography , engineering , medicine , metric (unit) , philosophy , operations management , alternative medicine , archaeology , pathology , epistemology , law
Recent theoretical, methodological, and technological advances in the spatial sciences create an opportunity for social scientists to address questions about the reciprocal relationship between context (spatial organization, environment, etc.) and individual behavior. This emerging research community has yet to adequately address the new threats to the confidentiality of respondent data in spatially explicit social survey or census data files, however. This paper presents four sometimes conflicting principles for the conduct of ethical and high-quality science using such data: protection of confidentiality, the social-spatial linkage, data sharing, and data preservation. The conflict among these four principles is particularly evident in the display of spatially explicit data through maps combined with the sharing of tabular data files. This paper reviews these two research activities and shows how current practices favor one of the principles over the others and do not satisfactorily resolve the conflict among them. Maps are indispensable for the display of results but also reveal information on the location of respondents and sampling clusters that can then be used in combination with shared data files to identify respondents. The current practice of sharing modified or incomplete data sets or using data enclaves is not ideal for either the advancement of science or the protection of confidentiality. Further basic research and open debate are needed to advance both understanding of and solutions to this dilemma.
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