Addressing Challenges For Geospatial Data-Intensive Research Communities: Research on Unique Confidentiality Risks and Geospatial Data Sharing within a Virtual Data Enclave
Geospatial Data Confidentiality
The AAG teamed up with the Inter-university Consortium for Political and Social Research at the University of Michigan to lead an NSF-funded program of research to address the challenges facing geospatial data-intensive research communities.
Research combining a variety of intensive geographically-referenced data streams is spreading across many scientific domains, ranging from environmental science to transportation to epidemiology, and opportunities to create new multi-disciplinary and data-intensive scientific collaborations are expanding.
Yet, the unique characteristics of geo-referenced data present special challenges to such collaborations. These data are highly identifiable when presented in maps and other visualizations. The potential opportunities and benefits of collaboration are constrained by the need to protect the locational privacy and confidentiality of subjects in research using geo-referenced data.
The project is entitled Addressing Challenges For Geospatial Data-Intensive Research Communities: Research on Unique Confidentiality Risks and Geospatial Data Sharing within a Virtual Data Enclave.
The focus is on the unique confidentiality characteristics of geospatial data and their visualizations, on disclosure risks, and on the potential for sharing geospatial data within a Virtual Data Enclave (VDE).
The aim is to engage the geospatial research community in an effort to:
- conduct research on the unique confidential characteristics of large geo-referenced data sets and on viable ways to manipulate these data and their geo-visualizations to protect confidentiality and privacy;
- conduct research on methods and procedures to assess and reduce disclosure risks in maps and other research projects derived from locationally identifiable data;
- conduct research regarding the viability of sharing and archiving confidential geo-referenced research data using a VDE to enable sophisticated analyses of these data under conditions that protect the privacy of research subjects; and
- test confidentiality methods within the geospatial VDE to reduce disclosure risk and develop standards for disclosure review.