Replication of scientific research: addressing geoprivacy, confidentiality, and data sharing challenges in geospatial research

Abstract

The ability to replicate, or reproduce, research is fundamental to the scientific process. Research combining a variety of georeferenced data is spreading rapidly across scientific domains and international borders. This suggests a growing potential for the use and integration of new and existing data sets to create new multi-disciplinary scientific collaborations. Yet, the unique characteristics of georeferenced data present special challenges to such collaborations. These data are highly identifiable when presented in maps and other visualizations or when combined with sensor data or other related geospatial data sets. The potential opportunities of collaboration may thus be constrained by the need to protect the locational privacy (geoprivacy) and confidentiality of subjects in research using georeferenced data. This paper reviews the obstacles to and potential methods for sharing georeferenced data in order to support a growing and dynamic geospatial research community and build capacity for data-intensive research across the social and environmental sciences. The development and implementation of a geospatial virtual data enclave methodology is proposed as an innovative and viable solution to share and archive georeferenced data among researchers while protecting the geoprivacy of research subjects and the confidentiality of these data. The ability to share confidential geospatial data among researchers is crucial to ensuring replicability of scientific research, and to enable researchers to verify and build upon the research of others.

Geospatial approaches to cancer control and population sciences at the United States cancer centers

“…Clearly, it arises because home address and other spatial data are personal identifiers linked to sensitive health and other data. Adequate tools to allow analysis of such data while safeguarding privacy exist but have not been widely tested or adopted, which in the meantime stymies data sharing and impedes other researchers from reproducing or expanding upon prior research [2, 29]. The Beau Biden Cancer Moonshot SM initiative is supporting NCI efforts to develop a Cancer Data Ecosystem [30]; such an “ecosystem” could include elements designed to facilitate analysis of geospatial data while protecting privacy.…”

Taking the pulse of COVID-19: a spatiotemporal perspective

“…These datasets involve strong privacy elements and should be protected while being used for research to fight against COVID-19 (Richardson et al 2013) and government to implement policies for tracking and tracing. A platform, such as Geospatial Virtual Data Enclave (GVDE) (Richardson et al 2015), that can enable the sharing but at the same time protect the privacy is critical to safely archive, access, share, analyze, and use confidential geospatial data in COVID-19 research and other public health programs (Richardson et al 2015;Barsky 2017).…”

How do people perceive the disclosure risk of maps? Examining the perceived disclosure risk of maps and its implications for geoprivacy protection

“…Second, perceived disclosure risk can be used to establish geoprivacy protection guidelines for mapping people's private locations. Recently, studies have called for establishing guidelines that inform researchers when creating maps with higher analytical utility while minimizing disclosure risk (Boulos et al, 2009;Kounadi & Resch, 2018;Leitner & Curtis, 2004;Richardson et al, 2015;VanWey et al, 2005). Although current privacy protection laws (e.g., the HIPAA in the United States) guide researchers to follow certain thresholds of disclosure risk (i.e., probability of reidentification), there is no clear consensus among scientists or society at large about which risk level should be used as the safe threshold (A.…”