Authors: Jason Tullis*, University of Arkansas, Bandana Kar, Oak Ridge National Laboratory
Topics: Geographic Information Science and Systems, Cyberinfrastructure, Hazards, Risks, and Disasters
Keywords: GIScience, provenance, replicability, reproducibility, privacy
Session Type: Paper
Start / End Time: 8:00 AM / 9:15 AM
Room: Plaza Court 5, Sheraton, Concourse Level
Presentation File: No File Uploaded
As replicability and reproducibility (R&R) crises develop within emerging transdisciplinary inquiry, ethical use of provenance information is intensely relevant to the establishment and preservation of trust in critical applications of GIScience and geospatial technologies. Today large volumes of geospatial data are generated at high velocity from satellite sensors and unmanned aircraft systems, citizens via social media, geolocation-based data services, global navigation satellite systems, etc. The extensive use of these data for some applications such as disaster analytics and humanitarian response raises the issue of R&R from competing perspectives of location privacy and geospatial data quality. While geospatial data from any source can be integrated and linked with contextual information to identify individuals’ movements, steps taken to ensure privacy can complicate the multiuser development of high-quality geospatial workflows. Provenance information as digital records of historical (retrospective) and potential future (prospective) geospatial processes is often overlooked, misunderstood, or inadequately addressed. We explore the relationship between provenance information, location privacy, and geospatial data quality in the context of R&R with a focus on disaster analytics. We argue that in the era of big data and deep learning, GIScientists and associated institutions bear greater responsibility for both geospatial workflow quality and for location privacy. Ethical use of international provenance standards and specifications, perhaps some already in existence, holds much promise for geospatial interoperability that balances both quality and privacy. Given vastly heterogenous computational landscapes, we provide practical recommendations for ethically driven provenance and R&R research and development within the GIScience community and beyond.