From Reproducible to Explainable GIScience (Short Paper)

Author Mark Gahegan

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Mark Gahegan
  • School of Computer Science / Centre for eResearch, University of Auckland, New Zealand

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Mark Gahegan. From Reproducible to Explainable GIScience (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 32:1-32:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Communicating deep understanding between humans is key to the effective application and sharing of science, and this is critical in GIScience because much of what we do has practical implications in the modelling and governance of societal and environmental systems. Reproducible and explainable science is needed for public trust, for informed governance, for productivity and for global sustainability [Vicente-Saez et al., 2021]. This article summarises some of the more recent research on reproducibility from outside of GIScience, gives practical guidance to current best practice from a GIScience perspective, provides a clearer road-map towards reproducibility and adds in the additional step of explainable GIScience as our final goal.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • GIScience
  • Reproducible
  • Explainable
  • discoverable


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