Reproducibility and replicability: opportunities and challenges for geospatial research

ABSTRACT

A cornerstone of the scientific method, the ability to reproduce and replicate the results of research has gained widespread attention across the sciences in recent years. A corresponding burst of energy into how to make research more reproducible and replicable has led to numerous innovations. This article outlines some of the opportunities for geospatial researchers to contribute to and learn from the broader reproducibility literature. We review practices developed in related disciplines to improve the reproducibility and replicability of research and outline current efforts to adapt those practices to geospatial analyses. The article then highlights the open questions, opportunities, and potential new directions in geospatial research related to R&R. We stress that the path ahead will likely require a mixture of computational, geospatial, and behavioral research that collectively addresses the many sides of reproducibility and replicability issues.

KEYWORDS:

• Reproducibility
• replicability
• geographic Information Science
• spatial analysis

Data and codes availability statement

Data sharing is not applicable to this article as no new data were created nor analyzed in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Peter Kedron

Peter Kedron is Assistant Professor in the School of Geographical Sciences and Urban Planning and a core faculty member of the Spatial Analysis Research Center at Arizona State University. Email: [email protected]. His research focuses on understanding how different processes create persistently uneven spatial patterns, developing new methods of spatial analysis, and using those methods to address economic, social, and environmental problems.

Wenwen Li

Wenwen Li is Associate Professor in GIScience in the School of Geographical Sciences and Urban Planning at Arizona State University. Email: [email protected]. Her research interests include cyberinfrastructure, geospatial big data, and machine learning, and their applications in data-intensive environmental and social sciences.

Stewart Fotheringham

Stewart Fotheringham is Professor of Computational Spatial Science and Director of the Spatial Analysis Research Center (SPARC) in the School of Geographical Sciences and Urban Planning, Arizona State University. Email: [email protected]. His research interests include spatial analysis, local statistical modeling, spatial interaction modeling, and GI Science.

Michael Goodchild

Michael Goodchild is Professor Emeritus in the Department of Geography at the University of California, Santa Barbara. E-mail: [email protected]. His research interests include geographic information science and systems, spatial data science, and uncertainty in geographic information.