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The number of reproduction and replication studies undertaken across the sciences continues to rise, but such studies have not yet become commonplace in geography. Existing attempts to reproduce geographic research suggest that many studies cannot be fully reproduced, or are simply missing components needed to attempt a reproduction. Despite this suggestive evidence, a systematic assessment of geographers’ perceptions of reproducibility and use of reproducible research practices remains absent from the literature, as does an identification of the factors that keep geographers from conducting reproduction studies. We address each of these needs by surveying active geographic researchers selected using probability sampling techniques from a rigorously constructed sampling frame. We identify a clear division in perceptions of reproducibility among geographic subfields. We also find varying levels of familiarity with reproducible research practices and a perceived lack of incentives to attempt and publish reproduction studies. Despite many barriers to reproducibility and divisions between subfields, we also find common foundations for examining and expanding reproducibility in the field. These include interest in publishing transparent and reproducible methods, and in reproducing other researchers’ studies for a variety of motivations including learning, assessing the internal validity of a study, or extending prior work. 

Despite recent calls to make geographical analyses more reproducible, formal attempts to reproduce or replicate published work remain largely absent from the geographic literature. The reproductions of geographic research that do exist typically focus on computational reproducibility—whether results can be recreated using data and code provided by the authors—rather than on evaluating the conclusion and internal validity and evidential value of the original analysis. However, knowing if a study is computationally reproducible is insufficient if the goal of a reproduction is to identify and correct errors in our knowledge. We argue that reproductions of geographic work should focus on assessing whether the findings and claims made in existing empirical studies are well supported by the evidence presented. We aim to facilitate this transition by introducing a model framework for conducting reproduction studies, demonstrating its use, and reporting the findings of three exemplar studies. We present three model reproductions of geographical analyses of COVID‐19 based on a common, open access template. Each reproduction attempt is published as an open access repository, complete with pre‐analysis plan, data, code, and final report. We find each study to be partially reproducible, but moving past computational reproducibility, our assessments reveal conceptual and methodological concerns that raise questions about the predictive value and the magnitude of the associations presented in each study. Collectively, these reproductions and our template materials offer a practical framework others can use to reproduce and replicate empirical spatial analyses and ultimately facilitate the identification and correction of errors in the geographic literature. 

The field of remote sensing has undergone a remarkable shift where vast amounts of imagery are now readily available to researchers. New technologies, such as uncrewed aircraft systems, make it possible for anyone with a moderate budget to gather their own remotely sensed data, and methodological innovations have added flexibility for processing and analyzing data. These changes create both the opportunity and need to reproduce, replicate, and compare remote sensing methods and results across spatial contexts, measurement systems, and computational infrastructures. Reproducing and replicating research is key to understanding the credibility of studies and extending recent advances into new discoveries. However, reproducibility and replicability (R&R) remain issues in remote sensing because many studies cannot be independently recreated and validated. Enhancing the R&R of remote sensing research will require significant time and effort by the research community. However, making remote sensing research reproducible and replicable does not need to be a burden. In this paper, we discuss R&R in the context of remote sensing and link the recent changes in the field to key barriers hindering R&R while discussing how researchers can overcome those barriers. We argue for the development of two research streams in the field: (1) the coordinated execution of organized sequences of forward-looking replications, and (2) the introduction of benchmark datasets that can be used to test the replicability of results and methods. 

Abstract. Reproducible open science with FAIR data sharing principles requires research to be disseminated with open data and standardised metadata. Researchers in the geographic sciences may benefit from authoring and maintaining metadata from the earliest phases of the research life cycle, rather than waiting until the data dissemination phase. Fully open and reproducible research should be conducted within a version-controlled executable research compendium with registered pre-analysis plans, and may also involve research proposals, data management plans, and protocols for research with human subjects. We review metadata standards and research documentation needs through each phase of the research process to distil a list of features for software to support a metadata-rich open research life cycle. The review is based on open science and reproducibility literature and on our own work developing a template research compendium for conducting reproduction and replication studies. We then review available open source geographic metadata software against these requirements, finding each software program to offer a partial solution. We conclude with a vision for software-supported metadata-rich open research practices intended to reduce redundancies in open research work while expanding transparency and reproducibility in geographic research. 

To hold the same privileged epistemological position as science, spatial data science must satisfy the self-corrective thesis. Doing so depends on the field’s capacity to reproduce and replicate published work, the willingness of researchers to do so, and our ability to assess the cumulative insights of such studies. We present some steps spatial data science might take to develop these capabilities and put forward a provisional vision of a veridical spatial data science.