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Open geographic information science (GIScience) aspires to make new forms of research and discovery possible by facilitating the reproduction, replication, reanalysis, and extension of prior studies. We used open science practices to conduct a series of open GIScience studies stemming from a single study of spatial accessibility to COVID-19 healthcare in Illinois. We conducted the studies with students while establishing a reusable model for open GIScience research practices, including an executable research compendium for use with Git version control and a public CyberGIS system. Contradicting the perceived burdens and low value of reproduction and replication studies, we used open GIScience to improve research quality and support discovery. We tested the ability to repeat the study methods replicating it with different data in Connecticut and reproducing it with the same data in Illinois and Chicago. We successfully repeated the study with modifications to improve reproducibility, manage large file sizes, and accommodate changes in computational environments and volunteered geographic data. We then reanalyzed the study to improve computational efficiency and improve validity in terms of data errors, missing data, edge effects, and boundary effects. Finally, we extended the study to research spatiotemporal accessibility of pharmacies in Vermont, finding that the lack of after-hours access exacerbates and exceeds urban-rural access disparities. The cascading improvements observable across our studies underscore the importance of reproducibility as a catalyst for research quality and innovation in GIScience, particularly in the context of unique phenomena and research challenges across geographic contexts.
Holler et al. (Mon,) studied this question.