6 min read

The reproducible geospatial science starter kit

2026-10-03

After giving a workshop on End-to-end Reproducible Spatial Analysis at South Dakota State University as a part of my sabbatical, one of my colleagues requested a “starter kit” for this type of work. I thought this was a great idea, but rather than write a lengthy email, I decided to put the content into a post. I’d strongly suggest reading this document in full before diving into any one piece described here. Additionally, while it makes sense to organizationally group these by topic, I actually think it’s a better idea to work on multiple sections of this starter kit at once rather than focus on one area at a time. Such is a limitation of a relatively linear endeavor like writing.

Perspective

  1. To click or not to click: A GIS Memoir by Yours Truly

    Now I wouldn’t be a self-respecting internet user in the year of our Ford 20261 without a little self-promotion. This piece will be especially useful for people needing convincing that a reproducible approach is worthwhile. Trust me; it is. Don’t trust me? Read that obnoxiously long technically-focused memoir above.

  2. Singleton, A. D., Spielman, S., & Brunsdon, C. (2016). Establishing a framework for open geographic information science. International Journal of Geographical Information Science, 30(8), 1507–1521.

    This article describes how to do reproducible science academically. I was fortunate enough to hear Alex present on this piece at AAG in New Orleans back in 2018 during an 8:00 AM Friday session.

  3. Shannon, J., & Walker, K. (2018). Opening GIScience: A process-based approach. International Journal of Geographical Information Science, 32(10), 1911–1926.

    This piece discusses not only reproducibility issues but also companion tools and applications for GIScience work. I love articles like this that demonstrate non-traditional modes of scholarship.

  4. Christensen, G., Freese, J., & Miguel, E. (2019). Transparent and Reproducible Social Science Research: How to Do Open Science. University of California Press.

    This book is a hefty undertaking but there is great content in here, and it really helps in framing work on R&R.

  5. Kedron, P., Li, W., Fotheringham, S., & Goodchild, M. (2021). Reproducibility and replicability: opportunities and challenges for geospatial research. International Journal of Geographical Information Science, 35(3), 427–445.

    Great piece that sets a modern stage for work on R&R. This was part of a flurry of special issues on the topic a few years ago.

Technical work

The resources below are language-specific to R, but analogous resources exist for Python, and this work could be applied there as well. I think R is much easier to learn for non-programmers and usually requires far fewer lines of code for geospatial tasks, but Python is admittedly used in production more often. I think it’s wise to work through the resources in the books below by physically typing in the code (rather than copy/pasting), inspecting intermediate objects, and taking a slow approach rather than blast through these as quickly as possible. Organize scripts by chapter, and avoid using LLMs except for asking clarification questions and really seeking to understand the “why” behind what you’re doing.

  1. Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science (2nd ed.). O’Reilly.

    This is a fantastic and comprehensive resource for learning R for the purpose of data wrangling/data science. I do, however, disagree with the authors that data visualization is the right place to start in learning R, so I’d recommend skipping over those chapters and returning to them after you have a decent handle on the language and syntax. In my view ggplot2 is not the most intuitive, and I think it can be a turn-off for budding spatial data scientists, who will be making maps with tmap and mapgl – both of which are simpler than ggplot2 – more than they will be making static non-spatial visualizations.

  2. Lovelace, R., Nowosad, J., & Muenchow, J. Geocomputation with R. CRC Press.

    A (mostly) vector-based approach to geospatial analysis with R that I’ve used for years within my research group. In parts it’s perhaps slightly too detailed (I think you can skip over some of the fine-grained details of sfc objects, for instance), but overall this is a great book.

  3. Pebesma, E., & Bivand, R. (2023). Spatial Data Science: With Applications in R. CRC Press.

    A (mostly) raster-based approach to geospatial analysis with R that I’ve admittedly not used, but the content looks great. If I was to start learning raster data analysis today, this is where I’d go. Plus, Edzer and Roger are some of the most influential contributors to the R spatial ecosystem.

References

  1. If I was to make any one piece of fiction mandatory reading it would be Aldous Huxley’s Brave New World.↩︎