Geography Geocomputation in Geography Education
Forrest Bowlick
  • LAST REVIEWED: 26 October 2023
  • LAST MODIFIED: 26 October 2023
  • DOI: 10.1093/obo/9780199874002-0277


Geocomputation refers broadly to various work using geographic data combined with computational technologies and methodologies. A dynamic area of research, geocomputation work covers many areas of application seeking to leverage the affordances of computing, especially high-performance computing, for solving spatial problems and working with geographic data. Embracing the rapid advances in computational capacity while engaging with long established problems in understanding geographic and spatial relationships results in an area of scholarship inviting a range of expertise to contribute. The difficulties of exploring the boundaries of what is or is not geocomputation reveal themselves especially in earlier work outlining the area, and concepts like geographic information systems/science (both referred to here as GIS) receive variant embrasure. New ideas for analysis and new names for what that analysis might be, emerging from/through and within/without geocomputation, prove successful in maintaining a dynamic and energetic area of practice. At the same time, this range of knowledge, skills, and experience provides a difficult landscape for educational efforts. Teaching and learning (taken together here as “education”) along the broad and deep interface between geocomputation and geography requires difficult choices, learning pathways, and execution in curriculum and instruction. Grappling with the balance and inclusion of computational components in geography education remains an open opportunity for investigation. Whether computing education includes geography is a different discussion.

General Overviews

Collections of ideas bind together early development of geocomputation emerging from conferences like the inaugural geocomputation conference in 1996. Longley, et al. 1998 and Abrahart, et al. 2000 collect various viewpoints and ideas along different tracks—sometimes complementary, sometimes contentious. Both develop in a way from ideas posed by Goodchild 1992 on the transition of GIS from a systems-based approach to a scientific one, though Gahegan 1999 draws clear lines on Gahegan’s view of the disabling nature of GIS. Kitchin 2013 and Wilson 2017 continue this critical tradition, while a new collection of ideas and practical advice put forth in Brunsdon and Singleton 2015 collect more voices—some heard again, and some heard for the first time. Singleton and Arribas-Bel 2021 continues to build out new forms and new definitions of geography and computers, while Solem, et al. 2021 continues the work to begin to understand the educational contexts. One way to teach, growing in utility and function not only in geography but across the physical and social sciences: the statistical programming language R. Lovelace, et al. 2018 positions R and geocomputation together in an accessible volume for R learners.

  • Abrahart, R. J., S. Openshaw, and L. M. See. GeoComputation. London: Taylor and Francis, 2000.

    DOI: 10.4324/9780203305805

    A principle and foundation building volume with emphasis on forward-looking approaches and framing for future considerations of computational and geographical problem-solving.

  • Brunsdon, C., and A. Singleton, eds. Geocomputation: A Practical Primer. Los Angeles: Sage, 2015.

    Application-driven discussions of geocomputation in many contexts. Reasonably comprehensive.

  • Gahegan, M. “What Is Geocomputation?” Transactions in GIS 3.3 (1999): 203–206.

    DOI: 10.1111/1467-9671.00017

    An at times sardonic editorial outlining the history of geocomputational development to the then current issues and topics. A meaningful dialogic glimpse into active discussions.

  • Goodchild, M. F. “Geographical Information Science.” International Journal of Geographical Information Systems 6.1 (1992): 31–45.

    DOI: 10.1080/02693799208901893

    One foundational argument for another definition of the “s” in “geographic information systems”: science. A broad coverage of ideas within which geocomputation and geography interface.

  • Kitchin, R. “Big Data and Human Geography: Opportunities, Challenges, and Risks.” Dialogues in Human Geography 3.3 (2013): 262–267.

    DOI: 10.1177/2043820613513388

    Represents the challenges of big data in scope and concept within geography well, linking geographic practices and computational influences.

  • Longley, P. A., S. Brooks, W. Macmillan, and R. A. McDonnell, eds. Geocomputation: A Primer. New York: John Wiley & Sons, 1998.

    A foundational primer of concepts and ideas paired with practices reflective of the computing access of the time. Still conceptually and theoretically essential.

  • Lovelace, R., J. Nowosad, and J. Muenchow. Geocomputation with R. Boca Raton, FL: CRC Press, 2018.

    Specific in terms of working within statistical programming language/software R, but with accessible conceptual materials and a reasonable learning curve for exercises.

  • Singleton, A., and D. Arribas-Bel. “Geographic Data Science.” Geographical Analysis 53.1 (2021): 61–75.

    DOI: 10.1111/gean.12194

    Explorations of the interfaces between the emergent data science and the geographic and spatial. Forward-looking and aspirational in establishing formal field definitions.

  • Solem, M., C. Dony, T. Herman, et al. “Building Educational Capacity for Inclusive Geocomputation: A Research-Practice Partnership in Southern California.” Journal of Geography 120.4 (2021): 152–159.

    DOI: 10.1080/00221341.2021.1933140

    An overview of a recent pilot effort to build capacities for geocomputation education within geography and computer science education communities. Reflective of challenges within the broader education space.

  • Wilson, M. W. New Lines: Critical GIS and the Trouble of the Map. Minneapolis: University of Minnesota Press, 2017.

    DOI: 10.5749/minnesota/9780816698523.001.0001

    A broader positioning of GIS within a critical framework integrating societal impacts, and an important way of thinking to acknowledge when working in geocomputation spaces.

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