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Geographical Python Teaching Resources: geopyter

Author

Listed:
  • Jonathan Reades

    (University College London)

  • Sergio J. Rey

    (University of California)

Abstract

geopyter, an acronym of Geographical Python Teaching Resources, provides a hub for the distribution of ‘best practice’ in computational and spatial analytic instruction, enabling instructors to quickly and flexibly remix contributed content to suit their needs and delivery framework and encouraging contributors from around the world to ‘give back’ whether in terms of how to teach individual concepts or deliver whole courses. As such, geopyter is positioned at the confluence of two powerful streams of thought in software and education: the free and open-source software movement in which contributors help to build better software, usually on an unpaid basis, in return for having access to better tools and the recognition of their peers); and the rise of Massive Open Online Courses, which seek to radically expand access to education by moving course content online and providing access to students anywhere in the world at little or no cost. This paper sets out in greater detail the origins and inspiration for geopyter, the design of the system and, through examples, the types of innovative workflows that it enables for teachers. We believe that tools like geopyter, which build on open teaching practices and promote the development of a shared understanding of what it is to be a computational geographer represent an opportunity to expand the impact of this second wave of innovation in instruction while reducing the demands placed on those actively teaching in this area.

Suggested Citation

  • Jonathan Reades & Sergio J. Rey, 2021. "Geographical Python Teaching Resources: geopyter," Journal of Geographical Systems, Springer, vol. 23(4), pages 579-597, October.
  • Handle: RePEc:kap:jgeosy:v:23:y:2021:i:4:d:10.1007_s10109-021-00346-6
    DOI: 10.1007/s10109-021-00346-6
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    References listed on IDEAS

    as
    1. Sergio Rey, 2009. "Show me the code: spatial analysis and open source," Journal of Geographical Systems, Springer, vol. 11(2), pages 191-207, June.
    2. Daniel Arribas-Bel & Jorge E Patino & Juan C Duque, 2017. "Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-25, May.
    3. Sergio J. Rey, 2019. "PySAL: the first 10 years," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(3), pages 273-282, July.
    4. Tsang, Eric W. K., 2014. "Old and New," Management and Organization Review, Cambridge University Press, vol. 10(03), pages 390-390, November.
    5. Paul Trafford & Yukari Shirota, 2011. "An Introduction to Virtual Learning Environments," Gakushuin Economic Papers, Gakushuin University, Faculty of Economics, vol. 48(3), pages 143-151.
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    Cited by:

    1. Antonio Páez, 2021. "Open spatial sciences: an introduction," Journal of Geographical Systems, Springer, vol. 23(4), pages 467-476, October.

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    More about this item

    Keywords

    Open Source; Spatial Analysis; Education;
    All these keywords.

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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