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Opening practice: supporting reproducibility and critical spatial data science

Author

Listed:
  • Chris Brunsdon

    (Maynooth University)

  • Alexis Comber

    (University of Leeds)

Abstract

This paper reflects on a number of trends towards a more open and reproducible approach to geographic and spatial data science over recent years. In particular, it considers trends towards Big Data, and the impacts this is having on spatial data analysis and modelling. It identifies a turn in academia towards coding as a core analytic tool, and away from proprietary software tools offering ‘black boxes’ where the internal workings of the analysis are not revealed. It is argued that this closed form software is problematic and considers a number of ways in which issues identified in spatial data analysis (such as the MAUP) could be overlooked when working with closed tools, leading to problems of interpretation and possibly inappropriate actions and policies based on these. In addition, this paper considers the role that reproducible and open spatial science may play in such an approach, taking into account the issues raised. It highlights the dangers of failing to account for the geographical properties of data, now that all data are spatial (they are collected somewhere), the problems of a desire for $$n$$ n = all observations in data science and it identifies the need for a critical approach. This is one in which openness, transparency, sharing and reproducibility provide a mantra for defensible and robust spatial data science.

Suggested Citation

  • Chris Brunsdon & Alexis Comber, 2021. "Opening practice: supporting reproducibility and critical spatial data science," Journal of Geographical Systems, Springer, vol. 23(4), pages 477-496, October.
    • Handle: RePEc:kap:jgeosy:v:23:y:2021:i:4:d:10.1007_s10109-020-00334-2
    DOI: 10.1007/s10109-020-00334-2
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    References listed on IDEAS

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    1. Nick Barnes, 2010. "Publish your computer code: it is good enough," Nature, Nature, vol. 467(7317), pages 753-753, October.
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    Citations

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    Cited by:

    1. Alexis Comber & Paul Harris, 2022. "The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions," Land, MDPI, vol. 11(3), pages 1-17, March.
    2. Rowe, Francisco & Calafiore, Alessia & Arribas-Bel, Dani & Samardzhiev, Krasen & Fleischmann, Martin, 2022. "Urban Exodus? Understanding Human Mobility in Britain During the COVID-19 Pandemic Using Facebook Data," OSF Preprints 6hjv3, Center for Open Science.
    3. Jonathan Reades & Loretta Lees & Phil Hubbard & Guy Lansley, 2023. "Quantifying state-led gentrification in London: Using linked consumer and administrative records to trace displacement from council estates," Environment and Planning A, , vol. 55(4), pages 810-827, June.

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    More about this item

    Keywords

    Critical data science; Open source; GIScience; Geocomputation;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • Z00 - Other Special Topics - - General - - - General

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