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Open data products-A framework for creating valuable analysis ready data

Author

Listed:
  • Dani Arribas-Bel

    (University of Liverpool)

  • Mark Green

    (University of Liverpool)

  • Francisco Rowe

    (University of Liverpool)

  • Alex Singleton

    (University of Liverpool)

Abstract

This paper develops the notion of “open data product”. We define an open data product as the open result of the processes through which a variety of data (open and not) are turned into accessible information through a service, infrastructure, analytics or a combination of all of them, where each step of development is designed to promote open principles. Open data products are born out of a (data) need and add value beyond simply publishing existing datasets. We argue that the process of adding value should adhere to the principles of open (geographic) data science, ensuring openness, transparency and reproducibility. We also contend that outreach, in the form of active communication and dissemination through dashboards, software and publication are key to engage end-users and ensure societal impact. Open data products have major benefits. First, they enable insights from highly sensitive, controlled and/or secure data which may not be accessible otherwise. Second, they can expand the use of commercial and administrative data for the public good leveraging on their high temporal frequency and geographic granularity. We also contend that there is a compelling need for open data products as we experience the current data revolution. New, emerging data sources are unprecedented in temporal frequency and geographical resolution, but they are large, unstructured, fragmented and often hard to access due to privacy and confidentiality concerns. By transforming raw (open or “closed”) data into ready to use open data products, new dimensions of human geographical processes can be captured and analysed, as we illustrate with existing examples. We conclude by arguing that several parallels exist between the role that open source software played in enabling research on spatial analysis in the 90 s and early 2000s, and the opportunities that open data products offer to unlock the potential of new forms of (geo-)data.

Suggested Citation

  • Dani Arribas-Bel & Mark Green & Francisco Rowe & Alex Singleton, 2021. "Open data products-A framework for creating valuable analysis ready data," Journal of Geographical Systems, Springer, vol. 23(4), pages 497-514, October.
  • Handle: RePEc:kap:jgeosy:v:23:y:2021:i:4:d:10.1007_s10109-021-00363-5
    DOI: 10.1007/s10109-021-00363-5
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    References listed on IDEAS

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    Cited by:

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    2. Higgins, Christopher D. & Páez, Antonio & Kim, Gyoorie & Wang, Jue, 2021. "Changes in accessibility to emergency and community food services during COVID-19 and implications for low income populations in Hamilton, Ontario," Social Science & Medicine, Elsevier, vol. 291(C).
    3. 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.
    4. David Rey-Blanco & Pelayo Arbués & Fernando A. López & Antonio Páez, 2024. "Using machine learning to identify spatial market segments. A reproducible study of major Spanish markets," Environment and Planning B, , vol. 51(1), pages 89-108, January.

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    More about this item

    Keywords

    Geographic data science; Open data; Open source;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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