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Introduction to the special issue on spatial machine learning

Author

Listed:
  • Kevin Credit

    (Maynooth University, National University of Ireland Maynooth)

Abstract

While, many of the machine learning (ML) and artificial intelligence (AI) methods that are now commonly being used to answer questions across scientific disciplines have been around for some time, their widespread application to spatial data and spatially-explicit research questions is much more recent. The large number of excellent review papers and special issues in leading journals published in the last few years—which this issue of the Journal of Geographical Systems takes its place among—attest to the growing interest in the application and development of cutting-edge methodologies for spatial data. This editorial begins by proposing a new inclusive definition for spatial ML, then provides a brief overview of each of the six papers in this special issue, and ends with a suggestion of several possible directions for future research in spatial ML.

Suggested Citation

  • Kevin Credit, 2024. "Introduction to the special issue on spatial machine learning," Journal of Geographical Systems, Springer, vol. 26(4), pages 451-460, October.
    • Handle: RePEc:kap:jgeosy:v:26:y:2024:i:4:d:10.1007_s10109-024-00452-1
    DOI: 10.1007/s10109-024-00452-1
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    References listed on IDEAS

    as
    1. Katarzyna Kopczewska, 2022. "Spatial machine learning: new opportunities for regional science," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(3), pages 713-755, June.
    2. Gignac, Gilles E. & Szodorai, Eva T., 2024. "Defining intelligence: Bridging the gap between human and artificial perspectives," Intelligence, Elsevier, vol. 104(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Spatial machine learning; Spatial data; Spatially-explicit models; GeoAI; Random forest;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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