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Spatial Machine Learning – New Opportunities for Regional Science

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  • Katarzyna Kopczewska

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

This paper is a methodological guide on using machine learning in the spatial context. It provides an overview of the existing spatial toolbox proposed in the literature: unsupervised learning, which deals with clustering of spatial data and supervised learning, which displaces classical spatial econometrics. It shows the potential and traps of using this developing methodology. It catalogues and comments on the usage of spatial clustering methods (for locations and values, separately and jointly) for mapping, bootstrapping, cross-validation, GWR modelling, and density indicators. It shows details of spatial machine learning models, combined with spatial data integration, modelling, model fine-tuning and predictions, to deal with spatial autocorrelation and big data. The paper delineates "already available" and "forthcoming" methods and gives inspirations to transplant modern quantitative methods from other thematic areas to research in regional science.

Suggested Citation

  • Katarzyna Kopczewska, 2021. "Spatial Machine Learning – New Opportunities for Regional Science," Working Papers 2021-16, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2021-16
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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/6633/
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    References listed on IDEAS

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    5. Kopczewska, Katarzyna & Ćwiakowski, Piotr, 2021. "Spatio-temporal stability of housing submarkets. Tracking spatial location of clusters of geographically weighted regression estimates of price determinants," Land Use Policy, Elsevier, vol. 103(C).
    6. Chasco, Coro & Le Gallo, Julie & López, Fernando A., 2018. "A scan test for spatial groupwise heteroscedasticity in cross-sectional models with an application on houses prices in Madrid," Regional Science and Urban Economics, Elsevier, vol. 68(C), pages 226-238.
    7. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.
    8. Julian Besag & James Newell, 1991. "The Detection of Clusters in Rare Diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(1), pages 143-155, January.
    9. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    10. Müller, Sven & Wilhelm, Pascal & Haase, Knut, 2013. "Spatial dependencies and spatial drift in public transport seasonal ticket revenue data," Journal of Retailing and Consumer Services, Elsevier, vol. 20(3), pages 334-348.
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    Cited by:

    1. Rodrigo García Arancibia & Pamela Llop & Mariel Lovatto, 2023. "Nonparametric prediction for univariate spatial data: Methods and applications," Papers in Regional Science, Wiley Blackwell, vol. 102(3), pages 635-672, June.
    2. Alessia Benevento & Fabrizio Durante, 2023. "Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information," Mathematics, MDPI, vol. 12(1), pages 1-15, December.
    3. Metz-Peeters, Maike, 2023. "The Effects of Mandatory Speed Limits on Crash Frequency - A Causal Machine Learning Approach," Ruhr Economic Papers 982, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, revised 2023.
    4. Rolf Bergs & Rüdiger Budde, 2022. "The potential of small-scale spatial data in regional science," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(2), pages 97-110, August.
    5. Muhammad Usman & Katarzyna Kopczewska, 2022. "Spatial and Machine Learning Approach to Model Childhood Stunting in Pakistan: Role of Socio-Economic and Environmental Factors," IJERPH, MDPI, vol. 19(17), pages 1-17, September.

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    More about this item

    Keywords

    spatial machine learning; clustering; spatial covariates; spatial cross-validation; spatial autocorrelation;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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