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Spatial Machine Learning – New Opportunities for Regional Science

Author

Listed:
  • 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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    Cited by:

    1. Roy Cerqueti & Antonio Iovanella & Raffaele Mattera, 2024. "Clustering networked funded European research activities through rank-size laws," Annals of Operations Research, Springer, vol. 342(3), pages 1707-1735, November.
    2. 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.
    3. Celbiş, Mehmet Güney & Bouzouina, Louafi, 2025. "To what extent walking and biking are substitutes or complements to public transport? Interpretable machine learning findings from the University of Lyon, France," Journal of Transport Geography, Elsevier, vol. 123(C).
    4. Kevin Credit, 2024. "Introduction to the special issue on spatial machine learning," Journal of Geographical Systems, Springer, vol. 26(4), pages 451-460, October.
    5. Eigo Tateishi, 2023. "The spatiotemporal socio-demography of the Tokyo capital region: a data-driven explorative approach," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 43(3), pages 467-519, December.
    6. Maria Kubara, 2024. "Spatiotemporal localisation patterns of technological startups: the case for recurrent neural networks in predicting urban startup clusters," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 72(3), pages 797-829, March.
    7. Feng Shi & Weiwei Cao & Runhua Huang & Wei Geng, 2025. "Spatially heterogeneous drivers of hukou transfer intentions in China: a geographically weighted logistic regression analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 74(2), pages 1-30, June.
    8. John I. Carruthers & Hanxue Wei, 2024. "What drives urban redevelopment activity? Evidence from machine learning and econometric analysis in three American cities," Journal of Geographical Systems, Springer, vol. 26(4), pages 565-599, October.
    9. 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.
    10. Milad Abbasiharofteh & Jan Kinne & Miriam Krüger, 2024. "Leveraging the digital layer: the strength of weak and strong ties in bridging geographic and cognitive distances," Journal of Economic Geography, Oxford University Press, vol. 24(2), pages 241-262.
    11. Fernando López & Konstatin Kholodilin, 2023. "Putting MARS into space. Non‐linearities and spatial effects in hedonic models," Papers in Regional Science, Wiley Blackwell, vol. 102(4), pages 871-896, August.
    12. 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.
    13. Katarzyna Kopczewska, 2023. "Spatial bootstrapped microeconometrics: Forecasting for out‐of‐sample geo‐locations in big data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 1391-1419, September.
    14. 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.
    15. 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.
    16. Shukai Li & Jifeng Chen & Wei Dai & Fangyuan Li & Yuting Gong & Hongmei Gong & Ziyi Zhu, 2025. "Multi-Province Collaborative Carbon Emission Forecasting and Scenario Analysis Based on the Spatio-Temporal Attention Mechanism—Empowering the Green and Low-Carbon Transition of the Transportation Sec," Sustainability, MDPI, vol. 17(19), pages 1-30, September.
    17. Hojun Lee & Hoon Han & Chris Pettit & Qishuo Gao & Vivien Shi, 2024. "Machine learning approach to residential valuation: a convolutional neural network model for geographic variation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 72(2), pages 579-599, February.
    18. Brian H. S. Kim & Martin Andersson & Janet Kohlhase, 2024. "Reflecting on a dynamic biennium: The Annals of Regional Science 2022–2023," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 72(3), pages 683-690, March.
    19. Doojin Ryu & Jengei Hong & Hyunjae Jo, 2024. "Capturing locational effects: application of the K-means clustering algorithm," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 73(1), pages 265-289, June.

    More about this item

    Keywords

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    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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