Spatial Machine Learning – New Opportunities for Regional Science

Katarzyna Kopczewska ()

No 2021-16, Working Papers from 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.

Keywords: spatial machine learning; clustering; spatial covariates; spatial cross-validation; spatial autocorrelation (search for similar items in EconPapers)
JEL-codes: C31 C49 R10 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-ure
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Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.wne.uw.edu.pl/index.php/download_file/6633/ First version, 2021 (application/pdf)

Related works:
Journal Article: Spatial machine learning: new opportunities for regional science (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2021-16

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