Spatial machine learning: new opportunities for regional science

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