Artykuł w czasopiśmie
Brak miniatury
Licencja
ClosedAccessDostęp zamknięty
 

Spatial machine learning: new opportunities for regional science

Uproszczony widok
dc.abstract.enThis paper is a methodological guide to 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 of using this developing methodology, as well as its pitfalls. It catalogues and comments on the usage of spatial clustering methods (for locations and values, both separately and jointly) for mapping, bootstrapping, cross-validation, GWR modelling and density indicators. It provides details of spatial machine learning models, which are 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 inspiration for transplanting modern quantitative methods from other thematic areas to research in regional science.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorKopczewska, Katarzyna
dc.date.accessioned2024-01-26T07:54:10Z
dc.date.available2024-01-26T07:54:10Z
dc.date.issued2022
dc.description.financeNie dotyczy
dc.description.volume68
dc.identifier.doi10.1007/S00168-021-01101-X
dc.identifier.issn0570-1864
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/120288
dc.identifier.weblinkhttps://doi.org/10.1007/s00168-021-01101-x
dc.languageeng
dc.pbn.affiliationeconomics and finance
dc.relation.ispartofAnnals of Regional Science
dc.relation.pages713-755
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.subject.enspatial clustering
dc.subject.enspatial regression
dc.subject.enspatial cross-validation
dc.subject.enperspectives
dc.titleSpatial machine learning: new opportunities for regional science
dc.typeJournalArticle
dspace.entity.typePublication