Mapping occupational health risk factors in the primary sector—A novel supervised machine learning and Area-to-Point Poisson kriging approach

dc.contributor.author	Gerassis, S.
dc.contributor.author	Boente López, Carlos
dc.contributor.author	Albuquerque, M. T. D.
dc.contributor.author	Ribeiro, M., M.
dc.contributor.author	Abad, A.
dc.contributor.author	Taboada, J.
dc.date.accessioned	2020-06-26T07:57:14Z
dc.date.available	2020-06-26T07:57:14Z
dc.date.issued	2020
dc.identifier.citation	Spatial Statistics, 42 (2020); doi:10.1016/j.spasta.2020.100434
dc.identifier.issn	2211-6753
dc.identifier.uri	http://hdl.handle.net/10651/55293
dc.language.iso	eng
dc.relation.ispartof	Spatial Statistics
dc.rights	© 2020 Elsevier
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081933433&doi=10.1016%2fj.spasta.2020.100434&partnerID=40&md5=a9aa496cc22e014e9a331787d06f4bbf
dc.title	Mapping occupational health risk factors in the primary sector—A novel supervised machine learning and Area-to-Point Poisson kriging approach
dc.type	journal article
dc.identifier.doi	10.1016/j.spasta.2020.100434
dc.relation.publisherversion	http://dx.doi.org/10.1016/j.spasta.2020.100434

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