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dc.contributor.author
Córdoba, Mariano
dc.contributor.author
Carranza, Juan Pablo
dc.contributor.author
Piumetto, Mario Andrés
dc.contributor.author
Monzani, Federico
dc.contributor.author
Balzarini, Monica Graciela
dc.date.available
2022-08-01T14:06:34Z
dc.date.issued
2021-07
dc.identifier.citation
Córdoba, Mariano; Carranza, Juan Pablo; Piumetto, Mario Andrés; Monzani, Federico; Balzarini, Monica Graciela; A spatially based quantile regression forest model for mapping rural land values; Academic Press Ltd - Elsevier Science Ltd; Journal of Environmental Management; 289; 7-2021; 1-10
dc.identifier.issn
0301-4797
dc.identifier.uri
http://hdl.handle.net/11336/163665
dc.description.abstract
Rural land valuation plays an important role in the development of land use policies for agricultural purposes. The advance of computational software and machine learning methods has enhanced mass appraisal methodologies for modeling and predicting economic values. New machine learning methods, like tree-based regression models, have been proposed as an alternative to linear regression to predict economic values from ancillary variables, since these algorithms are able to handle non-normality and non-linearity in the data. However, regression trees are commonly estimated assuming independent rather than spatially correlated data. This study aims to build a tree-based regression model that will help to tackle methodological problems related to the determination of prices of rural lands. The Quantile Regression Forest (QRF) algorithm was used to provide a regression model to predict and assess the uncertainty associated with model-derived predictions. However, the classical QRF ignores the autocorrelation underlying spatialized land values. The objective of this work was to develop, implement, and evaluate a spatial version of QRF, named sQRF, for computer-assisted mass appraisal of rural land values accounting for information from neighboring sites. We compared predictions of land values from sQRF with those obtained from spatial random forest, kriging regression, and linear regression models. sQRF performed well in predicting rural land values; indeed, it performed better than multiple linear regression. An important feature of sQRF is its ability to produce a direct uncertainty measure to assess the goodness of the predictions. Land values reflect a complex mix of agricultural returns, localization, and access to markets, which can be predicted from ancillary environmental variables. Good predictive models are essential to determine land values for multiple purposes including territorial taxation.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Academic Press Ltd - Elsevier Science Ltd
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
MASS APPRAISAL
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MACHINE LEARNING
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SPATIAL AUTOCORRELATION
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PREDICTION UNCERTAINTY
dc.subject.classification
Agricultura
dc.subject.classification
Agricultura, Silvicultura y Pesca
dc.subject.classification
CIENCIAS AGRÍCOLAS
dc.title
A spatially based quantile regression forest model for mapping rural land values
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-04-26T17:36:46Z
dc.journal.volume
289
dc.journal.pagination
1-10
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Córdoba, Mariano. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; Argentina
dc.description.fil
Fil: Carranza, Juan Pablo. Universidad Nacional de Córdoba. Rectorado. Instituto de Investigación y Formación en Administración Pública; Argentina
dc.description.fil
Fil: Piumetto, Mario Andrés. Universidad Nacional de Córdoba. Rectorado. Instituto de Investigación y Formación en Administración Pública; Argentina. Idecor; Argentina
dc.description.fil
Fil: Monzani, Federico. Idecor; Argentina
dc.description.fil
Fil: Balzarini, Monica Graciela. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; Argentina
dc.journal.title
Journal of Environmental Management
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0301479721005715
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.jenvman.2021.112509
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