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Article: Abandoned rural residential land: Using machine learning techniques to identify rural residential land vulnerable to be abandoned in mountainous areas

TitleAbandoned rural residential land: Using machine learning techniques to identify rural residential land vulnerable to be abandoned in mountainous areas
Authors
KeywordsChina
Land abandonment
Land use planning
Machine learning technique
Mountainous area
Rural residential land
Issue Date2019
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/habitatint
Citation
Habitat International, 2019, v. 84, p. 43-56 How to Cite?
AbstractRural residential land has been increasingly abandoned in China given the rapid and massive rural-to-urban migration. From the aspect of land-use planning and policy making, it is important to understand the determinants of residential land abandonment across rural areas as well as to know what rural residential land is vulnerable to be abandoned. However, neither of these can be known via qualitative evaluation of residential land abandonment or via remote sensing applications and land-use modelling. In this study, we develop an approach of combining machine learning techniques (Random Forest, Supported Vector Machine, and Naive Bayes) and land-as-an-object analysis to identify the rural residential land that has a high possibility of being abandoned in mountainous areas. We applied this approach to Fang County, Central China. The results indicate a reasonable and reliable prediction of rural residential land abandonment based on our approach, particularly in estimating the potential occurrence of local land abandonment. The geographic characteristics of the land and the living conditions of the land user were found to have relatively significant impacts on rural residential land use. Our approach also provides a pathway to evaluate specific land use and identify its potential change. This approach can be useful for the development of a standardized protocol for the evaluation of residential land abandonment across other rural areas and may be applicable for the investigation of other land types that may be vulnerable to abandonment. Quantitatively assessing the influential factors of residential land use can also provide alternative insights for the development of planning protocols in order to ultimately improve the quality of life and living environments in rural areas.
Persistent Identifierhttp://hdl.handle.net/10722/267491
ISSN
2017 Impact Factor: 3.0
2015 SCImago Journal Rankings: 1.038

 

DC FieldValueLanguage
dc.contributor.authorXu, F-
dc.contributor.authorHo, HC-
dc.contributor.authorChi, G-
dc.contributor.authorWang, Z-
dc.date.accessioned2019-02-18T09:03:09Z-
dc.date.available2019-02-18T09:03:09Z-
dc.date.issued2019-
dc.identifier.citationHabitat International, 2019, v. 84, p. 43-56-
dc.identifier.issn0197-3975-
dc.identifier.urihttp://hdl.handle.net/10722/267491-
dc.description.abstractRural residential land has been increasingly abandoned in China given the rapid and massive rural-to-urban migration. From the aspect of land-use planning and policy making, it is important to understand the determinants of residential land abandonment across rural areas as well as to know what rural residential land is vulnerable to be abandoned. However, neither of these can be known via qualitative evaluation of residential land abandonment or via remote sensing applications and land-use modelling. In this study, we develop an approach of combining machine learning techniques (Random Forest, Supported Vector Machine, and Naive Bayes) and land-as-an-object analysis to identify the rural residential land that has a high possibility of being abandoned in mountainous areas. We applied this approach to Fang County, Central China. The results indicate a reasonable and reliable prediction of rural residential land abandonment based on our approach, particularly in estimating the potential occurrence of local land abandonment. The geographic characteristics of the land and the living conditions of the land user were found to have relatively significant impacts on rural residential land use. Our approach also provides a pathway to evaluate specific land use and identify its potential change. This approach can be useful for the development of a standardized protocol for the evaluation of residential land abandonment across other rural areas and may be applicable for the investigation of other land types that may be vulnerable to abandonment. Quantitatively assessing the influential factors of residential land use can also provide alternative insights for the development of planning protocols in order to ultimately improve the quality of life and living environments in rural areas.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/habitatint-
dc.relation.ispartofHabitat International-
dc.subjectChina-
dc.subjectLand abandonment-
dc.subjectLand use planning-
dc.subjectMachine learning technique-
dc.subjectMountainous area-
dc.subjectRural residential land-
dc.titleAbandoned rural residential land: Using machine learning techniques to identify rural residential land vulnerable to be abandoned in mountainous areas-
dc.typeArticle-
dc.identifier.emailHo, HC: hcho21@hku.hk-
dc.identifier.authorityHo, HC=rp02482-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.habitatint.2018.12.006-
dc.identifier.scopuseid_2-s2.0-85059309207-
dc.identifier.hkuros296820-
dc.identifier.volume84-
dc.identifier.spage43-
dc.identifier.epage56-
dc.publisher.placeUnited Kingdom-

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