Centre de documentation
scientifique

Machine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices / Linchuan Yang in Annals of GIS, vol 27 n° 3 (July 2021)  

Public [article]  inAnnals of GIS > vol 27 n° 3 (July 2021) . - pp 273 - 284 Titre : Machine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices Type de document : Article/Communication Auteurs : Linchuan Yang, Auteur ; Yuan Liang, Auteur ; Qing Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 273 - 284 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de la valeur [Termes IGN] apprentissage automatique [Termes IGN] arbre de décision [Termes IGN] bien immobilier [Termes IGN] boosting adapté [Termes IGN] Chine [Termes IGN] Extreme Gradient Machine [Termes IGN] inférence [Termes IGN] logement [Termes IGN] transport public Résumé : (auteur) The adoption of bus rapid transit (BRT) systems has gained worldwide popularity over the past several decades. China is no exception as it has long been aiming at promoting public transportation. Prior studies have provided extensive evidence that BRT has substantial effects on house prices with traditional econometric techniques, such as hedonic pricing models. However, few of those investigations have discussed the non-linear relationship between BRT and house prices. Using the Xiamen data, this study employs a machine learning technique, namely the gradient boosting decision tree (GBDT), to scrutinize the non-linear relationship between BRT and house prices. This study documents a positive association between accessibility to BRT stations and house prices and a negative association between proximity to the BRT corridor and house prices. Moreover, it suggests a non-linear relationship between BRT and house prices and indicates that GBDT has more substantial predictive power than hedonic pricing models. Numéro de notice : A2021-629 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1080/19475683.2021.1906746 Date de publication en ligne : 27/03/2021 En ligne : https://doi.org/10.1080/19475683.2021.1906746 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98270 [article]